{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Customer_segmentation.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dpanagop/ML_and_AI_examples/blob/master/Customer_segmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YbF9C_0HTtCt",
        "colab_type": "text"
      },
      "source": [
        "#Load pandas, numpy, seaborn, matplotlib and scipy.stats libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1Y2cZUjlvmK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from scipy import stats\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nL3JZZiyUB3Q",
        "colab_type": "text"
      },
      "source": [
        "#Load and examine data from UCI website "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFaUdSXjmEVt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url=\"https://archive.ics.uci.edu/ml/machine-learning-databases/00292/Wholesale%20customers%20data.csv\"\n",
        "dat=pd.read_csv(url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZpNYwAGUHyH",
        "colab_type": "text"
      },
      "source": [
        "###Examine first rows of dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XXls62Bmc1J",
        "colab_type": "code",
        "outputId": "6a460a21-f5c8-45b3-bae6-0e0bbe0cd3a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "dat.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>Channel</th>\n",
              "      <th>Region</th>\n",
              "      <th>Fresh</th>\n",
              "      <th>Milk</th>\n",
              "      <th>Grocery</th>\n",
              "      <th>Frozen</th>\n",
              "      <th>Detergents_Paper</th>\n",
              "      <th>Delicassen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>12669</td>\n",
              "      <td>9656</td>\n",
              "      <td>7561</td>\n",
              "      <td>214</td>\n",
              "      <td>2674</td>\n",
              "      <td>1338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7057</td>\n",
              "      <td>9810</td>\n",
              "      <td>9568</td>\n",
              "      <td>1762</td>\n",
              "      <td>3293</td>\n",
              "      <td>1776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6353</td>\n",
              "      <td>8808</td>\n",
              "      <td>7684</td>\n",
              "      <td>2405</td>\n",
              "      <td>3516</td>\n",
              "      <td>7844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>13265</td>\n",
              "      <td>1196</td>\n",
              "      <td>4221</td>\n",
              "      <td>6404</td>\n",
              "      <td>507</td>\n",
              "      <td>1788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>22615</td>\n",
              "      <td>5410</td>\n",
              "      <td>7198</td>\n",
              "      <td>3915</td>\n",
              "      <td>1777</td>\n",
              "      <td>5185</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
              "0        2       3  12669  9656     7561     214              2674        1338\n",
              "1        2       3   7057  9810     9568    1762              3293        1776\n",
              "2        2       3   6353  8808     7684    2405              3516        7844\n",
              "3        1       3  13265  1196     4221    6404               507        1788\n",
              "4        2       3  22615  5410     7198    3915              1777        5185"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DssIw6FxUZtN",
        "colab_type": "text"
      },
      "source": [
        "###With *info* command, we can see the types of our variables and the number of completed records for each one of them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIUu7iKFme7u",
        "colab_type": "code",
        "outputId": "d712afdb-2577-4458-a541-906ce5157c60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        }
      },
      "source": [
        "dat.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 440 entries, 0 to 439\n",
            "Data columns (total 8 columns):\n",
            "Channel             440 non-null int64\n",
            "Region              440 non-null int64\n",
            "Fresh               440 non-null int64\n",
            "Milk                440 non-null int64\n",
            "Grocery             440 non-null int64\n",
            "Frozen              440 non-null int64\n",
            "Detergents_Paper    440 non-null int64\n",
            "Delicassen          440 non-null int64\n",
            "dtypes: int64(8)\n",
            "memory usage: 27.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fpJffGOKVHoe",
        "colab_type": "text"
      },
      "source": [
        "###while with *describe* command, we calculate the basic statistics for our variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBiuzNoansbY",
        "colab_type": "code",
        "outputId": "7eb3766e-b2fe-4009-ea27-65dea96a4729",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "dat.describe()"
      ],
      "execution_count": 0,
      "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>Channel</th>\n",
              "      <th>Region</th>\n",
              "      <th>Fresh</th>\n",
              "      <th>Milk</th>\n",
              "      <th>Grocery</th>\n",
              "      <th>Frozen</th>\n",
              "      <th>Detergents_Paper</th>\n",
              "      <th>Delicassen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>440.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>440.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.322727</td>\n",
              "      <td>2.543182</td>\n",
              "      <td>12000.297727</td>\n",
              "      <td>5796.265909</td>\n",
              "      <td>7951.277273</td>\n",
              "      <td>3071.931818</td>\n",
              "      <td>2881.493182</td>\n",
              "      <td>1524.870455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.468052</td>\n",
              "      <td>0.774272</td>\n",
              "      <td>12647.328865</td>\n",
              "      <td>7380.377175</td>\n",
              "      <td>9503.162829</td>\n",
              "      <td>4854.673333</td>\n",
              "      <td>4767.854448</td>\n",
              "      <td>2820.105937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3127.750000</td>\n",
              "      <td>1533.000000</td>\n",
              "      <td>2153.000000</td>\n",
              "      <td>742.250000</td>\n",
              "      <td>256.750000</td>\n",
              "      <td>408.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8504.000000</td>\n",
              "      <td>3627.000000</td>\n",
              "      <td>4755.500000</td>\n",
              "      <td>1526.000000</td>\n",
              "      <td>816.500000</td>\n",
              "      <td>965.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>16933.750000</td>\n",
              "      <td>7190.250000</td>\n",
              "      <td>10655.750000</td>\n",
              "      <td>3554.250000</td>\n",
              "      <td>3922.000000</td>\n",
              "      <td>1820.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>112151.000000</td>\n",
              "      <td>73498.000000</td>\n",
              "      <td>92780.000000</td>\n",
              "      <td>60869.000000</td>\n",
              "      <td>40827.000000</td>\n",
              "      <td>47943.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Channel      Region  ...  Detergents_Paper    Delicassen\n",
              "count  440.000000  440.000000  ...        440.000000    440.000000\n",
              "mean     1.322727    2.543182  ...       2881.493182   1524.870455\n",
              "std      0.468052    0.774272  ...       4767.854448   2820.105937\n",
              "min      1.000000    1.000000  ...          3.000000      3.000000\n",
              "25%      1.000000    2.000000  ...        256.750000    408.250000\n",
              "50%      1.000000    3.000000  ...        816.500000    965.500000\n",
              "75%      2.000000    3.000000  ...       3922.000000   1820.250000\n",
              "max      2.000000    3.000000  ...      40827.000000  47943.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkpS35kTVLIK",
        "colab_type": "text"
      },
      "source": [
        "#Preprocessing\n",
        "\n",
        "###The following code snippet, detects for every variable the records that lie beyond 4 sds from the mean\n",
        "###It is a common practise to omit these records when clustering as outliers/extreme cases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLLENx2Ymhlr",
        "colab_type": "code",
        "outputId": "2387f327-40b5-45fd-d6eb-58930941ff5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "headers=[ 'Fresh', 'Milk', 'Grocery', 'Frozen',\n",
        "       'Detergents_Paper', 'Delicassen']\n",
        "outliers=[]\n",
        "for hd in headers:\n",
        "  z=np.abs(stats.zscore(dat[hd]))\n",
        "  id=np.where(z>4)\n",
        "  print(\"outliers for \",hd,\":\", id)\n",
        "  outliers=np.union1d(outliers,id[0])\n",
        "  print(\"new outliers are \",outliers)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "outliers for  Fresh : (array([125, 181, 284]),)\n",
            "new outliers are  [125. 181. 284.]\n",
            "outliers for  Milk : (array([ 23,  47,  61,  85,  86, 183]),)\n",
            "new outliers are  [ 23.  47.  61.  85.  86. 125. 181. 183. 284.]\n",
            "outliers for  Grocery : (array([ 47,  61,  85, 333]),)\n",
            "new outliers are  [ 23.  47.  61.  85.  86. 125. 181. 183. 284. 333.]\n",
            "outliers for  Frozen : (array([ 93, 183, 325]),)\n",
            "new outliers are  [ 23.  47.  61.  85.  86.  93. 125. 181. 183. 284. 325. 333.]\n",
            "outliers for  Detergents_Paper : (array([ 47,  61,  65,  85, 333]),)\n",
            "new outliers are  [ 23.  47.  61.  65.  85.  86.  93. 125. 181. 183. 284. 325. 333.]\n",
            "outliers for  Delicassen : (array([ 23,  71,  87, 183]),)\n",
            "new outliers are  [ 23.  47.  61.  65.  71.  85.  86.  87.  93. 125. 181. 183. 284. 325.\n",
            " 333.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpDMm6jprg-w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dat=dat.drop(dat.index[outliers.astype(int)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xulAyZSoWB4i",
        "colab_type": "text"
      },
      "source": [
        "#Hierachical clustering\n",
        "\n",
        "###First all the variables are normalised. These prevents differences in scale from messing up our clustering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ig0EIqO4vYdY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#First,load preprocessing from sklearn\n",
        "from sklearn import preprocessing\n",
        "x = dat.values\n",
        "min_max_scaler = preprocessing.StandardScaler()\n",
        "x_scaled = min_max_scaler.fit_transform(x)\n",
        "df = pd.DataFrame(x_scaled,columns=dat.columns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vYIRfjKJW4Zn",
        "colab_type": "text"
      },
      "source": [
        "###The normalised data look like"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pguPG_JUvdIt",
        "colab_type": "code",
        "outputId": "123f1a00-150a-426b-e3c9-f1789c05ad9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "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",
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              "    }\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>Channel</th>\n",
              "      <th>Region</th>\n",
              "      <th>Fresh</th>\n",
              "      <th>Milk</th>\n",
              "      <th>Grocery</th>\n",
              "      <th>Frozen</th>\n",
              "      <th>Detergents_Paper</th>\n",
              "      <th>Delicassen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.465656</td>\n",
              "      <td>0.598855</td>\n",
              "      <td>0.145323</td>\n",
              "      <td>0.906348</td>\n",
              "      <td>0.064647</td>\n",
              "      <td>-0.777870</td>\n",
              "      <td>0.040019</td>\n",
              "      <td>0.049449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.465656</td>\n",
              "      <td>0.598855</td>\n",
              "      <td>-0.381089</td>\n",
              "      <td>0.936993</td>\n",
              "      <td>0.350437</td>\n",
              "      <td>-0.293367</td>\n",
              "      <td>0.213052</td>\n",
              "      <td>0.401536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.465656</td>\n",
              "      <td>0.598855</td>\n",
              "      <td>-0.447125</td>\n",
              "      <td>0.737604</td>\n",
              "      <td>0.082162</td>\n",
              "      <td>-0.092116</td>\n",
              "      <td>0.275389</td>\n",
              "      <td>5.279311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.682288</td>\n",
              "      <td>0.598855</td>\n",
              "      <td>0.201229</td>\n",
              "      <td>-0.777117</td>\n",
              "      <td>-0.410958</td>\n",
              "      <td>1.159518</td>\n",
              "      <td>-0.565738</td>\n",
              "      <td>0.411183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.465656</td>\n",
              "      <td>0.598855</td>\n",
              "      <td>1.078270</td>\n",
              "      <td>0.061432</td>\n",
              "      <td>0.012957</td>\n",
              "      <td>0.380494</td>\n",
              "      <td>-0.210726</td>\n",
              "      <td>3.141868</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Channel    Region     Fresh  ...    Frozen  Detergents_Paper  Delicassen\n",
              "0  1.465656  0.598855  0.145323  ... -0.777870          0.040019    0.049449\n",
              "1  1.465656  0.598855 -0.381089  ... -0.293367          0.213052    0.401536\n",
              "2  1.465656  0.598855 -0.447125  ... -0.092116          0.275389    5.279311\n",
              "3 -0.682288  0.598855  0.201229  ...  1.159518         -0.565738    0.411183\n",
              "4  1.465656  0.598855  1.078270  ...  0.380494         -0.210726    3.141868\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f3OgtYGp7-fY",
        "colab_type": "text"
      },
      "source": [
        "###Next we create and display a dendrogram. It is clear that there exist 2 groups, colored with green and red."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-rkd1lvex_X-",
        "colab_type": "code",
        "outputId": "3721e6d6-a49c-4acc-d5fd-8203f75a6c8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "#Load scipy.cluster.hierarchy\n",
        "import scipy.cluster.hierarchy as hc\n",
        "dend = hc.dendrogram(hc.linkage(df, method='ward'))\n",
        "#plt.axhline can be used to display a horizontal line at a specific height\n",
        "#plt.axhline(y=6, color='r', linestyle='--')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hrMU62vEY46B/FoC3AS8CPgGsBXqBQeDpGONZP+4a4CywEhgCrgAOYGJ9MzCO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\nhRCiIEjQhRCiIEjQhRCiIPx//FQXv1KCvu8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5cnTc7zzMhX",
        "colab_type": "code",
        "outputId": "0912276e-31e3-4242-829c-4cd959f3bc8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        }
      },
      "source": [
        "from sklearn.cluster import AgglomerativeClustering\n",
        "cluster = AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='ward')  \n",
        "cluster.fit_predict(df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,\n",
              "       0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1,\n",
              "       1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0,\n",
              "       1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0,\n",
              "       0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n",
              "       1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1,\n",
              "       1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,\n",
              "       1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,\n",
              "       0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7iBBpt8u8VZl",
        "colab_type": "text"
      },
      "source": [
        "###After performing a hierarchical clustering with 2 groups, we can profile each group.\n",
        "###A way to do this is to create scatterplots where datapoints from the two groups are displayed with differen colors.\n",
        "###For example, delw we create a scatterplot for variables Milk and Grocery."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9X-VAh60ImU",
        "colab_type": "code",
        "outputId": "5be4db1e-43a9-436f-a35a-be15aa3ee04c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "plt.figure(figsize=(10, 7))  \n",
        "plt.scatter(df['Milk'], df['Grocery'], c=cluster.labels_) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fb1208ea240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dVDEZFXQb5m712FIEAHQGXY47rgNAVFwkXF6ajgJA4eIxOb4uEZEvsagKQPs3\nHPIYBgHStwrZu+6QComoIBBCwGFzILMpA7Hx0dBoPJ8oCUUgslDO+wpFF4nCfS2qeTQdNYYY8dTA\nR3N8XSIiX2JRFYBi42NgDPHcJ89g0iO2WLQKifKfUwfO4tvBP2HC699g+/I9GYXAhrlb8fK9/dE+\ntCteqvYm1v+9ReWkgWHFrDV4plRvPBLWDY8X6oHZY/72Wlx16NMGepP7vD5JIyEqLhKVG1TIVYbB\nM99ArZbVoTfqYQ43wRxmQs/PuqPuQzVzdV2i/M5qseGvrxdhcLtP8XnPSTi684TakegOcj1RPSc4\nUT13Uq9b0DXhFaTdsLodD4sOxczTk2AONamULH+YP3UpJr7xHVxOF2SXAlOoEfXb1caDTzfCyG7j\n3YZNjSEGDJrxOpo83kDFxPnbujmbMaLbONjT/vtzM4UY0W3oE3jay1OipT+swvg+30CjkaDICgqX\niMUn8wcjvmwRn+S5djEJ1xNvoESFeK4apaCXlmJFn7qDcPnsVdjTHNBoJOhNevSf2hvNuzRRO16B\n4bfVfznBoir3Dm87ho+e+hxJl64DAihUIhZDZ7+FstVLqx1NVTeupqBzyV5w2pxux02hRoREhODa\nhSSPc+LLFsH3Ryf4K2LAefne/ji574zH8dDIEPxxdTo0Gs8H3narHUe2n0BoZAgSqpbk4gmiHJo5\n4g/89NFvcNz2mRYSYcbsS9+yAa+f+G31H6mjQu178P3RCTh/7CIkSUJ82SL8xYX0Sfw6vc6jqLJZ\n7G6bON/qwolLEELk2z+/xNOXsfi7f3DtwnXUalUDjTrUgVbnuclsXrl46rLX47Y0O6ypNoRGhHi8\nZjQbUe3+SnkdjSjorf19k0dB9a/ju06iUr3yfk5EmWFRFcAkSULxctzE9lZ6o87byntIGglGs8Fr\nYRUbH51vC6oti3Zg+BP/g+JS4HS4sOynNSh7byl8tuIDv31DLVWxGA5vO+5xPCwyBOawgj3UTJTX\nwqK973Mou2SERnp+oSF1caJ6kHE6nEi9bsl0hVYwq/NQTQjF82c3mPTo1K+dxwR/Y4gRz37wlL/i\nZYvskjGi63jY0xxw3mwnYEu14diuk1j4zTK/5XhxRFcYze5zl4whRvT4pIvXoT8i8p1HX28LU6j7\n55ZGkz46UbJicZVS0Z3wEzFI2K12jHlpIjpGPYcniryIZ8u9hm1Ld6kdy+9MIUZ88OdAmEKNMIeb\nYAo1wmDSo8u7j+GFj7ug52fdEVkoHBqtBhGFwtFzdDc8/FJLtWN7dWT7ccgu2eO4Pc2BZT+u8VuO\nWi2rY/icgbinRgIMJj2KlStAuZltAAAgAElEQVSKNyf3QruXW/ktA1FB1ahDXTz2ZnsYTPqMvVKL\nli2Cj+e+q3Y08oIT1YPEB499hi2LdriNvRtDDBi37hPcUyNBvWAqSUuxYuO8bbBZ7Kjbpqbb3nBC\nCNitDhjNhnw77AcAx3adxBtNhsCW6jlkWb1pFYz5Z7gKqYhIDUmJyTiw8XB6e5L65fP1Z1cw4kT1\nAuTKuaseBRUAOG1O/DL6Lwz+qZ9KydQTEm5G8y6Nvb4mSRJMXvp85Tdlq5dGRGwEbKnuE8VNoUa0\n79UaAHD28Hms+HktHFYnGnWsyw9boiAVHReJRh3qqh2D7oJFVRC4dOoK9Ea9R1GlKAJnDp5TKRXl\nliRJ+PjvQRjQYjhcdidkWYEQQLPOjfHg040wf+pSfP3Gd5CdMhRZwV8TFqJF1yboN7EnCysiIhWw\nqAoCJSsWg9PuueRWq9eiSsPcdbEuaBx2J/atOwhJklD1/orQG9TtAVPm3tKYdWYyNi/YjuTLN1C9\naRWUrFgcSYnJ+LrfdLdC2maxY/lPa9D8mSao/kCVPMt0/XIyZn76Bzb8vRVh0aF4rF87tOz2AAs5\nIirwWFQFgYjYcLTv1Qrzpy7P2MxWktJbCDw5oIPK6QLH1iW78PFTn0MgfZ6hRqPB0N/ewn3N71U1\nl8GoR+NO9d2ObV20ExqdFoB7MW1Ps2PV7PV5VlSlXrfglVoDcf3yjfQNjk8A41+diqPbj+OVL3rk\nyT2JiAIFV/8FiV5jnsOLI55BkYTCCIkIQf12tfDlxhEomhCndrSAkJSYjA8eGw3LjTSk3bAi7YYV\nqdctGNpxFG5cS1E7ngetTgOvz4UkCTp93n1Xmj9lKW5cS0kvqG6yWeyYO2kprnrpVk9EVJDwSVWQ\n0Gg06PT6w+j0+sNqRwlIq35Z77W/lRDA6tkb0b5X/mofUL9dLSiy4nHcYNSjRde82w9s+/I9cFg9\nh5r1Rh2Obj+O2Ha18+zeRET5HZ9UEQFISUrNaLB5K5fDhdSkVBUSZS40MhSDZ74Bo9mQ3ovLbLjZ\nj6sTKtS+J8/uWzQhDhqt58eGIiuILR6TZ/clIgoEfFJFBKB2q+r49bO/PLax0Rl0qN26hkqpMteo\nY13MPDMJ6+dsgd3qQP12tfJ8uLdT34ex/KfVsKc5Mo5pdVoULxdfIPuhERHdik+qiABUblAB9dvV\nctsOwhRqRJPH66N8rbIqJstcREw42rzQHB37tPHL/LmEqiUx5Of+iIqLhCnUCL1Rj6r3V8SIRe9x\n9R8RFXjsqE50k6IoWPP7Jiz9fiUkSULr55uhcad6LBa8kGUZF44nIiTchJii0WrHISLKU1ntqM6i\nioiIiCgT3KaGqICwWmxIungdhYrHwGAyqB2HiDKxZfFO/DJqDq6cu4aazarhmfceQ1zJQmrHIh9h\nUUUUoGRZxtS3f8C8yUsh3VyR13nQo3hm8GMcsiTKh/6euBhT3v4ho0nzxROJWD17Aybt/IyFVZDg\nRHWiAPX9B79i3pRlsFsdsKXaYEu1YdaIPzF/ylK1oxHRbRx2J75558eMggoAZJeMtBQrZn7yh4rJ\nyJdYVBEFIEVR8Of4BW4f0ED6NjU/j5yjUioiupNzRy4AXvZBkF0ydqzY4/9AlCdYVBEFIKfd6dYr\n6lbXE5P9nIaI7iYqLtJte6dbFS4R6+c0lFdYVBEFIIPJcMcP4ntqJvg3DBHdVXRcJGq3qg690X0q\nszHEiKcHdlQpFfkaiyoqEIRyHUryB1Au1YeSeD+UlDEQwqp2rByTJAmvjusBo9lwyzHAGGJAr/89\nq2IyIrqTd3/qh9qta0Jv0sMcboY53Iyen3VH3Tb3qR2NfIR9qijoCeGAuNIOkM8D+HczYCOgrwop\nZlZAr5TbvXo/vv/gV5w9fAH31EzA8x8+nacd4BVFwbkjF2AKNXHIgiiHki5dx/XEZBQvH882KAGC\nfaqI/mVbDCiX8V9BBQB2wHUQcG4DDHf9e5JvVX+gCv634gO/3Gvb0l0Y9dwEWFOskGUFZauXxtDZ\nbwXkUnC71Y6l36/GxnlbER0XiUdefShPN6ImulV0kShEF4lSOwblARZVFPSEczcg0ry84AKc+wO6\nqPKX88cuYlinz9xWGx7ZdhxvtxiO7w6ND6infbY0O/o2HIwLxy7BlmaHRiPhn1/W4bUvX0SbHs3V\njkdEAYxzqgoQoVyDkjIOytWuUJIHQziPqB3JP7SlAZg9j0t6QFvS73EC0bzJSyA73VcuKbKCpEvX\nsXftQZVS5cyCqctw/thF2G4WiIoiYE9z4Ku+0zKOERHlBIuqAkLIFyAutwUsUwHnFsD6J8TVxyHs\na9WOluckc4f0AsqtR4wGkCIBYxO1YgWUiycuw+WUvb525dw1P6fJnTW/b/TajkKj1eDQlqMqJCKi\nYMGiqoAQqeMAcQPAv79MZAA2iOT3oMZiBX+SNBGQYn8GdNWQPuKtAwz1IMX+DEkKnBHwdXM2o2+j\nwehW9lWMeelrXDp12W/3rtWyOkyhRo/jslNG5Qbl/ZbDF8Kjw7weV2QFoREhfk5DRMGERVVBYV+N\n9ELqNso1QLnk9zj+JunKQVPod0hxmyDFbYEm5ntI2qJqx8qy2WP+xohu43Fg4xFcOnkZS79fhd61\n3kbimSt+uX/L7g8gpmiUW48dY4gRLbo+gKIJcX7J4Csd+rSBKcS9QJQkCTHx0ezxRUS5wqKqoJDC\n7/CCACTv39yDkaQJh6QJVTtGttjS7Jgx7Nfb9gxTYE2x4ZdRuduSRjh3Q0nqB+XKY1BSRkPIiV7f\nZwoxYsLmkXj8zfYoXj4e5e5LwGvjX8Abk3vm6v5qqNO6Bjq/8yj0Rj1CIswwh5sQV6oQPpk/OKAm\n3BNR/sM+VQWEYpkFpIwEcGvDSz1gbApN9NdqxaIsOLrjBN5qNgxpNzyblZaqXBzf7hubo+sq1iVA\n8gAAdgACgB6QwiAVmgNJG5+rzIHg+uVk7N9wGBGx4ajSsAI0Gn7HJCLv2KeK3EghT0O4DgPW2YBk\nBIQzvfll5Ei1o9FdRBeNgtPufc+wuFKFMz1XlmVsXrADu1buRUx8NFp1b4roIlEQQgZShgKw3fJu\nJyBuQKSOhxQ5wnc/QD4VVTgSjTrUVTsGEQURFlUFhCRpIEUOgwh7Nb3ppbYYJB2bHQaC2Pho3Nei\nGnYs3+NWXBlDDOg86NE7nuewOfB2i+E4sec0rKk2GEx6/DB8Nj6e9y6q3x8JKF56d0EG7Ovy4Kcg\nIgp+LKoKGElbGNBm/nQjv5FdMjbO24bD244jvkwcmj7VEOYwL32ngth7M1/HnM8HwWnZj/OnQrB9\nVSG8NLoHajxY9Y7nzJ24GMd2noTdmr7i02FL7yj/aZexmHV6NADF+4maaF/HJyIqEFhUUb5mSbag\nX+P3kXjqMqypNphCjZj6zo8Yu+YjlKxYXO14d7Vt6S4smLoMtjQHmnW+H8063w+tTputawglCSb7\n0+j8ymUIkQahmCDpLNDEVsn0vGU/rs4oqG5lTbXh5P4bSCjZGLCvxX9tNgDADCn0hWzlI8oLlmQL\n9m88goiYMFSocw8XEVBAYFFF+dqMD37F+aMXMoa9bBY77GkOjH7+K3y54VOV02Xu23d/wpwJC2Gz\npK/a271qH5Z8vxIjFr4HrTbrhZW4MQKQzwJwQQIgaayA4oC4MQxS9KQ7nqfTe//rLRQBrV4LKfIz\niOuvAY7tgGQAhAMIfQ4wdcjOj0nkc7+PnYdpg2dBb9RBkRVEFYnEqMXvI75sEbWjEWWKRRXlayt/\nXucxSVsIgaPbj8OSbEFo5H/tEU4fPIfF01fgxrVUNGxfB/Xb18pW8eJLl05dxh/j5mcMuQHpBeGB\njYexecEONHwkG/sN2hcDuH2iugzYV0EIBZLkfdVau56tcHLfmYyi7l/RRaNQqlJxSJIEKWYGhOsM\noFwEdBUgaSKznosoD+xatQ/Th/wMh80Bhy39KaotzY53236M6QcDa59JKnhYVFG+lukH6C2vLZ+5\nBl+8PAkupwzZJWPlL+tRuUF5jFjwXraH23xhx4q90Gg9ix1bqh0b5m7NXlGVQ62ea4oti3Zi04Jt\nUGQBnUELvUGH4X++7fbnKulKAuAeiJQ//DVhkVtPNiD96erV80k4sv04KtTmAhvKv1hUkd8d2noM\n8yYuRvLVFDTuVB/NutwPvUHv9b0tuzfFn+MXwGn/74mPRiOhUv3yGVuKWFOt+KLnZLf5Q7ZUG/av\nO4SVv6xHi67+398vNDLEa98jrV6LiJhsNls1tgZs8+H+tEoLGB+441MqANBqtXj/1/44uuME9q49\niOgikWjYoQ4MJkP27k/kR8mXb3g9rtFqkJpk8XMaouxhUUXZ5nQ4se7Pzdi34TDiy8ahZbcHEBFz\np47t7uZNXoJJb82Aw+aEUAR2LN+Dv79ehM9XfwSD0bOw6jb0Cez8Zy/OHDwHh80Jg1kPc6gJA2e8\nlvGevWsPQqvzLC7sVgd+/Gi2KkVV/Yfv8/qkSqfTos0LzbN8ncPbjuH3MaF47k09ogsBBpMrvSO8\nFA4pYniWrlHuvjIod1+ZLN+TSE2NH6uHQ1uPemx6LTtlVKofWPtMUsHDooqyxZJsQd9G7+Hymauw\nptpgNBswY+gvGLNyOMrVzPwXt+VGGib2nwHHrU+ULHac3HcWy39cjbYvtvA4xxxqwpcbP8XOFXtx\nZPsJFC0Tl/605ZYCzGAyQCjedwY4d/Qiju06gXtq+LeoMJgMGLl4CN5rNwIOuxOSlL61TP+pvVCi\nQrEsXWP36v0Y/PAncFgdWPVbedRveQPl7nWg8ZOdkVD7BUgSnzhR8GnzYgvMn7IMF08mwp7mgCQB\nBrMBPUd3R0h4wWqlQoGH29RQtkwZ+D3mfLnQY/J46Sol8M3eLzI9d8uiHfi481ik3fBsOlm7dQ2M\nXDQkR5lkl4xOMc/Dmmrz+rreqEP3oU+i8zud/D7JVXbJ2LvuIBxWB6o1qQxzqCnL5/au9TaO7Tzp\ncbzMvaUwZdcYH6Ykyl+sFhsWT/8H6+ZsRlRcJB59rS2qNqqodiwqwLhNDeWJlT+v97plyvljF3Ht\nYhJiit65caQ53AwhPBtOShIQnt15RrfQ6rTo0KfNHTcXdtpd+OmTP6DICroOeSLH98lpthpN79yg\nMzMn9pz2fnzvaQghuAqKgpY51IRHX2uLR19rq3YUomzhDqKULVr9nVfS3W2VXZWGFTIml9/KYDbi\nkd6tc5Xr0dfbQm+883cEe5odv372N2RZztV9ssJhd2Lp96vwSZcvMOmtGThz6FyOrnOnQjM8OowF\nFRFRPsSiirKl7YvNYTS7z+XRaCSUu68MIgtFZHquRqPBiEVDEFssGiHhZoREmGEw6dF92JOo/kDm\n3cHvplCxGLzyxfMwmLyvIgTSi520G9Zc3edOhH09lKudIV9qgKMrmmLx1M+x8pf1mPPlQrxSeyDW\nzdmc7Ws+OeARGEOMbseMIUY8/mZ7X8UmIiIf4pwqyhaH3Ykh7UfgwMbDUGQFOr0O5ggzxq75CEUT\n4rJ0DVmWsXfNQaRet+DeJpUREZu1lYNZceH4JQxo9gESz1zxeC0iNhyzL33jtdVBbijWZUByfwD/\nzemyWSUM6VoWezamP20KjQrBb5e+vWOXc6/XVRR8885P+PurRdDoNJCdCh55pTV6ftbd5z8DERHd\nWVbnVPmkqJIkaRqA9gAShRDV7vZ+FlWBTQiBA5uO4PDWY4grWQj1Hr4vW8VCXtu1ah/ee/hTt75V\nxhAjXvniObR7uZXP76dcbn5zGxl3h3aa0ffhCgCAkAgzRi15H5XqZX9JuDXVistnr6FwiZgCt5E0\nEVF+4O+J6t8BmADgex9dj/IxSZJQpUEFVGlQIdvnupwubFuyC0mXklGtSWWUKB/v83w1mlbFx/Pe\nxdRBP+DU/nMoVCIGzw57Cs27NPb5vYRwALL3OVNlKv/35EqRFZiysfLvVuYwM0pVyv+bRxMRFXQ+\nKaqEEKslSUrwxbUoZ66cu4p5U5bh1L4zqNygAtq+2Bzh0TlfUZcXTh88hwHNPoA9zQ5FUaDICpo/\n0wT9p/b2+cTrms2q4avNo3x6Te/0gBQGiBSPV5Iup//1kiQJ0UWjcO7oBaSlWFG5fnlONCciCkI+\nm1N1s6iad6fhP0mSegLoCQClSpWqferUKZ/cl9K7bg9o9gFcThecdheMZgPM4WZ8tWUk4koWUjse\ngPQhwx6V+uH80Qu49T85U6gRb0zqpUrXc19RUr8GUicD+G8SvC1Ng28/LYWls4sCEHDZXdCb9BCK\nQFSRSIxa8j7iyxRRLTMRuUs8cwWrZ2+A3epA/Xa17trMmAqWrA7/+W22qxBiihCijhCiTuHChf11\n2wJhzEsTYU21ZfSPslsduHE1Bd+++5PKyf5z+uA5XD1/DbfX8DaLHXMnLVYnVCZuXEvBnAkL8e17\nM7Fl0Q4oimd/rX9Job2B0OcAyQzABEihcBl6oWqL9/H02x2hyAqcDhfSblhhTbXh0olEvP/ISKix\nSIQoLwghsHftAayYuQZnD59XO062rZi1Bj0q9sW092bi+w9+xRv3D8FX/abx7yhlW/6ZXUw5Yk21\n4tS+Mx7HFVnBpvnbVUjkndPmhKTxPuR1+x5fQPrKt50r9uLEntMoUSEeddrUhFabeR8sX9m/8TDe\neegjKC4FdqsDf45bgPK1ymDU0qFe9yeUJA2k8P4QYa8ByjVAE4MIyYDmXYB3HvrI4+dTFIGLJy/j\n9IGzKF2lpF9+JqK8cu1iEgY0H44rZ68CSN9FoFHHunjnh7537V2XH6QkpWLMS5PgsP23abvd6sCi\naSvwwBMNcW+Tyiqmo0DDoirAafU6SBoNAM8nKcaQ/LM3XJl7S0Fv0MMK961kDGYDmt02gdxyIw1v\nPTgM549ehMvhgs6oQ1ThSIxb9zGii0TlaU4hBD56agysKf/ltKfZsXfdQUwd9CP6jO1xx3MlyQBo\ni7odS7lu8fperU4DSx71zCLypxFdx+P80QuQXf99Bm2YuxVzJizE42/k/55qWxbt9Fr82dPsWDFz\nDYsqyhafDP9JkjQLwAYAFSVJOitJ0ou+uC7dncGoR8NHakNncK+PDWYD2vX0ffsAABDCDmFbDJH2\nK4TL+1Yqt9PqtHjnx74whhgzsprCTChduTg6vPqQ23u/GfQjTu0/mz6k6XDBmmJD4ukr+KLXZJ//\nLLc7feAsUpO8FEIC+GvCQly/nJyt6zV5rIH3hqQCKHcf52xQYLtxLQX71h10K6iA9KfPcycuUSlV\n9qQvGvE2zCel76FFlA2+Wv3XxRfXoZx5c0pvXGz9Ec4cPAeNRgOXS0btltXR5d1OPr+XcO6BuNYD\ngAwIBYACEfIMpPB37rqire5DNfHtvi+wcNoKXDl7FbVb1UCTx+t79Lj65+d1cDnc9xeUXTI2L9gB\n2SXn6ZCCRquB7LrDVjZCYNkPq/FE/0eyfL0Orz6EJd/9g8QzV2BPc0CjkaA36vH61y95HUokCiSO\nTIb1bWl2P6fJmXpta0JxeT7pN5gNaNntARUSUSDj8F8QCI8Ow1ebR+Lw1mO4cPwSylQvjdKVS/j8\nPkLIEEk9AXHD7biSOgt2e02EFr775qdFShfG88OfzvQ9iux9UrgQIs8njpaoUAzmMDOcds8WCUIA\nl710as9MSLgZX28bjSXfrcTGeVsRWzwGHfu04coiCgqx8dGILRaDC8cvuR3X6bW4/9F6KqXKntDI\nUAyc8RpGPTcBAKC4ZGh0WnTs8xCqNqqocjoKNNymhrJMOLZBJL0ECM/hsQ1LorBh1fN4Y1LPXHdX\nH9F9PFb9st7tiZFGI6H6g1Xx2bJhubp2ViyfuQYju433OG4KNWLgjNfR5LH6eZ6BKFDsXXcQ77b5\nBC6nCy6HC8YQIyILheOrLSMRVThS7XhZdu1iEtb8vgkOqwP12tXKky+mFLj83VGdVJR4+jKmDfkZ\nWxftRGikGZ36tUOHVx/y/f5wwg7A+6N+vcGFlT+vQ2ShcLw8qnuubtN7zHPYu/YAUq6mwppqgynU\nCFOIEf2n9M7VdbOqxTNNsOzH1di5Yg9cjvTCTm/UoXj5eDTqcNe/U0QFSrX7K+HbfZ9j/pRlOHfk\nAqo3rYpWzz4QcFsqxRSNRsc+bdSOQQGOT6oCXFJiMl6q+iZSr1syhs2MIUa06NoEb07u5dN7CWGD\nSGwAiDS341aLhAnvlsCy32JgDjPhr+Tvc90x3GFzYM3vm3BkxwmUrlwcD3a+H+YcbvOSEy6nC3Mn\nLcGCqcvgcsho0a0JHn+zvV8zEBFR/uDXDZWzi0WV73w37Bf8OvovOO1Ot+N6kx7fH/kShYrH+vR+\ninURkDwQTocNegNgTdXg4I4QvNe1LGSXBEmSsNA+KyD60xAREWUFh/8KiL1rDsBpdyChkg2SBJw8\naIIQEgxGPU7sOe3zokpjbgOhr4J/Jr0OR1oiNi8Px5blEVCU9CdTpaqUYEHlA5Ybafjhw9n4Z9Y6\nSBLQsvsD6DrkCY8nZWv/3ITZ//sbSZeSUeehGnhm8GM+/3dORERZw6IqwNV+UIMBnx1AeJQMCMCS\nosEnvRJwbJ8BcaXzZjsgSVcKZRp8jgHNhsFhdUBRBCSNBIPJgNe/9E+LsuO7T2HXyn2ILBSOhh3r\nBtWwnOyS8WaT93H28PmMrYf+HLcAO1bsxZcbPs2YKzdr5J+Y+fHvGUvXF0xdjlWzN2Dq7jGIKRqt\nWn4iooKKw3/5TOp1C/4cvwDr/96CqEIR6NSvHeq1vc/re4WSCvliE2g07qvx0lI1+PT1Dvh04eg8\nzXrqwFnM+vQPHNl+AglVS6DL4MfyvFWAoigY/fxXWPvHRiiygM6ghUajwailQ1Gxzj15em9/Wf/X\nFozsPh7WVPfu8+YwE4b9PgC1W9VAWooVTxV9CXar+xY4OoMOnfq2Rc/Rz/ozMhFRUOPwXwCyJFvw\nSq2BuHohKWOO1N51B9F1yOPoPMhLI0/bEmg0nkWxTi9h6I95v7VC6col8M4PffP8Prda+ct6rPtz\nU8Z+ev/+OQ3rOAozz0xyW/F45tA5fPnat9i1ch8MJj1aPfsgXh7dLdtPtQ5tPYZZn/6OUwfOoULt\nsnjmvcfvuNzalmbHql/X48zBcyhzb2k0eaJBtpt8Htl+3KOgAtIn7x/ZfgK1W9XAyX1noDXogNuK\nKpfDhe1L92TrfkRE5BssqvKReZOX4trFJLdJ5zaLHT8Mn432vVojLCrU/QTlCgDPzYgNRhkweDav\nDAYLv1kOm8WzU3NaihVHd5xAhdrpT6uSEpPRt+F7sCSnQQgBm8WORdNW4PTBs/jf8g+yfL9tS3dh\n2KOj4bA5IARw/sgFrP9rCz5f9SHK1yrr9t5Lpy7j9Qbvwmqxw5ZqgznMhGlDZmLCxhHZ2rOwSEIc\nTKFGj5/TYDagaEL6kG5sfDRcty1O+O/8vBn2ze9saXbILhmhESFqRyGiAsrHjYwoNzbN3+62U/q/\n9EY9Dm895nmCoS4AL09BpBBIhuDsp+RyurwelyQJLud/zULnT1l6sxD670me0+7E/vWHcGzXySzf\n78vXvoXdml5QAYCipBdok96a4fHesb0mI/lKCmw3nzJZU224ei7J63sz0/TJ9P0Cb21LIWkkmEKM\naHSzS3WR0oVRpWFFjz0fjSFGPDmgQ7buF+iSLl3H4Haf4tHo5/BE4RfwSp2BOL77lNqxiKgAYlGV\nj8QWi/G6f6fskhEV56Uzsb4mYGwA4NYme2ZAXwMwNMyrmKpq1b0pjCFGj+NavdZtTtXR7Se8FqhO\nuwvfvPNjlu7lsDlw4dhFr68d3HTU7Z9lWcb25Xs8ttiRXTLW/7UlS/f7lznMjLFrP0b52mWhM+ig\n02tRuX55jFv3idtQ4rDfB+C+5vdCb9TDHGZCWFQo3pj0MqrdXylb97uVoihY/3f6nK6xvSdj/8bD\nOb6WPyiKgv5Nh2L70t2QnTJcThlHt5/Amw+8j+QrN+5+ASIiH+Lw322EcAGuI4AUCklXyq/37tTv\nYWyYuyVjvhCQvsFv0TJFUOZezyySJAFRX0Gk/Q7YZqdvTmd+HFLIk7luvplftX7+Qaz8ZR0ObD4K\nW6oNeqMeGq0GQ35+M6OVw7wpS7Ft2e47XmPXyv04tf8MSlcpmem9dAYdDGaD1+HGiNgwt3+WJOmO\nG9rfacPZzJSsWBxfbR6JlKRUSJLkOfQLICwqFJ8uGIykxGTcuJqC4uWK5mqLIEVRMPzx/2H7st2w\nWeyQNBKW/bgaXYc8gS7v+H5zbl/Y+c8+XD2f5LEJtsspY/F3/+CpAR1VSkZEBRGLqlsI2z8QyYMA\nOAEhQ+hKQ4r6GpIu81++vlKlQQW89uWL+LrfdEgaCbJTRqnKxfHhX4PuWCRJkg5S6NNAaOabFAcL\nnV6HkUvex7alu7F92W5EF4lEi64PIDY+vYXA/KlLMan/DNjTPAuhDEJg88Kddy2qNBoN2vdqjbkT\nF7utsvM2xKbRaFC/XW1smr/d7Re8zqBD06caZetnvH45GXMnLsH+DYdQumpJPPpaW69F1b+i4yIR\n7e1JJoDLZ69i0fQVuHzmKmo2q4Ymj9eH3uB94vy2JbuwffmejCJSKAL2NAd+/HA2Wj3bFIWKxWTr\n5/CHiycSoSieG3A7rA6cOXhehUREVJCxqLpJuE5AXO8H4JZVV64jENeeBQovhyT5Z6S0TY/maN6l\nMY7tOoXwmDCUKB/vl/vmB0IInNx3BhACpauWvOPehRqNBnUfqom6D9X0eG3GsF8zL6gAaHRahIRn\nbQXgiyOeQUpSKlbMXAu9UQeXw4UOr7ZGp74Pe7z3jUk90e/+Ibh+ORlOuxN6ox5FShdGr8+y3t7g\nwolL6FPvHdgtdjhsTuxcsRfzpyzDqCXvo0qDClm+DgDsWrkPQx4ZAdmlwGl3YuXP6/DLqDkYu/Yj\nr/uyrZuzOWM+2K00OlcIlfsAACAASURBVA22L92N1s89mK37+0O5+xK8fuEwhRpRuX55FRIRUUHG\nouomkfYzgNsnQSuAuA44tgDG+n7LYjAZAv4XguySce3idYTHhMHkZQ7U7Q5tPYYPn/gfblxNBSSB\nsMhQvD/7rWwVEoqiIOni9Sy9t8njDbL0Pp1ehwHfvoqen3VH4ukriC9b5I6ry6KLRGH6wXHYvHAH\nzh6+gIRqJVG7VfVsbWw9ddCPsFxPy5ib5bo5T+iLnpMwdffnWb6OoigY0W2c29ClNdWGs4fP449x\n89H1vSfc3i+EwMWTl71eS5I0MIXe/d+hGirUvgeVG1TAvnUHM+bQafVaRMSGo9kzjVVOR0QFDYuq\nf8nn4VlUARAAFO+/bMi7RdNWYMrAH+Cwpq++a/VsU/QZ/8Idh50sN9IwqOWHsNz4b6NmW6od7zz0\nEX46ORHh0WFez7udRqNBoRIxuHL2msdrkkaCOcwEoQi8/2t/RMSGZ+tniogJR0TM3c/R6rRo+EjO\nV15uW7LbY7I7AJw5eB7WVKvXJ0zenDl0HpbkNI/jDpsTK2au9SiqJr81A3vWHLjj9eo9XCtL91XD\nx3PfwcxPfsfCaSvgtLtwf6d6eOGTZ4Kqyz4RBQYWVTdJxiYQ9tUArLe94gIM+fcXSlqKFVPe/gHL\nf1oNl1NGndY10Gf8CyiaEOeX+zvsTiyY9CNsV3+DMdSFlJT78OvY07Cn/bfybtkPq//P3nlHSVF8\nbfipnjybd1lyDqIgIIoRFREDimJWwCwomD8QEwqiKAhmMPwMIGICUYIKCCIqIopkQRDJOW7eyTNd\n3x+9aXZmllnYBZR6zuEctqe7qnpmoN+999Z7kbqk/3v9oo7xy1eLCIVCEcf1kOSnSQu5st8lca+n\n9/BevN7v/bAUoNVh5dqHu9H6nJa073IyNkfVRl38vgA5e3JJq5WC1W49rLEciXbc+ZFiSGgiwj6h\nIqx2C7oevVtC+TXm7Mvj63fmRDTlBqMm7Plvnogr2ni0sNqt3DGsJ3cM63m0l6JQKI5zlKgqxtEd\nXOMgtBMofiA7jN10prpHc2UxkVLyxKXD2LB8c0mPuD9mLmPt7+v5aP1oElJiFzdX1fyfPt2XHvf9\nitDAbJH4PFupUyeZUQ82BIxaF5/Hz9xP5tPv1dujRlpy9+ZGfaD7PD5y9+ZVak0X3dIJk9nM+CET\n2bt1P3Wa1qLPiJvpWOTvVJVIKflk2Jd88dJ0kEZQ87r+3bj92ZsqlfIry5X3XsLnw6eEFcZbrGbO\nvTa8wPzArmx2bdhDvRZ1Sor0y1KnSS3qNa/NltXbw7y67Am2CJG6+c+tWO2WqJ/BCR2a0fb8Vod0\nLwqFQnG8oURVEULYIeNLpHs8eL8zLBWct4D9iqO9tJisXbSezau2lQgqKDKndPuY89FPXPNQt2qd\nf9Uvy7jp3oXYHKUPbUeCzjld8znz4nwWfV+6I00zaeTszWP2+J/46rVvKcx1cUrnk+k94mZOPu8k\nLDYLoWB4gbk9wU6Ttg1Z8/s/1GlaK+YOt/J07tGRzj06AiADa8H/G9K9A+yXILT4UonxMHX0TCaN\nmh4WFfvqtRk4kx3c9OjVhzRmj8evZsvqbSycvhiL1UwwGKLFqU15+H/3AIb56Ut3vsUvXy3Carfg\n9wU4/7qzGDjuvgg7hWe+GsiATs/gLfQSCulIKTm7ewe69r4w7LzMBhkE/ZGpb82kHVcbJRQKheJw\nUaKqDEJLRCQ+AIkPHO2lxMW2NTuiHve5faxfvrna58/bMYdQ7cjjjgSdLtflhIkqk9nEV699y+zx\nP5WIkAVTF7F41nKufvAyGrVqwJa/tpe8ZnVYSUhxMuLmN7DYLPi9ATrdcDaPjL23RDzkHchn7e/r\nSa6RxElntgjbBSalROY/AZ5ZQAiwQMEwSPsAYT2tSu5/0shpETsNfW4fX4z6+pBFlcls4qnP+7N7\n8142r9pGnaa1aHJyqUfZ+CGT+HXqHwR8gZLI0oIpi8hsUIPew3uFjVWveR0+3fI2S+esJGt3Lq3O\nPoHGrSNtJBq0rEeL05ry9x8bwsSVxWbhuv7H7i8VCoVCcayhRNW/mPot6xLNcdLmtNKsXeNqnz8p\nI7k4wxeGrkMoWPqCzWmj55PXMGHoF+Eu59JIDU56aTp2p43UzGTDVFMIatRJZ9m8P/F7AyXX/PLV\n76TWSqHvS7fxybDJfDZ8KhabGalLMusn8vrsGiRYZ4L0grkZBLdQapERMFJ0OfdBzV8R4vC/+rn7\nozt252cVIKU8LAPWOk1qUadJrYjj5T2zwHgPv3lndoSoAmP34pndDi4in5v+OKNuf5Ol369E0zQS\nUxPo/15fmrZtdMj3oFAoFMcbSlT9i2l9TksatKzLllXbCBRFGIQmsNqtR8RT6KTzbiWw682I4z6P\nxrbNZ5Ba00tm/XR6PXUdSemJJRGnCKTRODoUyqXLzedx5/M9uaF27zBneWNcP9PfnIWn0MvcCT+H\nRWvuHrQGi+4CWbRzLvh3jFX7IbACYvRGlMENyILXIbActFqIxPsQ9ouintuoVX02r9oWcbzBiXVj\nCqrc/XnM/vBHtq/bRauzTqBzr3MrtUvNE8VHqqLj8ZKUlsiwr5+gMNeFO99NjfoZh1wXplAoFMcr\n6n/NfzFCCF6aO4QLenYsadfS/sI2jPl9eNw2BJVhy1/bGXn7GPq2H8grfd5h/45CXLyIz2vC49Lw\n+wR+n4afK7lz1Bgm7/mAt5eM4txrzqR245pRC6HLEvAG+P7jnwEozI3cAQdG774Z734fFq2p38xL\n246FWG2RVgSRCCD6eTK4AZl1Pfi+N2w0gquRuY+guz6Nev69r92BzRm+k87msHLvq3dEPX/jyi3c\n3uJBJgz9gtkf/sg7A8bTp3V/cvbFX4zf8vTmMY43i3q8siSmJlCzYaYSVAqFQnEIiLI7g44UHTp0\nkEuWLDni8/6XKf4cq6vn3+pf/+aJS58n4Augh3Q0k4bVYeXVn56l+SkZFO6ZAtJFYq2uCHP0B/9T\nVwxn+Q+rKxRXNeql8/n2d+l//mBWL4gVbQrnnK55DHx9GwnJcYgqkYSo+RtCRFof6DkPgW8OEaKr\ngmv+WriO8UMmsnXNDhqeWI/bn72JNuedFHXqfqc+ysYVW8KOmSwmLrn9AgbEsJsoz7olGxnY+RkC\nvgChoI7JrGGxWXnlp6GccFrVCCuFQqFQhCOEWCqlPKgJoRJVirjo234gm1ZujTh+8rkn8tr8YXGN\n4XF5GX3f+/z8xcKwHYvFWGwWrv2/y+kz4hbWL9vEgE5D8HsDUc0wy9LoBC+jZ/6D3Vn+uywAE4ap\nqw0QiLQ3Ebbzo46j7zsP9L1RXnEganxzWA22XXkurqvZm1Ag0o8ruUYSX+0bF/dYO9bvZvLLX7Nh\n+Waat2/CDQO7q116CoVCUY0oUaWoFKFgiIA/GNXkMRQKcZm1J9G+K2aLmVm+zys1l9/rZ+n3fzL0\n2pfCBFNmgww+WP0aziTDy2r7up1MGjmNZXNXcWB3NjIU+7s6cvIm2pzlwWQqK9ackPIiBP9CaGlg\nvxJhyow5hp51k1FLFYEVUfP3w7Jj8Li8XJt+B8EooiqjbhoTd7x3yGMrFAqFonqJV1SpwonjHK/b\nx6t3v0P35Fu5KuU2+pzcn9ULwtuVaJqGLYajdkJq9D54FWGxWfjw6c8jRFregXy2rS21iWjQsh4D\nx93P20tHYjabYo6XUTeNpCYTMCVeT3FECsupiIyJaI6uaEmPIBLuqlBQAYjEe4Hy5qR2cFxx2P5W\njgQ7p1x4MqZy92F1WLmsT5fDGluhUCgUxwZKVB3nDLvxFX749JeSNNvWNTt4ousLbF+3s+QcIQRX\n3nsxNkd4TZHZYuKkM1qQdyC6tUAsNv25ld2b9iLLtVHxewJMHT0r4vzUzBQeGNM7ar2YzWlj4Lj7\naXFqa7SU5xC1/kTUWouWMRFhOTHsXBnaiZ43GH3/ZejZfZD+xWGvC9sFkDwYRAqGuLIZgir5uUrd\nXywe/fB+6jStiSPJjj3Bhs1p4+SOJ9LzyWurZHyFQqFQHF2UpcJ/nJy9ufw8+Te8hV5Ov6x9mH/V\n7s17WTFvdYTNQcAX4MtXv6H/u6XF03c+35Ps3bnM//J3pK4TDIQIBkMs++FPbm58H0OnPEqHS9od\ndD1SShZOXxzdWgHYsX4Xfl8Aqy28+fLlfS6iadtGDLvxVbJ2ZWOxWZAS7hreM2xeQ3hFii8Z3IbM\nugakBwhCaCMy+w9kynA0R6nBpea8Hum4GvR9IFIQWtW1+kmvncbYNa+z8qe/2LN5H83bN6HFqU2r\nbHyFQqFQHF1UTdV/mN++WcILPV5DAqFACLPVxMW3XcBDb/VBCMGyH1bx3PUv48qLtC9odfYJvPHr\nCxHH50z4iTf6vRchipzJDibvHRshhsrzxn3v8/2EnyOcyIsRmkAAtRrX5P7Rd3Hm5ZHNrHdv2kvO\nvjyatGkYt8eTnvsIeGcQubMvDVFzIULETi9WBfu2H2DmBz+wd+t+Tr2wDZ1uPPuwmy8rFAqF4sig\naqqOcaT/D/Sch9Czb0d3fYqUh2feWB6Py8vwnq/j8/jxe/yEgiF8bj9zP/6ZZXP/BAzzymj2Bmar\niZPOagFAQU4hP036lV+mLMLj8rJwWuwo06r5aypc09a1O5jz0U8xBRWA1CW6Ltm9aS/DbniF1b9G\n2irUaVqLVmedUCnTTPyLiOpPJT2g74l/nENg5U9/0bvV//HFyGnMnfAzo+9/n36nPoYrP7oXV1Ww\n459dDLlqJN1TbqNng75MGjWNUCiySF6hUCgUVYdK/x0F9MKxUDga8BgH/CuQnkmQMRkhoheEV5bl\nc1ehmSI1s9flY+4n8znt4nZk1Emjc89z+WnSryXu5UIYjuzX9b+S2R/9yOh7P8Bk0RBCoIf0CtuW\nHCzoufyHVVCJwKjP4+eTYV/y4ndPx39RLLQMI6UXgV5UQ1U96LrOiFtH43WVCkmvy8eezfuY/Mo3\n3PHsTVU+54GdWTxw5pO48z1IKfEUePj4uclsX7eLgWPvKzlv+7qdTHvzO/Zs2kv7Lm24rE8XEpIr\nv/FAoVAoFAYqUnWEkXouFL5OiaAC4+/BreCZfmTWUEbY9H+vL7cMvoGMumnYE+2ccXl73lw0goAv\nwOj7PsDv9eMp8OLO9+B1+fhn6aaotgsAbTu1ijjmynMx473vmTD0C/bvyMJkrtxXbutf2yt1fixE\nwj1E7uyzgv2iw97ZVxG7NuzBleuKOB7wBfh50q/VMueUN2bi8/jDdlf63H7mfb6AA7uyAVj83XLu\nPe0xZrw7hz9mLWf8kInc3WZApTcdKBQKhaIUFak60viXg7CALJ8C8yC9cxDOG6tkmvZdTiYUxTTT\nnmDjoltKzS9NJhM9Hr+aHo9fHXbex8MmEwxEGnSaLCaatm3ExpVbCfgCmK1mhICnJ/aPqKfasHwz\nAy8cSigYwuvyYU+wxUwdxiJrdw4PnTOIpyf2p2bDii0RKkI4uiFD26DwHRBmkH6wnY9IHn7IY8aD\n1WGNaV5qc1RNVLI8a35bR9Af+dlZbRa2rdlBeu1UXrrr7bDeij63n5xgHp+PmEK/V+6olnUpFArF\nfx0VqTrSaMlEz4FpYMqosmkciQ6e/OQhrA4rVrvRF9DmtHLhzedx2sVtK7x23/YDzHh3LnowihjQ\nJRf2Oo9XfhzKbUNv5O6Rt/DJ5rc5vWv7sNOklAy78RVcee6S1JfX5cNkMjyvnEkOnMkObE4rV/S9\nmItu6xTVC0vqknWLNzKg0zOHXROkJd5rmHimf4LI/BEt7W2EVr3prpoNatCwVX00LXxHos1p48p7\nL6mWORu1ahA19RvwB6ndpCa7N+3Fk++JeD3oD/LrtMURxxUKhUIRHypSdaSxtAeRDNJNuLiyIhy9\nKjWU9H6HLHwX9ANgPQOR+HBYK5WOV5/Bxxvf5OcvfsNT6OWMy9rTvH2TCscMhUIMOH8IOXtzo88p\nJWdc1p46TWvFbO4LsGfzPrJ25UQcD/iDpKQ6efyjBzFbTLTueCJWmwUpJSed0Zyxgz7DXe6Br4d0\n8rMLWf7D6rhsGypCaE7QItOU1cmQyY8wsPNQCnNc6LqOrks6Xn1GpU0/pZR43T5sDmuFDY+vH3AF\nP3z6S9iGAKvdQrtOrajbrDY5e3OjRjGBStVUSSlZveBvdm3cQ/P2TcLsOhQKheJ4RImqI4wQGqR/\niMzuDTIH0EAGIelJhDV+waAXvg+Fb1JSm+WdgfT9BBnTEeb6Jeel107jmocuj3vc5T+sJj+7IGrK\nymQ2cf3A7tRpWgswHNBz9+dTp2mtSCuFCvo65+/PZ8z9H/D2kpEl1wkh6H5fV9Yt2cic8T9FXCN1\nnQM7suK+j2OJOk1qMWHjm6yYt5oDO7NpdfYJNGhZr1JjzJu4gPcGTiB3Xx42p40bHrmSXk9dF1Vc\nNWhZjxGznuK1vu+ya8NuNJOJC3p05IExvQFIq5XKiWc0Z81v6wiViUbanTaueTi+70regXwGXjiU\nvVv2IzEiiiefeyLPTX/8oLYaCoVC8V9FiaqjgDA3hcx5EPgTZCFY2lWqWFpKD7jKCCoAdJBupOsd\nREqkv1S87Nt2AD1Gj70OXU/hzud64Cn0MOr2t1g0cylmixkhBL1fvJnu915acm7txjXJbFiDHet2\nRVk/ZO3KZtqYmdwy+Iaw19p1as38yb+F7ZYD46F94pkt4r4Pv9ePZtIwW46Nr7jJZOK0iw8tyrZo\nxlJe7fNOSQ2UO9/DpJHTCYUktw+NXoPX5ryTGLfmdTyFHiw2S8T78PSk/jx+yTD2bN6HZtII+IJc\nfHsnLrn9grjW9HLvd9i+bldYg+hVv6zls+e/5I5hPQ/pPhUKheLfjqqpOkoIIRDWdghbx8rvPgtu\nIfpHFwL/4dXEtDy9GdFqvuyJds679kyAIkG1jIAviKfQi7vAw3uPfsyimctKzhdCMOSLASXNkcvj\n9wZYMOWPiOMX3HQOGXXTsdhKRYDNaeX0y9rTuHWDCtfu9wVYt2Qj95/xBFcm3cqVibfwfI9XKcgp\njOfWj1nGD5kUVlQORs/Gr179JupmgrI4Eh1RhWV67TTeW/kKL88bymPjH+Cj9WN46K27o7YCKo/f\n62fJd8vDBBWA3+Nn5th5cdyRQqFQ/DdRourfiFYTZIxddKbKpZXK06xdY9p3aRPW589iNZNeK4XO\nPTqSn1VQJKjC5/e5fUx8cWrYsSZtGvHaL89FLZoGMNsiH/ZWu5U3F43g6gcvp1bjTBqcWJe7XujF\n0xP7x1zz/h1ZPNn1ea5MuoUHzniCf5ZsRA8ZrXR+nbaYJy4ZFtG8+d/Eni3R/LUgGAhRGMWuIV6E\nELQ8vTkdrz6DzPrxb5IIBUMxPcmimckqFArF8cKxkRs5TpBSB/8fENoC5hZI0wngehu83xgnOLoj\nEu4/aL85YcpA2i4A389A2TSZHZHQL8ZV8fPMlwOZ8sZMZrz3PX6vn043nMPNT1+H1W5lz5b9mC2m\nqA/PAzuzI441bdsYk1mLWqMViGGvkJiawD2jbuWeUbfGXKMM7QOZTyBUn4fOGUT27tyocwT9Qbb9\nvZN1izdw4hktSq/1/QSYwN4ZoaXHnOdYoHHrBqxeEOksb3NaSUqvPo+tWDgSHTRp05ANyzeHHTeZ\nTZx9xUG7OCgUCsV/FiWqjhBSz0Fm3wKhnSB1jEpuidE6pSi145qA9C2EjClGQXsFiJRRyPynwPs9\nCBNgg6SnELYzD3utZouZGwd258aB3SNeq9O0JkKLTBFpJo12F7SOOO51+8KKocuyc0Pl28PIUBYy\n92EIrARh4rdvU3Hl1o3pBQVGP8Gd6/dw4hkt0F2fQsGLgGZ8BPnPIlNGoDm6VXotR4rew3vxRNfn\nw1KANqeNO567CZOpensWxmLguPsY0GkIQX8QvzeA3WkjIdVJ7xdvPirrUSgUimMBJaqOEDLv2aJa\nqNLojKT8Jjk/un8zJv8vYOtU4XhCcyJSX0PqhaDngKkOQlT/x2mxWrh71K38b8BHJVv2NZPAkWjn\n5qevizzfZsZiNePz+CNeSz6EKIvMuRuCfwNBkLBzU2LR2LFrgfSQTtN2jZDBLUWCqii6V5zCynsC\naT0LUYU+YVXJyeeexAszBvH+45+wZfV2atRN49ZnbqTLzecdtTU1a9eY8f+MYdbYH9i2dgetzmrJ\nRbeeH7OGTqFQKI4HlKg6AkgZAt8cILyoOJoMkNJD/t5FpDSsWFSVjKElQpRCd93zLRSOAX0vmFsi\nkh5DWE87hNVHcsU9F5OZuYRJL/3Cgd1W2p7jptf/7aN2gwKgVti5JpOJrr0vZNbYefjLCythGI3W\nbFAjrnll4B8IbqTs+9i0lQebXcfjih6xsdottDmvFU1Obohe8DYQzUBUA9/34OwR1zqOBu06tebN\n30cc7WWEkVYzhV5PXnu0l6FQKBTHDEpUHRGK03wHx+fRWDhnF5fdB3u37mfKGzP4Z+kmmrdrzLX9\nu1GnSa2DjmGkuEZRYrkQWI7MvhPSP0JY21d4bTxI/2JOP3sip08pa+kgDO+tzB8jUpf3vHQbezbv\nY9GMZWHHD+zM5vGLn2Pc2jfi2nWGvr+oxUzpodMuKKBWAz87NzkIFGk2oQmQkJSRyOV9LuLWZ4pt\nGwJE/xx0o22NQqFQKBSHgdr9dwQQwgzW0ykfm5KyqLyqiFAIfB7B95NsbPpzK3e3fYSv35rN6l/W\n8u27c+jbbiDrl22qcC4pQ1D4GuEeVgBeZMErVXI/0v1plPElyHyj1qkcVpuFWo0yI2qxpC7J2pXD\n2t//wZXvZuvaHXhc3tgTW06K6JloMsErU7dz6a11SUxNICHFSde7LuTL/WP5at84eg/vVWowar8I\nsEYZGLBdWOE979myjzW//4OnMLK9i0KhUCgUoCJVVYKUEun5ElzvgZ4NllMRSY8iLCeUnCOShyGz\nbiwSBR6kdJCbFSBrt5lGLQ2hsGGVg5f/rzHtL2nOWw+Pw1NQ+gAPBkIEAyHGPPABoxdW0ARYz4nS\nrLl4kHX8OX8N6xZvpFajGpx1ZYdDc7/W82K8IAwz0yjs23YAqUfuwxdCMOHZyfw5fw1miwk9qHPd\ngCu447keEdEroaUjnXeCZwLI4vfGQmJaEg+9+ywPv59a4bKFpTXS2RPcn1OyOQArJN4f5kJfloKc\nQp697mXW/v4PZpuFkD/ILUOup8fj11Q4VzHBQJA9m/eRnJFEckZSXNcoFAqF4t+JElVVgCwcDa5x\nlERv/POR2YshYyrCbPTaE+bGkDkX6ZkGwfUIc2vGDdrOz1+swGJ1I6XAlW/CkWjnhke6c+eJD0ed\n6+9FG5BSxk6XackgtKg9m7dv0Hiq23CCgSAWmwVHgp3XFzxf0nYmbmyXgn8pUC6qJINGb8MonHZJ\nO5bPWx3Wjw6M3YGr5q8h4A2UWCx89doM0munctX9l0WMI5IGgOVEpHs86Llg64xI6IvQKhZUxWjJ\nTyLtlyO934EwI+zdEJYTY54/vOfr/LVwXckuN4BPn/+KBi3r0fHqMyqca/ZHP/JO//GEAiFCwRCn\nd23P4xMeVMXcCoVC8R9Fpf8OARnai577GPre09H3ngOudwlPh0mQPmThO2HXCS0ZLeE2tJRhaAk9\n6P9ef655uBtSJONxWWh9Tkte/fk56jSthSPRHnVum9NaYf2REFZw3AaEP7iDAQvjhmfgdfkI+kN4\nCrzk7Mtj+M1vRL9HPdewL5ASGViPDG4oMdAUzmvA3AxE8RwaYDf6F8Zwh7/0jgtIr52KpUxkTGgC\nXddLxEoxPrePSaOmx7g/gXB0Q8uYjJb5PVryoErv2hPWdmjJj6MlPVKhoMrZm8vKn9cQ9IdvMPC6\nfEx++esK51j501+Muf8DXLluvC4fAV+Qxd8tZ3iv1yu1VoVCoVD8e1CRqkoi9UJk1rVGmi/qTrJi\nQlHri8pitpjpPbwXvYf3initW9+LmTZ6ZpgVgdVh4bI+XQ66RpHUHynM4B5vpAK1NN4blsnCWeGp\nPqlL/lm6kfysgpLUlAztROYOgMBqSsNdJkADLQ3SxiAsbSBjEni+Rfpmg0hHJPREWNrGXJMj0cHb\nS0by5StfM2X0TLwuX9R0YDF5+wsOep/VTX5WQUyj05y9sVKgBhNHTo1oLRPwBVn+wyqydueQUSet\nSteqUCgUiqOPilRVEun5CvRCKhZUAAJMjQ55ntufvZEzrzgNi91CQooTq91Ch0tOoc+Ig5srCqGh\nJT2MqLkEUfMPROYCfv0ueopPD+os/m4FAFIGkFk9isRgAMO6IIjh6+QBfRcy+3akXogQVpYvbM7d\n5zq5NH0D19V5jc9fnIqux97lmJiawNndT0fqskJBBdDitKYHvc/qpl6LOlFb7JgsJjpcWnFz5H3b\nsqIeN1vN5OzJrZL1KRQKheLYokoiVUKIrsAbGCGND6SUL1bFuFWFlEGjpUtwE5ibg+18hDhEJ+rA\nciJ3vkXDhki899DmwDDZHDxpAHu37mf7ul3UP6EOUsL2dbto2rZRSQpQ6tlI90QI/AWWkxCOHgiT\n4fskhAlEIlLPpucjDXj70SxCwcjU4ecjphhGkr75RYXmFdg/yBB4Z/HXirYMuWpkSSStILuQT5//\nCleeu0Lht/Knvwj4YzcBFkJgdVjp98ptcbxLVYvH5aUwx0V6LTNaYBZacDPPTWrFkB4rceUa0Sqz\n1UxCsoOegyr2Z2p3QSt2btgd0XRYD+nUb1m32u5BoVAoFEePwxZVwlAnbwEXAzuAxUKIr6WUaw53\n7KpA6tnIrJtAP2CkwoTNaEicMRGhHUIKxtQMY1t+eV+johQZgFYDkfxMlXhC1WqUSWGui3tPe4zC\nHKN5rsli4oExvel2V3Nk1g1Fu/18zPt8BR+Nmsv+XTbqNqtNnxdv5qzO34F7Ml2vszCmf/Toz471\nu42/hHYaxeYV5vzGOgAAIABJREFU4gP9ABOe/SLCJd3n9jFt9ExuGXw9dqct6tXJNZKxWM0RYkNo\ngsTUBNp1as1tQy+n8ckNDvreVBV+X4BxT7zB79/8htUuGTX5b5xJGmazn5PbOZm4MonXH7+ILWsL\nOfXiNlw/4ErSa1f83enxxDX8OPFX3PmekhY6NqeN24beGPO9USgUCsW/GyFjtZuPdwAhzgaGSikv\nLfr5SQApZUz75w4dOsglS5Yc1rzxouc8BL65hLuZm8F+OVrqy5UeT4b2IQ9cCtIVPp65GaR/icAD\nIqU0khRYjSx832iibDkNkdgHYYo/UhEKhbgi4ZaIYmmAcX9YqVd/CSCZPTGNt56qj89Tmq6yOTQG\nvbOdsy45AMCNJ7cmLztSR9duUpOPN76F9C9HZt9BhZE44USkvU+vZh9EbaBsT7Dx7oqXqdusdtTL\nXfluejXshzvfE3HdhHUPk2IdBsENgADbeYiU4dXa8FgGd7BreU8yau4raclosUi0sECmGRzXoKW8\nUKmx927dzyfDJrN83mrSa6dx02NXHXTHoEKhUCiOPYQQS6WUB+0YXxU1VfWA7WV+3lF0rPyC7hFC\nLBFCLNm/f38VTHtwpJTg+4Hy7WEgCN7ZB7lWR/oWoOe/hF44FhnaB4Aw1USkfwLmlhiBPgvYOiHS\nP0LTbAgttVRQeX9EZvUC33cQXAueScgDVyKDW+O+h2//NyeqoAKYPWEHIJESxr9YJ0xQAfg8OmNf\nKN0Z16v/HmyO8AhRcfQEAMspYGkHRN95CHbDMsHSIWYkSUrIqBs7ipOQ7OTF2YNJr5OKI9GOI8lO\nSmYyz399Lymme0r7+hEA33xk9q0crvCPhZRBQgd6UrPOXqw2ic0hsdnLCyqI5/sSjVqNMnnkg/v4\nZNPbjF74ghJUCoVC8R/niO3+k1K+B7wHRqTqSM0b1bCpwuNFBds59xj1U9IN2JCu0ZD6NsLWEWFp\njajxDVIvAGFBiEgRoofyIe9xwr2cAiBDyIJXEGmj41r9lr92xHxt1xYjjRQMCHL2R/8od28tdRC/\n6q4s9JDgs9dr4S60kpiWyB3DenDxrUafQSEEpH9gWEG4PgTcGC7wJtBqQ8JdCOdNCCG4behNrJq/\nFp/Hj9Wu06Ktm4DfQYdul2NzVJzeOunMFry78mV++PgXrE4rl97ZGbP/PSgsv8suaKQkA8uQlrbG\njkRhAXOriFY4h4R/AcgCTHH9KzjEGjyFQqFQHDdUhajaCZQNW9QvOnbUEUIgbecbRephu/VMFbcl\n8UwD/zJK02A+w3oq9/+g5m9G2xlAaNEdsnXPd5D3qHFd5KvgXxT3PZx1xWl8+785UV9rcGJNYAdm\nS5Dk9CB5WZHu6Jn1SuuehIBr7znA1X0d+J3f4ki0R7qWCysytIXS90sCQdAPICytEMKY46QzW/DC\njEEs/fY5brp3FVIXWO0mzPYJyOC5CHPDmPc05Y1vGfvkZ5itZqSUfPLcl7z/q0aiPXr/PemdAzl9\nMQrodRDJkPY/hKVVzDniIrQHIeLpyWgFx1WHN5dCoVAo/vNURfpvMdBCCNFECGEFegAVOyMeQUTy\nUNAyQTiLDjhBq4lIHhzzGumZRvS6oiAEVlU4nwztq0BQFRGn+zfAmZefGjWdppk0rnnwBkBHCLj1\nkcjUHsDe7TZGPtgIn0dQbNJpSn0OZ5Ijqomo1LPB+32U9XuRhf8LO9K2I9wxcC2OBB1nUgizxQ+h\nLcicO2Km7P5auI6xgz7D7w3gzvfgKfCSvTuHL0dvo7xhqbGgELg/NfoKykIjcqjvMawdYrXjiRdL\nO6LZqOo6+LwCKS3G98XcEpEY3eFeoVAoFIpiDjtSJaUMCiEeAGZj5EjGSSn/OuyVVRHCVBsyvwfv\nbGRwA8J8AtgvMZzHY14U622RcDArBu93B1mRAxJ6V3iG9C83BExoG1hPZfzfjzGsxxcsmbMSGZI0\nOKkeQ74YQHLCYPAbkZYrbs9G02DciDoU5pkobt4cCgoWzEgjGEzhqQ+bIBL6ICwtY08e2gPCCjJK\n1Ci0LXyd7k+I3AWpG/0HAysgyu7HaW/Owu+JNNOcNjaRmwc4sJiL/bEAbEbaUd9Vcl72PjMzP8lg\n05pETjz7DS7vd+8h99QTlpOQtnPBt4DiNK3fJ8jLSYaE/tTMDID5RLCeUaGLvUKhUCgUUEU1VVLK\nmcDMqhirOhDCBo7uUaMSUc933ID0ryAiWiUSwHxyxRdLD5GF8cWYwHkzwnFDzMt1z/eQ9wgltVie\nzViZxrCPL0I4eoPtwpL0o77fKJ4PhWDpT0m4C01k1vNTmOcMG9Pvlfw220pBcDApluQKl69THxny\no0XEMDWwljO8DB0guqeVMIRVFDau2BL1uKdAsuSPwZx94Xxjc4Gwg+MmCO0D7yQAtvxtp/9VzQn6\nBX6fxuJ5S5n8+kO8uejFyvcvLF5p6huGOHRPAunDmt6Nmg36xkztKhQKhUIRC9WmJhr2y406LO9s\njBoeM6AhUt8+eIG0rRMUvkWk47oV0sejWWPvyJRSQsGzhBe360ipI3yzkP6fjchJ+gQj0mY7j/wd\n2xhwdRMO7Lbg9wn0UHTpaLaYydmbR0qN2KJqy1/bebTLs1zTO5Ore+/G7iwWTAKEA5FwX7l77Qz+\nP4gQnzIA1lNi32MstEy01JHh53vnIn3fgHTzxmP1cRdoFEfh/F6dgN/F2/3HM2z647HHrQAhLIiE\nOyHhzkO6XqFQKBSKYlSbmigIoaGlvoTImIRIehSR/CwicwGifKQm2rWWE8F5E0Z9kCj64wBnjwoF\nFQB6FuiRLUxKMk/Sje5fg3RPNo4n3M3/nqnD7q1WPC4ToaCGlIJoOxulrlOnac2YU0spearbcHL3\n5fHhiAzGPFmXbettFOSayMk5FZExGWEOb7sjnNeAqR5hFgzCAfZuyJx70Peegn6gm1FoXkSTk6MX\nsJutZmo1yox8wdYZzK0IBR2sXeaEcvFGqUuWzf0z5n0pFAqFQnGkUJGqChCWk8ByUuWvSxoE9kuQ\nnunGz47uYDn94BdqiQc/RXjB8zUk3Iww1eCXGSkEA+WjU8XCqvR4as0URGROr4T1yzZRkF1Ycv3c\nyRnMnWx4XJ3ZrS3Pf9M84hohHJAx2WiT45sNIgWsHYoidUXRtuB6ZO5AZPJzaM6rufb/uvHHrGVh\nzYaFJqjdpCbNTmkcZQ4TpI9HFH6JyTyVYJRSL6s9ctejQqFQKBRHGhWpqgaEEAjr6Wgpz6OlPI+I\ns9BZCDs4rgAq9nkqyCtNLUo9+kcoBKRmBkhJN4rCc/bmxrRmAPC6fAgt+hrLu5+HzaMloCX2Rsv4\nAi39/aJCfW+5s7xQOBLd9wetz8rgnpduw+awkpDswJ5go/4JdRkx66mY75EQVsxJvejcoxMWW/jv\nAVa7ha53VWCPoVAoFArFEUJFqo4xRPJQpO4mUDgHk8mwSyirNTwujR1bzyOlmfHzWVeeysLpS8Ia\nJWsmyZld8hk6fgv7dlq49fRW+Nx+Zo//kWsf7hZ13hPPaB613snmtHLBTR3jv4HgxujH9SzI6Ysk\nwBW9ruCiW95l/dKtJKUn0qRNw7hE5/2j72Ln+t1s+nMrQtPQQyFO7ngidw7rEf/6FAqFQqGoJpSo\nOsYQwo5IG80v30xlybfv0e/ZLZjMEk2TaBrMm5LBmT16lZx//+i+/L1oEwU5B/C6TDgSQjiTQjww\nwnBiT0kv3YmoFaX/QqEQv3+zlAVTFuFMdtD1rgtpcWpTBrzXj5fveptgIEgoqGNPsNGoVX263tU5\n/hsw1YHQ5hgvFvVL9MzEnlifdhc8UKn3JiHZyRu/vsD6ZZvY8c9uGp/cIGaNlkJR3UjfIqR3GsgQ\nwnElWM9V1hsKxXHOYTdUPhSOZEPlw0FKv1Ev5JliHHBci3D2qNjjqlLj+4zmwVqG4adVBp/Hx72n\nPU7Wrj20OyeblPQg61akc8pF3bjv9fCdan6vn/kfXsWWtS4atfRy7uV52BzG56qHYOn8RF7p34Jb\nhtxFt74XMbj7SFbNX4PX5UPTBBa7hd4jbuaaBy9n2987mfn+XHL25nJmt9M4//qzMFvMSN1V1JKn\n4nvXPTMg70kiU4DlEGloteJ3llcojiX0/BHgnojxPZeAAxyXI5KHK2GlUPwHibehshJVRUi9EOl6\nBzzfACawXwOBxRBYSqnvlDD6zmVMifofpyvfzYRnPiJn+yyEppNYowu3Pdc7qo2B7poIhSONMWUA\nLKcg0sYgyrituws8TBszk5+/+A1nsoOr7u9KpxvPie6E7puPnvMAUvdGeEz5fYLNf9flhC5z+e2b\nJYy8/U28heGix2Kz8Pn2/0WsVfpXIvOfKkrrSYzgphMc3RBJDyO0VCNtGFwL+gGwtEF6v4eCVw0X\n9JieXRa02seMR6yiipBSB/98pO930GogHFcjTDWO9rKqFBncgDxwDRFdB4QDkTYhrl3CCoXi34US\nVZVAygAy6xoIbqHUIdyCIQiivD9JQ9ESepW5Xke6PiN70yhSa3gJBiDgF5jMMHZ4G/q99TkWa+kO\nNelbiMy5l3B/JwtY2qNlfFK5tfvmIwtehuAWvN4k9GAWzsRIQ04pLWg1f2bEbZ/w4+e/RrzuTHIw\n4P1+nH9dE6TrIwj8BeaGRSIzWtTJYtgppI01+vLpOwGT4cSecA8i8QGQLmTWLRBaG+Xy09EyPq3U\nvVY1OXtzGfPgWH772vgunt29Aw+O6U1arfjbCClKkdKPzL4Tgn8VNSK3gjAhUt9F2M462surMqTr\nQ2TBK0R2ExCQcB9akmpppFD814hXVKmaKgDfPAjtIPw/ychWKiW4P4SyoirvCULumaTXNK632sBq\nM8RYn6dWsXTWd5x11ZWl57vGEtlbMACBlcjQLoSpblzLlt4fkbkPUyx67HYvepGekhJWL0pgxa+J\nJKcF6Xi5B2/WPyyetSL6YALSauxBHri/qEVNwIjURXVML1qvvg+ybwd9N2Fmp66xYGmNsHeBlKHI\n7DswfqvXAQ2EHZH8VFz3GHHPod2GMauwIa0XIrTkQ0q3BPwBHjx7EAd2ZBMKGmtfOG0x/yzZyPh1\nozFb1D+NyiLdXxT1xiwW4f4yjch/Newx/gsIB0ZHrvJYEFrCkV6NQqE4hlBPDowUl/GbdZyUMeiU\nwW3gnYVJi2KghLFzTwRmAaWiCn1f9HGFxUihVSCqpJRsXrWN7D25tGv7IqZyUSRNg2AQhvVpzIoF\niXg9GjabZOwLYLa9iysv+n0KTdCq7WQIusreaMx1GItxg/QR6R7vQbo/MkSVqaHh9RVYQbEzO8nD\nEJZWFY8dBb3wPSgcY4xDAAghEUjLqYjkZwzj1Tj57esl5GcVlAgqgFAwRH5WAQunL+b868+u9PqO\nezzTiB7V9EHwb7C0PtIrqh7sl0D+8CgvCLBH312rUCiOD5SoAoS5AVI4ivr2xYG5zMMhsMpoYyN9\n0U81S9JrlyvutnaE4CYio2EhMLeIOW32nhwGXTacnRt2o5lNTP5zi5GlLMePU9NY/ksiPo/x27TP\na0RyfJ7ogspsNTF8xiC04FUx545OsZ9WeVEF6DlIKZE5dxTVYxWH0FyQ9xTS0g5hbhD3TDKwBgrf\nJKKOBQmBpcjsnlBjVkTBfyy2rd2JpzBSAHgLfWxbuzPudSnKUFEj8qiRnX8nQkuHtNFGBK64a4IM\nQsoIhKnOYY3t8/j4btw85n/5O4mpCVx1f1dOvahtlaxboVBUP0pUAdivgIKX4jzZDEkPI/3LgCBS\nq0HUuqsi/D5BgzaXIWWwpBGySOiD9E4HPZ9SYeWAxEcMl/IYPHvdy2z5axuhoCFQDuw2U7uhcf1P\n01L5+JVaZO2xIAQlgioeZEiyaMYyWtxrxWQ6yK69MGwgQlEEpRVsF0PgTwhtJ7JYPYD0fI5Ieiz+\nNXqmEVnDUvaEANL9KSLpkbjGa9SqPo5EO56C8Pu1Jxo2Esc6Ukqj72JwtRHZtHWpsl2ph4pw3oTM\nW0tkI/JUMLc8KmuqLoTtAshcCP5fAR2sHRFxdESoCL8vwP+dO5jt63aWdBxYNvdPejx+NTc/ff3h\nL1qhUFQ7ylEdEFoSmCPb0USt4RcpkHsfMqc3Mqcf5PQzjkUhGASbA6yegch9Z6O7PjOGMNVAZHwN\nzlvB1AysZyPS3kRLuC3mGvdt28+G5ZtLBBXAhJdq43ULvv4wg1cfqc+OjXY8LhPuwspFBUIhnc9H\nTOWbj1Lxe+P8SphOQNSYCEnPYvT+K77ODqaaiITbQd9F+V59BkEIxvKyioH0U5F4BT9bVnzH/h1Z\ncQ13dvcOpNRIxmQufa9MZhMpNZI5u/tBaxGPKlJ6kdk9kLl9kQWvIPMGIfd3NlLRRxP7VWC/EOP7\nYAWRACIZkfb2f9JmQGhOhP1ihP3SwxZUAPM+W8COf3aFtXDyunx8NnwKufvzDnt8hUJR/ahIFcZD\nyqj5CSfqc0BmRT7b9fBIjJTGH7MZjJN9RjSnYCTSlI6wd0WYMhHJTwBPxLE+P668/ZgsJvCWpgx/\n+Codk0Xy68zUuCJTZquZoD+6xYGUkg+ey6RWPQ8dOhdisTmLIlACI71XfJ0dbJ3Q0sYAIMzNkZam\nSNfHENoNtk4I500ILRFpbm2kRSJwgPXMg663LMJ+GdI7NWaK1u8V/DbLy9QPHuX91a+RVjO60C3G\nbDEzeuELvPXwhyyc/gcA51x1Bve/cecxX6QuC/8HgTWUpEJlEKQHmTcQkfHFUVuXEBoi9TUjVetf\nDFoG2LtUGH1VlLJw+h94XZFlBGarmdUL/ubcayr3b0ahUBx5ju2nx5FCxrBOiHaqjCa2ina1FdUN\nlW8tU4oHWTgGYe8a31y6C5n/DHhn0TBT8uYsM68NqMfqP0p/K543pSa6LolVVG4yCyxWDaGZeXpS\nf4ZcNSos2lWWgF9j6J2Nqdc0yLhVDyEszUBLRBa8Bb45RpG582aE85aw64TlZETqyIjxhLkh0n4p\neOdQWsBsBi0J4bgurvegBOsZRhGwZwbgCWsXreuGhcXXH2bgLvAw9Y0Z3PVCrwoGM0irlcrTE/tX\nbh3HAp6pRNaW6RD4C6nnhnmdHQ2EpRUcwkaE453Umilomij691yKlJKk9MOPhCkUiupHiSooiqq0\nMAwsKyAYBFPUgFAAnXqE/DuxHKysJbQn7nXJ3PvBvwQIIID6TYOMnLyR2RPTmfp+Jnt3JJFcI4mC\nrEJ8wch6owYtfFxyUwHJaUHO65ZPQt0lnHvdWfw2fTF+b2zLiN1brUjr+WhFNytSBgOD4153WUTK\nSKSlDbg/MXYL2i5CJD5opFwrM44QkPwCOK5FembiyVqEkBuxWHX+/C2Rt5+uR/Zew1ts+bzVh7TW\nfw8V7co8yI5NxTFL93svZd5nv4Sl/4SAhBQnbc6LLE9QKBTHHselqJKBv5Ceb4Egwn45wtoekTIC\nmXUt0R5KUjeiITGrQoQTV/A+9v3zPHWb+NB1iTNRRo9WxfkbvAxuAf9Syhdnm8xw2c3ZXHxjHiuX\nXEXrLoOZNHIaU8fMwucujV7YHDp3D97JmRcVlF5c+BYD3x3LCG+AxbNXoAf1MEsBAE0TnNqlDabo\n6rHSCGEy6qsSbq+CsQRYOyCsHcjbv5u+7Qbi84S/P0IT1G1W67DnOqaxdzNEath3Q4C5ubEzTfGv\npHn7JjwwpjdvPjgOk1lD1yUpNZIYPvOpkr6dCoXi2Oa4E1V64VtQ+C7GA0kiPV8gHTeiJT+FtF8F\n3mmUTwUGg4aostmjjWgGy2kkZV7LvWfPJS1jB7Ub+qnVwMctA/Zid5Ydy45IHBjfQkPbQVgjdtYV\npxatthCnd5yBSH6CO57vgWYxMfX1Gfh9AVLSrdw9ZHO4oALAj1V+y7NTnyVnby6bV21j1B1v4c53\n4yn0Yk+wYU+w8/D/7olvjUeRuo29tDjFy99LIBgofeBY7VauH3BlBVf++xGJDyD9CyC007CowGH0\nZUx5+WgvTXGYdL3zQjrdeA5/L1qPM9nJCac1/U8W+SsU/1WOqzY1MrgNeaAbkfUodkTGZ0iSIOsS\nyooqKY2mxAG/KCeQijA1hYxp4PkS976P2L99H3O/TGPhrCRueWQ/7c8rIDndimZti0h+BGEJ95yR\nwY3IwFojKhVYBlotREJvMDdC7r+ICm0ERAIi+XmEwzAcDIVCeF0+HOZ5UDC46IFb/lavRksdVfKj\nz+Nj/uTf2bhyM41aNeCCHh1xJERVj0cMKf1I1wTwTAaCYO+OSOhT4lYtpR+5vxMF2XmMeqg+y+cn\noZnAkaDzf+89TMerL6ji9ejgm4v0TAc0hONasF1wRB52UkrwzTHcymUQ4bwK7N2NF33zkIE/Eab6\nYL+iSnagKRQKhSIS1fsvCtI1AVnwEpGiSoOEfgjr6UU9+cK9i4rfoshnqAD7ZSC94FtYcl0wqKFp\nOlLX0Mw6QjjBeiYi9e2SVh1SL0Tm9DW8nChvF+CA5EEQWA6emRHrKZ0+AZEyCmG/OHy9eg5y3/mR\n9ymciJTXEPbOMd6ho49hGNq7qJas+L5tYG5S1MjajPR+h8x7skQ05ueYcBeYyKynYUp9FC3h1qpd\nT94j4J0HFJmnCgfYr0RLeb7K5omFnjeo6DtQbNzqMFKgae8jhEoJKRQKxZEgXlF1fP2vLKxEr4yS\noGdB8B+i1VTF3s1nA+vZYYIKwGzW0TQwmXVjNukG/yLwflc6Y94gCKzEED7lha0HCl6EpCGQ+CCI\njOj3I4NgOzdyvVoaJA3C8Asqro1ygFYP6XobPacf0vdb9DGPNoGVRbVkZYWkD0LbwPeD8WNoD8jS\nQvvktBC1G/oN49LQripezwrw/kCpqMGwdfB8jQz8XbVzlUMG/gHPt+Fz44HAUvAvrNa5FQqFQlF5\nji9RZbuY6NYJEjzTkMHdRv+9eEnsD6H9xIwkhU3hRnq+Mv4qPUUCoSKH8BAitBkt8W5EzV+BaLvl\n9JgiQkvoicj4Cpx3gP160BINYRJYaaSNcvqhuz4snU56kd45SM/XyNCBg99PdRH4k6htb6S7yMUe\nsLQjatsT4URYT63S5UjfAqJ/vqEiN+1qxP87UXfzSTfSV81zKxQKhaLSHFeiSpgyIGUU0W/bZ9Tw\niErsngptA9/8+M/3L0DffyUy8GccJ3uRoqjjfWAJiGhb5XWke2LMEYSlBVry42BuDHoB4elADxS8\nZqQh/YuR+85B5j2OzBuC3N8Z3TUOMNKUeuG76LlPoHtmGPVF1YmpVgxhawdTPeOvllPA2t44VoIN\nTI3AVrWpTaNOKYpPhjAbjuHViZYao5+eFbS06p1boVAoFJXmuBJVAJrjMjDFauTrAtv5YIqzT5ln\nIgQrmQIKrYPsu4kajQlDQwQ3ACCDe0D6+esPJw9e3oIrGreh16knMX1cKtIX6QQfge9HokZbhBnp\nX2bUdsnCoholN+CDgtfR3VOQ+06HwlfAOwXy+iP3dUTXozdmrhJsnY2apfJpWmFCOIxdfUIIRNp7\nRmrU1MT4PBP6INI/K+mvWGXYu0WupeS1+ExcDxlbF6L/E9UQju7VO7dCoVAoKs1xJ6oA0DJjv+b5\nFEJbQKsdz0DE68QejpeDmzTajdYjUoJnEv+sNPNkz2b8s8JJwK+RtcfK2Bfq8PHIfeiF7x5kmTWI\nKgykG9wfxliLH/KfJkL8ySzIffggaz90hLAi0j8r6sVoozgCJdI/NmrFypynJd6NljkbLfMHtKSH\nS3YHVul6TDURqW+AcIJILPkjUt+ududyoSUg0sYZ7V5EQtHcSYi0MQhTPN9PhUKhUBxJjqvdf8VI\n7w/I3Acp7WcXDcHBBZMAc5siJ/bYDuWHhhWR+TOEdiCzb2PwrbVZPC8JKcPFkc2hM3n1euyNFsbc\nUi/9S5DZdxG9NsjEwaNmkddotSt2n68KZGgvEAKtzlH36pHSC/4/AA2sZyDEwazzq3LuUFGtWRAs\n7Y7o3AqFQqFQu/8qRNi7GGKoQuIRmxKCaygVJRqH/5ZqgB2SHjFqwAIrAZ1Na+wRggpA0yQH9tiR\n3tlI369IPT/iHGHtAElPAI4o64slqCryqjo0IS6l5Ot3ZnNLk/u4IvEWBnQawrolG2OeL0y1EKa6\nR11QAQhhR9jOR9jOPeKiRgiT4fpvPV0JKoVCoTiGOe4c1aHIULHS0ZlYlI12CdAagr75EMbRQEsH\n26UI53UIy8nGYVMdEGYaNPdxYHfkA1UPCTJq5UP+UKSwgAwgE+9HS+wXPnpCL3THlbDvtIrXgG6k\nuixngX8+UaN5pqaHcH8w4dkv+PLlb/AWtdPZsGw1GxfcRrN6+WgiBNZzEMlDEOaGhzS+QqFQKBRH\nk+NSVBFYUeRJVdWEQN8FIhPk/kpc5wBTLaNuyFSub52tE+Dg1kf2smZJAj5PaaTJ5gjR7dYs7E4d\n8JW2tHG9g7SciLBdEDaUEIlIbESanwIkgvNakIUI26Vg64T0TIH8QeXOE2BuZdgbWNrHHUXyun1M\nfvmbMv0JJcM/30Tzkz1ooijy5V+AzLoeMr9HaClxjatQKBQKxbHCcZn+M9y6K6qnOgyEBRIfooL2\ny+UvAGsXRI3vkMGt6Pkvo3umIYsFUmgXEKT1GR4Gv7+Fek18CCFxJoW44b599Bm8O3JI6UG6xkfO\nJAQ4rsYoAC+LDZw90ZKfRkt5EWHvjBAawnE1pLwBlrYgkiipM/N9g8y5E5n/XJz3CHu37kczlb4n\nLU/x0OQkL1Z72VSiDtKLdH8V97gAMrQbvWAMet6TSM+3SFmB/5dCoVAoFNXE8RmpMmVgeA9Vg7CS\nheD+iPjrjiQE/0TuvwD0PaVH8x5D2m+C4HKQeYDk9AsLOP3CvwkFQTPFcnkvQs+JelgkD0KGdhiu\n5cJsOJPbzkUkhe/o0z1zinb/+Q3ndgJl7kkWuYp/gW49Dc1xxUHvska9dEKB0pRrwxNiGaZ6iwr/\n40P6fkPKKPatAAAQj0lEQVTm9MP4LANIzyxwvQvpE6tlN2DMdcggYDom6r8UCoVCcXQ4PiNVtkuh\nOvumhTZU7nx9W5igKsE7qShNGS7QTOaDCCq0Io+jSIRwoKV/iKgxFZHyEqLGt2hp74QVQMvAGsgb\nCDLXsF2I6E1YTADyHkXP6XvQ6FBCspNLbr8Am9OYZ9v68tGyYuxFdgoHR0odmTcQ8FC6+9INwS1I\n90dxjXG4yMAa9KzrkXtbI/e2Qc97GlmdPl4KhUKhOGY5LkWV0BIQ6RNAq4kRrPuvRRd0cH2IXjgu\nptgR5mYIexeEuVHEa4YgiTeFFgLfb8jCdw565gNjenPV/V2xJ9hY/2ciuzYnoutlg6UaCDvCeV2c\nU28qaaocjq+oZ171IkO7kNk3F9kdSMAPnunI3PuqfW6FQqFQHHscn+k/ABkCPY9qq6066rigcBTS\nNw/SJyBiROaklEarG/cHoOeCtQOEDnBwc9KyeMHzBZRLIcrgNqTrXfAvB3MTtMS+3D3yVu4a3ouA\nL4jV5ofCF8HzNRAE69mI5GcqUaRuhVhtc0RFlhBVg3R/AhGi1Qf+ZcjgRoS5WbWvQaFQKBTHDset\nqJL5g4m+C+5IE4/JKFCya89M/EJQh8BSpO8XhL1T1DNkwUvg/hQjhQb4fuKQApgyvEZKBjcgs24o\nOh6C0Eak7xdIfQPNdj428QMyf4YRmUp7H6xnVroeSZgbIk0NitKtZd9DB8LZq/L3UFkCfxPV9FVY\nILgFlKhSKBSK44rjMv0nZQCC6472MoqQwMGaOCdA0pOgNaXy/lohcH8efWa9ANwfUyKoStYTaw4T\niJpRjmtQzr5BFrxcVI9VPJYEvMj8Z5DZ9yDzB4Hve/j/9u49OK7yvOP49zl71UorX4RtwKbGCYTC\nhMGhNIECxSaAjetwDYRwKS3J4GQgQEMSSpvSQgLhj7TThtJpPVzaDIkhAdxQSMulOKWZUIwBEyCG\nxiEQcNvYxtiydVtp9+kfZ40ltCtb2qM90tHvM6Oxds/Re57zWpYev+97nrf3X/B3V+C7/mYU97SH\nzbg93HbIWgmLm+Ygfyq0nDOm9kYlcyQ1N1v2EqQPGf/ri4jIhDJFR6pS7PsIURMEM6Cyrf5xy4QV\n4Cuvj639cp2q5QOvh237Po7YWQsU/wh2fj18apASkIegFSt+eei5pXXU7N/Kluq06+BErge67sQL\n52OpA/ctlt0hpQ+GWT+C0o+hvAWyRzdt2s0KF1WnAAc/GZmD3Ik116qJiEiyTdGkyiGYC5W34g4k\nVPkV71Uzr8W7oOu2MTXtDmZ11iilDqyxJmi3Gkmn92P5pZA7Ae++DwY2QmYhVvgkFrQPPTeYAeXh\nW+aEbfYMf9sC6PsJFD458g3VitTSw0bKmsFSs6HjfrzzZig9EyadhU9hbV9oeiwyNb227hfc/dVV\nbHzhlxywYDaX/Pn5fPT0j8QdlsiUNeWm/yrlHfg754aVzycM30tSYND/87228tOnW9m8KUNPV0D3\nroBtW9LceNnBbOusXUfKUrMgexK1vw0KQOZ97w3gO78BViQoXkUw41sEbZcNT6gACp8lnI4bLAfp\nQ6mdywcQFPdyhxOPpRcQzLyDYP+XCOasJSheq/35pCk2PPNzrl10A8899iI7tnTy6tqN3HTeN3ly\n1X/GHZrIlDUlRqp84HW85yHoewYG1hPdvn9RGYBgPuROg77HahzvB980YguVCty8Yj7bt6aZf1gv\nqTT88md5gpTx3W/2cdXf1fnC/IlQenL4+5aF7FLoe5A9I1blsGRAeRM28+4R47HC+Xj5rbAQanVP\nQnKLoe3z8M6nGL7Y3qpb8ojIvrjjunvo6x460tzXXeLvr/02iy84QYVoRWKQ6KTK3fGdt0L3t5l4\nidQg1oJlFkD+C/i7Zehfw9CpwBHWfmWOg6CNX//3f9DTFQDGm6/tGSEqD8AzP1xf/+t7HqJ235QI\nnzZMMTQB6oPSc/jA61i6/sbKZoa1fwlvWwHlNyA4AEvtB0Cl/SbovCGs6I4DaWzGSqwJZRBEkmLj\n+tobt+/ctotd27sozmhrckQikuikitLT0P1dJnRChQFZPHsCbF1eXbA+ihpRxesIskdQWPAOlcoV\n1LrX4syRfriO8L/Z8q+oWb7B0jDwJoyQVL13alCE4Mgh7wWFs/D8KVB6FiyLZ46B/vV477+GmzSn\n9t9ruyJTXceBM+nuHD6Cnc6kKBTfP/UuIs2Q6DVV3vVPTIxaVPUYpI/AOlZBz71Q2QrU2xOvTgvV\nop4zZndw1EkfJp1JDTmeb81x7jX19+YLq5fX+AFsecgeS+2SAf0N12CyoA3LL4bUQbB1Cb79c/iO\nP8G3nEKl81bcJ8iTmSIT1MVfPZdcYeh2T7lClrOuPJ1UOlXnq0RkPCU2qXJ3KP1X3GGMIAPFGwj2\nW42lD4HeJ9j3rWF2M0h/6L1X13/nag49+gPkCllapxXI5DIs/9xpnHLJ79ZvIn8m5I4nTKyC8Ak2\na8Wm/y3W+vtgOYaOZuUhtwhL/8YoYx3O3fF3V4T7HnpXdcuZEvSsqrO2TER2O/nCE/nsrRfSOq1A\nrpAlV8jxic8v4Q++fkHcoYlMWYmd/vP+55kYW9DUq4fl2OBRqWDa6GcpW68asv1Me0eRbz19C29u\neJutb7/DBxcezPRZI2/5YpaC6bdD/wtQWhuWQsifvueJvo7v4523DCoZ8Gms7YpRBlpH+RdQ/h+G\nTXd6D951D5ZfEs11RBLqrCuXsXzFaWzfvIP2jiLZvJ48FYlTIpOqSvd90HkzNbcQGXcp9kyZVaBw\ncTjy4t3DzvT+N6D/NSxzGFa4FO98FXxwDacAUnMheyKUt0NpDdADVgxHuQpn1oxg/uHzmH/4vH2O\n2Mwge3T48f5j6Q9gM+/Y57ZGxbvBUnVyzl3jc02RhEln0uw3tyPuMESEBCZVXlpbTahGtzYpGgWY\n9g3Mq0Uvcx+HoAMfeBVKzzO06GUZeu/He3+At/4h1nYN9L8cPqlo1Y2C03OxGXdhqTn7HIG7Q/+z\neM/DQAprORPLLozyJqOT/k1qL5TPQ/70ZkcjIiLSkOQlVbvuZPwSqpG2tsmESVB+yZApOQBmrISe\n1Xj3vTDwSrWN3XvslaHrbsgvJWj/Ct56GfS/BKnZ4SL2Udaa8c6boOdBwj4wvOdBvPVSguIXR3mv\n488si7ffAju+TDiqWAZaID0PK1wUc3QiIiKjk7ikisqvx6dd2x/orDmNBwb5JVj7DcMTKsAsA4Xz\ngX68cyPDk74S3vsoljk8rOWUWjymEL3/Jeh5YFD71S1huu7GW87G0gvG1O54ClqW4JkP4l2roPJ/\nWG4RtHxCNatERGTSSV5SlTsx3JNu1E/S7YV5WLZ8mCzklmLFL2HB9L00Uu9hSxvh2L7z3jXUvm+H\nvh/BBEyqACx9CDbtz+IOQ0REpCHJK6lQuIhwsXjELAe544D3j6D0Q9/j+JZTqey6feQ2ch+n9vRh\nBmtZ1niIlqf2vafCulMiIiIybhpKqszsPDN7xcwqZnZMVEGNlXsvbFvB0AXhUUhD7iRs+m3QdjkE\nc9izwLo6xUYf7FqJl56t24qlZkP7XwA5wuQsG37edlVYq6pR+WXU/it1UHkCERGRcdXo9N/LwDnA\nP0QQS8O8+34ob2yghXoL0cvQ/T2cFFa8HjIL8e1X1Xjsvxfvvh/L/nbdKwSFc/HcCdD7eNhu7mQs\nfVADMQ+KPj0P372vHunq7ZRh2l9iwcxIriEiIiK1NZRUufsGYOLsht77Q8ZemyqAlvOg79+hUga2\nsyfBcqAPuu8Lp/C83tOFXq0KPjJLzYHWi8cY58iCwtl4fjH0PQUE4QhbUByXa4mIiMgeTVtTZWaX\nm9k6M1u3ZcuWcbrI3nZlDxi+Jqoqfz7BtK8RzP4JNv1rYK01TurFe/8Zsh8L978bdv0Clm98bVSj\nLJiOtZyBtSxXQiUiItIke02qzOwJM3u5xkftct51uPtKdz/G3Y+ZNWvW2CMeKdbWC8OtVGofhfw5\n0PEwZI5iz60XoHgdNu3GwdHWacPBK2Gi0n4DYYK2e2F4ATK/pbVLIiIiU9Rep//c/ZRmBBIFyy3C\nC5dC112ESVN177/cqdB2JUHm0PB1x/fxShfgWFBjdCt7PHiNfQOtBWs5A4CgcB6eOQrveQC8E8ud\nGk61mXaHFxERmYoSV6cqKH4RL1wEpecgmA7Zj9VMdCyoNb23+1gbPu1W2HEd4ajVAJCD/HLI/s6e\n8zIfwjLXR38TIiIiMuk0lFSZ2dnAbcAs4BEzW+/usc9/WWoONFj3KWhZhmePht5H8Eo3ll+EZY6M\nKEIRERFJmkaf/lsNrI4olgnHUvtD62dqbvkrIiIiMljyKqqLiIiIxEBJlYiIiEgElFSJiIiIREBJ\nlYiIiEgElFSJiIiIREBJlYiIiEgElFSJiIiIREBJlYiIiEgElFSJiIiIREBJlYiIiEgElFSJiIiI\nREBJlYiIiEgElFSJiIiIRMDcvfkXNdsCvNmES+0HbG3CdaYS9en4UL9GT30aPfXp+FC/Ri/qPp3v\n7rP2dlIsSVWzmNk6dz8m7jiSRH06PtSv0VOfRk99Oj7Ur9GLq081/SciIiISASVVIiIiIhFIelK1\nMu4AEkh9Oj7Ur9FTn0ZPfTo+1K/Ri6VPE72mSkRERKRZkj5SJSIiItIUiU+qzOw8M3vFzCpmpqcr\nGmBmS83sNTPbaGZ/HHc8SWBmd5nZZjN7Oe5YksLMDjKzNWb2s+q//avjjmmyM7O8ma01sxerfXpj\n3DElhZmlzOwFM3s47liSwMzeMLOXzGy9ma1r9vUTn1QBLwPnAE/FHchkZmYp4HbgdOAI4NNmdkS8\nUSXCPwJL4w4iYQaAa939COBY4Ap9rzasDzjZ3Y8CFgJLzezYmGNKiquBDXEHkTCL3X2hSiqMA3ff\n4O6vxR1HAnwU2Ojur7t7CbgXODPmmCY9d38K2BZ3HEni7v/r7s9XP99J+AtrbrxRTW4e2lV9mal+\naEFug8xsHvB7wB1xxyLRSHxSJZGZC7w16PXb6BeVTHBmdjDwEeCZeCOZ/KrTVOuBzcDj7q4+bdxf\nA18BKnEHkiAOPGZmz5nZ5c2+eLrZFxwPZvYEsH+NQ3/q7j9odjwiEj8zawMeAK5x986445ns3L0M\nLDSz6cBqM/uwu2st4BiZ2XJgs7s/Z2aL4o4nQU5w901mNht43Mxerc4INEUikip3PyXuGKaATcBB\ng17Pq74nMuGYWYYwofqOuz8YdzxJ4u7bzWwN4VpAJVVjdzxwhpktA/JAu5nd4+4XxxzXpObum6p/\nbjaz1YRLV5qWVGn6T/bVs8ChZrbAzLLABcBDMcckMoyZGXAnsMHd/yrueJLAzGZVR6gwsxbgVODV\neKOa3Nz9enef5+4HE/48fVIJVWPMrNXMirs/B06jyYl/4pMqMzvbzN4GjgMeMbNH445pMnL3AeBK\n4FHChb/fc/dX4o1q8jOzVcDTwGFm9raZfSbumBLgeOAS4OTqY9Xrq6MBMnYHAGvM7KeE/8F63N1V\nAkAmmjnAj83sRWAt8Ii7/1szA1BFdREREZEIJH6kSkRERKQZlFSJiIiIREBJlYiIiEgElFSJiIiI\nREBJlYiIiEgElFSJiIiIREBJlYiIiEgElFSJiIiIROD/AVNlv3+qHOvCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iu66LbfuMGKR",
        "colab_type": "text"
      },
      "source": [
        "#K-means\n",
        "###Before applying k-means, we should point out that it is good practise to disregard highly correlated variables when clustering. This is because their importance will be hightened if we include all of them.\n",
        "###The same applies to categorical data, hence we will omit Region and Channel varaiables. \n",
        "###The correlations plot below, shows that Milk is highly correlated with both Grocery and Detergents_Paper. We will exclude the last two from our clusterin data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1OJ9K2AN9mV",
        "colab_type": "code",
        "outputId": "439d6ffb-5e4a-41ae-f9e8-173626239436",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "corr = dat.iloc[:,2:8].corr()\n",
        "#corr.style.background_gradient(cmap='coolwarm').set_precision(2)\n",
        "sns.heatmap(corr, annot = True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb12060a240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pfuz0KqlJ16gyqC0NXunHz8Pft77mpGv8EPayU2O8LeP4fyARGQoMtSuaZYyZ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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brI-mbPaSTfE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Exclude Region, Channel, Groceries and Detergents_Paper variables from df dataframe\n",
        "df2=df.iloc[:,[2,3,5,7]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WkgLZrRF2E_",
        "colab_type": "text"
      },
      "source": [
        "###To perform k-means clusterring, firstly we load the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-5yDX1A0iy3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sQKNqAFGhAg",
        "colab_type": "text"
      },
      "source": [
        "###One critical decision is the number of clusters that k-means will create. For this we use the **elbow method**.\n",
        "###For this, we apply k-means and create clustering solutions for 2,3,..,10 clusters.\n",
        "###Then we plot the sum of squared distances of data to their closest cluster center \n",
        "###According to the elbow method the best number of clusters is where an elbow appears at the graph."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUyIWKUCIigY",
        "colab_type": "code",
        "outputId": "c63c742b-24f4-45c3-c707-d40a48da7097",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "wcss = []\n",
        "for i in range(2, 10):\n",
        "    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
        "    kmeans.fit(df2)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "plt.plot(range(2, 10), wcss)\n",
        "plt.title('Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('Sum of sq distances of data to their closest center')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T43nm4t48eHY3Zq3ewelPfsOMVTZH1JhQ8TIB8G0RSRaRBsBCYLGI3BH60Izx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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTb4qztR9_ce",
        "colab_type": "text"
      },
      "source": [
        "###Unfortunatelly, the best number of clusters is not clear from the results of the elbow method. Possible candidates are 3, 5 and 6.\n",
        "###Normaly, we would test all the above by profilling the clusters and making a business driven decission.\n",
        "###For our purposes, we will examine only the slution with 3 groups."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GYVKms_JAfb",
        "colab_type": "code",
        "outputId": "75e13ec8-ddfc-4c0b-93ca-a401825658ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
        "kmeans.fit(df2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=0, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJrQap7E_aAm",
        "colab_type": "text"
      },
      "source": [
        "###In ordder to best understand the clusters, we append a column indicating the cluster of each record to the non-ormalised data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5xfa9GiA1SE",
        "colab_type": "code",
        "outputId": "72aeb87f-6696-4370-8dc7-1d0c7d11cd7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "dat['cluster']=kmeans.labels_\n",
        "dat.head()"
      ],
      "execution_count": 0,
      "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>Channel</th>\n",
              "      <th>Region</th>\n",
              "      <th>Fresh</th>\n",
              "      <th>Milk</th>\n",
              "      <th>Grocery</th>\n",
              "      <th>Frozen</th>\n",
              "      <th>Detergents_Paper</th>\n",
              "      <th>Delicassen</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>12669</td>\n",
              "      <td>9656</td>\n",
              "      <td>7561</td>\n",
              "      <td>214</td>\n",
              "      <td>2674</td>\n",
              "      <td>1338</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>7057</td>\n",
              "      <td>9810</td>\n",
              "      <td>9568</td>\n",
              "      <td>1762</td>\n",
              "      <td>3293</td>\n",
              "      <td>1776</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>6353</td>\n",
              "      <td>8808</td>\n",
              "      <td>7684</td>\n",
              "      <td>2405</td>\n",
              "      <td>3516</td>\n",
              "      <td>7844</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>13265</td>\n",
              "      <td>1196</td>\n",
              "      <td>4221</td>\n",
              "      <td>6404</td>\n",
              "      <td>507</td>\n",
              "      <td>1788</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>22615</td>\n",
              "      <td>5410</td>\n",
              "      <td>7198</td>\n",
              "      <td>3915</td>\n",
              "      <td>1777</td>\n",
              "      <td>5185</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Channel  Region  Fresh  Milk  ...  Frozen  Detergents_Paper  Delicassen  cluster\n",
              "0        2       3  12669  9656  ...     214              2674        1338        0\n",
              "1        2       3   7057  9810  ...    1762              3293        1776        1\n",
              "2        2       3   6353  8808  ...    2405              3516        7844        1\n",
              "3        1       3  13265  1196  ...    6404               507        1788        2\n",
              "4        2       3  22615  5410  ...    3915              1777        5185        1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ShmiVcKhMP3Z",
        "colab_type": "text"
      },
      "source": [
        "###Display clusters' size and median for variables Fresh, Milk, Fresh, Frozen and Delicassen"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEeSA58aBA2o",
        "colab_type": "code",
        "outputId": "31a384ea-188b-4e62-fff9-ff09e3e706a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "source": [
        "print('Records per cluster')\n",
        "#dat.groupby('cluster')['cluster'].count()\n",
        "dat.groupby('cluster').agg({\"cluster\":\"count\",\n",
        "                            \"Fresh\":\"median\",\"Milk\":\"median\",\"Fresh\":\"median\",\"Frozen\":\"median\",\"Fresh\":\"median\",\"Delicassen\":\"median\",\"Grocery\":\"median\",\"Detergents_Paper\":\"median\"})"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Records per cluster\n"
          ],
          "name": "stdout"
        },
        {
          "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>cluster</th>\n",
              "      <th>Fresh</th>\n",
              "      <th>Milk</th>\n",
              "      <th>Frozen</th>\n",
              "      <th>Delicassen</th>\n",
              "      <th>Grocery</th>\n",
              "      <th>Detergents_Paper</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>269</td>\n",
              "      <td>6211</td>\n",
              "      <td>2521</td>\n",
              "      <td>1069</td>\n",
              "      <td>654</td>\n",
              "      <td>3450</td>\n",
              "      <td>706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>75</td>\n",
              "      <td>6134</td>\n",
              "      <td>11093</td>\n",
              "      <td>1535</td>\n",
              "      <td>2563</td>\n",
              "      <td>14855</td>\n",
              "      <td>5079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>81</td>\n",
              "      <td>23632</td>\n",
              "      <td>2770</td>\n",
              "      <td>6422</td>\n",
              "      <td>1365</td>\n",
              "      <td>2961</td>\n",
              "      <td>411</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         cluster  Fresh   Milk  Frozen  Delicassen  Grocery  Detergents_Paper\n",
              "cluster                                                                      \n",
              "0            269   6211   2521    1069         654     3450               706\n",
              "1             75   6134  11093    1535        2563    14855              5079\n",
              "2             81  23632   2770    6422        1365     2961               411"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBilIJ2KMpC-",
        "colab_type": "text"
      },
      "source": [
        "###Create a barplot dispalying number of records for each Channel per cluster "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XNKK-XnGq0C",
        "colab_type": "code",
        "outputId": "f262b549-64ee-4337-de21-53fd75672193",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        }
      },
      "source": [
        "dat_agg=dat.groupby(['cluster','Channel']).count() #agg({\"cluster\":\"median\"})\n",
        "dat_agg['Frozen'].unstack().plot(kind='bar',title='Channel per cluster')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb11f4bcc18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N67O2QvuYFhFNEdFUV1dXShlmZlaEkoJeUv+8p8cDbWfkzAROkbS9pMHAUODJ8ko0M7Ny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\n9GZmifv/lBxcXhH/fWMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWxGRnvYM6cT",
        "colab_type": "text"
      },
      "source": [
        "###Create a barplot dispalying number of records for each Region per cluster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4kRXgsbKXAO",
        "colab_type": "code",
        "outputId": "dd179f1b-ecc4-4119-bc77-5cfe0a3d3566",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        }
      },
      "source": [
        "dat_agg=dat.groupby(['cluster','Region']).count() #agg({\"cluster\":\"median\"})\n",
        "dat_agg['Frozen'].unstack().plot(kind='bar',title='Region per cluster')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb11f589f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qMu-YVeSMLpm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}