{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6bde01a5",
   "metadata": {},
   "source": [
    "## <font color='purple'> Introduction: </font>\n",
    "\n",
    "In the development of a cancer diagnosis prediction model, I utilized a K-Nearest Neighbors (KNN) classifier with 3 neighbors. My primary goal was to optimize the model to accurately identify cancer cases, reducing the number of false negatives, that represent undetected cancer cases. I focused on improving recall, which measures the model's ability to correctly identify positive cases.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "d13293ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "ae14f335",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\Teni\\Desktop\\Git-Github\\Datasets\\KNN\\breast-cancer.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f15e75a",
   "metadata": {},
   "source": [
    "### <font color='black'> Data Info Analysis </font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "1fcdea81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "54b305b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "2857dac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.diagnosis.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "d04dd32f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['M' 'B']\n"
     ]
    }
   ],
   "source": [
    "print(df.diagnosis.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "958c79f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                           int64\n",
      "diagnosis                   object\n",
      "radius_mean                float64\n",
      "texture_mean               float64\n",
      "perimeter_mean             float64\n",
      "area_mean                  float64\n",
      "smoothness_mean            float64\n",
      "compactness_mean           float64\n",
      "concavity_mean             float64\n",
      "concave points_mean        float64\n",
      "symmetry_mean              float64\n",
      "fractal_dimension_mean     float64\n",
      "radius_se                  float64\n",
      "texture_se                 float64\n",
      "perimeter_se               float64\n",
      "area_se                    float64\n",
      "smoothness_se              float64\n",
      "compactness_se             float64\n",
      "concavity_se               float64\n",
      "concave points_se          float64\n",
      "symmetry_se                float64\n",
      "fractal_dimension_se       float64\n",
      "radius_worst               float64\n",
      "texture_worst              float64\n",
      "perimeter_worst            float64\n",
      "area_worst                 float64\n",
      "smoothness_worst           float64\n",
      "compactness_worst          float64\n",
      "concavity_worst            float64\n",
      "concave points_worst       float64\n",
      "symmetry_worst             float64\n",
      "fractal_dimension_worst    float64\n",
      "Unnamed: 32                float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "0f530925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                           0\n",
       "diagnosis                    0\n",
       "radius_mean                  0\n",
       "texture_mean                 0\n",
       "perimeter_mean               0\n",
       "area_mean                    0\n",
       "smoothness_mean              0\n",
       "compactness_mean             0\n",
       "concavity_mean               0\n",
       "concave points_mean          0\n",
       "symmetry_mean                0\n",
       "fractal_dimension_mean       0\n",
       "radius_se                    0\n",
       "texture_se                   0\n",
       "perimeter_se                 0\n",
       "area_se                      0\n",
       "smoothness_se                0\n",
       "compactness_se               0\n",
       "concavity_se                 0\n",
       "concave points_se            0\n",
       "symmetry_se                  0\n",
       "fractal_dimension_se         0\n",
       "radius_worst                 0\n",
       "texture_worst                0\n",
       "perimeter_worst              0\n",
       "area_worst                   0\n",
       "smoothness_worst             0\n",
       "compactness_worst            0\n",
       "concavity_worst              0\n",
       "concave points_worst         0\n",
       "symmetry_worst               0\n",
       "fractal_dimension_worst      0\n",
       "Unnamed: 32                569\n",
       "dtype: int64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "\n",
    "# There are no null data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02931756",
   "metadata": {},
   "source": [
    "\n",
    "### <font color='black'> Data Exploratory Analysis </font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "fa6dc28e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "90e010a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.diagnosis.value_counts().plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "154bb01e",
   "metadata": {},
   "outputs": [],
   "source": [
    "diagnosis_mapping = {'M': 1, 'B': 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "6084800c",
   "metadata": {},
   "outputs": [],
   "source": [
    " df.diagnosis = df.diagnosis.map(diagnosis_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "3897692f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # if the datatype is not in string format\n",
    "# df['diagnosis'] = df['diagnosis'].astype(str)\n",
    "\n",
    "# # Strip any leading/trailing spaces and convert to uppercase\n",
    "# df['diagnosis'] = df['diagnosis'].str.strip().str.upper()\n",
    "\n",
    "# # Create the mapping dictionary\n",
    "# diagnosis_mapping = {'M': 1, 'B': 0}\n",
    "\n",
    "# # Apply the mapping to the 'diagnosis' column\n",
    "# df['label'] = df['diagnosis'].map(diagnosis_mapping)\n",
    "\n",
    "# # Display the DataFrame to check results\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "e4e94604",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>1</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>1</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>1</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>1</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>1</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302          1        17.99         10.38          122.80     1001.0   \n",
       "1    842517          1        20.57         17.77          132.90     1326.0   \n",
       "2  84300903          1        19.69         21.25          130.00     1203.0   \n",
       "3  84348301          1        11.42         20.38           77.58      386.1   \n",
       "4  84358402          1        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "8d53a49d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
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       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
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       "      <th>concavity_mean</th>\n",
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       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
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       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
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       "      <th>concave points_worst</th>\n",
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       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
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       "      <td>0.4601</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>1</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
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       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>1</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>1</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>1</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>564</th>\n",
       "      <td>926424</td>\n",
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       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>926682</td>\n",
       "      <td>1</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
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       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>1</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
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       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>1</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
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       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>0</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
       "0      842302          1        17.99         10.38          122.80   \n",
       "1      842517          1        20.57         17.77          132.90   \n",
       "2    84300903          1        19.69         21.25          130.00   \n",
       "3    84348301          1        11.42         20.38           77.58   \n",
       "4    84358402          1        20.29         14.34          135.10   \n",
       "..        ...        ...          ...           ...             ...   \n",
       "564    926424          1        21.56         22.39          142.00   \n",
       "565    926682          1        20.13         28.25          131.20   \n",
       "566    926954          1        16.60         28.08          108.30   \n",
       "567    927241          1        20.60         29.33          140.10   \n",
       "568     92751          0         7.76         24.54           47.92   \n",
       "\n",
       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
       "0       1001.0          0.11840           0.27760         0.30010   \n",
       "1       1326.0          0.08474           0.07864         0.08690   \n",
       "2       1203.0          0.10960           0.15990         0.19740   \n",
       "3        386.1          0.14250           0.28390         0.24140   \n",
       "4       1297.0          0.10030           0.13280         0.19800   \n",
       "..         ...              ...               ...             ...   \n",
       "564     1479.0          0.11100           0.11590         0.24390   \n",
       "565     1261.0          0.09780           0.10340         0.14400   \n",
       "566      858.1          0.08455           0.10230         0.09251   \n",
       "567     1265.0          0.11780           0.27700         0.35140   \n",
       "568      181.0          0.05263           0.04362         0.00000   \n",
       "\n",
       "     concave points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0                0.14710  ...        25.380          17.33           184.60   \n",
       "1                0.07017  ...        24.990          23.41           158.80   \n",
       "2                0.12790  ...        23.570          25.53           152.50   \n",
       "3                0.10520  ...        14.910          26.50            98.87   \n",
       "4                0.10430  ...        22.540          16.67           152.20   \n",
       "..                   ...  ...           ...            ...              ...   \n",
       "564              0.13890  ...        25.450          26.40           166.10   \n",
       "565              0.09791  ...        23.690          38.25           155.00   \n",
       "566              0.05302  ...        18.980          34.12           126.70   \n",
       "567              0.15200  ...        25.740          39.42           184.60   \n",
       "568              0.00000  ...         9.456          30.37            59.16   \n",
       "\n",
       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0        2019.0           0.16220            0.66560           0.7119   \n",
       "1        1956.0           0.12380            0.18660           0.2416   \n",
       "2        1709.0           0.14440            0.42450           0.4504   \n",
       "3         567.7           0.20980            0.86630           0.6869   \n",
       "4        1575.0           0.13740            0.20500           0.4000   \n",
       "..          ...               ...                ...              ...   \n",
       "564      2027.0           0.14100            0.21130           0.4107   \n",
       "565      1731.0           0.11660            0.19220           0.3215   \n",
       "566      1124.0           0.11390            0.30940           0.3403   \n",
       "567      1821.0           0.16500            0.86810           0.9387   \n",
       "568       268.6           0.08996            0.06444           0.0000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                  0.2654          0.4601                  0.11890  \n",
       "1                  0.1860          0.2750                  0.08902  \n",
       "2                  0.2430          0.3613                  0.08758  \n",
       "3                  0.2575          0.6638                  0.17300  \n",
       "4                  0.1625          0.2364                  0.07678  \n",
       "..                    ...             ...                      ...  \n",
       "564                0.2216          0.2060                  0.07115  \n",
       "565                0.1628          0.2572                  0.06637  \n",
       "566                0.1418          0.2218                  0.07820  \n",
       "567                0.2650          0.4087                  0.12400  \n",
       "568                0.0000          0.2871                  0.07039  \n",
       "\n",
       "[569 rows x 32 columns]"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df.columns[-1], axis=1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "1c3b00b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1076.74x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, vars=['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean'], hue='diagnosis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888c06bf",
   "metadata": {},
   "source": [
    "\n",
    "### <font color='black'> Define X and y </font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "15292286",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('diagnosis', axis=1)\n",
    "y= df['diagnosis']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "200b1ef9",
   "metadata": {},
   "source": [
    "\n",
    "### <font color='black'> Train_Test_Split </font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "22c6f858",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "519003d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9ae1394",
   "metadata": {},
   "source": [
    "#### <font color='black'> Data Scaling </font>\n",
    "\n",
    "- Scaling the data because the values in the data are widely apart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "abc2c938",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "c7b48f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "04c77894",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "ed834bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261e1ca5",
   "metadata": {},
   "source": [
    "- model selction\n",
    "\n",
    "- will be using KNNClassification to identify the ditance/relationship of the variables in forcasting new data enties\n",
    "\n",
    "- also, will use GridsearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "564e4dd0",
   "metadata": {},
   "source": [
    "### <font color='black'> Data Modelling </font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "84693df5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "6aaa3f0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "2db019ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=2)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "d476e8de",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deaec10f",
   "metadata": {},
   "source": [
    "### <font color='black'> Data Metrics </font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "61d08332",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "bde99a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9532163742690059"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "1d271bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.953"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(accuracy_score(y_test, y_pred), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "ab9226e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[107,   1],\n",
       "       [  7,  56]], dtype=int64)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "b524444a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1418eb93a60>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(knn, scaled_X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "cd878173",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.96       108\n",
      "           1       0.98      0.89      0.93        63\n",
      "\n",
      "    accuracy                           0.95       171\n",
      "   macro avg       0.96      0.94      0.95       171\n",
      "weighted avg       0.95      0.95      0.95       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b566347f",
   "metadata": {},
   "source": [
    "- the metrics for precisiona and recall are different because the data is unbalanced\n",
    "- It's better to have False Positives than False negatives.\n",
    "- Finetuning oroptimizing the Recall metric will be at the cost of a lower precision score (which is alriht in our data context)\n",
    "- Due to the context of the data, need to further optimize the model's ability to significantly minimize false negatives- which means ethe recall would be enhanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "efba5574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04678362573099415"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_rate = 1-(accuracy_score(y_test, y_pred))\n",
    "\n",
    "error_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a99db44e",
   "metadata": {},
   "source": [
    "#### <font color='black'> Hypertune the parameters to improve the Metrics Score </font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "d24f7e17",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "error_rate = [ ]\n",
    "\n",
    "for k in range (1, 50):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(scaled_X_train, y_train)\n",
    "    y_pred = knn.predict(scaled_X_test)\n",
    "\n",
    "    error = 1-(accuracy_score(y_test, y_pred))\n",
    "\n",
    "    error_rate.append(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "b97d46fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.04678362573099415,\n",
       " 0.04678362573099415,\n",
       " 0.040935672514619936,\n",
       " 0.040935672514619936,\n",
       " 0.040935672514619936,\n",
       " 0.03508771929824561,\n",
       " 0.040935672514619936,\n",
       " 0.040935672514619936,\n",
       " 0.0292397660818714,\n",
       " 0.040935672514619936,\n",
       " 0.0292397660818714,\n",
       " 0.040935672514619936,\n",
       " 0.03508771929824561,\n",
       " 0.04678362573099415,\n",
       " 0.040935672514619936,\n",
       " 0.04678362573099415,\n",
       " 0.04678362573099415,\n",
       " 0.04678362573099415,\n",
       " 0.052631578947368474,\n",
       " 0.04678362573099415,\n",
       " 0.052631578947368474,\n",
       " 0.04678362573099415,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.04678362573099415,\n",
       " 0.052631578947368474,\n",
       " 0.04678362573099415,\n",
       " 0.04678362573099415,\n",
       " 0.04678362573099415,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.04678362573099415,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.052631578947368474,\n",
       " 0.04678362573099415,\n",
       " 0.052631578947368474]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "81cce846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'range')"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, 50), error_rate)\n",
    "plt.ylabel('error rate')\n",
    "plt.xlabel('range')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4bf6747",
   "metadata": {},
   "source": [
    "**Zone in on the graph, since the minimu error rate falls within the KNN range of 9 and 11**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "a7aa0ac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8.0, 12.0)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, 50), error_rate)\n",
    "plt.ylabel('error rate')\n",
    "plt.xlabel('range')\n",
    "\n",
    "plt.xlim(8, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "c3f78f58",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "knn_2 = KNeighborsClassifier(n_neighbors=9)\n",
    "\n",
    "knn_2.fit(scaled_X_train, y_train)\n",
    "y_pred_2 = knn.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "f0ce67be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9473684210526315"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "c4297ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.947"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(accuracy_score(y_test, y_pred), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bacac60e",
   "metadata": {},
   "source": [
    "Since the accuracy has no much diff- leave the knn as it was"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a1eaaf",
   "metadata": {},
   "source": [
    "#### <font color='black'> Hypertune the parameters using GrodSearchCV to improve the Recall score </font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "5ff4644b",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "5263350e",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "c2332fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "operations = [('scaler', scaler), ('knn', knn)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "6fc83957",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "c21d5dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(operations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "d0ee6124",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "981de645",
   "metadata": {},
   "outputs": [],
   "source": [
    "k_values = range(1, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "1556d961",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'knn__n_neighbors': k_values}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "90ca7c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import make_scorer, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "0487e1a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "scorer= make_scorer(recall_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "45f0456e",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_cv_classifier = GridSearchCV(pipe, param_grid, cv=5, scoring=scorer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "62d3ba82",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                                       ('knn', KNeighborsClassifier())]),\n",
       "             param_grid={'knn__n_neighbors': range(1, 50)},\n",
       "             scoring=make_scorer(recall_score))"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_cv_classifier.fit(scaled_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "eecadc67",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_cv_classifier.predict(scaled_X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "27ce06ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "C:\\Users\\Teni\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1418f2c0be0>"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(full_cv_classifier, scaled_X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "4d014e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'knn__n_neighbors': 3}"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_cv_classifier.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "eb25373b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9062068965517242"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_cv_classifier.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb854140",
   "metadata": {},
   "source": [
    "- The best knn value from the range using GridsearchCV is 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c17400b",
   "metadata": {},
   "source": [
    "\n",
    "## <font color='purple'> Conclusion: </font>\n",
    "\n",
    "Through careful adjustments, I improved the model's performance significantly. I reduced false negatives from 7 to 4, ensuring more cancer cases were correctly identified. Additionally, precision increased from 1 to 3, meaning the model made fewer incorrect positive predictions. Overall, my refined model is more reliable for early cancer detection. These improvements not only enhance the model's accuracy but also its potential utility in clinical settings for better patient outcomes."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}