9 Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>CHURNRISK</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>CHILDREN</th>\n",
       "      <th>ESTINCOME</th>\n",
       "      <th>HOMEOWNER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>TOTALDOLLARVALUETRADED</th>\n",
       "      <th>TOTALUNITSTRADED</th>\n",
       "      <th>LARGESTSINGLETRANSACTION</th>\n",
       "      <th>SMALLESTSINGLETRANSACTION</th>\n",
       "      <th>PERCENTCHANGECALCULATION</th>\n",
       "      <th>DAYSSINCELASTLOGIN</th>\n",
       "      <th>DAYSSINCELASTTRADE</th>\n",
       "      <th>NETREALIZEDGAINS_YTD</th>\n",
       "      <th>NETREALIZEDLOSSES_YTD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1703</td>\n",
       "      <td>Medium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>28766.90</td>\n",
       "      <td>N</td>\n",
       "      <td>47</td>\n",
       "      <td>6110.61</td>\n",
       "      <td>58</td>\n",
       "      <td>1527.6525</td>\n",
       "      <td>152.76525</td>\n",
       "      <td>8.70</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>152.76525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1704</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>91272.20</td>\n",
       "      <td>Y</td>\n",
       "      <td>25</td>\n",
       "      <td>26992.70</td>\n",
       "      <td>13</td>\n",
       "      <td>13496.3500</td>\n",
       "      <td>1349.63500</td>\n",
       "      <td>3.25</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1349.6350</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1705</td>\n",
       "      <td>Low</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>73228.30</td>\n",
       "      <td>N</td>\n",
       "      <td>42</td>\n",
       "      <td>22472.25</td>\n",
       "      <td>28</td>\n",
       "      <td>11236.1250</td>\n",
       "      <td>1123.61250</td>\n",
       "      <td>7.00</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1123.6125</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1706</td>\n",
       "      <td>High</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>64792.30</td>\n",
       "      <td>N</td>\n",
       "      <td>52</td>\n",
       "      <td>13051.31</td>\n",
       "      <td>36</td>\n",
       "      <td>6525.6550</td>\n",
       "      <td>652.56550</td>\n",
       "      <td>9.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>652.56550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1707</td>\n",
       "      <td>High</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>93322.10</td>\n",
       "      <td>Y</td>\n",
       "      <td>40</td>\n",
       "      <td>29922.99</td>\n",
       "      <td>8</td>\n",
       "      <td>14961.4950</td>\n",
       "      <td>1496.14950</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1496.14950</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2061</th>\n",
       "      <td>837</td>\n",
       "      <td>High</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>64319.20</td>\n",
       "      <td>N</td>\n",
       "      <td>41</td>\n",
       "      <td>3010.30</td>\n",
       "      <td>37</td>\n",
       "      <td>752.5750</td>\n",
       "      <td>75.25750</td>\n",
       "      <td>5.55</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>150.51500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2062</th>\n",
       "      <td>838</td>\n",
       "      <td>Medium</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>73338.00</td>\n",
       "      <td>N</td>\n",
       "      <td>48</td>\n",
       "      <td>20922.09</td>\n",
       "      <td>9</td>\n",
       "      <td>10461.0450</td>\n",
       "      <td>1046.10450</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1046.10450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2063</th>\n",
       "      <td>839</td>\n",
       "      <td>Medium</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>433.74</td>\n",
       "      <td>N</td>\n",
       "      <td>61</td>\n",
       "      <td>3300.33</td>\n",
       "      <td>40</td>\n",
       "      <td>825.0825</td>\n",
       "      <td>82.50825</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>82.50825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2064</th>\n",
       "      <td>840</td>\n",
       "      <td>Low</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>16962.20</td>\n",
       "      <td>N</td>\n",
       "      <td>45</td>\n",
       "      <td>18601.86</td>\n",
       "      <td>8</td>\n",
       "      <td>9300.9300</td>\n",
       "      <td>930.09300</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>930.0930</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2065</th>\n",
       "      <td>841</td>\n",
       "      <td>Low</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>10708.30</td>\n",
       "      <td>N</td>\n",
       "      <td>26</td>\n",
       "      <td>20832.08</td>\n",
       "      <td>117</td>\n",
       "      <td>10416.0400</td>\n",
       "      <td>1041.60400</td>\n",
       "      <td>29.25</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1041.6040</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2066 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID CHURNRISK GENDER STATUS  CHILDREN  ESTINCOME HOMEOWNER  AGE  \\\n",
       "0     1703    Medium    NaN      M         2   28766.90         N   47   \n",
       "1     1704       Low    NaN      M         2   91272.20         Y   25   \n",
       "2     1705       Low    NaN      S         0   73228.30         N   42   \n",
       "3     1706      High      M      M         1   64792.30         N   52   \n",
       "4     1707      High      F      S         0   93322.10         Y   40   \n",
       "...    ...       ...    ...    ...       ...        ...       ...  ...   \n",
       "2061   837      High      F      S         0   64319.20         N   41   \n",
       "2062   838    Medium      F      S         1   73338.00         N   48   \n",
       "2063   839    Medium      F      S         0     433.74         N   61   \n",
       "2064   840       Low      F      M         2   16962.20         N   45   \n",
       "2065   841       Low      M      M         2   10708.30         N   26   \n",
       "\n",
       "      TOTALDOLLARVALUETRADED  TOTALUNITSTRADED  LARGESTSINGLETRANSACTION  \\\n",
       "0                    6110.61                58                 1527.6525   \n",
       "1                   26992.70                13                13496.3500   \n",
       "2                   22472.25                28                11236.1250   \n",
       "3                   13051.31                36                 6525.6550   \n",
       "4                   29922.99                 8                14961.4950   \n",
       "...                      ...               ...                       ...   \n",
       "2061                 3010.30                37                  752.5750   \n",
       "2062                20922.09                 9                10461.0450   \n",
       "2063                 3300.33                40                  825.0825   \n",
       "2064                18601.86                 8                 9300.9300   \n",
       "2065                20832.08               117                10416.0400   \n",
       "\n",
       "      SMALLESTSINGLETRANSACTION  PERCENTCHANGECALCULATION  DAYSSINCELASTLOGIN  \\\n",
       "0                     152.76525                      8.70                   2   \n",
       "1                    1349.63500                      3.25                   4   \n",
       "2                    1123.61250                      7.00                   4   \n",
       "3                     652.56550                      9.00                   3   \n",
       "4                    1496.14950                      2.00                   4   \n",
       "...                         ...                       ...                 ...   \n",
       "2061                   75.25750                      5.55                   1   \n",
       "2062                 1046.10450                      2.25                   1   \n",
       "2063                   82.50825                      6.00                   5   \n",
       "2064                  930.09300                      2.00                   1   \n",
       "2065                 1041.60400                     29.25                   1   \n",
       "\n",
       "      DAYSSINCELASTTRADE  NETREALIZEDGAINS_YTD  NETREALIZEDLOSSES_YTD  \n",
       "0                     13                0.0000              152.76525  \n",
       "1                     10             1349.6350                0.00000  \n",
       "2                      5             1123.6125                0.00000  \n",
       "3                      6                0.0000              652.56550  \n",
       "4                      9                0.0000             1496.14950  \n",
       "...                  ...                   ...                    ...  \n",
       "2061                  18                0.0000              150.51500  \n",
       "2062                   9                0.0000             1046.10450  \n",
       "2063                  11                0.0000               82.50825  \n",
       "2064                   5              930.0930                0.00000  \n",
       "2065                   8             1041.6040                0.00000  \n",
       "\n",
       "[2066 rows x 17 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import model_selection\n",
    "from sklearn import preprocessing\n",
    "from sklearn.compose import ColumnTransformer, make_column_transformer\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn import tree\n",
    "from sklearn import ensemble\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "churn_data = pd.read_csv(\"https://raw.githubusercontent.com/IBM/ml-learning-path-assets/master/data/mergedcustomers_missing_values_GENDER.csv\")\n",
    "churn_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                             int64\n",
       "CHURNRISK                     object\n",
       "GENDER                        object\n",
       "STATUS                        object\n",
       "CHILDREN                       int64\n",
       "ESTINCOME                    float64\n",
       "HOMEOWNER                     object\n",
       "AGE                            int64\n",
       "TOTALDOLLARVALUETRADED       float64\n",
       "TOTALUNITSTRADED               int64\n",
       "LARGESTSINGLETRANSACTION     float64\n",
       "SMALLESTSINGLETRANSACTION    float64\n",
       "PERCENTCHANGECALCULATION     float64\n",
       "DAYSSINCELASTLOGIN             int64\n",
       "DAYSSINCELASTTRADE             int64\n",
       "NETREALIZEDGAINS_YTD         float64\n",
       "NETREALIZEDLOSSES_YTD        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                           2066\n",
       "CHURNRISK                    2066\n",
       "GENDER                       2063\n",
       "STATUS                       2066\n",
       "CHILDREN                     2066\n",
       "ESTINCOME                    2066\n",
       "HOMEOWNER                    2066\n",
       "AGE                          2066\n",
       "TOTALDOLLARVALUETRADED       2066\n",
       "TOTALUNITSTRADED             2066\n",
       "LARGESTSINGLETRANSACTION     2066\n",
       "SMALLESTSINGLETRANSACTION    2066\n",
       "PERCENTCHANGECALCULATION     2066\n",
       "DAYSSINCELASTLOGIN           2066\n",
       "DAYSSINCELASTTRADE           2066\n",
       "NETREALIZEDGAINS_YTD         2066\n",
       "NETREALIZEDLOSSES_YTD        2066\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.count()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_churn_data = churn_data[~churn_data[\"GENDER\"].isna()]\n",
    "cleaned_churn_data = cleaned_churn_data.drop([\"ID\"], axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_cols = [\"GENDER\", \"STATUS\", \"HOMEOWNER\"]\n",
    "\n",
    "cleaned_churn_data.loc[:,categorical_cols].head(5)\n",
    "\n",
    "onehot_encode =  preprocessing.OneHotEncoder(handle_unknown='ignore')\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[('onehot',onehot_encode)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessorForCategorical = ColumnTransformer(transformers=[('cat', categorical_transformer, categorical_cols)],\n",
    "                                            remainder=\"passthrough\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = preprocessorForCategorical.fit_transform(cleaned_churn_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['F', 'M'], dtype=object),\n",
       " array(['D', 'M', 'S'], dtype=object),\n",
       " array(['N', 'Y'], dtype=object)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder =  preprocessing.OneHotEncoder(handle_unknown='ignore')\n",
    "encoder.fit(cleaned_churn_data.loc[:,categorical_cols])\n",
    "encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 'High', 1, 64792.3, 52,\n",
       "       13051.31, 36, 6525.655, 652.5655, 9.0, 3, 6, 0.0, 652.5655],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[0] #transformed entries put leftmost, and then untransformed after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHURNRISK</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>CHILDREN</th>\n",
       "      <th>ESTINCOME</th>\n",
       "      <th>HOMEOWNER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>TOTALDOLLARVALUETRADED</th>\n",
       "      <th>TOTALUNITSTRADED</th>\n",
       "      <th>LARGESTSINGLETRANSACTION</th>\n",
       "      <th>SMALLESTSINGLETRANSACTION</th>\n",
       "      <th>PERCENTCHANGECALCULATION</th>\n",
       "      <th>DAYSSINCELASTLOGIN</th>\n",
       "      <th>DAYSSINCELASTTRADE</th>\n",
       "      <th>NETREALIZEDGAINS_YTD</th>\n",
       "      <th>NETREALIZEDLOSSES_YTD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>64792.30</td>\n",
       "      <td>N</td>\n",
       "      <td>52</td>\n",
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       "      <td>652.56550</td>\n",
       "      <td>9.00</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000</td>\n",
       "      <td>652.56550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>93322.10</td>\n",
       "      <td>Y</td>\n",
       "      <td>40</td>\n",
       "      <td>29922.99</td>\n",
       "      <td>8</td>\n",
       "      <td>14961.4950</td>\n",
       "      <td>1496.14950</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1496.14950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>High</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>49910.70</td>\n",
       "      <td>Y</td>\n",
       "      <td>52</td>\n",
       "      <td>27422.74</td>\n",
       "      <td>23</td>\n",
       "      <td>13711.3700</td>\n",
       "      <td>1371.13700</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1371.13700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>High</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>76161.40</td>\n",
       "      <td>Y</td>\n",
       "      <td>45</td>\n",
       "      <td>11961.20</td>\n",
       "      <td>25</td>\n",
       "      <td>5980.6000</td>\n",
       "      <td>598.06000</td>\n",
       "      <td>6.25</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000</td>\n",
       "      <td>598.06000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>High</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>55860.00</td>\n",
       "      <td>Y</td>\n",
       "      <td>15</td>\n",
       "      <td>16011.60</td>\n",
       "      <td>10</td>\n",
       "      <td>8005.8000</td>\n",
       "      <td>800.58000</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000</td>\n",
       "      <td>800.58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2061</th>\n",
       "      <td>High</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>64319.20</td>\n",
       "      <td>N</td>\n",
       "      <td>41</td>\n",
       "      <td>3010.30</td>\n",
       "      <td>37</td>\n",
       "      <td>752.5750</td>\n",
       "      <td>75.25750</td>\n",
       "      <td>5.55</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000</td>\n",
       "      <td>150.51500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2062</th>\n",
       "      <td>Medium</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>73338.00</td>\n",
       "      <td>N</td>\n",
       "      <td>48</td>\n",
       "      <td>20922.09</td>\n",
       "      <td>9</td>\n",
       "      <td>10461.0450</td>\n",
       "      <td>1046.10450</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1046.10450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2063</th>\n",
       "      <td>Medium</td>\n",
       "      <td>F</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>433.74</td>\n",
       "      <td>N</td>\n",
       "      <td>61</td>\n",
       "      <td>3300.33</td>\n",
       "      <td>40</td>\n",
       "      <td>825.0825</td>\n",
       "      <td>82.50825</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000</td>\n",
       "      <td>82.50825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2064</th>\n",
       "      <td>Low</td>\n",
       "      <td>F</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>16962.20</td>\n",
       "      <td>N</td>\n",
       "      <td>45</td>\n",
       "      <td>18601.86</td>\n",
       "      <td>8</td>\n",
       "      <td>9300.9300</td>\n",
       "      <td>930.09300</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>930.093</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2065</th>\n",
       "      <td>Low</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>10708.30</td>\n",
       "      <td>N</td>\n",
       "      <td>26</td>\n",
       "      <td>20832.08</td>\n",
       "      <td>117</td>\n",
       "      <td>10416.0400</td>\n",
       "      <td>1041.60400</td>\n",
       "      <td>29.25</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1041.604</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2063 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CHURNRISK GENDER STATUS  CHILDREN  ESTINCOME HOMEOWNER  AGE  \\\n",
       "3         High      M      M         1   64792.30         N   52   \n",
       "4         High      F      S         0   93322.10         Y   40   \n",
       "5         High      M      M         2   49910.70         Y   52   \n",
       "6         High      M      M         0   76161.40         Y   45   \n",
       "7         High      F      M         2   55860.00         Y   15   \n",
       "...        ...    ...    ...       ...        ...       ...  ...   \n",
       "2061      High      F      S         0   64319.20         N   41   \n",
       "2062    Medium      F      S         1   73338.00         N   48   \n",
       "2063    Medium      F      S         0     433.74         N   61   \n",
       "2064       Low      F      M         2   16962.20         N   45   \n",
       "2065       Low      M      M         2   10708.30         N   26   \n",
       "\n",
       "      TOTALDOLLARVALUETRADED  TOTALUNITSTRADED  LARGESTSINGLETRANSACTION  \\\n",
       "3                   13051.31                36                 6525.6550   \n",
       "4                   29922.99                 8                14961.4950   \n",
       "5                   27422.74                23                13711.3700   \n",
       "6                   11961.20                25                 5980.6000   \n",
       "7                   16011.60                10                 8005.8000   \n",
       "...                      ...               ...                       ...   \n",
       "2061                 3010.30                37                  752.5750   \n",
       "2062                20922.09                 9                10461.0450   \n",
       "2063                 3300.33                40                  825.0825   \n",
       "2064                18601.86                 8                 9300.9300   \n",
       "2065                20832.08               117                10416.0400   \n",
       "\n",
       "      SMALLESTSINGLETRANSACTION  PERCENTCHANGECALCULATION  DAYSSINCELASTLOGIN  \\\n",
       "3                     652.56550                      9.00                   3   \n",
       "4                    1496.14950                      2.00                   4   \n",
       "5                    1371.13700                      5.75                   3   \n",
       "6                     598.06000                      6.25                   2   \n",
       "7                     800.58000                      2.50                   2   \n",
       "...                         ...                       ...                 ...   \n",
       "2061                   75.25750                      5.55                   1   \n",
       "2062                 1046.10450                      2.25                   1   \n",
       "2063                   82.50825                      6.00                   5   \n",
       "2064                  930.09300                      2.00                   1   \n",
       "2065                 1041.60400                     29.25                   1   \n",
       "\n",
       "      DAYSSINCELASTTRADE  NETREALIZEDGAINS_YTD  NETREALIZEDLOSSES_YTD  \n",
       "3                      6                 0.000              652.56550  \n",
       "4                      9                 0.000             1496.14950  \n",
       "5                     10                 0.000             1371.13700  \n",
       "6                      5                 0.000              598.06000  \n",
       "7                      8                 0.000              800.58000  \n",
       "...                  ...                   ...                    ...  \n",
       "2061                  18                 0.000              150.51500  \n",
       "2062                   9                 0.000             1046.10450  \n",
       "2063                  11                 0.000               82.50825  \n",
       "2064                   5               930.093                0.00000  \n",
       "2065                   8              1041.604                0.00000  \n",
       "\n",
       "[2063 rows x 16 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_churn_data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting all numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CHILDREN', 'ESTINCOME', 'AGE', 'TOTALDOLLARVALUETRADED',\n",
       "       'TOTALUNITSTRADED', 'LARGESTSINGLETRANSACTION',\n",
       "       'SMALLESTSINGLETRANSACTION', 'PERCENTCHANGECALCULATION',\n",
       "       'DAYSSINCELASTLOGIN', 'DAYSSINCELASTTRADE', 'NETREALIZEDGAINS_YTD',\n",
       "       'NETREALIZEDLOSSES_YTD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_cols = cleaned_churn_data.select_dtypes(include =[float, int]).columns\n",
    "numerical_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "standard_scaler = preprocessing.StandardScaler()\n",
    "numerical_transformer = Pipeline(steps=[('scale',standard_scaler)])\n",
    "preprocessingForNum = ColumnTransformer(transformers=[('scale', numerical_transformer, numerical_cols)],\n",
    "                                            remainder=\"passthrough\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining both preprocessors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessorForAll= ColumnTransformer(transformers=[('cat', categorical_transformer, categorical_cols),\n",
    "                                                    ('num',numerical_transformer,numerical_cols)],\n",
    "                                                    remainder=\"passthrough\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_name = \"CHURNRISK\"\n",
    "features = cleaned_churn_data.drop([target_name], axis = 1)\n",
    "target_churn = cleaned_churn_data.loc[:,target_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()\n",
    "target = label_encoder.fit_transform(target_churn)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split data into training and testing data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(features, target, train_size = 0.8, random_state = 0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessorAll&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;GENDER&#x27;, &#x27;STATUS&#x27;,\n",
       "                                                   &#x27;HOMEOWNER&#x27;]),\n",
       "                                                 (&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scale&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;CHILDREN&#x27;, &#x27;ESTINCOME&#x27;, &#x27;AGE&#x27;, &#x27;TOTALDOLLARVALUETRADED&#x27;,\n",
       "       &#x27;TOTALUNITSTRADED&#x27;, &#x27;LARGESTSINGLETRANSACTION&#x27;,\n",
       "       &#x27;SMALLESTSINGLETRANSACTION&#x27;, &#x27;PERCENTCHANGECALCULATION&#x27;,\n",
       "       &#x27;DAYSSINCELASTLOGIN&#x27;, &#x27;DAYSSINCELASTTRADE&#x27;, &#x27;NETREALIZEDGAINS_YTD&#x27;,\n",
       "       &#x27;NETREALIZEDLOSSES_YTD&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;classifier&#x27;,\n",
       "                 LogisticRegression(max_iter=1000, random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessorAll&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;GENDER&#x27;, &#x27;STATUS&#x27;,\n",
       "                                                   &#x27;HOMEOWNER&#x27;]),\n",
       "                                                 (&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scale&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index([&#x27;CHILDREN&#x27;, &#x27;ESTINCOME&#x27;, &#x27;AGE&#x27;, &#x27;TOTALDOLLARVALUETRADED&#x27;,\n",
       "       &#x27;TOTALUNITSTRADED&#x27;, &#x27;LARGESTSINGLETRANSACTION&#x27;,\n",
       "       &#x27;SMALLESTSINGLETRANSACTION&#x27;, &#x27;PERCENTCHANGECALCULATION&#x27;,\n",
       "       &#x27;DAYSSINCELASTLOGIN&#x27;, &#x27;DAYSSINCELASTTRADE&#x27;, &#x27;NETREALIZEDGAINS_YTD&#x27;,\n",
       "       &#x27;NETREALIZEDLOSSES_YTD&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;classifier&#x27;,\n",
       "                 LogisticRegression(max_iter=1000, random_state=0))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessorAll: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;GENDER&#x27;, &#x27;STATUS&#x27;, &#x27;HOMEOWNER&#x27;]),\n",
       "                                (&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler())]),\n",
       "                                 Index([&#x27;CHILDREN&#x27;, &#x27;ESTINCOME&#x27;, &#x27;AGE&#x27;, &#x27;TOTALDOLLARVALUETRADED&#x27;,\n",
       "       &#x27;TOTALUNITSTRADED&#x27;, &#x27;LARGESTSINGLETRANSACTION&#x27;,\n",
       "       &#x27;SMALLESTSINGLETRANSACTION&#x27;, &#x27;PERCENTCHANGECALCULATION&#x27;,\n",
       "       &#x27;DAYSSINCELASTLOGIN&#x27;, &#x27;DAYSSINCELASTTRADE&#x27;, &#x27;NETREALIZEDGAINS_YTD&#x27;,\n",
       "       &#x27;NETREALIZEDLOSSES_YTD&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;GENDER&#x27;, &#x27;STATUS&#x27;, &#x27;HOMEOWNER&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;CHILDREN&#x27;, &#x27;ESTINCOME&#x27;, &#x27;AGE&#x27;, &#x27;TOTALDOLLARVALUETRADED&#x27;,\n",
       "       &#x27;TOTALUNITSTRADED&#x27;, &#x27;LARGESTSINGLETRANSACTION&#x27;,\n",
       "       &#x27;SMALLESTSINGLETRANSACTION&#x27;, &#x27;PERCENTCHANGECALCULATION&#x27;,\n",
       "       &#x27;DAYSSINCELASTLOGIN&#x27;, &#x27;DAYSSINCELASTTRADE&#x27;, &#x27;NETREALIZEDGAINS_YTD&#x27;,\n",
       "       &#x27;NETREALIZEDLOSSES_YTD&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=0)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessorAll',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('cat',\n",
       "                                                  Pipeline(steps=[('onehot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                                  ['GENDER', 'STATUS',\n",
       "                                                   'HOMEOWNER']),\n",
       "                                                 ('num',\n",
       "                                                  Pipeline(steps=[('scale',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  Index(['CHILDREN', 'ESTINCOME', 'AGE', 'TOTALDOLLARVALUETRADED',\n",
       "       'TOTALUNITSTRADED', 'LARGESTSINGLETRANSACTION',\n",
       "       'SMALLESTSINGLETRANSACTION', 'PERCENTCHANGECALCULATION',\n",
       "       'DAYSSINCELASTLOGIN', 'DAYSSINCELASTTRADE', 'NETREALIZEDGAINS_YTD',\n",
       "       'NETREALIZEDLOSSES_YTD'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier',\n",
       "                 LogisticRegression(max_iter=1000, random_state=0))])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = linear_model.LogisticRegression(max_iter = 1000,random_state=0,multi_class='auto',solver='lbfgs')\n",
    "\n",
    "lr_model = Pipeline(steps = [(\"preprocessorAll\",preprocessorForAll), ('classifier', lr)])\n",
    "\n",
    "lr_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_lr = lr_model.predict(X_test)\n",
    "label_encoder.classes_\n",
    "y_test = label_encoder.inverse_transform(y_test)\n",
    "y_test_lr = label_encoder.inverse_transform(y_test_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_matrix = pd.DataFrame([y_test, y_test_lr])\n",
    "conf_matrix = conf_matrix.T\n",
    "conf_matrix.columns = [\"Actual Test\", \"Predicted (RFC)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_matrix_LR = pd.crosstab(conf_matrix[\"Predicted (RFC)\"], conf_matrix[\"Actual Test\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Actual Test', ylabel='Predicted (RFC)'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9cf3mbaxcu0Vjn409EZGYOudrTPEfBY86b6s/i1KphHUZq9d1TzJYtGQlwkOXICbmFI79FYMRw4fAzbUiVvy4Tu7QSGJBX05Gjz6d8cmHE/AkPQP2DuUBAI/T0qHKejq3zaZcWbi85QSHf6tflas9/dlyL/k+UpLvyxO4PpJpGMXb2/uNQ6Yff/wxPv7445eec3V1RVRUVLHjUAg6MnC7YcMGLF26FJcuXQIA1KxZE+PGjcOgQYMAAJmZmerVKW+Sk3JV0liLom6rl2+qMndqAHp3e7pqZNrcb3A76S7Cli547X0uTg74fdt/66B3/34QoRv/hyvXbsDC3Bw1qlbCkH690d6rYInrTftsAE839Zr3zTKcuXAJZa3LoF/vbvjkuWTDb+wknDh5psB9vbq0x7zpE17Zr5wsXN6VOwRZjRrpi4kTPoGzswPOnruEiRODcfjIX3KHJZsqNoaxzPPyvRMvbZ88Lhg7Nv8KAHhvQHd8+V1wgWu+W/AjvvvqRynD0xmv+nMSU+amIFH6sRg4680X6SCdSTbEpIvJBsnL0JMN0mQoyQYVTokkGxvE2TDNYvAcUfopaToxjEJERKTXDPypr7IlG3Z2drh8+TLs7e1ha2v70k29nnnw4EEJRkZERCQyHV/6KjXZko1Fixap91VftGjRa5MNIiIiKr1kSzZ8fX3Vu32+uA87ERGRXtG/6ZFakXXORrly5QpV0cjLyyuBaIiIiCTCYRT5HDx4UP21IAjo2rUrVq1ahYoVK8oYFREREYlJ1mTDy8tL47WxsTGaN2+OKlWqyBQRERGRBFjZICIiIkkZ+NJXnXjEPBEREekvnatscAksERHpGyGfq1Fk8+KS16ysLIwaNQpWVpoP89q+fXtJhkVERCQuztmQj42NjcbrIUOGyBQJERERSUXWZCM0NFTOtyciIioZBj5BVOfmbBAREekdztkgIiIiSRn4nA0ufSUiIiJJsbJBREQkNQOvbDDZICIikpqBP/WVwyhEREQkKVY2iIiIpMZhFCIiIpKUgS995TAKERERSYqVDSIiIqlxB1EiIiKSFIdRiIiIiKTDygYREZHEBK5GISIiIkkZ+DAKkw0iIiKpGfgEUc7ZICIiIkmxskFERCQ1DqMQERGRpAx8giiHUYiIiEhSTDaIiIikli+Ic2jp0KFD6NGjB1xcXKBQKLBz506N835+flAoFBpH8+bNNa5RqVQYN24c7O3tYWVlhZ49e+LWrVtaxcFkg4iISGpCvjiHlp48eQIPDw8sXbr0ldd07twZiYmJ6mPPnj0a5/39/bFjxw5s3rwZR44cQXp6Orp37468vLxCx8E5G0RERKWESqWCSqXSaFMqlVAqlS+9vkuXLujSpctr+1QqlXBycnrpuUePHmH16tVYt24d2rdvDwBYv349XF1dsX//fnTq1KlQcbOyQUREJDWRhlFCQkJgY2OjcYSEhBQrtMjISDg4OKBGjRoYMWIEkpOT1ediYmKQk5ODjh07qttcXFxQt25dREdHF/o9WNkgIiKSmFjblQcGBiIgIECj7VVVjcLo0qUL+vbtC3d3dyQkJGDGjBlo27YtYmJioFQqkZSUBDMzM9ja2mrc5+joiKSkpEK/D5MNIiKiUuJ1QyZF0b9/f/XXdevWhaenJ9zd3bF79274+Pi88j5BEKBQKAr9PhxGISIikppMq1G05ezsDHd3d8THxwMAnJyckJ2djdTUVI3rkpOT4ejoWOh+mWwQERFJrZQkG/fv38fNmzfh7OwMAGjcuDFMTU0RERGhviYxMRFnz55Fy5YtC90vh1GIiIikJtOD2NLT0/HPP/+oXyckJCAuLg52dnaws7NDcHAw+vTpA2dnZ1y7dg1Tp06Fvb093nvvPQCAjY0Nhg8fjgkTJqB8+fKws7PDxIkTUa9ePfXqlMJgskFERKSnTpw4gTZt2qhfP5tc6uvri+XLl+PMmTNYu3YtHj58CGdnZ7Rp0wZbtmyBtbW1+p5FixbBxMQE/fr1Q2ZmJtq1a4ewsDAYGxsXOg6FIAh693SYnJSrcodAOsbC5V25QyAdUsXGWe4QSIdcvndC8vdID+gpSj9lFv4sSj8ljZUNIiIiiQkG/tRXThAlIiIiSbGyQUREJDUDr2ww2SAiIpKaSDuIllYcRiEiIiJJsbJBREQkNQ6jEBERkaQMPNngMAoRERFJipUNIiIiienh/plaYbJBREQkNQMfRmGyQUREJDUDTzY4Z4OIiIgkpZeVDT50i16UOqye3CGQDrFbc0buEMjAGPqzUfQy2SAiItIpBp5scBiFiIiIJMXKBhERkdQM+9EoTDaIiIikZuhzNjiMQkRERJJiZYOIiEhqBl7ZYLJBREQkNQOfs8FhFCIiIpIUKxtEREQSM/QJosVONlQqFZRKpRixEBER6ScOo2hn37598PPzQ9WqVWFqagpLS0tYW1vDy8sL8+bNw507d6SIk4iIqNQS8gVRjtKq0MnGzp07UbNmTfj6+sLIyAiff/45tm/fjn379mH16tXw8vLC/v37UaVKFYwaNQr37t2TMm4iIiIqJQo9jDJ//nx8/fXX6NatG4yMCuYo/fr1AwDcvn0bS5Yswdq1azFhwgTxIiUiIiqtDHwYpdDJxvHjxwt1XcWKFbFgwYIiB0RERKRvBANPNrSes5GWlob8/IJ/anl5eUhLSxMlKCIiItIfWiUbO3bsgKenJ7KysgqcU6lUaNKkCX755RfRgiMiItIL+SIdpZRWycby5csxadIkWFpaFjhnaWmJyZMnY+nSpaIFR0REpA+EfHGO0kqrZOPs2bPw9vZ+5fnWrVvjzJkzxY2JiIiI9IhWm3qlpqYiNzf3ledzcnKQmppa7KCIiIj0SimuSohBq8pGpUqVcOLEiVeeP3HiBNzd3YsdFBERkT7hMIoWfHx8MG3aNNy9e7fAuaSkJEyfPh19+vQRLTgiIiJ9IFeycejQIfTo0QMuLi5QKBTYuXOn+lxOTg4mT56MevXqwcrKCi4uLvjwww8L7ATu7e0NhUKhcQwYMECrOLQaRpkyZQp27dqF6tWrY8iQIahZsyYUCgUuXLiADRs2wNXVFVOmTNEqACIiIpLGkydP4OHhgaFDhxYoBmRkZCA2NhYzZsyAh4cHUlNT4e/vj549exYYxRgxYgRmz56tfm1hYaFVHFolG9bW1vjzzz8RGBiILVu2qOdn2NraYsiQIZg/fz6sra21CoCIiEjfyTUE0qVLF3Tp0uWl52xsbBAREaHR9t1336Fp06a4ceMG3Nzc1O2WlpZwcnIqchxab+plY2ODZcuWISUlBXfv3kVSUhJSUlKwbNkylCtXrsiBEBER6S1BIcqhUqmQlpamcahUKtHCfPToERQKRYHf5xs2bIC9vT3q1KmDiRMn4vHjx1r1q1Wy8ccff6hXoygUClSoUAEODg5QKBRavSkRERFpLyQkBDY2NhpHSEiIKH1nZWVhypQpGDRoEMqWLatuHzx4MDZt2oTIyEjMmDED27Ztg4+Pj1Z9KwRBKPQza42NjZGYmAgHBwcAQPPmzbFt2zZUrFhRqzeVmomZbsVD8ksdVk/uEEiH2K3hfkD0n5zs25K/R1Jrb1H6sY3YV6CSoVQqoVQq33ivQqHAjh070Lt37wLncnJy0LdvX9y4cQORkZEaycaLYmJi4OnpiZiYGDRq1KhQcWs1Z+PFvOTcuXOilm+IiIj0kZAvzghAYRMLbeTk5KBfv35ISEjAH3/88dpEAwAaNWoEU1NTxMfHS5NsEBERkf54lmjEx8fj4MGDKF++/BvvOXfuHHJycuDs7Fzo99Eq2Xi2vvZVr4mIiKgguVajpKen459//lG/TkhIQFxcHOzs7ODi4oL3338fsbGx+PXXX5GXl4ekpCQAgJ2dHczMzHDlyhVs2LABXbt2hb29Pc6fP48JEyagYcOGaNWqVaHj0HoYpV27djAxeXpbRkYGevToATMzM43rYmNjtemWiIhIrwmCPP8wP3HiBNq0aaN+HRAQAADw9fVFcHAwfv75ZwBAgwYNNO47ePAgvL29YWZmhgMHDmDJkiVIT0+Hq6srunXrhqCgIBgbGxc6Dq2SjaCgII3XvXr10uZ2IiIiKkHe3t4F5ls+701rRFxdXREVFVXsOIqVbBAREdGblebnmohB6029XicrKwtff/21mF0SERGVekK+QpSjtNI62UhJScHu3bvx+++/Iy8vD8DT2axLlixBpUqV8MUXX4geJBERUWkmCOIcpZVWwyjR0dHo1q2bejtTT09PhIaGonfv3sjPz8f06dMxbNgwqWIlIiKiUkirysaMGTPQqVMnnD59Gp9++in+/vtvdO/eHdOnT0d8fDzGjh0LS0tLqWIlIiIqlQx9GEWr7crt7e0RFRWFOnXqICMjA9bW1ti8eTP69u0rZYxa43bl9CJuV07P43bl9LyS2K78WoMOovRTKS7izRfpIK0qGw8ePECFChUAPH3crKWlJRo2bChJYERERKQftN5B9PHjxzA3N4cgCFAoFMjIyEBaWprGdW/aV52IiMiQlObJnWLQegfRGjVqaLx+vrLxLAF5tkqFiIiIxHsQW2mlVbJx8OBBqeIgIiIiPaVVsuHl5SVVHERERHpLrmej6IpCTxB98uSJVh1rez0REZG+EvLFOUqrQicb1apVw/z583Hnzp1XXiMIAiIiItClSxd8++23ogRIREREpVuhh1EiIyMxffp0zJo1Cw0aNICnpydcXFxgbm6O1NRUnD9/HkePHoWpqSkCAwPx8ccfSxk3ERFRqZFv4MMohU42atasia1bt+LWrVvYunUrDh06hOjoaGRmZsLe3h4NGzbEypUr0bVrVxgZifp8NyIiolLN0OdsaLWDaGnBHUTpRdxBlJ7HHUTpeSWxg+jFGl1F6efty3tE6aeksQRBREREktJq6SsRERFpT//GELTDZIOIiEhihr6DKIdRiIiISFI6kWwMHjwYP/74Iy5fvix3KERERKLLFxSiHKVVoYdRTp8+XehO69evr1UQZcqUwcKFCzFq1Cg4OTnBy8sLXl5e8Pb2xttvv61VX0RERLqGS18LufTVyMgICoVC/WTX1ynqU1+TkpIQGRmJyMhIREVF4fLly3BwcEBiYqJW/XDpK72IS1/peVz6Ss8riaWvZyr3EKWfegm/iNJPSSv0MEpCQgKuXr2KhIQEbNu2DZUrV8ayZctw8uRJnDx5EsuWLUPVqlWxbdu2IgdjbW0NW1tb2Nraoly5cjAxMYGTk1OR+yMiItIFgiDOUVoVehjF3d1d/XXfvn3x7bffomvX/zYpqV+/PlxdXTFjxgz07t1bqyAmT56MqKgonDp1CnXr1kXr1q0RGBiI1q1bo1y5clr1RUREpGtK83wLMRRp6euZM2dQuXLlAu2VK1fG+fPnte7vq6++QoUKFRAUFIRevXqhVq1aRQmLnjNqpC8mBIyCs7MDzp2/jAkTgnDkz+Nyh0XPMa5WF2Yd34eRW3UYlSuPzOWzkHvqaOHurVobFgFfIf/ONWTMGyNpnEYulaAcMBrGlWpCyHiMnEN7kL1no/q8SYNWMPXqBqO3qkBhYor8xBtQ/boeeedjJI2Liuadd5phwoRP0KhhPbi4OKHP+8Pw88/75A6L9FyRVqPUqlULc+fORVZWlrpNpVJh7ty5RUoUTp48iWnTpuH48eNo3bo1nJyc0L9/fyxfvhwXLlwoSogGrW/fnlj4TTBCvvgWnk074ciR4/j1l/VwdXWROzR6ntIcebcSoNq8TLv7zC1h7jcReRfjih2CorwjrH/47bXvZfHpfAiPHiDji/FQbV4Osw59YNreR32JcfW6yLsQi8ylM/EkZBxyL52CxehgGLlWLXZ8JD4rK0ucPn0en/pPlzsUgyIIClGO0qpIlY0ffvgBPXr0gKurKzw8PAAAp06dgkKhwK+//qp1fx4eHvDw8MD48ePVfS1evBjjx49Hfn5+kSecGqrPPh2BNaGbsSZ0EwBgwsQgdOzohVEjP8S06V/IHB09k3fuBPLOndD6PvPB45FzPBIQ8mHi0aLAeZMWHWDWsS+M7J2Qf/8ucg7uQk6U9n8vAcC0aRsoTM2QFf4NkJuD/DvXke1YEWbtfZCzfzsAQLV1hcY92bvCYOLRAib1miH75pUivS9JZ9++g9i376DcYRic0jzfQgxFSjaaNm2KhIQErF+/HhcvXoQgCOjfvz8GDRoEKyurIgVy8uRJ9UqUw4cPIy0tDQ0aNECbNm2K1J+hMjU1RaNG9fHlV99rtEdERKFFc0+ZoiKxmLToAKMKLsgKXQCzroMKnDd9pzPMun8A1eZlyLv5D4xdq8F8yKcQVFnIPbZf6/czrlILufFngNwcdVvu+Rgo3xsGRXlHCPfvFrxJoYDC3AJCxmOt349IX3HORhFZWlri448/FiUIW1tbpKenw8PDA97e3hgxYgRat26NsmXLvvFelUoFlUql0VaY5bn6yt7eDiYmJki+m6LRnpycAkcnB5miIjEoHFygfG8YMr6eCOTnv/Qas66DoPrfSuTG/QkAyL1/F9nObjB7t2uRkg1FWVvkv5BQCGmp/56ze2myYdq+DxRm5siNOaT1+xGRfipysrFu3TqsWLECV69exdGjR+Hu7o5FixahSpUq6NWrl9Z9FTa5eFFISAhmzZql0aYwKgOFsfZ96ZMXt095tkcKlVIKI1gMm4LsX9ZBSH75ngCKMjYwsnOA+Yf+wJBP/zthbAwh84n6peXMFTCy+zfx/DcpL7N4h/p8/oNkZMwe+d/9L37bqBP5gt9PJp7eUHYfgszlsyA8flTYT0ek90rzfAsxFCnZWL58OWbOnAl/f3/MnTtXPafC1tYWixcv1jrZ6N69u/rrW7duQaFQoGLFwm3MFRgYiICAAI022/KGu+toSsoD5ObmwtGpgkZ7hQrlkXz3nkxRUbGZW8C4Ug0YuVaFcsC/q08UCiiMjFDm+93I/HYq8u9cBwBkrV+CvISLmvc/VwnJXDoDMDYGABiVs4flhK/wZN7o/659bo6UkJYKhY2tRlcK63Lqc88zadwa5h/6I/PH+ci7eLI4n5ZI7xj6MEqRVqN89913WLlyJaZNmwYTk//yFU9PT5w5o/3OfPn5+Zg9ezZsbGzg7u4ONzc3lCtXDnPmzEH+K8rFzyiVSpQtW1bjMNQhFADIyclBbOxptG/XWqO9ffvWOHpM+8mIpCOyMvBk9khkzButPnIO70Ze0k1kzBuNvISLEB4/RH7qPRjZO0O4l6h5PDfcITxIVrfnP0h+2vb8tf+2AUDe1QswqVYXMP7v77lJrUbIf5ii0aeJpzfMfScga/WXyDvLJdZEpKlIyUZCQgIaNmxYoF2pVOLJkycvueP1pk2bhqVLl+KLL77AyZMnERsbi/nz5+O7777DjBkzihKiQVu0ZCWGDxsIP9/+ePvtavjmq2C4uVbEih/XyR0aPU9pDqO3qsDorSoAAIW909O9KmyfVqXMeg+Fud/Ep9cKAvLvXNc4hMePgJzspxWN7KfzlrJ/3QCzzv1g2rYXFA4VYeRSCSYtOsC0nc9LQ3iTnOMHIeTmwNx3Aoxc3GHSoCXMugxA9r8rUYB/E42hE6HathJ5CRehKGsLRVlbwNyyGH84JBUrK0t4eNSBh0cdAEDlSm7w8KjDpfESE0Q6tHXo0CH06NEDLi4uUCgU2Llzp2ZcgoDg4GC4uLjAwsIC3t7eOHfunMY1KpUK48aNg729PaysrNCzZ0/cunVLqziKNIxSuXJlxMXFaewqCgB79+5F7dq1te4vPDwcq1atQs+ePdVtHh4eqFixIkaPHo158+YVJUyDtXXrzyhvZ4vp0z6Ds7MDzp67hB49P8CNG9Lv/0+FZ+xeA5YBC9Svzfs+nSeRczQCWeHfwMjGDgo77Sb15vz5G4TsLJh1eB/K94YD2Srk3U5Azh87ixZkVgYyl0yFcuAYWAZ+ByEjHdn7t6uXvQKAaeuuUBibwHzgWGDg2P9i+fdzkG5p3NgDB/b/T/3666+DAQBr1/6E4R99JlNU+k+uYZQnT57Aw8MDQ4cORZ8+fQqcX7BgARYuXIiwsDDUqFEDc+fORYcOHXDp0iVYW1sDAPz9/fHLL79g8+bNKF++PCZMmIDu3bsjJiYGxv8Oyb5JoR/E9rzQ0FDMmDED33zzDYYPH45Vq1bhypUrCAkJwapVqzBgwACt+jM3N8fp06dRo0YNjfZLly6hQYMGyMzM1Ko/PoiNXsQHsdHz+CA2el5JPIgt2rngL/qiaJlY9OePKRQK7NixQ/1IEUEQ4OLiAn9/f0yePBnA0yqGo6MjvvzyS4wcORKPHj1ChQoVsG7dOvTv3x8AcOfOHbi6umLPnj3o1KlTod67SMMoQ4cORVBQECZNmoSMjAwMGjQIP/zwA5YsWaJ1ogE8rWIsXbq0QPvSpUu1flw9ERGRrhFrB1GVSoW0tDSN48XtHworISEBSUlJ6Nixo7pNqVTCy8sL0dHRAICYmBjk5ORoXOPi4oK6deuqrymMIi99HTFiBEaMGIGUlBTk5+fDwaHoezgsWLAA3bp1w/79+9GiRQsoFApER0fj5s2b2LNnT5H7JSIi0gWvX+pQeC/b7iEoKAjBwcFa95WUlAQAcHR01Gh3dHTE9evX1deYmZnB1ta2wDXP7i+MIlU22rZti4cPHwIA7O3t1YlGWloa2rZtq3V/Xl5euHz5Mt577z08fPgQDx48gI+PD86dO4fQ0NCihEhERKR3AgMD8ejRI40jMDCwWH2+uIKzMBtjart5ZpEqG5GRkcjOzi7QnpWVhcOHDxelS7i4uBSYCHrq1CmEh4djzZo1ReqTiIhIFwgQZ4KoUqmEUqkUpS8nJycAT6sXzs7O6vbk5GR1tcPJyQnZ2dlITU3VqG4kJyejZcuWhX4vrSobp0+fxunTpwEA58+fV78+ffo0Tp48idWrVxd6My4iIiJDkS+Ic4ipcuXKcHJyQkREhLotOzsbUVFR6kSicePGMDU11bgmMTERZ8+e1SrZ0Kqy0aBBAygUCigUipcOl1hYWOC7777TpksiIiK9ly9SZUNb6enp+Oeff9SvExISEBcXBzs7O7i5ucHf3x/z589H9erVUb16dcyfPx+WlpYYNOjpgx5tbGwwfPhwTJgwAeXLl4ednR0mTpyIevXqoX379oWOQ6tkIyEhAYIgoEqVKjh+/DgqVPhvS2wzMzM4ODgUes0tERERSevEiRMaT09/9ngPX19fhIWFYdKkScjMzMTo0aORmpqKZs2a4ffff1fvsQEAixYtgomJCfr164fMzEy0a9cOYWFhWv2+L9I+G2Lx8Xn9roYPHz5EVFSU+tkrhcV9NuhF3GeDnsd9Nuh5JbHPxgHH/qL00+7uFlH6KWlFmiAaEhICR0dHDBs2TKN9zZo1uHfvnnpzkDexsbF54/kPP/ywKCESERHpDLGWvpZWRUo2VqxYgY0bNxZor1OnDgYMGFDoZIPLWomIiPRfkZKNF5fJPFOhQgUkJiYWOygiIiJ9ItbS19KqSJt6ubq64s8//yzQ/ueff8LFhU8OJCIiel6+SEdpVaTKxkcffQR/f3/k5OSol8AeOHAAkyZNwoQJE0QNkIiIiEq3IiUbkyZNwoMHDzB69Gj1TqLm5uaYPHlysbdNJSIi0jeluSohhiIlGwqFAl9++SVmzJiBCxcuwMLCAtWrVxdtC1UiIiJ9YuhzNor81FcAKFOmDJo0aSJWLERERKSHCp1s+Pj4ICwsDGXLln3jZlzbt28vdmBERET6It+wCxuFTzZsbGzUj5N902ZcRERE9B+5no2iKwqdbDy/ARc34yIiIio82Z4LoiOKtM8GERERUWEVurLRsGFD9TDKm8TGxhY5ICIiIn3Dpa+F1Lt3b/XXWVlZWLZsGWrXro0WLVoAAI4dO4Zz585h9OjRogdJRERUmuUX8h/r+qrQyUZQUJD6648++gjjx4/HnDlzClxz8+ZN8aIjIiKiUq9Icza2bt360ke/DxkyBNu2bSt2UERERPpEEOkorYqUbFhYWODIkSMF2o8cOQJzc/NiB0VERKRP+CC2IvD398cnn3yCmJgYNG/eHMDTORtr1qzBzJkzRQ2QiIiISrciJRtTpkxBlSpVsGTJEmzcuBEAUKtWLYSFhaFfv36iBkhERFTacQfRIurXrx8TCyIiokIw9B1Ei7yp18OHD7Fq1SpMnToVDx48APB0f43bt2+LFhwRERGVfkWqbJw+fRrt27eHjY0Nrl27ho8++gh2dnbYsWMHrl+/jrVr14odJxERUalVmleSiKFIlY2AgAD4+fkhPj5eY/VJly5dcOjQIdGCIyIi0gf5CnGO0qpIlY2///4bK1asKNBesWJFJCUlFTsoIiIifVKal62KoUiVDXNzc6SlpRVov3TpEipUqFDsoIiIiEh/FCnZ6NWrF2bPno2cnBwAgEKhwI0bNzBlyhT06dNH1ACJiIhKO+4gWgRff/017t27BwcHB2RmZsLLywvVqlWDtbU15s2bJ3aMREREpRrnbBRB2bJlceTIEfzxxx+IjY1Ffn4+GjVqhPbt24sdHxEREZVyWicbubm5MDc3R1xcHNq2bYu2bdtKERcREZHeMPQJolonGyYmJnB3d0deXp4U8RAREekdQ082ijRnY/r06QgMDFTvHEpERET0KkWas/Htt9/in3/+gYuLC9zd3WFlZaVxPjY2VpTgiIiI9IFQiid3iqFIyUavXr2gUBj4nxwREVEhGfowSpGSjeDgYJHDICIiIn2l1ZyNjIwMjBkzBhUrVoSDgwMGDRqElJQUqWIjIiLSC/kiHdqoVKkSFApFgWPMmDEAAD8/vwLnmjdvXuzP+jJaVTaCgoIQFhaGwYMHw9zcHJs2bcInn3yCrVu3ShIcERGRPpBj98+///5bY+Xo2bNn0aFDB/Tt21fd1rlzZ4SGhqpfm5mZSRKLVsnG9u3bsXr1agwYMAAAMGTIELRq1Qp5eXkwNjaWJEAiIqLSTqzdP1UqFVQqlUabUqmEUqkscO2Lzyr74osvULVqVXh5eWnc6+TkJE5wr6HVMMrNmzfx7rvvql83bdoUJiYmuHPnjuiBERERkaaQkBDY2NhoHCEhIW+8Lzs7G+vXr8ewYcM0FnhERkbCwcEBNWrUwIgRI5CcnCxJ3FpVNvLy8gqUWExMTJCbmytqUERERPpErNUogYGBCAgI0Gh7WVXjRTt37sTDhw/h5+enbuvSpQv69u0Ld3d3JCQkYMaMGWjbti1iYmIK1ac2tEo2BEGAn5+fRhBZWVkYNWqUxl4b27dvFy9CIiKiUk6sZONVQyZvsnr1anTp0gUuLi7qtv79+6u/rlu3Ljw9PeHu7o7du3fDx8dHlHif0SrZ8PX1LdA2ZMgQ0YIhIiIicV2/fh379+9/YyHA2dkZ7u7uiI+PFz0GrZKN52esEhERUeHIsRrlmdDQUDg4OKBbt26vve7+/fu4efMmnJ2dRY+hSM9GISIiosLLV4hzaP2++fkIDQ2Fr68vTEz+qy+kp6dj4sSJOHr0KK5du4bIyEj06NED9vb2eO+990T85E8VaQdRIiIi0n379+/HjRs3MGzYMI12Y2NjnDlzBmvXrsXDhw/h7OyMNm3aYMuWLbC2thY9DiYbREREEpPr2SgdO3aEIBQcxLGwsMC+fftKLA4mG0RERBKTc86GLuCcDSIiIpIUKxtEREQSyzfw2oZeJhsibUFPesRuzRm5QyAd0t6xvtwhkIGRa86GrtDLZIOIiEiXGHZdg3M2iIiISGKsbBAREUmMwyhEREQkqaLs/qlPOIxCREREkmJlg4iISGJc+kpERESSMuxUg8MoREREJDFWNoiIiCTG1ShEREQkKUOfs8FhFCIiIpIUKxtEREQSM+y6BpMNIiIiyXHOBhEREUmKczaIiIiIJMTKBhERkcQMu67BZIOIiEhyhj5ng8MoREREJClWNoiIiCQmGPhACpMNIiIiiXEYhYiIiEhCrGwQERFJzND32WCyQUREJDHDTjU4jEJEREQSY2WDiIhIYhxGISIiIkkZ+moUJhtEREQSM/R9Njhng4iIiCTFygYREZHEDH0YhZUNIiIiiQki/aeN4OBgKBQKjcPJyem/mAQBwcHBcHFxgYWFBby9vXHu3DmxPzoAJhtERER6q06dOkhMTFQfZ86cUZ9bsGABFi5ciKVLl+Lvv/+Gk5MTOnTogMePH4seB4dRiIiIJCbXMIqJiYlGNeMZQRCwePFiTJs2DT4+PgCA8PBwODo6YuPGjRg5cqSocbCyQUREJLF8QRDlUKlUSEtL0zhUKtUr3zc+Ph4uLi6oXLkyBgwYgKtXrwIAEhISkJSUhI4dO6qvVSqV8PLyQnR0tOifn8kGERFRKRESEgIbGxuNIyQk5KXXNmvWDGvXrsW+ffuwcuVKJCUloWXLlrh//z6SkpIAAI6Ojhr3ODo6qs+JicMoREREEhNrl43AwEAEBARotCmVypde26VLF/XX9erVQ4sWLVC1alWEh4ejefPmAACFQqEZpyAUaBMDKxtEREQSy4cgyqFUKlG2bFmN41XJxousrKxQr149xMfHq+dxvFjFSE5OLlDtEAOTDSIiIgOgUqlw4cIFODs7o3LlynByckJERIT6fHZ2NqKiotCyZUvR31tnhlGysrJw+vRpJCcnIz9fc95uz549ZYqKiIio+OTYrnzixIno0aMH3NzckJycjLlz5yItLQ2+vr5QKBTw9/fH/PnzUb16dVSvXh3z58+HpaUlBg0aJHosOpFs/Pbbb/jwww+RkpJS4JxCoUBeXp4MUREREYlDjqWvt27dwsCBA5GSkoIKFSqgefPmOHbsGNzd3QEAkyZNQmZmJkaPHo3U1FQ0a9YMv//+O6ytrUWPRSEIguxPh6lWrRo6deqEmTNnijJWZGpWUYSoiEhftXesL3cIpEP23twr+Xv0de8lSj9br+8SpZ+SphNzNpKTkxEQECDJpBQiIiKSl04kG++//z4iIyPlDoOIiEgScjwbRZfoxJyNpUuXom/fvjh8+DDq1asHU1NTjfPjx4+XKTIiIqLiM/SnvupEsrFx40bs27cPFhYWiIyM1NhQRKFQMNkgIiIqxXQi2Zg+fTpmz56NKVOmwMhIJ0Z2iIiIRKMDazFkpRPJRnZ2Nvr3789Eg4iI9FJ+KZ5vIQad+O3u6+uLLVu2yB0GERERSUAnKht5eXlYsGAB9u3bh/r16xeYILpw4UKZIiMiIio+ThDVAWfOnEHDhg0BAGfPntU4J8XT54iIiEpSaV62KgadSDYOHjwodwhEREQkEZ1INoiIiPSZoU8Q1Ylko02bNq8dLvnjjz9KMBoiIiJxcemrDmjQoIHG65ycHMTFxeHs2bPw9fWVJygiIiKRcIKoDli0aNFL24ODg5Genl7C0RAREZGYdGKfjVcZMmQI1qxZI3cYRERExWLoD2LT6WTj6NGjMDc3lzuMUuedd5phx44wXL8Wg5zs2+jZs5PcIZGM+P1guLp90A3Lfl+Gbee3Ydv5bVi4cyE8vT3V58vZl0PAwgCsP7EeOy7vwJx1c+BSyUXGiPVXPgRRjtJKJ4ZRfHx8NF4LgoDExEScOHECM2bMkCmq0svKyhKnT59HePgWbP1pldzhkMz4/WC4UhJTEBoSijvX7gAA2vdtj5mrZ2Jsl7G4cfkGZq6aidzcXMwePhtPHj+BzwgfzN80HyPbjoQqUyVz9KRPdCLZsLGx0XhtZGSEmjVrYvbs2ejYsaNMUZVe+/YdxL593LuEnuL3g+H6a/9fGq/DF4Sj2wfd8HbDt5GXk4dajWthZLuRuHH5BgDg+2nfY1PcJnj38sa+zfvkCFlvcTWKDggNDZU7BCIivWZkZIR3u78LcwtzXIy9CFPl08dC5Khy1Nfk5+cjNzsXdZrWYbIhstI8BCIGnUg2ikOlUkGl0iz3CYLAbc6JiABUersSFu5cCDOlGTKfZGLOiDm4EX8DxibGuHvzLvwm++G7wO+QlZGF90a8BztHO9g52MkdNukZ2SaI2tnZISUlBQBga2sLOzu7Vx6vExISAhsbG40jP/9xSXwEIiKdd+vKLYzpPAaf9foMu9ftxoRFE+BW3Q15uXmYO3IuKlapiK1nt2Ln5Z2o36I+/v7jb+TnGfquEOIz9NUoslU2Fi1aBGtrawDA4sWLi9xPYGAgAgICNNrsyr9dnNCIiPRGbk4uEq8lAgDiT8ejhkcN9BrWC98Ffod/zvyDsZ3HwtLaEqampnj04BEW/bwI8afjZY5a/+RzzoY8nt8ZtDi7hCqVSiiVSo02DqEQEb2cQqFQz9d4JuNxBgDApZILqtevjnVfr5MjNNJjsiUbaWlphb62bNmyEkaif6ysLFGtWmX168qV3ODhUQcPHqTi5s07MkZGcuD3g+HyneyLEwdP4N6de7AsYwmvnl6o16IeZnzwdEuBd7q9g0f3H+HenXuo9HYljAoehaP7jiL2UKzMkesfw65ryJhslCtXrtAViLy8PImj0S+NG3vgwP7/qV9//XUwAGDt2p8w/KPPZIqK5MLvB8Nla2+Lzxd/DjsHOzx5/AQJFxIw44MZOHn4JADAzsEOH8/8GOXsy+FB8gMc2HYAm5Zskjlq/WToq1EUgkyLf6OiotRfX7t2DVOmTIGfnx9atGgB4OnuoeHh4QgJCdF6mMXUrKKosRKRfmnvWF/uEEiH7L25V/L3aFGxjSj9HL1dOvfMka2y4eXlpf569uzZWLhwIQYOHKhu69mzJ+rVq4cff/yRT34lIiIqxXTi2ShHjx6Fp6dngXZPT08cP35choiIiIjEIwiCKEdppRPJhqurK3744YcC7StWrICrq6sMEREREYmHD2LTAYsWLUKfPn2wb98+NG/eHABw7NgxXLlyBdu2bZM5OiIiIioOnahsdO3aFZcvX0bPnj3x4MED3L9/H7169cLly5fRtWtXucMjIiIqFu4gqiNcXV0xf/58ucMgIiISXWmebyEGnahsAMDhw4cxZMgQtGzZErdv3wYArFu3DkeOHJE5MiIiIioOnUg2tm3bhk6dOsHCwgKxsbHqp7g+fvyY1Q4iIir1DH2CqE4kG3PnzsUPP/yAlStXwtT0vz37W7ZsidhYbptLRESlmxxLX0NCQtCkSRNYW1vDwcEBvXv3xqVLlzSu8fPzg0Kh0DieLdQQk04kG5cuXULr1q0LtJctWxYPHz4s+YCIiIhKuaioKIwZMwbHjh1DREQEcnNz0bFjRzx58kTjus6dOyMxMVF97NmzR/RYdGKCqLOzM/755x9UqlRJo/3IkSOoUqWKPEERERGJRKwhEJVKpZ5q8MzLnn4OAL/99pvG69DQUDg4OCAmJkbjH/hKpRJOTk6ixPcqOlHZGDlyJD799FP89ddfUCgUuHPnDjZs2ICJEydi9OjRcodHRERULGItfQ0JCYGNjY3GERISUqgYHj16BACws7PTaI+MjISDgwNq1KiBESNGIDk5WfTPL9uD2F40bdo0LFq0CFlZWQCeZloTJ07EnDlztO6LD2Ijotfhg9joeSXxILa6juLMg4i5EVXoysbzBEFAr169kJqaisOHD6vbt2zZgjJlysDd3R0JCQmYMWMGcnNzERMT88Y+taEzyQYAZGRk4Pz588jPz0ft2rVRpkyZIvXDZIOIXofJBj2vNCUbZ+8eK9J9Y8aMwe7du3HkyBG89dZbr7wuMTER7u7u2Lx5M3x8fIoaZgGyztkYNmxYoa5bs2aNxJEQERFJR87dP8eNG4eff/4Zhw4dem2iATydQ+nu7o74+HhRY5A12QgLC4O7uzsaNmxo8LurERGR/sqX4XecIAgYN24cduzYgcjISFSuXPmN99y/fx83b96Es7OzqLHImmyMGjUKmzdvxtWrVzFs2DAMGTKkwMQVIiIi0t6YMWOwceNG7Nq1C9bW1khKSgIA2NjYwMLCAunp6QgODkafPn3g7OyMa9euYerUqbC3t8d7770naiyyrkZZtmwZEhMTMXnyZPzyyy9wdXVFv379sG/fPlY6iIhIb8jxILbly5fj0aNH8Pb2hrOzs/rYsmULAMDY2BhnzpxBr169UKNGDfj6+qJGjRo4evQorK2tRf38OjVB9Pr16wgLC8PatWuRk5OD8+fPF2mSKCeIEtHrcIIoPa8kJojWqOApSj+X750QpZ+SphP7bDzzbKtUQRCQn58vdzhEREQkAtmTDZVKhU2bNqFDhw6oWbMmzpw5g6VLl+LGjRtFXvpKRESkS+QYRtElsk4QHT16NDZv3gw3NzcMHToUmzdvRvny5eUMiYiISHRyrEbRJbLO2TAyMoKbmxsaNmwIhULxyuu2b9+uVb+cs0FEr8M5G/S8kpizUdW+kSj9XEkpnU9Cl7Wy8eGHH742ySAiItIHpXkIRAyyb+pFRESk7wTBsBc96MQj5omIiPSZWI+YL61kX41CRERE+o2VDSIiIonp0P6ZsmCyQUREJDEOoxARERFJiJUNIiIiiXEYhYiIiCRl6DuIchiFiIiIJMXKBhERkcS4gygRERFJytDnbHAYhYiIiCTFygYREZHEDH2fDSYbREREEjP0YRQmG0RERBLj0lciIiIiCbGyQUREJDEOoxAREZGkDH2CKIdRiIiISFKsbBAREUmMwyhEREQkKa5GISIiIpIQKxtEREQS44PYiIiISFIcRiEiIiKSECsbREREEuNqFCIiIpIU52wQERGRpAy9ssE5G0RERHps2bJlqFy5MszNzdG4cWMcPny4xGNgskFERCQxQRBEObS1ZcsW+Pv7Y9q0aTh58iTeffdddOnSBTdu3JDgU76aQtDD2o6pWUW5QyAiHdbesb7cIZAO2Xtzr+TvYSLS76Xc7NtaXd+sWTM0atQIy5cvV7fVqlULvXv3RkhIiCgxFQYrG0RERKWESqVCWlqaxqFSqV56bXZ2NmJiYtCxY0eN9o4dOyI6OrokwlXTywmiOVpmfvpIpVIhJCQEgYGBUCqVcodDOoDfE/Q8fj+ULG0rEq8SHByMWbNmabQFBQUhODi4wLUpKSnIy8uDo6OjRrujoyOSkpJEiaew9HIYhYC0tDTY2Njg0aNHKFu2rNzhkA7g9wQ9j98PpZNKpSpQyVAqlS9NGO/cuYOKFSsiOjoaLVq0ULfPmzcP69atw8WLFyWP9xm9rGwQERHpo1clFi9jb28PY2PjAlWM5OTkAtUOqXHOBhERkR4yMzND48aNERERodEeERGBli1blmgsrGwQERHpqYCAAHzwwQfw9PREixYt8OOPP+LGjRsYNWpUicbBZENPKZVKBAUFceIXqfF7gp7H7wfD0L9/f9y/fx+zZ89GYmIi6tatiz179sDd3b1E4+AEUSIiIpIU52wQERGRpJhsEBERkaSYbBAREZGkmGzoibCwMJQrV06re/z8/NC7d29J4iEi3RUZGQmFQoGHDx8CKNrPDyJtMNkoBV6VFDz/A6N///64fPlyyQdHsmGyqL/8/PygUCheujxx9OjRUCgU8PPzE+39+PODpMZkQ09YWFjAwcFB7jCISCSurq7YvHkzMjMz1W1ZWVnYtGkT3NzcRH0v/vwgqTHZ0BMvK4POnTsXDg4OsLa2xkcffYQpU6agQYMGBe79+uuv4ezsjPLly2PMmDHIyckpmaBJMlFRUWjatCmUSiWcnZ0xZcoU5ObmAgB++eUXlCtXDvn5+QCAuLg4KBQKfP755+r7R44ciYEDB8oSOz3VqFEjuLm5Yfv27eq27du3w9XVFQ0bNlS3CYKABQsWoEqVKrCwsICHhwf+97//afS1Z88e1KhRAxYWFmjTpg2uXbumcf7Fnx8vq5r5+/vD29tb/drb2xvjxo2Dv78/bG1t4ejoiB9//BFPnjzB0KFDYW1tjapVq2LvXukf3066j8mGntqwYQPmzZuHL7/8EjExMXBzc8Py5csLXHfw4EFcuXIFBw8eRHh4OMLCwhAWFlbyAZNobt++ja5du6JJkyY4deoUli9fjtWrV2Pu3LkAgNatW+Px48c4efIkgKeJib29PaKiotR9REZGwsvLS5b46T9Dhw5FaGio+vWaNWswbNgwjWumT5+O0NBQLF++HOfOncNnn32GIUOGqP9/3rx5Ez4+PujatSvi4uLU//AQQ3h4OOzt7XH8+HGMGzcOn3zyCfr27YuWLVsiNjYWnTp1wgcffICMjAxR3o9KMYF0nq+vr2BsbCxYWVlpHObm5gIAITU1VQgNDRVsbGzU9zRr1kwYM2aMRj+tWrUSPDw8NPp1d3cXcnNz1W19+/YV+vfvL/VHIhH4+voKvXr1KtA+depUoWbNmkJ+fr667fvvvxfKlCkj5OXlCYIgCI0aNRK+/vprQRAEoXfv3sK8efMEMzMzIS0tTUhMTBQACBcuXCiRz0EFPft/e+/ePUGpVAoJCQnCtWvXBHNzc+HevXtCr169BF9fXyE9PV0wNzcXoqOjNe4fPny4MHDgQEEQBCEwMFCoVauWxvfD5MmT1T87BEEo8PPjZd9bn376qeDl5aV+7eXlJbzzzjvq17m5uYKVlZXwwQcfqNuefS8dPXq0mH8iVNqxslFKtGnTBnFxcRrHqlWrXnn9pUuX0LRpU422F18DQJ06dWBsbKx+7ezsjOTkZPECpxJ34cIFtGjRAgqFQt3WqlUrpKen49atWwCelsAjIyMhCAIOHz6MXr16oW7dujhy5AgOHjwIR0dHvP3223J9BPqXvb09unXrhvDwcISGhqJbt26wt7dXnz9//jyysrLQoUMHlClTRn2sXbsWV65cAfD0+6F58+Ya3w/PP268OOrXr6/+2tjYGOXLl0e9evXUbc+eLMqfKcRno5QSVlZWqFatmkbbs18cr/L8Dxfg6djui0xNTQvc82wsn0onQRBe+f/+Wbu3tzdWr16NU6dOwcjICLVr14aXlxeioqKQmprKIRQdMmzYMIwdOxYA8P3332uce/Z3dffu3ahYsaLGuWfPPHnZ3/s3MTIyKnDfy+Zyveznx/Ntz77f+DOFWNnQUzVr1sTx48c12k6cOCFTNFSSateujejoaI1fFtHR0bC2tlb/Qno2b2Px4sXw8vKCQqGAl5cXIiMjOV9Dx3Tu3BnZ2dnIzs5Gp06dNM7Vrl0bSqUSN27cQLVq1TQOV1dX9TXHjh3TuO/F1y+qUKECEhMTNdri4uKK/2HIYDHZ0FPjxo3D6tWrER4ejvj4eMydOxenT58u8C9eKt0ePXpUYHjt448/xs2bNzFu3DhcvHgRu3btQlBQEAICAmBk9PSvvI2NDRo0aID169erVxi0bt0asbGxuHz5ssaqA5KXsbExLly4gAsXLmgMeQKAtbU1Jk6ciM8++wzh4eG4cuUKTp48ie+//x7h4eEAgFGjRuHKlSsICAjApUuXsHHjxjdOAm/bti1OnDiBtWvXIj4+HkFBQTh79qxUH5EMAIdR9NTgwYNx9epVTJw4EVlZWejXrx/8/PwKVDuodIuMjNRYBgkAvr6+2LNnDz7//HN4eHjAzs4Ow4cPx/Tp0zWua9OmDWJjY9WJha2tLWrXro07d+6gVq1aJfURqBDKli37ynNz5syBg4MDQkJCcPXqVZQrVw6NGjXC1KlTAQBubm7Ytm0bPvvsMyxbtgxNmzbF/PnzC6xqeV6nTp0wY8YMTJo0CVlZWRg2bBg+/PBDnDlzRvTPRoaBj5g3IB06dICTkxPWrVsndyhERGRAWNnQUxkZGfjhhx/QqVMnGBsbY9OmTdi/fz8iIiLkDo2IiAwMKxt6KjMzEz169EBsbCxUKhVq1qyJ6dOnw8fHR+7QiIjIwDDZICIiIklxNQoRERFJiskGERERSYrJBhEREUmKyQYRERFJiskGERERSYrJBpGBUigU2Llzp9xhEJEBYLJBJLHo6GgYGxujc+fOWt9bqVIlLF68WPyg3kChULz28PPzK3Lfcn0mIpIPdxAlktiaNWswbtw4rFq1Cjdu3ICbm5vcIb3R80/83LJlC2bOnIlLly6p2ywsLOQIi4hKKVY2iCT05MkT/PTTT/jkk0/QvXv3lz5t8+eff4anpyfMzc1hb2+v3uXV29sb169fx2effaauKABAcHAwGjRooNHH4sWLUalSJfXrv//+Gx06dIC9vT1sbGzg5eWF2NjYQsft5OSkPmxsbKBQKDTaDh06hMaNG8Pc3BxVqlTBrFmzkJubq74/ODgYbm5uUCqVcHFxwfjx41/7mYhIvzHZIJLQli1bULNmTdSsWRNDhgxBaGgont+0d/fu3fDx8UG3bt1w8uRJHDhwAJ6engCA7du346233sLs2bORmJioUW14k8ePH8PX1xeHDx/GsWPHUL16dXTt2hWPHz8u9mfat28fhgwZgvHjx+P8+fNYsWIFwsLCMG/ePADA//73PyxatAgrVqxAfHw8du7ciXr16hX7MxFR6cVhFCIJrV69GkOGDAEAdO7cGenp6Thw4ADat28PAJg3bx4GDBiAWbNmqe/x8PAAANjZ2cHY2BjW1tZwcnLS6n3btm2r8XrFihWwtbVFVFQUunfvXpyPhHnz5mHKlCnw9fUFAFSpUgVz5szBpEmTEBQUhBs3bsDJyQnt27eHqakp3Nzc0LRp02J/JiIqvVjZIJLIpUuXcPz4cQwYMAAAYGJigv79+2PNmjXqa+Li4tCuXTvR3zs5ORmjRo1CjRo1YGNjAxsbG6Snp+PGjRvF7jsmJgazZ89GmTJl1MeIESOQmJiIjIwM9O3bF5mZmahSpQpGjBiBHTt2aAyxEJHhYWWDSCKrV69Gbm4uKlasqG4TBAGmpqZITU2Fra1tkSZaGhkZ4cXnJ+bk5Gi89vPzw71797B48WK4u7tDqVSiRYsWyM7OLtqHeU5+fj5mzZr10icIm5ubw9XVFZcuXUJERAT279+P0aNH46uvvkJUVBRMTU2L/f5EVPqwskEkgdzcXKxduxbffPMN4uLi1MepU6fg7u6ODRs2AADq16+PAwcOvLIfMzMz5OXlabRVqFABSUlJGglHXFycxjWHDx/G+PHj0bVrV9SpUwdKpRIpKSmifLZGjRrh0qVLqFatWoHDyOjpjxQLCwv07NkT3377LSIjI3H06FGcOXPmlZ+JiPQbKxtEEvj111+RmpqK4cOHw8bGRuPc+++/j9WrV2Ps2LEICgpCu3btULVqVQwYMAC5ubnYu3cvJk2aBODpnhSHDh3CgAEDoFQqYW9vD29vb9y7dw8LFizA+++/j99++w179+5F2bJl1e9RrVo1rFu3Dp6enkhLS8Pnn38u2nLVmTNnonv37nB1dUXfvn1hZGSE06dP48yZM5g7dy7CwsKQl5eHZs2awdLSEuvWrYOFhQXc3d1f+ZmISL+xskEkgdWrV6N9+/YFEg0A6NOnD+Li4hAbGwtvb29s3boVP//8Mxo0aIC2bdvir7/+Ul87e/ZsXLt2DVWrVkWFChUAALVq1cKyZcvw/fffw8PDA8ePH8fEiRM13mPNmjVITU1Fw4YN8cEHH2D8+PFwcHAQ5bN16tQJv/76KyIiItCkSRM0b94cCxcuVCcT5cqVw8qVK9GqVSt15eaXX35B+fLlX/mZiEi/KYQXB3+JiIiIRMTKBhEREUmKyQYRERFJiskGERERSYrJBhEREUmKyQYRERFJiskGERERSYrJBhEREUmKyQYRERFJiskGERERSYrJBhEREUmKyQYRERFJ6v/FZIOlT/TUEwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(conf_matrix_LR, annot = True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "AP157",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}