{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Week4_Assignment.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "zZKLWQvm2d3b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 170
},
"outputId": "bfe75ff4-5b82-4001-8624-5cfda1924b81"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/data/')\n",
"data_dir = '/data/My Drive/Colab Notebooks/Experiment'\n",
"!ls '/data/My Drive/Colab Notebooks/Experiment'\n",
"!pip install matplotlib"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /data/\n",
"Iris.csv m_data.csv w_data.csv\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (3.2.2)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.4.7)\n",
"Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.18.5)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.2.0)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.8.1)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qkXPqdBaH8fQ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "620bfeda-e873-43d5-a24b-2e6bdb2ea407"
},
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(data_dir+'/Iris.csv')\n",
"df.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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>SepalLengthCm</th>\n",
" <th>SepalWidthCm</th>\n",
" <th>PetalLengthCm</th>\n",
" <th>PetalWidthCm</th>\n",
" <th>Species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n",
"0 1 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 2 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 3 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5 5.0 3.6 1.4 0.2 Iris-setosa"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "h_XZLhyeIRjz"
},
"source": [
"import numpy as np \n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6JqPVcX7JLaM",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
},
"outputId": "b1670e08-a489-4544-9e34-bcadc3ba45ea"
},
"source": [
"df.info()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 150 entries, 0 to 149\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Id 150 non-null int64 \n",
" 1 SepalLengthCm 150 non-null float64\n",
" 2 SepalWidthCm 150 non-null float64\n",
" 3 PetalLengthCm 150 non-null float64\n",
" 4 PetalWidthCm 150 non-null float64\n",
" 5 Species 150 non-null object \n",
"dtypes: float64(4), int64(1), object(1)\n",
"memory usage: 7.2+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "M6JqhSioKnow",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "8799a8c1-a612-4d63-e9eb-7ce137336bef"
},
"source": [
"df['Species'].value_counts()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Iris-setosa 50\n",
"Iris-virginica 50\n",
"Iris-versicolor 50\n",
"Name: Species, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hSAoi01UO5Sz"
},
"source": [
"df.drop('Id', axis=1, inplace=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "L4Wtn1qMPXdg",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"outputId": "38e7af2d-c207-42b2-b670-68af36b335d3"
},
"source": [
"df"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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>SepalLengthCm</th>\n",
" <th>SepalWidthCm</th>\n",
" <th>PetalLengthCm</th>\n",
" <th>PetalWidthCm</th>\n",
" <th>Species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>6.7</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.3</td>\n",
" <td>Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>1.9</td>\n",
" <td>Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.0</td>\n",
" <td>Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>6.2</td>\n",
" <td>3.4</td>\n",
" <td>5.4</td>\n",
" <td>2.3</td>\n",
" <td>Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>5.1</td>\n",
" <td>1.8</td>\n",
" <td>Iris-virginica</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>150 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n",
"0 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5.0 3.6 1.4 0.2 Iris-setosa\n",
".. ... ... ... ... ...\n",
"145 6.7 3.0 5.2 2.3 Iris-virginica\n",
"146 6.3 2.5 5.0 1.9 Iris-virginica\n",
"147 6.5 3.0 5.2 2.0 Iris-virginica\n",
"148 6.2 3.4 5.4 2.3 Iris-virginica\n",
"149 5.9 3.0 5.1 1.8 Iris-virginica\n",
"\n",
"[150 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "1y1Lzt6bPbrY",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 404
},
"outputId": "6124b280-8344-449c-910c-59428bd31917"
},
"source": [
"figure = df[df['Species']=='Iris-setosa'].plot.scatter(x='SepalLengthCm', y='SepalWidthCm', color='red', label='Setosa')\n",
"df[df['Species']=='Iris-versicolor'].plot.scatter(x='SepalLengthCm', y='SepalWidthCm', color='blue', label='versicolor', ax=figure)\n",
"df[df['Species']=='Iris-virginica'].plot.scatter(x='SepalLengthCm', y='SepalWidthCm', color='green', label='virginica', ax=figure)\n",
"\n",
"figure.set_xlabel(\"Sepal Length\")\n",
"figure.set_ylabel(\"Sepal Width\")\n",
"figure.set_title(\"Length VS Width\")\n",
"figure=plt.gcf()\n",
"figure.set_size_inches(10, 6)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "b8tJhJdtdyOI",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 404
},
"outputId": "28cdd5b5-efa8-460c-b01c-48b429ac2039"
},
"source": [
"figure = df[df['Species']=='Iris-setosa'].plot.scatter(x='PetalLengthCm', y='PetalWidthCm', color='red', label='Setosa')\n",
"df[df['Species']=='Iris-versicolor'].plot.scatter(x='PetalLengthCm', y='PetalWidthCm', color='blue', label='versicolor', ax=figure)\n",
"df[df['Species']=='Iris-virginica'].plot.scatter(x='PetalLengthCm', y='PetalWidthCm', color='green', label='virginica', ax=figure)\n",
"\n",
"figure.set_xlabel(\"Petal Length\")\n",
"figure.set_ylabel(\"Petal Width\")\n",
"figure.set_title(\"Length VS Width\")\n",
"figure=plt.gcf()\n",
"figure.set_size_inches(10, 6)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "85aTyIcQlqt-",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 390
},
"outputId": "bf24b69d-0eb9-4c7f-f743-5605d257376b"
},
"source": [
"df.hist(edgecolor='green', linewidth=1.2)\n",
"figure=plt.gcf()\n",
"figure.set_size_inches(12, 6)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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tc2vV5zS1Tu04UrfdYmq3eKA9YzIzs/aUafaJ9Jaf1wNfiIgvpYsflrQofX0RyWTvZmZmZmYdJ8vsEyIZbXxvRKyveOkGkul5wNP0mJmZmVkHy3Ld/JXARcCW9PafAB8ALgOulfROYAdwQT4hmpmZmZnlq2pSHBG3AZrh5bOaG46ZmZmZWev5jnZmZmZm1vOcFJuZmZlZz3NSbGZmZmY9r7EJas263K7RXUBy05CRvSO5lNHf18/C+Qtz2beZmZll46TYbBoTPAkxl9M2ngbA4MmDrFq/KpeyFh52NDvWPODE2MzMrEBOis2mERwAHeT4/RuYG0fRN9HPkn2bml7OQe1hF2sYK485KTYzMyuQk2KzWcyNo5jHMYjxfG4rPe3N0c3MzKzVPNDOzMzMzHqezxSbmZl1sNL+EmPlsdzLmRx4bNatnBSbmZl1qNL+EiduOInSgd1Fh2LW8ZwUm5mZdaix8hilA7sPDQrO01PazqPzL821DLMiOSk2MzPrcJODgvN0MPbkun+zonmgnZmZmZn1PJ8ptqZp1iCM2e4e54EeZmZmlgcnxdawqXd/a1Sed48zMzMzm46TYmvY1Lu/NWq2u8d5oIeZdYrJqdJmu/rVKF89M2ueqkmxpE8D5wKPRMRL0mUfAn4HeDRd7QMRcVNeQVpnaNZAj9nuHueBHmbWCSqnSvPVL7POkOVM8WeBjwOfm7L8iogYbHpEZmZmHa5yqrS+iaUzXv1qlK+emTVP1aQ4Im6VtCz/UMzMzLrL3DgKMS+36dJ89cyseRrpU/xuSW8F7gTWRkz/lylpNbAaYGBggKGhoQaKrM3o6Gjm8soTZQZPHqRvoh8xnltMAwvmMnjyIFvv2Mq2OdtyKwey1WlgAaxd3lh9g0WU5zTvvZstpmaXNZOp5TTjfZq+nH7Kc+o7Hmo5vluh3eKB9ozJzMzaU71J8SeBjwCR/r4ceMd0K0bERmAjwIoVK2LlypV1Flm7oaEhspY3sneEVetXsWTfplwnQH/v8v2su28dw+cOs/iIxbmVA9nqtHb5OJdvaWy85QG2s2vBuqa9d7PF1OyyZjK1nGa8T9MZp8TwgvqOh1qO71Zot3igPWNqV5JOIOkmN0DStm+MiI9JOhq4BlgGbAcumOkkiJlZJ6vr5h0R8XBEHIyICeDvgNObG5aZmbXYOMlVvxcDrwB+X9KLgUuAb0XEC4Fvpc/NzLpOXUmxpEUVT38NuKc54ZiZWREiYmdE3JU+3gvcCywBzgMmR4ltAt5UTIRmZvnKMiXbVcBK4FhJDwGXAislnUJyiW078Ls5xtiVWjG3pOevNLN6pIOrTwVuBwYiYmf60i6S7hVT12947Ei39f+uHNOR15gEaN04i5nKyqNurRs7MvuYjm47Jiu5btPLMvvEm6dZfGVdpRkw0dS7v5mZNZOkw4HrgTUR8YSkQ69FREiKqds0Y+xIt/X/rhzT8f7lC3MZkwCtG2cxU1l5jLdoVZ2qjenotmOykus2Pd/RruWiqXd/m43nrzSzWkjqI0mIvxARX0oXPyxpUUTsTLvOPVJchGZm+XFSXJBm3f1tNp6/0syyUnJK+Erg3ohYX/HSDcDFwGXp768UEJ6ZWe6cFJuZGcArgYuALZLuTpd9gCQZvlbSO4EdwAUFxWdmlisnxWZmRkTcBmiGl89qZSxmZkWoa0o2MzMzM7Nu4qTYzMzMzHqek2IzMzMz63lOis3MzMys5zkpNjMzM7Oe56TYzMzMzHqek2IzMzMz63lOis3MzMys5zkpNjMzM7Oe56TYzMzMzHqek2IzMzMz63lOis3MzMys51VNiiV9WtIjku6pWHa0pJsl/ST9fVS+YZqZmZmZ5SfLmeLPAudMWXYJ8K2IeCHwrfS5mZmZmVlHqpoUR8StwO4pi88DNqWPNwFvanJcZmZmZmYtM6/O7QYiYmf6eBcwMNOKklYDqwEGBgYYGhqqs8jajY6OZi6vPFFm8ORB+ib6EeO5xTSwoK8l5QAEiyjPmb2sgQWwdnljcWQppxazxdTssmYytZxmvE/Tl9NPec4gW+/YyrY522ratpbjuxXaLR5oz5jMzKw91ZsUHxIRISlmeX0jsBFgxYoVsXLlykaLzGxoaIis5Y3sHWHV+lUs2beJeRyTW0zvXT7GuvvW5V4OwAG2s2vB7GWtXT7O5VsaOwyylFOL2WJqdlkzmVpOM96n6YxTYnjBOobPHWbxEYtr2raW47sV2i0eaM+YzMysPdU7+8TDkhYBpL8faV5IZmZmZmatVW9SfANwcfr4YuArzQnHzMzMzKz1ql4PlnQVsBI4VtJDwKXAZcC1kt4J7AAuyCvA0v4SY+WxurYtT5QZ2TuSad1do7vqKsPMzMzMOl/VpDgi3jzDS2c1OZZnKe0vceKGkygdmDr5RTaDJw+yav2qJkdlZmZmZt2m+SOHmmisPEbpwG6O37+BuVH7/UH6JvpZsm9T9RWBp7SdR+dfWnMZZmZmZtb52jopnjQ3jqprpgExnnm7g7Gn5v2bmZmZWXeod6CdmZmZmVnXcFJsZmZmZj2vI7pPmJmZmXWyRmbTqkV/Xz8L5y/MvZxu5KTYzMyQ9GngXOCRiHhJuuxo4BpgGbAduCDCAzDMatXobFq1WHjY0exY84AT4zo4KTYzM4DPAh8HPlex7BLgWxFxmaRL0ufvLyA2s47W6GxaWR3UHnaxhrHymJPiOjgpNjMzIuJWScumLD6P5OZNAJuAIZwUm9Wt3tm0Mov8dt0LPNDOzMxmMhARO9PHu4CBIoMxM8uTzxSbmVlVERGSpj0PJWk1sBpgYGCAoaGhmvc/Ojpa13btqjxRZvDkQfom+hlYAGuXj+dSTrCI8pykHJFPGbOVlUfdWlWnoJ/ynEG23rGVbXO2Pev1Zh6TlcdDkXWa1G1/b5UaqZuTYjMzm8nDkhZFxE5Ji4BHplspIjYCGwFWrFgRK1eurLmgoaEh6tmuXY3sHWHV+lUs2beJ9y9fyOVb8vm6PcB2di1Yx5J9m/K9LD9DWWuXjze9bq2q0zglhhesY/jcYRYfsfhZrzfzmKw8Hoqs06Ru+3ur1EjdnBSbmdlMbgAuBi5Lf3+l2HDMmm/X6K5pl5cnyozsHcm1DGsvTorNzAxJV5EMqjtW0kPApSTJ8LWS3gnsAC4oLkKz5prgSYi5nLbxtGlfHzx5kFXrV7U4KiuSk2KzNlDPWYR6zmK0alL3Vk1SD56ovlki4s0zvHRWSwMxa5HgAOjgjNOk9U30s2TfpqaU9ZS28+j8S5uyL8uPk2KzAlU7UzGbes5itGJS91ZOUg+eqN7MGjPTNGlivGn9fw/6njcdwUmxWYGqnamYTa1nMVo1qXurJqkHT1RvZmbN01BSLGk7sBc4CIxHxIpmBGXWa+qZ0L3msxgtntQ990nqwRPVm5lZ0zTjTPF/i4ifNmE/ZmZmZmaFcPcJsx6T19RAkwP/PPWQmZl1okaT4gC+kd7l6P+kE7g/QyN3Omr0DjC13GmnVXfQGVjQ15K72kC2OjXjbkTNfu9mi6l1dzp6Zjl53ZGqkfrUGlOwgPKcQW4ZuqXWMDNZethSrrrxKoAWHuOtuyOVdYdWzYzifw7NOk+jSfGrImJY0s8CN0v6UUTcWrlCI3c6avQOMLXcaadVd9B57/Ix1t1X3N2HpmrG3Yia/d7NFlOrPqep5eRx16bpyqlFrTFNlpXXALj3LZ/HFVuOOzT1UCuO8Vbekco6X6tnRjGzztLQt3xEDKe/H5H0ZeB04NbZtzKzIuU1AG5y4J+nHrJ21cqZUTwvrVnnqTspltQPzImIvenjVcCfNi0yMzOzHLRiZhT/c2jWeRo5UzwAfFnS5H7+ISK+3pSozMzMzMxaqO6kOCIeAF7axFjMzMzMzAoxp+gAzMzMzMyK5qTYzMzMzHqek2IzMzMz63lOis3MzMys5/k2z2ZmVriDcZCRvSO5luG7zJnZbJwUm5lZoUr7S2x5+B7OXn920aGYWQ9zUmxmZoUaK49xMMZzv9Oc7zJnZrNxUmxmZm0h7zvN+S5zZjYbD7QzMzMzs57npNjMzMzMep6TYjMzMzPree5TbGZmZtZFqk0/WJ4oN2UKxPGJcebNyT+V7O/rZ+H8hbmX46TYzMzMrAtM8CTEXE7beNqs6w2ePMiq9asaLk/MIxhveD/VLDzsaHaseSD3xNhJsZmZmVkXCA6ADlad3rBvop8l+zY1VNbkFId5T6V4UHvYxRrGymNOis3MzMwsu2rTG4rxhqc/nJziMO+pFIn8dj2VB9qZmZmZWc9rKCmWdI6kH0u6T9IlzQrKzMzah9t6M+sFdSfFkuYCnwBeB7wYeLOkFzcrMDMzK57bejPrFY30KT4duC8iHgCQdDVwHvDDZgRW6aD21NWnJOhnnFLGMkoNlZU9pr6WlJOUUb1OtbxHjZRTi9liatXnNLWcZrxPWcqpRa0x5f3eTcbTqs/oUBmWt7Zv67Pvv5XH5tNl5dV+TC2niO+UPOpWVDs/VTPr1i51mtSO3/0zl9O6dl4R9dVE0vnAORHxrvT5RcDLI+LdU9ZbDaxOn74I+HH94dbsWOCnLSwvi3aLqd3iAceUVbvF1G7xwMwxnRgRx7U6mE6Upa1vUjvfjsdPs7huncl160yVdauprc999omI2AhszLuc6Ui6MyJWFFH2TNotpnaLBxxTVu0WU7vFA+0ZUzdqRjvfzZ+V69aZXLfO1EjdGhloNwycUPF8abrMzMy6h9t6M+sJjSTFdwAvlPQ8Sc8BLgRuaE5YZmbWJtzWm1lPqLv7RESMS3o38E/AXODTEbG1aZE1RyHdNqpot5jaLR5wTFm1W0ztFg+0Z0wdpYVtfTd/Vq5bZ3LdOlPddat7oJ2ZmZmZWbfwHe3MzMzMrOc5KTYzMzOzntcVSbGkuZK+J+nGaV57m6RHJd2d/ryrRTFtl7QlLfPOaV6XpL9Ob5v6A0kvKzielZJKFe/TB/OMJy3zSEnXSfqRpHslnTHl9Za+Rxljaun7JOlFFWXdLekJSWumrNOy9yljPEUcS/9L0lZJ90i6StL8Ka8fJuma9D26XdKyvGOyZ5J0gqRbJP0w/azeM806Lf+bb5aM9Wv530YzSJov6buSvp/W7cPTrNORf2MZ61ZIHtEsmj1H6sjPbVKVutX8ueU+T3GLvAe4F3juDK9fM/WmIi3y3yJipsmxXwe8MP15OfDJ9HdR8QB8OyLOzTmGSh8Dvh4R5ysZ1f4zU14v4j2qFhO08H2KiB8Dp8Ch2+0OA1+eslrL3qeM8UAL3yNJS4A/AF4cEfskXUsyQ8JnK1Z7J7AnIl4g6ULgL4HfbEV8dsg4sDYi7pJ0BLBZ0s0RUXlnvCL+5pslS/2g9e1sMxwAzoyIUUl9wG2SvhYR/16xTqf+jWWpGxSXRzTDbDlSp35uk5qa/3X8mWJJS4FfBT5VdCw1Og/4XCT+HThS0qKig2oVSQuB1wBXAkTEUxHx+JTVWvoeZYypSGcB90fEjinLizqWZoqnCPOABZLmkfwjMzLl9fOATenj64CzJKmF8fW8iNgZEXelj/eSfJEtmbJax7aLGevXkdLPYzR92pf+TB2l35F/Yxnr1rEy5Egd+blBPvlfxyfFwAbgfcDELOv8enop7jpJJ8yyXjMF8A1Jm5XcAnWqJcCDFc8fIt8GtFo8AGekl5C+JukXc4wF4HnAo8Bn0ksfn5LUP2WdVr9HWWKC1r5PlS4Erppmeavfp2rxQAvfo4gYBgaB/wR2AqWI+MaU1Q69RxExDpSAY/KMy2aWXqI9Fbh9yktFHctNNUv9oLj2oyHpZeq7gUeAmyNixs+u0/7GMtQNiskjmqFajtSxnxs55H8dnRRLOhd4JCI2z7LaV4FlEfFfgJt5+j+ivL0qIl5Gcjnw9yW9pkXl1hvPXST3CH8p8DfAP+YczzzgZcAnI+JUYAy4JOcyq8kSU6vfJwDSrhxvBL7YivKqqRJPS98jSUeRnO14HrAY6Jf023mWafWTdDhwPbAmIp4oOp5mq1K/QtqPZoiIgxFxCskdDU+X9JKiY2qWDHUrKo9oSMYcqSPllf91dFIMvBJ4o6TtwNXAmZI+X7lCRDwWEQfSp58CTmtFYOnZKyLiES1GHBwAACAASURBVJI+l6dPWaWlt06tFk9EPDF5CSkibgL6JB2bVzwkZ4AeqviP/DqShLRSq28vWzWmAt6nSa8D7oqIh6d5rYjb8M4YTwHv0dnAf0TEoxFRBr4E/PKUdQ69R2kXi4XAYznGZNNI+2xeD3whIr40zSodfUvpavUrsP1omrRL2S3AOVNe6vi/sZnqVlQe0QRVcyQ693PLJf/r6KQ4Iv4oIpZGxDKSS7n/HBHPOEM0pT/aG0n6eeVKUn860IL08vsq4J4pq90AvFWJV5Bc8t1ZVDySjp/sRyTpdJJjI7c/jIjYBTwo6UXporOAqQNSWvYeZY2p1e9ThTczc1eFlr5P1eIp4D36T+AVkn4mLfcsnv13fgNwcfr4fJK2omv6DXaC9LO5Erg3ItbPsFoRx3JTZKlfge1HQyQdJ+nI9PEC4LXAj6as1pF/Y1nqVkQe0QxZciQ69HPLK//rltknnkHSnwJ3RsQNwB9IeiPJyODdwNtaEMIA8OW07ZsH/ENEfF3S7wFExN8CNwGvB+4DngTeXnA85wP/XdI4sA+4sAV/GP8T+EJ6Kf4B4O0FvkdZY2r5+5T+I/Na4HcrlhX2PmWIp6XvUUTcLuk6kkvT48D3gI1T2oErgb+XdB9JO3BhXvHYjF4JXARsSftvAnwA+Dko9G++WbLUr4h2thkWAZuUzDgzB7g2Im7skr+xLHUrIo/ITZd8btNq9HPzbZ7NzMzMrOd1dPcJMzMzM7NmcFJsZmZmZj3PSbGZmZmZ9TwnxWZmZmbW85wUm5mZmVnPc1JsZmZmZj3PSbGZmZmZ9TwnxWZmZmbW85wUm5mZmVnPc1JsZmZmZj3PSbGZmZmZ9TwnxZY7SR+S9Pmi46gk6W2Sbis6DjOzbtRIuy/pA5I+Ncvr2yWdPcvrQ5LeVU/Z1tucFPe4tHHZJ2lU0sOSPivp8Crb1N3gSFomKSTNqy/i1pUp6Vck3Sppr6RHJf2LpDfmFaeZWSsU0O7/k6T3VzxfkrbJ0y07PiL+PCIylVVP8i3pOel2P5E0lr4fn5a0rJb9WPdxUmwAb4iIw4GXASuA/11wPIWTdD7wReBzwFJgAPgg8IYi4zIza5JWtvu3Aq+peP4a4EfTLPtJROzKMY5J1wFvBN4CLAReCmwGzmpB2dbGnBTbIRExDHwNeImkV0j6jqTHJX1f0koASX8GvBr4eHqW4ePp8o9JelDSE5I2S3p1reVLWijpSkk7JQ1L+qikuelrb5N0m6RBSXsk/Yek11Vs+7yKs7rflPSJirMHt6a/H09jPqNiu2ftT5KA9cBHIuJTEVGKiImI+JeI+J2KeP5V0hXpe/SApF9Olz8o6RFJF9f6HpiZtVKL2v1bgVdKmsw5Xg1sAFZMWXZrut9nnP2VdJGkHZIek/THFcvPAT4A/GYa1/cryjwxbaP3SvqGpGPTbc4GXgucFxF3RMR42sZ/IiKuTNcZSr9/vpPu96uSjpH0hbSud/iscndyUmyHSDoBeD2wE/i/wEeBo4F1wPWSjouIPwa+Dbw7Ig6PiHenm98BnJKu/w/AFyXNrzGEzwLjwAuAU4FVQOUltJcDPwaOBf4KuDJNYEnL/C5wDPAh4KKK7SbPRhyZxvxvVfb3IuAEkrMJs3k58IO0zH8ArgZ+KY3/t0m+QGa9JGlmVqQWtfvfBQ4jOSMLSZt8M3DflGW3Tt1Q0ouBT5K06YtJ2tulABHxdeDPgWvSuF5aselbgLcDPws8J60PwNnAdyPiwSpvzYVpmUuA5wP/Bnwmreu9wKVVtrcO5KTYAP5R0uPAbcC/AA8BN0XETekZ0puBO0kazmlFxOcj4rH0v+7LSRrAF2UNQNJAuv81ETEWEY8AV5A0TJN2RMTfRcRBYBOwCBiQ9HMkyegHI+KpiLgNuCFDsdPuj6TRheRLYjb/ERGfSbe/hiSR/tOIOBAR3wCeIkmQzczaTcva/Yg4ANwOvEbS0cDCiHiAJNGeXPbiNI6pzgdujIhb0/38CTCRoX6fiYhtEbEPuJYkeYekfa/Wtk9uf39ElEjOpN8fEd+MiHGSrnWnZtiHdRgnxQbwpog4MiJOjIj/QZIY/kZ6Ce3xtOF8FUnSOC1J6yTdK6mUrr+Q5AxsVicCfcDOijL/D8l/+ZMO9TWLiCfTh4eTnD3YXbEMoNpZgNn291j6eMb6ph6ueLwv3c/UZT5TbGbtqNXt/mS/4lcD/5ouu61i2YMRsWOa7RZT0Z5HxBhPt9Gzqeyb/CRPt8WPUb1th2e3727be0DLZgCwjvIg8PeT/WenEZVP0n5k7yMZpLA1IiYk7QE03cazlHkAODb9T7wWO4GjJf1MRXJ7wkzxZvDjNJ5fBwZr3NbMrBPl3e7fCvwesJ3kDDEkyfGn0mXP6jqR2gn8QkW5P8PTV/OeFVcG3wTeI2lpRDxU47bW5Xym2KbzeeANSqYkmytpvqSVkpamrz8MnFSx/hEkfYEfBeZJ+iDw3CplHJbud37aB+1h4BvA5ZKeK2mOpOdL+q/Vgk3PLtwJfEjJVDtn8MxZIh4ludx20nTbT7O/AN4L/Imkt1fE8ypJG7Psw8ysw+Td7v8bcCTJeItvA0TEnnT732bmpPg64Ny0/X0O8Kc8M3d5GFhWMWBvVhHxTZL+zF+WdJqkeZKOkPR7kt6RZR/WvZwU27OkAxDOIxnV+yjJGYQ/5Onj5WPA+Upmbfhr4J+ArwPbgB3Afqp3XxgluQQ1+XMm8FaSARE/BPaQNIZZLnMB/BZwBsmlsY+S9PE9kNbnSeDPgH9NLwu+otrOIuI64DeBdwAjJA3vR4GvZIzHzKxj5N3up90eNpO08fdUvPRtkm5y0ybFEbEV+H2SgXw7Sb4bKs/wfjH9/ZikuzJW93zgJpLviVIazwqSs8jWw5ScFDPrLpKuAX4UER4hbGZmZlX5TLF1BUm/lHa3mJPOXXke8I9Fx2VmZmadwQPtrFscD3yJZADGQ8B/j4jvFRuSmZmZdQp3nzAzMzOznufuE2ZmZmbW81rafeLYY4+NZcuW1bTN2NgY/f39+QSUE8fcGo45f50WL2SLefPmzT+NiONaFFJPqWznO/H4ycp160yuW2eqt261tvUtTYqXLVvGnXfeWdM2Q0NDrFy5Mp+AcuKYW8Mx56/T4oVsMUua7s5Z1gSV7XwnHj9ZuW6dyXXrTPXWrda23t0nzMzMzKznOSk2MzMzs57npNjMzMzMep6TYjMzMzPreW1/846DcZCRvSMtKau/r5+F8xe2pCwzM3taaX+JsfJY7uW4nTezmbR1UlzaX2LLw/dw9vqzW1LewsOOZseaB9xgmllPkjQXuBMYjohzJT0PuJrkTpGbgYsi4qlml1vaX+LEDSdROrC72bt+FrfzZjaTtk6Kx8pjHIxxjt+/gblxVK5lHdQedrGGsfKYG0sz61XvAe4Fnps+/0vgioi4WtLfAu8EPtnsQsfKY5QO7M69rXc7b2azaeukeNLcOIp5HJNvIb7btZn1MElLgV8F/gx4ryQBZwJvSVfZBHyIHJLiSbm39W7nzWwWHZEUm5lZ7jYA7wOOSJ8fAzweEePp84eAJdNtKGk1sBpgYGCAoaEhAEZHRw89nk15oszgyYP0TfQjxquuX6+gn/KcQbbesZVtc7Y1tK+sdetErltnct0a56TYzKzHSToXeCQiNktaWev2EbER2AiwYsWKmLzzVNa7UI3sHWHV+lUs2bcp1zPF45QYXrCO4XOHWXzE4ob25buHdSbXrTO1qm5Ois3M7JXAGyW9HphP0qf4Y8CRkualZ4uXAsMFxmhmlivPU2xm1uMi4o8iYmlELAMuBP45In4LuAU4P13tYuArBYVoZpY7J8VmZjaT95MMuruPpI/xlQXHY2aWG3efMDOzQyJiCBhKHz8AnF5kPGZmreIzxWZmZmbW85wUm5mZmVnPc1JsZmZmZj3PSbGZmZmZ9TwnxWZmZmbW85wUm5mZmVnPc1JsZmZmZj3PSbGZmZmZ9TwnxWZmZmbW85wUm5mZmVnPc1JsZmZmZj3PSbGZmZmZ9TwnxWZmZmbW86omxZLmS/qupO9L2irpw+ny50m6XdJ9kq6R9Jz8wzUzMzMza74sZ4oPAGdGxEuBU4BzJL0C+Evgioh4AbAHeGd+YZqZmZmZ5adqUhyJ0fRpX/oTwJnAdenyTcCbconQzMzMzCxn87KsJGkusBl4AfAJ4H7g8YgYT1d5CFgyw7argdUAAwMDDA0NZQ6uPFFm6WFLed/yeYjx6hs0IOinPGeQrXdsZducbQ3ta3R0tKZ6tgPH3BqdFnOnxQudGbOZPVNpf4mx8ljT91ueKDOyd+TQ8/6+fhbOX9j0cqwzZUqKI+IgcIqkI4EvAz+ftYCI2AhsBFixYkWsXLkyc3Aje0e46saruGLLcczjmMzb1WOcEsML1jF87jCLj1jc0L6GhoaopZ7twDG3RqfF3GnxQmfGbGZPK+0vceKGkygd2N30fQ+ePMiq9asOPV942NHsWPOAE2MDMibFkyLicUm3AGcAR0qal54tXgoM5xGgmZmZ9Y6x8hilA7s5fv8G5sZRTd1330Q/S/ZtAuCg9rCLNYyVx5wUG5AhKZZ0HFBOE+IFwGtJBtndApwPXA1cDHwlz0DNWi2Py3dTL92BL9+ZmU1nbhzV9KvEYvzpfUZTd21dIMuZ4kXAprRf8Rzg2oi4UdIPgaslfRT4HnBljnGatVRel++mXroDX74zMzNrB1WT4oj4AXDqNMsfAE7PIyizouV1+a7y0h348p2ZmVm7qKlPsVmvafblu2dcugNfvjMzM2sTvs2zmZmZmfU8J8VmZoak+ZK+K+n7krZK+nC6/HmSbpd0n6RrJD2n6FjNzPLgpNjMzAAOAGdGxEuBU4BzJL2CZLahKyLiBcAe4J0FxmhmlhsnxWZmRiRG06d96U8AZwLXpcs3AW8qIDwzs9x5oJ2ZmQGQTr25GXgB8AngfuDx9CZNAA8BS6bZbjWwGmBgYODQbbaz3nK7PFFm8ORB+ib6EeNV169X0E95ziBb79jKtjnbGtpXN99OvOi65Xk8DCyAtcuTfTbzeGgHRX9ueWpV3ZwUm5kZABFxEDhF0pHAl4Gfz7jdRmAjwIoVK2LyNttZb7k9sneEVetXsWTfpqbfrKHSOCWGF6xj+NxhFh+xuKF9dfPtxIuuW57Hw9rl41y+JUl9mnk8tIOiP7c8tapu7j5hZmbPEBGPk9y19AzgSEmTJ1CWAsOFBWZmliOfKbaOM3n75elumdwsu0Z35bJfs3Yl6TigHBGPS1oAvJZkkN0twPnA1cDFwFeKi9LMLD9Oiq2jVN5+ebpbJptZ3RYBm9J+xXOAayPiRkk/BK6W9FHge8CVRQZpZpYXJ8XWUSpvv9w3sfQZt0xupqe0nUfnX5rLvs3aUUT8ADh1muUPAKe3PiIzs9ZyUmwdaW4chZiX26Ccg7Enl/2amZlZe/JAOzMzMzPreU6KzczMzKznOSk2MzMzs57npNjMzMzMep6TYjMzMzPreU6KzczMzKznOSk2MzMzs57npNjMzMzMep6TYjMzMzPreU6KzczMzKznOSk2MzMzs57npNjMzMzMep6TYjMzMzPreU6KzczMzKznOSk2MzMzs543r9oKkk4APgcMAAFsjIiPSToauAZYBmwHLoiIPfmFamZmZtaZSvtLjJXHctt/eaLMyN4R+vv6WTh/YW7ldLOqSTEwDqyNiLskHQFslnQz8DbgWxFxmaRLgEuA9+cXqpmZmVnnKe0vceKGkygd2J1bGYMnD7Jq/SoWHnY0O9Y84MS4DlWT4ojYCexMH++VdC+wBDgPWJmutgkYwkmxmZmZ2TOMlccoHdjN8fs3MDeOyqWMvol+jt+/gV2sYaw85qS4DlnOFB8iaRlwKnA7MJAmzAC7SLpXmJmZmdk05sZRzOOYXPYtxnNLuHtF5qRY0uHA9cCaiHhC0qHXIiIkxQzbrQZWAwwMDDA0NJQ5uPJEmaWHLeV9y+chxjNvV4+gn/KcQbbesZVtc7Y1tK/R0dGa6tkOOiXm8kSZwZMH6ZvoZ2ABrF2ez3ERLKI8Jymnmcfe1JibedzloVOOi0qdGLOZmRUvU1IsqY8kIf5CRHwpXfywpEURsVPSIuCR6baNiI3ARoAVK1bEypUrMwc3sneEq268iiu2HJfbf1aTxikxvGAdw+cOs/iIxQ3ta2hoiFrq2Q46JeaRvSOsWr+KJfs28f7lC7l8S00XOzI7wHZ2LVjHkn2bmnrsrV0+/oyYm3nc5aFTjotKnRizmZkVr+qUbEpOCV8J3BsR6yteugG4OH18MfCV5odnZmZmZpa/LPMUvxK4CDhT0t3pz+uBy4DXSvoJcHb63MzMOpCkEyTdIumHkrZKek+6/GhJN0v6SfrbnRbNrCtlmX3iNkAzvHxWc8MxM7OCePpNM+tpvqOdmZkRETsj4q708V6gcvrNTelqm4A3FROhmVm+8hmlZGY12TW6qyXl+E5HloWn3zSzXuSk2KxAEzwJMZfTNp7WkvJ8pyOrpp7pN2eaejPr9HiVUy3mOf1mr0+9mVXRdcvzeKicFrOVU2K24hgfWADvW97e03zWq1XHpJNiswIFB0AHc73L0aSD2uM7Hdms6p1+c6apN7NOj1c51WKe02/2+tSbWRVdtzyPh8ppMVs5JWYrjvG1y8f5qy1jbT3NZ71adUw6KTZrA3ne5eiQaW+vY5bIMP3mZXj6TTPrYk6KzcwMnp5+c4uku9NlHyBJhq+V9E5gB3BBQfGZmeXKSbGZmXn6TTPreZ6SzczMzMx6npNiMzMzM+t5TorNzMzMrOc5KTYzMzOznuek2MzMzMx6npNiMzMzM+t5TorNzMzMrOc5KTYzMzOznuek2MzMzMx6npNiMzMzM+t5TorNzMzMrOc5KTYzMzOznjev6ADaza7RXQ3vozxRZmTvyIyv9/f1s3D+wobLMTMzM7PmcFKcmuBJiLmctvG0hvc1ePIgq9avmvH1hYcdzY41DzgxNjMzM2sTTopTwQHQQY7fv4G5cVRD++qb6GfJvk3TvnZQe9jFGsbKY06KzczMrGOV9pcYK4/lXs7BOJh7GeCk+FnmxlHM45iG9iHGZ95HNLRrMzMzs8KV9pc4ccNJlA7szr2sK160gRX7V+R+MtFJsZmZmZnVZKw8RunA7qZcYZ/NQe3hYIy35Aq7k2IzMzMzq0szrrDPKgAezW//FTwlm5mZmZn1PCfFZmZmZtbz3H3CrMfUMhd3tTm3Z+K5uM3MrNNUTYolfRo4F3gkIl6SLjsauAZYBmwHLoiIPfmFaWaNqmcu7mpzbs/Ec3GbmVmnyXKm+LPAx4HPVSy7BPhWRFwm6ZL0+fubH56ZNUs9c3HPNuf2TDwXt5mZdaKqSXFE3Cpp2ZTF5wEr08ebgCGcFJt1hFpGCs865/ZMPBe3mZl1oHr7FA9ExM708S5gYKYVJa0GVgMMDAwwNDSUuZDyRJmlhy3lfcvnIcbrDDWbYBHlOYP0TfQ3XNbAAli7fPp9BP2U5wyy9Y6tbJuzraFymml0dLSmz6Yo5Ykygycnn9Ns73Ojmnk8VJoac17lTKeesup5j4s+xjvlWG437ipnZr2u4YF2ERGSZjw3FBEbgY0AK1asiJUrV2be98jeEa668Squ2HJcvnPgAQfYzq4F61iyb1PDZa1dPs7lW6Z/a8cpMbxgHcPnDrP4iMUNldNMQ0ND1PLZFGVk7wir1q9iyb5NvH/5whnf50Y183ioNPXYyKuc6dRT1mzH8kyKPsY75VhuQ5/FXeXMrIfVOyXbw5IWAaS/H2leSGZm1moRcSsw9X6t55F0kSP9/aaWBmVm1kL1JsU3ABenjy8GvtKccMzMrI1k7ipnZtbpskzJdhXJoLpjJT0EXApcBlwr6Z3ADuCCPIM0M7NizdZVbqaxI1n7d1eOFcizb30z+7t3c9/1ouuW5/FQOU6ileMfWnGMDyyA9y3vrjpB8jkNHHZYS+qUZfaJN8/w0llNjsXMzNrLw5IWRcTO2brKzTR2JGv/7sqxAnn2rW9mf/du7rtedN3yPB4qx0m0cvxDK47xtcvH+astY11VJ0g+p//10kd59S+9Ovc6+Y52ZmY2k8mucpfhrnJmHaOWO5e2cxmt5qTYmqK0v8RYeSz3crrxj9CsHbirnFnnq+fOpfY0J8XWsNL+EiduOInSgakD182sU7irnFnnq+fOpfV6Stt5dP6luZbRak6KrWFj5TFKB3b7j9DMOkIzrjiVJ8qM7B2ZdZ3+vv6uu9V5K64K+opg42q5c2m9DnbhfXycFFvT+I/QzNpZMy8tD548yKr1q2ZdZ+FhR7NjzQNdkxj7qqB1OyfFZmbWE5p5ablvop8l+zbN+PpB7WEXaxgrj3VNUtyqq4K+ImhFcVJsZmY9pRlXtcT47PuYdkbn7pD3VUFfEbSiOCk2MzPrYM3u5ztTf2n39bVu56TYzMysQ+XRzzdLf2mzbuSk2MzMrEPl0c93pv7S7utr3c5JsZmZWYdrZj/fmfpLd2tfX9/9zSY5KTYzM7Oe47u/2VROis0sF606M9KNN0gws/z57m82lZNiM2uqVp996bYbJJhZa/nGUzbJSbGZNVUrz7504w0SzMysGE6KzSwXrTj70s03SDAzs9aaU3QAZmZmZmZFc1JsZmZmZj3PSbGZmZmZ9Tz3Ke5ypf0lxspjVdeb6V73WXhScjMzM+t0Toq7WGl/iRM3nETpwO6q6/pe92ZmZtbLnBR3sbHyGKUDuzNNjTXTve6z8KTkZmZm1umcFPeALFNjzXSv+yw8KbmZmZl1Og+0MzMzM7Oe56TYzMzMzHqek2IzMzMz63lOis3MzMys5zU00E7SOcDHgLnApyLisqZEZWZmbcNtff3ynsfd88SbNU/dSbGkucAngNcCDwF3SLohIn7YrODMzKxYbuvrM8GTEHM5beNpRYdiZhk1cqb4dOC+iHgAQNLVwHmAG0ozs+7htr4OwQHQwUzzxDfC88SbNU8jSfES4MGK5w8BL28snOkd1B6IPPZcWUapaWUF/YxTmqGcZE7fVlzymiwjS51mi7maZr53tZTVSMy1lNPMOk2Nuaj3LmtZ9bzHra2T58huga5p61vVzleW1SrNfO9mqlur/rbzLKeybu3e/tYq6O+6Oh3af4soor6aSDofOCci3pU+vwh4eUS8e8p6q4HV6dMXAT+usahjgZ/WFWRxHHNrOOb8dVq8kC3mEyPiuFYE0+mytPWztPOdePxk5bp1JtetM9Vbt5ra+kbOFA8DJ1Q8X5oue4aI2AhsrLcQSXdGxIp6ty+CY24Nx5y/TosXOjPmNle1rZ+pne/mz8J160yuW2dqVd0amZLtDuCFkp4n6TnAhcANzQnLzMzahNt6M+sJdZ8pjohxSe8G/olkmp5PR8TWpkVmZmaFc1tvZr2ioXmKI+Im4KYmxTKTurteFMgxt4Zjzl+nxQudGXNba6Ct7+bPwnXrTK5bZ2pJ3eoeaGdmZmZm1i18m2czMzMz63ltmxRL+rSkRyTdU3QsWUk6QdItkn4oaauk9xQdUzWS5kv6rqTvpzF/uOiYspA0V9L3JN1YdCxZSNouaYukuyXdWXQ8WUg6UtJ1kn4k6V5JZxQd02wkvSh9fyd/npC0pui4up2kcyT9WNJ9ki6Z5vXDJF2Tvn67pGWtj7I+Ger2NkmPVhxz7yoiznpU+45V4q/Tuv9A0staHWO9MtRtpaRSxef2wVbHWK8seUanfnYZ65bvZxcRbfkDvAZ4GXBP0bHUEPMi4GXp4yOAbcCLi46rSswCDk8f9wG3A68oOq4Mcb8X+AfgxqJjyRjvduDYouOoMeZNwLvSx88Bjiw6phpinwvsIpmjsvB4uvUnfZ/vB05Kj5HvT23zgP8B/G36+ELgmqLjbmLd3gZ8vOhY66zfrN+xwOuBr6XfEa8Abi865ibWbWWnfHdME3vVPKNTP7uMdcv1s2vbM8URcSuwu+g4ahEROyPirvTxXuBekrtBta1IjKZP+9Kftu5oLmkp8KvAp4qOpVtJWkjyxXIlQEQ8FRGPFxtVTc4C7o+IHUUH0uUO3QI6Ip4CJm8BXek8kn+wAK4DzpKkFsZYryx161gZvmPPAz6Xfkf8O3CkpEWtia4xnZg/ZJUxz+jIz64dcqi2TYo7XXqJ8FSSM69tLe2KcDfwCHBzRLR7zBuA9wETRQdSgwC+IWlzevevdvc84FHgM2k3lU9J6i86qBpcCFxVdBA9YLpbQE/9Eju0TkSMAyXgmJZE15gsdQP49fQS9XWSTpjm9U6Vtf6d6oy02+DXJP1i0cHUY5Y8o+M/uyo5VG6fnZPiHEg6HLgeWBMRTxQdTzURcTAiTiG5U9Xpkl5SdEwzkXQu8EhEbC46lhq9KiJeBrwO+H1Jryk6oCrmkVx+/GREnAqMAc/qU9mO0htMvBH4YtGxWNf7KrAsIv4LcDNPnxG39nYXSdeqlwJ/A/xjwfHUrNPyjFpUqVuun52T4iaT1EfyYX4hIr5UdDy1SC+P3wKcU3Qss3gl8EZJ20kuZ54p6fPFhlRdRAynvx8BvkxyabadPQQ8VHHV4DqSJLkTvA64KyIeLjqQHlD1FtCV60iaBywEHmtJdI3JcnvrxyLiQPr0U8BpLYqtFbJ8th0pIp6Y7DYYyRzcfZKOLTiszDLkGR372VWrW96fnZPiJkr7yV0J3BsR64uOJwtJx0k6Mn28AHgt8KNio5pZRPxRRCyNiGUkl8j/OSJ+u+CwZiWpX9IRk4+BVcD/394dq9QRRAEY/g/6CAG1sfUNQsAy2KSwstAixlRioQ+QRvAp0lhISAQb4VZ5CDshldgoJG0abYRjMWMTInclXvdu5v+qZatzmB32DJyZmepTVTLzF3AVEUv11VvgR48hPcUGtk68lC5XQI+AD/V5jTJnp3rfQjU2tz/6NFcpTRBZUQAAAR9JREFUPZD/ixGwWU8yeAP8zsyffQf1HCJi/qGvPSJeU2qhISzUutYZgxy7LrlNeuz+6Ua7SYqIY8ouw1cRcQ3sZ+Zhv1GNtQy8B85rjy7Ap7qamVYLwFFEzFA+rpPMHMQxZwMyB5zWeTwLfMvM7/2G1Mku8LUWBJfAx57jGasuOlaA7b5jaUE+cgV0RBwAZ5k5ovzkvkTEBWXz03p/EXfXMbe9iFgF7ii5bfUW8BP97R9L2WhNZn6m3GD4DrgAbhjA/H/QIbc1YCci7oBbYH0gCzV4pM4AFmHwY9clt4mOnTfaSZIkqXm2T0iSJKl5FsWSJElqnkWxJEmSmmdRLEmSpOZZFEuSJKl5FsWSJElqnkWxJEmSmmdRLEmSpObdA/eAUh/oV5EnAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x432 with 4 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "d_yjSNfRnInd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 405
},
"outputId": "8fdf0927-dca9-48ec-c421-a0bb8b7c7cbb"
},
"source": [
"plt.figure(figsize=(12,6))\n",
"plt.subplot(2,2,1)\n",
"sns.violinplot(x='Species',y='SepalLengthCm',data=df)\n",
"plt.subplot(2,2,2)\n",
"sns.violinplot(x='Species',y='SepalWidthCm',data=df)\n",
"plt.subplot(2,2,3)\n",
"sns.violinplot(x='Species',y='PetalLengthCm',data=df)\n",
"plt.subplot(2,2,4)\n",
"sns.violinplot(x='Species',y='PetalWidthCm',data=df)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7c10691080>"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 4 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GQ0ZfH8RqjkB"
},
"source": [
"from sklearn.linear_model import LogisticRegression \n",
"from sklearn.model_selection import train_test_split \n",
"from sklearn.neighbors import KNeighborsClassifier \n",
"from sklearn import svm \n",
"from sklearn import metrics \n",
"from sklearn.tree import DecisionTreeClassifier"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NEhVPS6erVVm",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "217a9367-2cc5-4f81-c2c3-27dc74a5762a"
},
"source": [
"df.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(150, 5)"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "aq6hGJuGW2tV",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
},
"outputId": "56a8d2a8-d72a-4dcf-e923-c57660634cac"
},
"source": [
"plt.figure(figsize=(7,4)) \n",
"sns.heatmap(df.corr(),annot=True,cmap='cubehelix_r') \n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mfXG8Dz5sP8o",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "60ca21ac-9c42-4c69-b2e2-3edfeda3165d"
},
"source": [
"train, test = train_test_split(df, test_size = 0.3)\n",
"\n",
"print(train.shape)\n",
"print(test.shape)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(105, 5)\n",
"(45, 5)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6fgkTig9tTVd"
},
"source": [
"train_x = train[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']]\n",
"train_y=train['Species']\n",
"test_x= test[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']] \n",
"test_y =test['Species'] "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1EwidrXaS9YQ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "b6e397de-ab3e-49af-ea75-b6d6fad762cc"
},
"source": [
"train_x.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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>SepalLengthCm</th>\n",
" <th>SepalWidthCm</th>\n",
" <th>PetalLengthCm</th>\n",
" <th>PetalWidthCm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>6.4</td>\n",
" <td>2.9</td>\n",
" <td>4.3</td>\n",
" <td>1.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>7.2</td>\n",
" <td>3.0</td>\n",
" <td>5.8</td>\n",
" <td>1.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>5.4</td>\n",
" <td>3.9</td>\n",
" <td>1.3</td>\n",
" <td>0.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>6.7</td>\n",
" <td>3.3</td>\n",
" <td>5.7</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>5.1</td>\n",
" <td>3.8</td>\n",
" <td>1.5</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm\n",
"74 6.4 2.9 4.3 1.3\n",
"129 7.2 3.0 5.8 1.6\n",
"16 5.4 3.9 1.3 0.4\n",
"124 6.7 3.3 5.7 2.1\n",
"19 5.1 3.8 1.5 0.3"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TXgWkAH0TG9p",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"outputId": "7588f433-f704-46ae-b61a-6849aab26660"
},
"source": [
"train_y.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"74 Iris-versicolor\n",
"129 Iris-virginica\n",
"16 Iris-setosa\n",
"124 Iris-virginica\n",
"19 Iris-setosa\n",
"Name: Species, dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hVclcdEpTPvd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "09c76a59-938b-4d04-f013-ffac637630e3"
},
"source": [
"model = svm.SVC()\n",
"model.fit(train_x, train_y)\n",
"prediction = model.predict(test_x)\n",
"print('the accuracy rate of SVM is:', metrics.accuracy_score(prediction, test_y))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"the accuracy rate of SVM is: 0.9555555555555556\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "B2On4MUuVBFt",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 170
},
"outputId": "0063cc7b-8b36-4fbf-fc3b-645a3fd7aa79"
},
"source": [
"model = LogisticRegression()\n",
"model.fit(train_x,train_y)\n",
"prediction=model.predict(test_x)\n",
"print('The accuracy of the Logistic Regression is',metrics.accuracy_score(prediction,test_y))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"The accuracy of the Logistic Regression is 0.9555555555555556\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3nOqYmNyWMrw",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "656cd29a-2269-4aca-fb24-7376ab61d9b5"
},
"source": [
"model = DecisionTreeClassifier()\n",
"model.fit(train_x, train_y)\n",
"prediction = model.predict(test_x)\n",
"print('The accuracy of Decision Tree is:', metrics.accuracy_score(prediction, test_y))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"The accuracy of Decision Tree is: 0.9555555555555556\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ejUy3j17Y622",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "3b4070c9-9eed-4038-f2cf-d120a3db6b65"
},
"source": [
"model = KNeighborsClassifier(n_neighbors=3)\n",
"model.fit(train_x, train_y)\n",
"prediction = model.predict(test_x)\n",
"print('The accuracy of the KNeighbors is:', metrics.accuracy_score(prediction, test_y))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"The accuracy of the KNeighbors is: 0.9777777777777777\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Nu1DN5S-Zls3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 490
},
"outputId": "e0bc7745-4584-4379-e3eb-099fe8aea946"
},
"source": [
"a_index=list(range(1,11))\n",
"a=pd.Series()\n",
"x=[1,2,3,4,5,6,7,8,9,10]\n",
"for i in list(range(1,11)):\n",
" model=KNeighborsClassifier(n_neighbors=i) \n",
" model.fit(train_x,train_y)\n",
" prediction=model.predict(test_x)\n",
" a=a.append(pd.Series(metrics.accuracy_score(prediction,test_y)))\n",
"plt.plot(a_index, a)\n",
"plt.xticks(x)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
" \n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"([<matplotlib.axis.XTick at 0x7f7c075da748>,\n",
" <matplotlib.axis.XTick at 0x7f7c075da710>,\n",
" <matplotlib.axis.XTick at 0x7f7c075da358>,\n",
" <matplotlib.axis.XTick at 0x7f7c0757ce10>,\n",
" <matplotlib.axis.XTick at 0x7f7c075882e8>,\n",
" <matplotlib.axis.XTick at 0x7f7c07588780>,\n",
" <matplotlib.axis.XTick at 0x7f7c07588c18>,\n",
" <matplotlib.axis.XTick at 0x7f7c07590160>,\n",
" <matplotlib.axis.XTick at 0x7f7c07588a90>,\n",
" <matplotlib.axis.XTick at 0x7f7c075901d0>],\n",
" <a list of 10 Text major ticklabel objects>)"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9iiD7oA7q90L"
},
"source": [
"petal=df[['PetalLengthCm','PetalWidthCm','Species']]\n",
"sepal=df[['SepalLengthCm','SepalWidthCm','Species']]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "GtDjOBD9r0Sh"
},
"source": [
"train_p,test_p=train_test_split(petal,test_size=0.3,random_state=0) \n",
"train_x_p=train_p[['PetalWidthCm','PetalLengthCm']]\n",
"train_y_p=train_p.Species\n",
"test_x_p=test_p[['PetalWidthCm','PetalLengthCm']]\n",
"test_y_p=test_p.Species\n",
"\n",
"\n",
"train_s,test_s=train_test_split(sepal,test_size=0.3,random_state=0)\n",
"train_x_s=train_s[['SepalWidthCm','SepalLengthCm']]\n",
"train_y_s=train_s.Species\n",
"test_x_s=test_s[['SepalWidthCm','SepalLengthCm']]\n",
"test_y_s=test_s.Species"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "KRIDNnSgsTT5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "63681a2e-1220-47c9-ff6a-73683d54dbee"
},
"source": [
"model=svm.SVC()\n",
"model.fit(train_x_p,train_y_p) \n",
"prediction=model.predict(test_x_p) \n",
"print('The accuracy of the SVM using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
"\n",
"model=svm.SVC()\n",
"model.fit(train_x_s,train_y_s) \n",
"prediction=model.predict(test_x_s) \n",
"print('The accuracy of the SVM using Sepal is:',metrics.accuracy_score(prediction,test_y_s))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"The accuracy of the SVM using Petals is: 0.9777777777777777\n",
"The accuracy of the SVM using Sepal is: 0.8\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zwB0lwUWsXkO",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "fadcdcce-4642-44fe-8375-155cf61d6483"
},
"source": [
"model = LogisticRegression()\n",
"model.fit(train_x_p,train_y_p) \n",
"prediction=model.predict(test_x_p) \n",
"print('The accuracy of the Logistic Regression using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
"\n",
"model.fit(train_x_s,train_y_s) \n",
"prediction=model.predict(test_x_s) \n",
"print('The accuracy of the Logistic Regression using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"The accuracy of the Logistic Regression using Petals is: 0.9777777777777777\n",
"The accuracy of the Logistic Regression using Sepals is: 0.8222222222222222\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6FkjP3i0sdST",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "2e351036-1c93-4b39-a76c-e53ea63fb9f2"
},
"source": [
"model=DecisionTreeClassifier()\n",
"model.fit(train_x_p,train_y_p) \n",
"prediction=model.predict(test_x_p) \n",
"print('The accuracy of the Decision Tree using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
"\n",
"model.fit(train_x_s,train_y_s) \n",
"prediction=model.predict(test_x_s) \n",
"print('The accuracy of the Decision Tree using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"The accuracy of the Decision Tree using Petals is: 0.9555555555555556\n",
"The accuracy of the Decision Tree using Sepals is: 0.6666666666666666\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "0Aw_hBNbskwQ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "daac23c8-e67b-4328-a328-22a939682762"
},
"source": [
"model=KNeighborsClassifier(n_neighbors=3) \n",
"model.fit(train_x_p,train_y_p) \n",
"prediction=model.predict(test_x_p) \n",
"print('The accuracy of the KNN using Petals is:',metrics.accuracy_score(prediction,test_y_p))\n",
"\n",
"model.fit(train_x_s,train_y_s) \n",
"prediction=model.predict(test_x_s) \n",
"print('The accuracy of the KNN using Sepals is:',metrics.accuracy_score(prediction,test_y_s))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"The accuracy of the KNN using Petals is: 0.9777777777777777\n",
"The accuracy of the KNN using Sepals is: 0.7333333333333333\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HA-mlPvzs5IU"
},
"source": [
"Using Petals over Sepal for training the data gives a much better accuracy"
]
}
]
}