artificial_neural_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset is the Telco Customer Churn dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "--TFEsIzy075"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zQQfuPemhLHm"
   },
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8vOuliT3hTrq"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/content/sample_data/telco_customer_churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "id": "hAtqmKqCQvEA",
    "outputId": "1452a8aa-757b-4408-89a0-e966a5402653"
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   "outputs": [
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       "      <th>0</th>\n",
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       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
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      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
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     "execution_count": 475,
     "metadata": {},
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   "source": [
    "df.head()"
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    {
     "data": {
      "text/plain": [
       "(7043, 21)"
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     "execution_count": 476,
     "metadata": {},
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    "df.shape"
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    "id": "tG0PWnqSiPFu"
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   "source": [
    "The feature customerID wasn't helpful so it was dropped. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9tcjaovXQvIj"
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   "outputs": [],
   "source": [
    "df.drop('customerID', axis=1, inplace=True)"
   ]
  },
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       "  "
      ],
      "text/plain": [
       "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  Female              0     Yes         No       1           No   \n",
       "1    Male              0      No         No      34          Yes   \n",
       "2    Male              0      No         No       2          Yes   \n",
       "3    Male              0      No         No      45           No   \n",
       "4  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           No   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85        29.85   \n",
       "1               No               Mailed check           56.95       1889.5   \n",
       "2              Yes               Mailed check           53.85       108.15   \n",
       "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
       "4              Yes           Electronic check           70.70       151.65   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  "
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VPkVQsDZG-51",
    "outputId": "452ae23f-d839-449d-9796-5b95528fb79f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges         object\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "v0vJPXljj8R3",
    "outputId": "d8fb9d27-de41-4d94-ac6c-04b20d0a5f1c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "TotalCharges        0\n",
       "Churn               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shows that there are no Null values.  However, object dtypes need to be converted to numeric\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "buA_mYSxG-9o",
    "outputId": "97dfb4e8-b780-4920-a280-9748a5fada58"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "7038    False\n",
       "7039    False\n",
       "7040    False\n",
       "7041    False\n",
       "7042    False\n",
       "Name: TotalCharges, Length: 7043, dtype: bool"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "tried to convert with pd.to_numeric(df.TotalCharges), but got error b/c there are \" \" values.\n",
    "  Need to take out spaces\n",
    "errors= igonres errors and displaysw\n",
    "\"\"\"\n",
    "pd.to_numeric(df.TotalCharges, errors='coerce').isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 614
    },
    "id": "zG4C9m6MG_Bb",
    "outputId": "9b22eb94-c45a-43e0-c6b5-cccceeec7c2b"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "  <div id=\"df-8e75f15e-feb9-4d26-bce5-75703a6bc497\">\n",
       "    <div class=\"colab-df-container\">\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
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       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
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       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>52.55</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.25</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>80.85</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.75</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1340</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>56.05</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.85</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3826</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>25.35</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4380</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>20.00</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5218</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>No internet service</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>19.70</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6670</th>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>73.35</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6754</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>61.90</td>\n",
       "      <td></td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e75f15e-feb9-4d26-bce5-75703a6bc497')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
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       "\n",
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       "          const element = document.querySelector('#df-8e75f15e-feb9-4d26-bce5-75703a6bc497');\n",
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       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "        }\n",
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       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "488   Female              0     Yes        Yes       0           No   \n",
       "753     Male              0      No        Yes       0          Yes   \n",
       "936   Female              0     Yes        Yes       0          Yes   \n",
       "1082    Male              0     Yes        Yes       0          Yes   \n",
       "1340  Female              0     Yes        Yes       0           No   \n",
       "3331    Male              0     Yes        Yes       0          Yes   \n",
       "3826    Male              0     Yes        Yes       0          Yes   \n",
       "4380  Female              0     Yes        Yes       0          Yes   \n",
       "5218    Male              0     Yes        Yes       0          Yes   \n",
       "6670  Female              0     Yes        Yes       0          Yes   \n",
       "6754    Male              0      No        Yes       0          Yes   \n",
       "\n",
       "         MultipleLines InternetService       OnlineSecurity  \\\n",
       "488   No phone service             DSL                  Yes   \n",
       "753                 No              No  No internet service   \n",
       "936                 No             DSL                  Yes   \n",
       "1082               Yes              No  No internet service   \n",
       "1340  No phone service             DSL                  Yes   \n",
       "3331                No              No  No internet service   \n",
       "3826               Yes              No  No internet service   \n",
       "4380                No              No  No internet service   \n",
       "5218                No              No  No internet service   \n",
       "6670               Yes             DSL                   No   \n",
       "6754               Yes             DSL                  Yes   \n",
       "\n",
       "             OnlineBackup     DeviceProtection          TechSupport  \\\n",
       "488                    No                  Yes                  Yes   \n",
       "753   No internet service  No internet service  No internet service   \n",
       "936                   Yes                  Yes                   No   \n",
       "1082  No internet service  No internet service  No internet service   \n",
       "1340                  Yes                  Yes                  Yes   \n",
       "3331  No internet service  No internet service  No internet service   \n",
       "3826  No internet service  No internet service  No internet service   \n",
       "4380  No internet service  No internet service  No internet service   \n",
       "5218  No internet service  No internet service  No internet service   \n",
       "6670                  Yes                  Yes                  Yes   \n",
       "6754                  Yes                   No                  Yes   \n",
       "\n",
       "              StreamingTV      StreamingMovies  Contract PaperlessBilling  \\\n",
       "488                   Yes                   No  Two year              Yes   \n",
       "753   No internet service  No internet service  Two year               No   \n",
       "936                   Yes                  Yes  Two year               No   \n",
       "1082  No internet service  No internet service  Two year               No   \n",
       "1340                  Yes                   No  Two year               No   \n",
       "3331  No internet service  No internet service  Two year               No   \n",
       "3826  No internet service  No internet service  Two year               No   \n",
       "4380  No internet service  No internet service  Two year               No   \n",
       "5218  No internet service  No internet service  One year              Yes   \n",
       "6670                  Yes                   No  Two year               No   \n",
       "6754                   No                   No  Two year              Yes   \n",
       "\n",
       "                  PaymentMethod  MonthlyCharges TotalCharges Churn  \n",
       "488   Bank transfer (automatic)           52.55                 No  \n",
       "753                Mailed check           20.25                 No  \n",
       "936                Mailed check           80.85                 No  \n",
       "1082               Mailed check           25.75                 No  \n",
       "1340    Credit card (automatic)           56.05                 No  \n",
       "3331               Mailed check           19.85                 No  \n",
       "3826               Mailed check           25.35                 No  \n",
       "4380               Mailed check           20.00                 No  \n",
       "5218               Mailed check           19.70                 No  \n",
       "6670               Mailed check           73.35                 No  \n",
       "6754  Bank transfer (automatic)           61.90                 No  "
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This shows rows regardles of errors\n",
    "#TotalCharges show which rows have no values\n",
    "df[pd.to_numeric(df.TotalCharges, errors='coerce').isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ADQFmrxgJ3iJ",
    "outputId": "ab9587ba-59b7-4bfa-e6a7-375c1414bfd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                                 Female\n",
       "SeniorCitizen                               0\n",
       "Partner                                   Yes\n",
       "Dependents                                Yes\n",
       "tenure                                      0\n",
       "PhoneService                               No\n",
       "MultipleLines                No phone service\n",
       "InternetService                           DSL\n",
       "OnlineSecurity                            Yes\n",
       "OnlineBackup                               No\n",
       "DeviceProtection                          Yes\n",
       "TechSupport                               Yes\n",
       "StreamingTV                               Yes\n",
       "StreamingMovies                            No\n",
       "Contract                             Two year\n",
       "PaperlessBilling                          Yes\n",
       "PaymentMethod       Bank transfer (automatic)\n",
       "MonthlyCharges                          52.55\n",
       "TotalCharges                                 \n",
       "Churn                                      No\n",
       "Name: 488, dtype: object"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# row number 488\n",
    "df.iloc[488]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "8QzcqRPRJ3s0",
    "outputId": "cd5808a8-8c1e-498a-8359-ee3219c448b4"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "' '"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[488]['TotalCharges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2urfLdU1J3yw"
   },
   "outputs": [],
   "source": [
    "#deleting the rows that have ' ' because there are only 11 and creating new databases without these rows\n",
    "df1 = df[df.TotalCharges!=' ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 339
    },
    "id": "ZHGjQimkLE7x",
    "outputId": "0006459e-2220-4849-9e47-fce48b36a919"
   },
   "outputs": [
    {
     "data": {
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       "      <td>No</td>\n",
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      "text/plain": [
       "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  Female              0     Yes         No       1           No   \n",
       "1    Male              0      No         No      34          Yes   \n",
       "2    Male              0      No         No       2          Yes   \n",
       "3    Male              0      No         No      45           No   \n",
       "4  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           No   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85        29.85   \n",
       "1               No               Mailed check           56.95       1889.5   \n",
       "2              Yes               Mailed check           53.85       108.15   \n",
       "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
       "4              Yes           Electronic check           70.70       151.65   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  "
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bN1IBjD3LFGj",
    "outputId": "5d8f55b0-2878-4f6a-af39-bd7124ab20cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         29.85\n",
       "1       1889.50\n",
       "2        108.15\n",
       "3       1840.75\n",
       "4        151.65\n",
       "         ...   \n",
       "7038    1990.50\n",
       "7039    7362.90\n",
       "7040     346.45\n",
       "7041     306.60\n",
       "7042    6844.50\n",
       "Name: TotalCharges, Length: 7032, dtype: float64"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df1.TotalCharges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aUzYpFCALFN7",
    "outputId": "8ed8b278-1579-4cfc-c9b2-9102fa75a248"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges         object\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zLBHnYA_LFSG"
   },
   "outputs": [],
   "source": [
    "df1.iloc.TotalCharges = pd.to_numeric(df1.TotalCharges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_VffMm1z7LAR"
   },
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7lN-SgnSMp8D",
    "outputId": "03a3c700-448e-4d34-fdf1-23090cf97f30"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7032 entries, 0 to 7042\n",
      "Data columns (total 20 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   gender            7032 non-null   object \n",
      " 1   SeniorCitizen     7032 non-null   int64  \n",
      " 2   Partner           7032 non-null   object \n",
      " 3   Dependents        7032 non-null   object \n",
      " 4   tenure            7032 non-null   int64  \n",
      " 5   PhoneService      7032 non-null   object \n",
      " 6   MultipleLines     7032 non-null   object \n",
      " 7   InternetService   7032 non-null   object \n",
      " 8   OnlineSecurity    7032 non-null   object \n",
      " 9   OnlineBackup      7032 non-null   object \n",
      " 10  DeviceProtection  7032 non-null   object \n",
      " 11  TechSupport       7032 non-null   object \n",
      " 12  StreamingTV       7032 non-null   object \n",
      " 13  StreamingMovies   7032 non-null   object \n",
      " 14  Contract          7032 non-null   object \n",
      " 15  PaperlessBilling  7032 non-null   object \n",
      " 16  PaymentMethod     7032 non-null   object \n",
      " 17  MonthlyCharges    7032 non-null   float64\n",
      " 18  TotalCharges      7032 non-null   object \n",
      " 19  Churn             7032 non-null   object \n",
      "dtypes: float64(1), int64(2), object(17)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "z2qr99roMtDH"
   },
   "outputs": [],
   "source": [
    "tenure_churn_no = df1[df1.Churn == 'No'].tenure\n",
    "tenure_churn_yes = df1[df1.Churn == 'Yes'].tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PZ2XKNhLMtIC",
    "outputId": "c703a8e9-a75f-41b0-8989-72e027b84106"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1       34\n",
       "3       45\n",
       "6       22\n",
       "7       10\n",
       "        ..\n",
       "7037    72\n",
       "7038    24\n",
       "7039    72\n",
       "7040    11\n",
       "7042    66\n",
       "Name: tenure, Length: 5163, dtype: int64"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tenure_churn_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fntGg0iJMtMC",
    "outputId": "dbec19f3-6069-4c11-9dcc-dae00f1e30e2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2        2\n",
       "4        2\n",
       "5        8\n",
       "8       28\n",
       "13      49\n",
       "        ..\n",
       "7021    12\n",
       "7026     9\n",
       "7032     1\n",
       "7034    67\n",
       "7041     4\n",
       "Name: tenure, Length: 1869, dtype: int64"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tenure_churn_yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ddI8IsfHolDT"
   },
   "outputs": [],
   "source": [
    "def feature_w_hue(df, col= None, hue_col=None, rot=None, figsize=None):\n",
    "    \"\"\"\n",
    "    plots a seaborn countplot for column and hue w/customization\n",
    "    Args\n",
    "    df (dataframe)\n",
    "    col (int or str)\n",
    "    hue_col (int or str)\n",
    "    rot(rotate x label)\n",
    "    figsize (dict)\n",
    "    \"\"\"\n",
    "    \n",
    "    fig,ax = plt.subplots(figsize=figsize)\n",
    "    sns.countplot(data=df, x=col, hue=hue_col, ax=ax)\n",
    "    label_font = {'weight':'bold','size':15}\n",
    "    ax.set_ylabel('Counts',fontdict=label_font)\n",
    "    ax.set_xlabel(col,fontdict=label_font)\n",
    "    ax.set_title(f'How {col.title()} relates to {hue_col.title()}',fontdict=label_font)\n",
    "    ax.set_xticklabels(ax.get_xticklabels(), fontdict={'rotation':rot,'ha':'right'}); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jDDSXxHfow5y"
   },
   "outputs": [],
   "source": [
    "def feature_distribution(data, col= None, ticklabels=None, figsize=None):\n",
    "    \"\"\"\n",
    "    plots a seaborn countplot for feature w/customization\n",
    "    \n",
    "    Args\n",
    "        df (df)\n",
    "        col (int or str)\n",
    "        figsize (dict)\n",
    "    \"\"\"\n",
    "    print(data[col].value_counts(dropna=False))\n",
    "    print('\\n')\n",
    "    print(round(data[col].value_counts(normalize=True),3))\n",
    "    dashes = dashes='---'*15\n",
    "    print(dashes)\n",
    "    \n",
    "    fig,ax = plt.subplots(figsize=figsize)\n",
    "    sns.countplot(data=data, x=col, ax=ax)\n",
    "    label_font = {'weight':'bold','size':15}\n",
    "    ax.set_ylabel('Counts',fontdict=label_font)\n",
    "    ax.set_xlabel(col,fontdict=label_font)\n",
    "    ax.set_title(f'Distribution of {col.title()}',fontdict=label_font)\n",
    "    ax.set_xticklabels(ticklabels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XjB8FqnCpgmQ"
   },
   "source": [
    "The below shows that the feature 'Churn' has 2 categories of 'No' and 'Yes'.  'No' is 73% of the feature and 'Yes' is 27%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "UTEHuHreozFQ",
    "outputId": "3de07451-ee0b-4792-b1d3-471cbb2ba3b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No     5163\n",
      "Yes    1869\n",
      "Name: Churn, dtype: int64\n",
      "\n",
      "\n",
      "No     0.734\n",
      "Yes    0.266\n",
      "Name: Churn, dtype: float64\n",
      "---------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_distribution(df1, col='Churn',ticklabels=['No / 0','Yes / 1',],figsize=(8,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "id": "2Jskuc0Dqe0K",
    "outputId": "69054132-041c-48b4-a1e2-9951821f7ff6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_w_hue(df1, col='gender', hue_col='Churn',rot=40,figsize=(14,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-hBGpd_bsM9U",
    "outputId": "9f0cd2a8-6b6b-4d33-dd22-81f9135d7b11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month-to-month    3875\n",
       "Two year          1685\n",
       "One year          1472\n",
       "Name: Contract, dtype: int64"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Contract'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wa68HRcpsZE2"
   },
   "source": [
    "The below shows that the category 'Month-to-Month' are the largest group of customers with 3,875.  These are also the largest category that either didn't churn (0) or did (1).  The sceond largest category is 'One year' with 1685 customers with over 1400 didn't churn and around 200+ did churn.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "id": "6AEj5RxIrdI3",
    "outputId": "54eaf140-a017-421d-f508-3d44c39c3711"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_w_hue(df1, col='Contract', hue_col='Churn',rot=40,figsize=(14,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O9aSlmL7vupG",
    "outputId": "ebdd37fd-5de1-4180-9f8a-8686cc9badf3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 500,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1['tenure'].value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KUIpUHC8wiTM"
   },
   "outputs": [],
   "source": [
    "def count_value_status(data, feature):\n",
    "    #function provides count and percentage of each value\n",
    "    vc= data[feature]\n",
    "    count = vc.value_counts(dropna=False)\n",
    "    percent = vc.value_counts(normalize=True)\n",
    "    percent100 = vc.value_counts(normalize=True).mul(100).round(2).astype(str) + '%'\n",
    "    count_df = pd.DataFrame({'count': count, 'per': percent,'%': percent100})\n",
    "    return count_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "qfvFWi6Bwn_H",
    "outputId": "883161ca-3390-4021-d772-e2f4cb5fb96b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-e47745c6-4890-41c9-89d3-624da092d60c\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>count</th>\n",
       "      <th>per</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>613</td>\n",
       "      <td>0.087173</td>\n",
       "      <td>8.72%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>362</td>\n",
       "      <td>0.051479</td>\n",
       "      <td>5.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>238</td>\n",
       "      <td>0.033845</td>\n",
       "      <td>3.38%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200</td>\n",
       "      <td>0.028441</td>\n",
       "      <td>2.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176</td>\n",
       "      <td>0.025028</td>\n",
       "      <td>2.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>59</td>\n",
       "      <td>0.008390</td>\n",
       "      <td>0.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>57</td>\n",
       "      <td>0.008106</td>\n",
       "      <td>0.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>56</td>\n",
       "      <td>0.007964</td>\n",
       "      <td>0.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>51</td>\n",
       "      <td>0.007253</td>\n",
       "      <td>0.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>50</td>\n",
       "      <td>0.007110</td>\n",
       "      <td>0.71%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 3 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e47745c6-4890-41c9-89d3-624da092d60c')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-e47745c6-4890-41c9-89d3-624da092d60c button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-e47745c6-4890-41c9-89d3-624da092d60c');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "    count       per      %\n",
       "1     613  0.087173  8.72%\n",
       "72    362  0.051479  5.15%\n",
       "2     238  0.033845  3.38%\n",
       "3     200  0.028441  2.84%\n",
       "4     176  0.025028   2.5%\n",
       "..    ...       ...    ...\n",
       "38     59  0.008390  0.84%\n",
       "28     57  0.008106  0.81%\n",
       "39     56  0.007964   0.8%\n",
       "44     51  0.007253  0.73%\n",
       "36     50  0.007110  0.71%\n",
       "\n",
       "[72 rows x 3 columns]"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_value_status(df1, 'tenure')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3qsUbeRTxYCC"
   },
   "source": [
    "The largest categories of customers are those with a tenure of 1 month and 72 months.  of the customers who have been with bank 72 months around 355 didn't churn and around 5 did churn. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "ilv2Sp-EvDoL",
    "outputId": "fa06049a-1d46-44ab-b83e-4edf2dec5b88"
   },
   "outputs": [
    {
     "data": {
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m8Otf/7oT1ZMk9SPvQZIkCTj00ENZe+212W+//TpdFUnSbKi/n7Wd+1Gr+vUMUkSMjIjbIuL/RcT9EXFcGX5RRPwtIu4ur3FleETEGRHxcETcExFr92d9JUlDx6KLLsoee+zB+eef//awDTfckMsvvxyASy+9lE022aRT1ZMk9ZP+vsTuVWBiSmktYBywdUSsX747PKU0rrzuLsO2AVYqr/2Bs/q5vpKkIeSwww7jueeee/vzmWeeyYUXXsiaa67Jt7/9bb7+9a93sHaSpP7Qr5fYpZQS8FL5OLy8UoufTAIuKb/7fUQsHBFLpZSe7OOqSpI6oKeXQfSGl1566e33Sy65JC+//PLbn5dddlluvvnmfq+TJKlz+v0hDRExLCLuBp4BfppS+kP56oRyGd1pETGiDBsNPF75+fQyTJIkSZJ6Xb8nSCmlN1NK44AxwISIWB04ElgFWBdYFPhcT8qMiP0j4o6IuOPZZ5/t9TpLkiRJGho69pjvlNI/gVuArVNKT6bsVeBCYEIJewJYuvKzMWVYfVnTUkrjU0rjR40a1ddVlyRJkjSH6u+n2I2KiIXL+3mBDwAPRsRSZVgAOwH3lZ9cB+xdnma3PvCi9x9JkiRJ6iv9/X+QlgIujohh5OTsipTSjyLi5ogYBQRwN/DJEn8DsC3wMPAy4D+nkCRJktRn+vspdvcA720wfGKT+AQc0Nf1kiRJkiTo4D1IkiR1WkqJjTfemBtvvPHtYd///vfZeuutO1grSVIn9fcldpIkNfXY8Wv0annLHHNvy+8jgrPPPpvdd9+dzTbbjDfeeIOjjjqKm266qVfrIUkaPEyQJElD2uqrr84OO+zAiSeeyH/+8x8+/OEPc8IJJ3Dffffx+uuvM3XqVCZNmsT999/Pfvvtx2uvvcZbb73FVVddxUorrdTp6kuSepkJkiRpyDv22GNZe+21mWeeedh+++2ZOHEiF1xwAf/85z+ZMGECW2yxBWeffTaHHHIIe+21F6+99hpvvvlmp6stSeoDJkiSpCFvvvnmY88992T++efniiuu4Ic//CEnn3wyAK+88gqPPfYYG2ywASeccALTp09nl1128eyRJM2hTJAkSQLmmmsu5pprLlJKXHXVVay88sozfL/qqquy3nrrcf3117PttttyzjnnMHFiw4ewSpIGMZ9iJ0lSxVZbbcWZZ55J/k8TcNdddwHwyCOPsPzyy3PwwQczadIk7rnnnk5WU5LUR0yQJEmqOProo3n99ddZc801WW211Tj66KMBuOKKK1h99dUZN24c9913H3vvvXeHaypJ6gteYidJGjC6eyx3X5o6derb788555yZvj/iiCM44ogj+rFGkqRO8AySJEmSJBUmSJIkSZJUmCBJkiRJUmGCJEnqqNrT4ga7OWU6JGmoM0GSJHXMyJEjef755wd9cpFS4vnnn2fkyJGdrookaTb5FDtJUseMGTOG6dOn8+yzz3a6KrNt5MiRjBkzptPVkCTNJhMkSVLHDB8+nOWWW67T1ZAk6W1eYidJkiRJhQmSJEmSJBUmSJIkSZJUmCBJkiRJUmGCJEmSJEmFCZIkSZIkFSZIkiRJklSYIEmSJElSYYIkSZIkSUW/JkgRMTIibouI/xcR90fEcWX4chHxh4h4OCK+FxHzlOEjyueHy/dj+7O+kiRJkoaW/j6D9CowMaW0FjAO2Doi1gdOBE5LKa0IvAB8tMR/FHihDD+txEmSJElSn+jXBCllL5WPw8srAROBK8vwi4GdyvtJ5TPl+80jIvqpupIkSZKGmH6/BykihkXE3cAzwE+BvwL/TCm9UUKmA6PL+9HA4wDl+xeBxfq3xpIkSZKGin5PkFJKb6aUxgFjgAnAKrNbZkTsHxF3RMQdzz777GzXUZIkSdLQ1LGn2KWU/gncAmwALBwRc5evxgBPlPdPAEsDlO8XAp5vUNa0lNL4lNL4UaNG9XndJUmSJM2Z+vspdqMiYuHyfl7gA8CfyInSbiVsH+Da8v668pny/c0ppdR/NZYkSZI0lMzdfUivWgq4OCKGkZOzK1JKP4qIB4DLI+JLwF3A+SX+fODbEfEw8A9gcj/XV5IkSdIQ0q8JUkrpHuC9DYY/Qr4fqX74K8Du/VA1SZIkSercPUiSJEmSNNCYIEmSJElSYYIkSZIkSYUJkiRJkiQVJkiSJEmSVJggSZIkSVJhgiRJkiRJhQmSJEmSJBUmSJIkSZJUmCBJkiRJUmGCJEmSJEmFCZIkSZIkFSZIkiRJklSYIEmSJElSYYIkSZIkSYUJkiRJkiQVJkiSJEmSVJggSZIkSVJhgiRJkiRJhQmSJEmSJBUmSJIkSZJUmCBJkiRJUmGCJEmSJEmFCZIkSZIkFSZIkiRJklT0a4IUEUtHxC0R8UBE3B8Rh5ThUyPiiYi4u7y2rfzmyIh4OCIeioit+rO+kiRJkoaWuft5fG8Ah6WU/hgRCwB3RsRPy3enpZROrgZHxHuAycBqwLuAn0XEu1NKb/ZrrSVJkiQNCf16Biml9GRK6Y/l/b+BPwGjW/xkEnB5SunVlNLfgIeBCX1fU0mSJElDUcfuQYqIscB7gT+UQQdGxD0RcUFELFKGjQYer/xsOq0TKkmSJEmaZR1JkCJifuAq4NCU0r+As4AVgHHAk8ApPSxv/4i4IyLuePbZZ3u9vpIkSZKGhn5PkCJiODk5ujSldDVASunplNKbKaW3gHPpuozuCWDpys/HlGEzSClNSymNTymNHzVqVN9OgCRJkqQ5Vn8/xS6A84E/pZROrQxfqhK2M3BfeX8dMDkiRkTEcsBKwG39VV9JkiRJQ0t/P8VuI2AKcG9E3F2GHQV8MCLGAQl4FPgEQErp/oi4AniA/AS8A3yCnSRJkqS+0q8JUkrp10A0+OqGFr85ATihzyolSZIkSUXHnmInSZIkSQONCZIkSZIkFW0nSBGxUUT8T0R8oHzeJiIeioh/RMSlETFf31VTkiRJkvpeT84gHQ58DRgTESOAS8lPlVsYmAwc3fvVkyRJkqT+05ME6b3l763AeuTE6EHyo7iD/HhuSZIkSRq0epIgLVH+TgfWID+S+xRgnzJ8dC/WS5IkSZL6XU8e8/0yMA85ERpfhv2Z/P+JAAbM/yda5/BLZhp2zQIdqIgkSZKkQaUnCdJfgHWBu4H5yYnR3cAy5fsne7dqkiRJktS/enKJ3anl74Lld5eklP4NbF2G/643KyZJkiRJ/a3tM0gppSsi4jFgQ+DvwNXlq7uA/YDbe796kiRJktR/2k6QImJvgJTSqdXhKaVbI2L53q6YJEmSJPW3ntyDdBHwFjDzExDg4fJdT8qTJEmSpAGlJ/cgQf5/RzMOiBjZ7DtJkiRJGkxanvGJiEnApLphF9SFrVj+/rsX6yVJkiRJ/a67S+LG0fWPYCGfJdqnSeydvVIjSZIkSeqQdu4Zql06l+o+17xAfoLdwb1VKUmSJEnqhJYJUkrpOOA4gIh4Kw9KPb1vSZIkSZIGhZ48dW6zPquFJEmSJA0APflHsb+IiGERsSGwNDCiQUyjR4BLkiRJ0qDQk38UuzZwNTk5aiTR+H8kSZIkSdKg0JNL7L4FLNNXFZEkSZKkTutJgrQG+SzRxcAVwCt0PdlOkiRJkga9niRIjwKrAIemlP7VN9WRJEmSpM7pySO7jyl/P9UXFZEkSZKkTuvJGaQDgX8DX46Ig4G/Am9Uvk8ppc17s3KSJEmS1J96kiC9n3zPUQBLAe+sfBe0cT9SRCxNftLdkiV+Wkrp6xGxKPA9YCz5Ur49UkovREQAXwe2BV4G9k0p/bEHdZYkSZKktvUkQXqM2X8owxvAYSmlP0bEAsCdEfFTYF/g5ymlr0bEEcARwOeAbYCVyms94KzyV5IkSZJ6XU/+UezY2R1ZSulJ4Mny/t8R8SdgNDAJ2LSEXQzcSk6QJgGXpJQS8PuIWDgilirlSJIkSVKv6slDGnpVRIwF3gv8AViykvQ8Rb4ED3Ly9HjlZ9PLMEmSJEnqdW2fQYqIC7qLSSl9pM2y5geuojwyPN9q9HYZKSJ6dClfROwP7A+wzDLLsHhPfixJkiRJRU/uQdqX5vcg1R7S0G2CFBHDycnRpSmlq8vgp2uXzkXEUsAzZfgTwNKVn48pw2aQUpoGTAMYP3588r/XSpIkSZoVPb3ELpq82vtxPlV0PvCnlNKpla+uA/Yp7/cBrq0M3zuy9YEXvf9IkiRJUl9pO0FKKc1VfZHPPq0IXEh+BPe6bRSzETAFmBgRd5fXtsBXgQ9ExF+ALcpngBuAR4CHgXOBT7dbX0mSJEnqqZ5cYjeDlNJbwCMR8TFgD+BkYGI3v/k1zc84zfRPZsvT6w6Y1TpKkiRJUk/McoIEEBHzAFsB78D/TyRJkiRpkOvJU+ze7Cbk8W6+lyRJkqQBrSdnkFo9jCEBx89mXSRJkiSpo3qSIF3cYNir5H/eenVK6YHeqZIkSZIkdUbbCVJKab++rIgkSZIkdVqPH9IQEe8DtgRGAc8BP04p/bK3KyZJkiRJ/a0nD2mYC/gusHvdV0dExPeBD5VHf0uSJEnSoNT2P4oFPkP+f0fR4LV7+V6SJEmSBq2eJEj7kJ9WdzOwKbBi+ftzcpK0b+9WTZIkSZL6V0/uQVqp/J2cUnquvH8kIj4EPA2s0Ks1kyRJkqR+1pMzSK+Vv0vWDa99fn32qyNJkiRJndOTM0h3AZsAN0bEucDjwBjg4+RL7+7q/epJkiRJUv/pSYJ0CvA+YDQwtTI8yAnSqb1XLUmSJEnqf21fYpdS+iFwAPASMz7B7iXg4JTSdX1SQ0mSJEnqJy3PIEVEAAsApJT+lVI6KyK+DWwILE7+R7EPAP+KiEgppb6usCRJkiT1le7OIO0PvABcXxuQUnoppfSTlNJ3U0o/Aa4oMR/vu2pKkiRJUt/rLkHas/z9aouYr5AvtZvcKzWSJEmSpA7pLkFapfz9ZYuYW+tiJUmSJGlQ6i5BWrT8bfU/jt6oi5UkSZKkQam7x3w/C7wL2Aq4tknMlpVYSZIkSepV6xx+yQyf7/za3n02ru7OIP2afH/R2RHxgfovI2IL4Gzy/0H6de9XT5IkSZL6T3dnkM4E9gCWAG6KiAeBh8p3K5PvO6r9o9gz+qqSkiRJktQfWiZIKaXfRsRU4LgyaBVmfBhDlL/HppR+1/vVkyRJkqT+090ldqSUvgjsDtxDToiqr3uAXVNKX+rLSkqSJElSf+juEjsAUkpXAVdFxDuBZciX1D2WUnq6LysnSZIkSf2p2zNIVSmlp1JKt6WUbp+V5CgiLoiIZyLivsqwqRHxRETcXV7bVr47MiIejoiHImKrno5PkiRJknqiRwlSL7gI2LrB8NNSSuPK6waAiHgPMBlYrfzmWxExrN9qKkmSJGnI6dcEKaX0S+AfbYZPAi5PKb2aUvob8DAwoc8qJ0mSJGnI6+8zSM0cGBH3lEvwFinDRgOPV2Kml2GSJEmS1CcGQoJ0FrACMA54EjilpwVExP4RcUdE3PHss8/2dv0kSZIkDREdT5BSSk+nlN5MKb0FnEvXZXRPAEtXQseUYY3KmJZSGp9SGj9q1Ki+rbAkSZKkOVbHE6SIWKrycWeg9oS764DJETEiIpYDVgJu6+/6SZIkSRo62vo/SL0lIi4DNgUWj4jpwLHAphExjvy/lR4FPgGQUro/Iq4AHgDeAA5IKb3Zn/WVJEmSNLT0a4KUUvpgg8Hnt4g/ATih72okSZIkSV06fomdJEmSJA0UJkiSJEmSVJggSZIkSVJhgiRJkiRJhQmSJEmSJBUmSJIkSZJUmCBJkiRJUmGCJEmSJEmFCZIkSZIkFSZIkiRJklSYIEmSJElSYYIkSZIkSYUJkiRJkiQVc3e6AgPNY8evMdOwZY65twM1kSRJktTfPIMkSZIkSYUJkiRJkiQVJkiSJEmSVJggSZIkSVJhgiRJkiRJxZB/it06h18yw+drFuhQRSRJkiR1nGeQJEmSJKkwQZIkSZKkwgRJkiRJkoohfw+SJEmSpP5X/yyAO7+2d4dqMiPPIEmSJElS0a9nkCLiAmB74JmU0upl2KLA94CxwKPAHimlFyIigK8D2wIvA/umlP7Yn/WtN1CzXEmSJEm9o7/PIF0EbNshUyoAACAASURBVF037Ajg5ymllYCfl88A2wArldf+wFn9VEdJkiRJQ1S/JkgppV8C/6gbPAm4uLy/GNipMvySlP0eWDgiluqfmkqSJEkaigbCPUhLppSeLO+fApYs70cDj1fippdhkiRJktQnBkKC9LaUUgJST38XEftHxB0Rccezzz7bBzWTJEmSNBQMhATp6dqlc+XvM2X4E8DSlbgxZdhMUkrTUkrjU0rjR40a1aeVlSRJkjTnGggJ0nXAPuX9PsC1leF7R7Y+8GLlUjxJkiRJ6nX9/Zjvy4BNgcUjYjpwLPBV4IqI+Cjwd2CPEn4D+RHfD5Mf871ff9ZVkiRJ0tDTrwlSSumDTb7avEFsAg7o2xrNnseOX2OmYcscc28HaiJJkiSpN/RrgjSU+E9lJUmSpMHHBEmSJElSU/UH/mHOPvg/EB7SIEmSJEkDgmeQJEmSpCFmqJ0V6gkTJEmSJEm9Yk64D99L7CRJkiSp8AxSP/GR4JIkSdLA5xkkSZIkSSo8gzSH6smNd96kJ0mSJGUmSBp05oSb/yRJkjQweYmdJEmSJBWeQZIkSZL6WV9dEeOVNrPPM0iSJEmSVHgGST3iUYmBwXbQnMwHx0iSOskESdKAYeInaSiwr5MGNhOkQWYwdaqdrqtHoedsnV6+JGkoGEx9rdt99RYTpAFgMHU+kiRJ0pzMBEkDgkmiJEnqL311tskn080ZfIqdJEmSJBWeQRqAHjt+jRk+L3PMvR2qiTT0eJROklTldmHoMUGS1KfcsEiSpMHEBEnqocF23bIkDST2dZIGOhMkSZKk2WTiJ805TJAkaRb4/zakOZ9JjzQ0mSBJ0gDiDpmkwcr+S3OKAZMgRcSjwL+BN4E3UkrjI2JR4HvAWOBRYI+U0gudqqOkwWlO3WjPqdOlwXeGstPL4mCbX+3qyXT1VWxf6fQyI7UyYBKkYrOU0nOVz0cAP08pfTUijiifP9eZqkmDkxshSYPRQNiJlzQ0DbQEqd4kYNPy/mLgVkyQpD7hzoj6ksuXJGmwGEgJUgJ+EhEJOCelNA1YMqX0ZPn+KWDJjtVuDuA/oG3OR3dL6mv2MwOD82vwsc3U3wZSgrRxSumJiFgC+GlEPFj9MqWUSvI0k4jYH9gfYJlllmHxvq+rJM2x2t0ZGWxnhdzJch5IUjsGTIKUUnqi/H0mIq4BJgBPR8RSKaUnI2Ip4Jkmv50GTAMYP358aphFSdIQ5o6xJEntmavTFQCIiPkiYoHae2BL4D7gOmCfErYPcG1naihJkiRpKBgoZ5CWBK6JCMh1+m5K6aaIuB24IiI+Cvwd2KODdVQD9fc1QfN7m3oSq4FtsF1a1WnOr8HHM27OA0lD14BIkFJKjwBrNRj+PLB5/9dIkiRJ0lA0IBIkDTw+8U6S2uOZFkmaswyIe5AkSZIkaSDwDNIg55keSUOZZ28kSb3NBEmDXk+SRBNKSZIktWKCJPUCEy9JkqQ5g/cgSZIkSVJhgiRJkiRJhQmSJEmSJBXeg6QByXt6JEmS1AmeQZIkSZKkwjNIUj+qPzMGnh2TJEkaSEyQpAFqTk2m5tTpkiRJcwYvsZMkSZKkwjNIUhOD6UERg6mufaUnZ6acX5IkqRnPIEmSJElSYYIkSZIkSYWX2ElDjJeXqadcZiRJQ4kJkqSmerJj7E60JEmaE5ggSdIcwgdVSJI0+0yQJKmFTicSg+3/RvXV/OqLcvtq3nZ6HsxOotwqdiAYTMtBX+l0nyQNBSZIkgYsd1w0UHQ66ZFmhcuXwOVgVpggSVIHuMFyHihzORC4HCgbKAdGTZAkSXOMgbJx1ZzJ5ct5MKczUc1MkCRJ0hyn0zt6g+0poIPp/i4veR0Yde2L+yIHCv9RrCRJkiQVg+IMUkRsDXwdGAacl1L6aoerJEnSkOJj5DUYuSzOufryLO2AT5AiYhjwTeADwHTg9oi4LqX0QGdrJkmSpKGmrx6lPxgvRZtTDYZL7CYAD6eUHkkpvQZcDkzqcJ0kSZIkzYEGQ4I0Gni88nl6GSZJkiRJvSpSSp2uQ0sRsRuwdUrpY+XzFGC9lNKBlZj9gf3Lx5WBhxoUtTjwXBujbDfO2IEx/sEW2+nxD4TYTo9/sMV2evyDLbbT4x8IsZ0e/2CL7fT4B1tsp8c/EGI7Pf7BFtvp8TeLXTalNKphdEppQL+ADYAfVz4fCRw5C+Xc0Ztxxg6M8Q+22E6PfyDEdnr8gy220+MfbLGdHv9AiO30+AdbbKfHP9hiOz3+gRDb6fEPtthOj7+nsSmlQXGJ3e3AShGxXETMA0wGrutwnSRJkiTNgQb8U+xSSm9ExIHAj8mP+b4gpXR/h6slSZIkaQ404BMkgJTSDcANs1nMtF6OM3ZgjH+wxXZ6/AMhttPjH2yxnR7/YIvt9PgHQmynxz/YYjs9/sEW2+nxD4TYTo9/sMV2evw9jR34D2mQJEmSpP4yGO5BkiRJkqR+YYI0wEXEkj2IfW9ERF/WR93rSRsMhPbqizoMtnkA7dcjInq93xwI86uv6tAX86unOtlmtlfPuY4NjH6x08tNp+dXp8c/K9yO9V65He8Ie1NEDGsjZq+I2CkiRrQR+6mImBwRi7cRe0pEfKzNel4WEfu2EXclcFBELNRG7MXAxcA8bcQeHxH/ExGbtBE7JSK2i4jh3cXOqdpZrkrcvACpjetWI2KBdmPrftdrK39EzFf3ebb7g9o86OH4h5XPTactIhZtt34RsVhETGhnXkXEEhFxYETM26otImLJiLggIhZPKb3VTZkLlTrMWz43rXdEvKPuc6vYkdC1zPTWslDrC9tcbuet+9yqzRYu5b7VzXQtGhHLtlnXURGxa+QnmnYXu0REnBARC7bRZgtExIhaP9fNdLXVDv3VXt3Utdfbq8S21WaDqb3Kd73eJ5ZyOtov9kWfWGIHQr/odmxwbcc6vo61a9AnSGWBOxwgpfRms5kdEYtExM3AtsARwAkRMX+LMm8ENgL2Br4cEU0faBERywF7AHtFxKQybKaFupT7A+C/KaWLWpQ3d0RcAvwnpfSFlNKLLWIXK2W+BkSpb7PY+SPiV8CywPLAlPoFuy72t8BEYF9gakTs3iR29YhYNyKWbjbuSuwC3cVUYleJiHFtbojXiIiNI2K1NmLXj4jJbdZhfeDQiFiwm7gNgPMiYs02yhwP3BkRn2gnNiL2jIgtofVObESsFvks4pg2yt0AuDkivk5u2yg7RlEXt1HkAwXddj5lXl0XEWcCR3UTux7w44g4Hfhyq2mLfIDgLuDCNuowAbgVWL6NjfsE4NfASKDpxqLE3UD+n2wbdlPmesAvgVOAy2vztUns+sCNEfFF4OhmbVBiNyTP2xMjYp8Sm5rFRsROrepZV4fLIuKkiPhgG9N2bUR8lfKPuVu02TuB+yLi7BLXbLomAL8BxrVR1/WAPwBL0c0Dhkq5NwM7AGPLsGaJzHol9gzgpG6ma33gioj4fER8uBbbYL3pt/ZqUddeb68S21abDab2KrFt9YkldtD0i33RJ1ZiO90vuh0bXNuxjq9jPdKTf5o00F7AisBDwDPA6ZXhURc3Avg2cFL5PAr4LbB9gzLnBq4BvlI+zw98HxjdpA4BLAicCewJ/BBYu0HcXMD1wK2VYauQNx7RIP5MYLHyfhtgXWDVupj5yI8//2L5vB3w3drvGpS5A3BReT8MuApYGVioQez2wIXl/QLA6cBPgO3q4iYDfwG+A1wNfKJRG5RhHwf+HzCujbadQu5Mvlde27aI3auU+w3gR8BmLWJHlvZ9GRjfrK5l+IdKubs0W7bKsPcDfwf2aGO6JgN3Az8A/qe2bDSJ/SBwL3ACcAewQxvz4OtlWf94k3UhyAnyvcDuZbm6BLipfhqBTYD/AI8DOwNztxj/DsCfgd2ASeSnxezeIvZ+8kGF95VltuH6VeIXBm4BHqXFP4kGtgKeqLZX/bRX5sEI8g7Wh7pprz2BPwK7AkfT1Yc0Wg5WK9O1O7BQma9LN1p2gFWBPwG7kPuxC4EfNKnru4FHyvyaApwLnN+k3A+QD5a8AmzUzfK9G7n/3AP4X/L6M6FF7D1l+f0EcBGwYIv59i7g58A/gSMaLefAjuS+Y9du2mAuch/0XWDPVm1b3u8N3FbqfAEwtUXZawMPlnmwAXAesECTcscBD5dytwPOBs6oW7cGSnvt2tvt1W6bDab2qtR3bJlfLfvE8v59tN8v7kj7/eKO9HK/CGxNL/eJJX4y7feLq9NGv1jq8B7a6BfL+6WB+7prM/KB7pfbbK/tyvLdTnttBzzQg/ZapLv2qvQJ/9ddm5X3w8n7it1tx3Yn70+10161bVM727FVyOtjO+31rspy0N06tgHw3zbbbBu61vOWbdbT12wX0MkXsAz5bNAocrb7mRax46kkDsAXgP2axI6qvL+8rIQ3AQcACzf5zRXkHbRPl98cAaxbF7NuWZg2IR/5uon8j3A/RyVJIXd8N5c6H0g+OnAxOQHZsa7MZSvvNwKurda/LnZ94IUy/m+Td6ivLyvYJnWx7wd+DyxXPn+41OGU2spCTiavBbYun9cDngYmlc9zVcobTz4i8hPgHGCZFm21MzkhWJacfH4KOJa8IavfydqFfDRz1fL5FGCdyveNNvKHl3rcB6xYhtUnEluWlXOX8nlkmd4RDabtYLqSnXcAY4B31pdblo3fASsA7wX+WlueGox/EeCnwIbl82dL+zUa/0Jlet5bPm8KvAhs2WgekHdezgPmrwz7HXB2XdzmZVnZgbzztEl9PSuxR1ASOPJlnl8DDmwS+2m6ktPR5J2jXYGJLZaJg8id5p9pkigCnyEv16NLWx0N7Afs1iT+/NLO7yAn+KcCX618H8DJlfm6CnnH971NylsLOK3SfveWes80H0rsmZXPWwH/Ak5uELsM8M3yfji5v/sJ5cBIXewHy3yaQt4YjW20fJVhX6+02fzk/mWvJtN2PKU/I599/jn57PJ7msQPK+2xCvAPGhy0IO+MnVvejwAOAXYCNm5S5rnASmX5PQc4krLe1cV9h3KQirzO3EZZzxvEbl5rc/IG/A7ygZGZlhlgC+CUyvQdAPyNklBU4kYD57TZXnuS16u92miv04Cd22yvqcAGPWivEcChrdqrxJ1O14Gzlm1G3llasVV7lfl4MSXRa6O9NqOsI22010RKQtSqvcr385H7xHdUhs3UJ1bqsAn5IGJ3/eJnab9f/CQ97xcPpEW/SN42fafMq3b7xA/QpE+sxJ1I+/3i6pQD2HTfL65OG/0ieZ1apCxXTduMfAB6a/K+Ucv2Im+3j6YcBG7VXiX2YLr6mabtBSxJ2Qa30V6jyfsnF3fXZsAS5e+5rdqMvP04HViru/YClgDWoaufa9pepQ3WAb7eRnutU+bDtFbtVYatR97naKfNNiRvm7pts1l5zXYBnX4B85W/65OPzE9qEjd33edvAYdXPjea+YsCZ5X3E8hH7CbXxdQW/FPJic07yEcVplMSFWbcSf4g+Yjhd8vn95Vy6xf+/cnJ0wXl84LknZ6zgXkb1bfE3UjlKFmD7w8kJ0S/LZ9XJO/cHkVOQGpHXRYAjgN+QT4C+Udgn7ICLlNiRpYVb+NK+dsBzwIrV6cdWA5Yvbz/DjmRqSas9Ufodqh83hW4rsn0rAO8q7xfAXiKfBTyJPLp6WodasnFQeQd1KOAn5W6rVlX7obkHbjdyQnHNeSNx3eAFerKnVLm4UplPl1ETq5qOyjDyt/NqSTY5CMoRzeZrmXIyee4Mo1PkhPv31J2cuha9pYldyLzlM+LkZPvJ6jsaJB3wiaRj5b+HNi88t07yAnjHuRkeNtSTm39+p9Sn9Vr01OZ9i3IHfuoyjz5ODN3fFPIHeiC5fPi5FP+l5I3Nr8CPlKp606UzrTM9/eRjzD+nXwUbktyEvNh8gZiudKm55CTzzPIG5fbgH0q5e5C3sH8AnnD8zVyJ/te8kGDo0pdN6ckkeQdrOHkHYO969p/L/JyvyZ5J/ek0v5nkTv4vwOHVWK3JR8Jf56yI0reefss+ShfLbH9FHn5W598VHPDyrx8d2nDiZXfT6IkEGXYieQDR/M16AO2KW25OF3L51HA5xrEbk/XQZF3kS+bupy8Dl9BOWhT6jsZWLJ8/iH5DPX65I3mw+QDJQeRd1o2IPdnJ5GXvbPJG9Dr6NrgfYrcZ65A3uHelXwpy0llnv+droMTB5ZyR1fa7F3kHYj3160zny7za0vykcpjyOvYuaWcOygH0Cqx25H7l9pBi8OAz5P73PeW+bEvua/+CzP2i/XtdSoluWmjvU4ry8Fcpb3mqbTXZxvE7ln5vHSL9joF+Fgl9vom7TW8lLsb+WDM2eW3M7UZ+YBC7ez1haXezdrrdCrbPfLOTbP2OoW87owp7XVci/Y6hZw0LVvaa+NG7VWGXUY+g7V4aZ8tG/WJ5fP3yMviCLr6xc/QuF/8HnlZXYjcL9bWsUb94vdq7Vs+L0HzfvEySl9WPn+Xxv3i90p7jSInpufRvE+8rDLvvgB8jAZ9Yvn+Cma8omIe8k58o37x8jK/li/z8WTgMRr3i7XYd5P7xc1b9ItXlrrV1qmGbUa+QuYYcpLUXXtdTd6GL9dGe11NXvcWL5+XbNFeVwJfpOtA6GVN2itKfY8gr4PdtdmV5CtLFmnVZqWunwUWqbRXs+3YlWV+jW+jva4sdVq/jfa6mLxvPpa8v7VVi3XsEvK6XN33aNZm3yYfHB9NXn/nbtZms/rqeILTmy9yh3QvXRvINRvE1GbiWXQd2TmZusu+aHzm4Qs036E9hNxp30LeWHyLSmZdFzuRGY/eHwt8vi7mHeRE5v8qC/DmlCOTDcqsrdDrlQVn2RbzaSPge5XP+9IgqSInSfuTj2hsUlkoL6nEfAW4vu53nyPv0C7GjElobWdzUfLG+HC6dg4WLStWbVoXrPxuTeDKyuf62NpljmeUaVmZ3DncTO7sDq+r3550XUJ5C/m63QNq5VbidijTO528w7w2uQP5eV3cHuQN0ufp6sAOIHdu72TmnZjh5e9+dB0NjTL+6vw6jbzz8TTw6TLss+QdlyXrYr9PPkM3gbzTciB5ef0SuRO9ubTJbeTLcw4s5S5VKeOgUufvkM8efo2yEajU5yLyBnGNEvMd8g7YScx4FvRQ4Bvl/arkxK5W7snkHYd5qVwiRN5AXlip6+/LOEeU79Yld4T/R97xGVM3XcfQldjuWyn3Q+SNTS32D2XeTCb3F1fQtQFZD/h3ac/fM3Mi/wXg2vK+Ol9vJyeRm5TXBZXfrE/eMNxaiT24zKM/lnH9mrzOf458IOJG8rJ3UxnnJ8nJ11KlzHnJ/cYnK7E3kJPDkZVxXwlcVmmHW0vsjeTlZBhd69FJlP6Nrp2PWrnTSuz8dO3kz0teh0+oq0PtoM7h5ER/YfLO7It10/UV8s7CtXStN/OV3x1fib2xjOND5LMb0yrTtx75TOuPK+M/r24enAV8v9J31Mr9MXmd3YR81vobld/sQrkEpBJ7NLl/mU7eGfo1eeP8jRL/ODnBWZvcdzxA18GbWntNIe+EPU7ue+qvCKi211LAxiX2Vrq2V9X2Oqa8H0VeP2rl7lTrc+naeam11xfq6rBbpb2WrbTXP8vwWuwvyrxanryN27euzWoHBm4lb2PeX9rr3Lr2uoG8LNbGv3PdPHi7verG/0vyzt/yZX5/s669ziUf8KvFvr8Mn05OJKrttSX5MufqpY+7kfvEd1aGfZS8LM4QW1ff0+nqF9ckr+vNYj9DV7/4HvKZxfPrYhr1ixc1qgNd/eIocr/4NPnARHW6lif3ifs06BPr58Gm5EsNr6DrEv9Vyf3k9S2m62i6+sVFG5S7Ull2qsPWJ/cx9fWtXb53OXkHuNYv7k5eJ6v7HzuVaV6yMuzj5feX0PzMea29li91vaTJdP1Ppb2WJicxl9TFzEs581dpk2nkfuPiBu21DjkJrm3HapexVadrmdJme9e12bdK7MWV4RuXNrucrqRtdfK6d1mT6Tqm0l5zNxj/cqW9zqtrrx+T16Vq7G5lfl9G3s5Xt2M/IK+X95OTrM1pvI59sRJ7L/DRuvpW9z3WIq9j55KTq4/UxVb3Pd5FOSs/K69Z+tFAfpE3YPeTL2X7eIu4z5N3Hm+kjWyTvEP6SyqdTN33W5SGrW2w1qhvuBbl/qq6IlS+W4y8IbqUvGG4BTi2m/JWIO+0Nb3PpyzAZ5E7tcXJG7TDW8TXNsgbk4/e/YMZT4PfTjnTVj6vSj5q8FBZGaqnYGvJwVplerYmd2jPNYitHVHbCPh1ef9hcqfyNJX7zsp3i1TejyUnDX+ujy3lHUXeGPyttNsZlfpWr1PfhXI0s3zejHydfn1dv1N+v21l2A2l/Prx15LZVcq0rEvX/XRP183b+chHYKtn2K4jJzJPM+NO3Znknc7aZUs7kncsvw2cWFnefkfekB9FPtK0JDkJuR24vcQ1vE+P3EHfQL4G/wfNYsnr4b5l2p6lJOSV2Bl2DMt3nyHvVJ5YF7s5eUP4JPnSgK3JSdYzwNdK7BJlujYD5q0r9zByn3BiXey65A3WA+SdqRHkDcBD5LOjjaZrGF2XUtTP19+Sj5IvTeWgQRnH3+i67rvWBuuRE/vlK7GnAHcCXy6fFyLvNC9K3qj9iq714njyteK12AVocL8kOYH4EfmswGXNYsk78DuQN+DPUjbCldiZLoslX8r1QF0driz1/kiZl4+Qz1g9Cjxd4hYscQtQd0lwma5qmQuSd0yCnNxPpxwAIq+fj7WaB+Q+7hbyen9Ng3n7ztIW11V+U7scq74Ow8g7VStXYs8hHyQ5k7xzdz25D9yfvFNea6/jyjxuec9qaa8fkpfZnZvFlrK2J59lvpW8nlRj123QXseUV7UOPyInxFuTl6dHyGd6zyUf+a3F1qZthSbLwRfr4pYk79Q9UWmvncg7Y/XjX7tBe63PjPf4Ti6xa5EPjvyw8ptPkXdK62OXIq/Xq9S11+3ALdVtVvl7Enn9rp0FPbQsB9XY2r3D81SGnVXG91/gvgaxte3evuQDY+8lX+5+d13smAbz9uDSJvV1WISccD9Wvt+WvF14ka6zjKuQl/Ol6sr8TIMyVyWfpfwUeYf3/WX4+8h97y8aTFftgPOwMk93Km3faH6tAVxdGb5HWTbq6zA3uQ99V2X4yWW+Ve/N3o687l5LTt5rbXYIebmtv4d7jUp5Z5f2epy8D1gfW7vapdZe48gHzH5QF7s+8O66eXsQuR+vv498TeCr5H3TWnudSE4Cv1mJ3ZZ8P+umdeUeWuZD/TxYlpyk/6bSZpuQD6S+q266Viuf5yafqandDnFm3fjXJSc+36mMf3dycvSNuvEvR97/re5/nU7erzq+fN6Brm3Plyn7HeXz4eRtw/GVMr9L3v+t3kpwVmmr/9J1wKsWuyhd/ey+5VVrsw/Ur1PtvmbpRwP5Rd5B+i/wv93EXU4+c9DwPqRK3LylzIeAQ1vEDWPGFXB4N+UOIyccfwEO6Sb2mLJQzXS9fZP4M4EpLb4P8lmsH5GP4HyujTI3Kiv19XTd8/XZ8t0CZTpqRyYPLAvmMTS4P4yuTnU8eSf3KfLlaw3vJSNfinM+eWfo3rKitLzvjLyDc0tdHWqXdixE7vCnk49szUU+EvHlSuz/lti5mPG07hfIHdzYEnd4Gf4OcuJwXJlX+5F3Nk7oZh4cRe5MlmfG++lqp7KHkxPeWn0+Tj5a9MUG7VB/H9PnyB31BGY8C3IsXafXzyQf+bmc3ElNqpvW2qUXwyq/fbG0cdN7+sgb2efJO/sHdBM7N3ln+u4yfdXYo8lHokaQNyLrVr6b3CB2v7p58NFS7scazIN9yvvPko9QXVrado8m86DWZqeSN4LrNZiufcv7a8kbrBPJlxt8ui72mEq5tQMQB5I3aptV4i4n7yjfQL4U6C7y0d1zSux2dbG1+yUPpGsjfxw5OZrcJPYg8hHLQ8v0386Ml2nVl1vbmdyPvKHbpkF9bySf3bqKciafcs16XVytzFUqZd7ToMw/kfuePckJxBnknZy7mfEyrfq6rka+rORKclJbf39pra4fJC+rPyOvN3dTzsJUYh+ozK/aPPg0ue9bha77UA8gb7QPKt9dW+r6MF0PYmh0z+r6de21TZPYo8hnHz5X5svtdN0H2qjc8eQ+f9/SXhvUxR5I7gPOK9NYPRq+ToPYWrnr1LXZBpW4g8jr00fJBw7OIffbd1N2WJrUdQJdSe6ulXrUz9uDyf3VLyvttVld7EFluo6k6/65T9F1E371fuCfkrdZHynvLyx1epC8HDe7d7i2w1nrFw9qEnsEeZuxH1394oEtyq2tDx8hr/P718X+mHwm/BPkdax2f8m65ISuGlcrs3bZe61P/HhdmT8pZe5fpv8rpQ3vKvO7WV1r/UytX1y/QextZXwPkfc5TirlHtCgDreXNqtdSl7rFzdhxnuzf0e+nP4c8uVf3yCfjf8LOcFrdA937Z7iqaW9dqHx/d7XkM8y7llpr52bxF5NV6Lx0TJd29bF/qbM00vI60r10veN6mJ/Sz5bcjVdZ41rbbZNk3lwfhnHF0qb3VPiW82D00p7LdakrueR95F/WGmvnZrU9Qd0nbU+qLTXNnXT+CNg0fL5G+T1+HLyMjG5Lvbte+npOrhQW8emNIutrOPPlTZ7+/LLWXn1SZLSqRd5Z/IaKhu2FrHbVBuwRVyQV/imN0o2+E3Dp5I1iFuDyn0gHZhfc1M5Nd1N7DvKwlh/z1ft8ozlyaeuLyZ3sis1iJ1UV+Yh5CM4K7SKJR8JeIvcsbYsl3xE6RS6HoZQH1vrHDZmxiMeI7spdwHy0Z/f07VBqMXtWj6PLp3DaeQd2ne3MQ8+Wsqdp0Vd1yAnp5eTNx4rNojdqa6uXyRvKJaj8f13R1Y+r07u0FdsEFe9jG+lMm2rNimzGvslcie99DOMcwAACm1JREFUbBuxnyTvlKzUIPYsZr5EsXakqLtyp5B3qhuVWz8PlirzctlWZZZhm5flrLsyP0be+Vuxjbq+n7yhrx7prt4DuQF5I7V7eb8vM25kG90v+aGy7FzGjGc86mO/SU4QziMfrFimjXIPIu+ENKvDBmVZqd1nM28bZX6Msnw3id2QfNnnHuT+ZkdmfEhNo3Jr45/CjGeV6ss9kZxA/S85QVmpjfpuSd7e1HZmq/ehPlReC5PXl33LctDynlXymZHLyJcJt4pdvMyLp8jJbXflfpi8g9OsDveSz0iPqbZXG+XuWtpspQZxfyb3QYuUebgjuT9oVeYS1fZqEvsgeQdsMfIycxzlQFc3dV23tFctUai/H3himae7kLcdO9P10Ixm9w7vUdrsW3QtB81idydfufIQXffxtordh5wArtiivqdSDmZU2qxVXXcjHwRoVuZm5LMUu5Z5+H66lomm5ZbP29L1cKJGdf0q+V6+vciXrjWrQ7W+E8g74LV5W39v9qJlmr5B3k/YmZyItrqHe9W69moUuzd52/PVuvZqVu5Z5ISh2l71sYuQE66zG/SJrcrdr67NGs2DPcj9+Jham7WaBw3aq1GZO5P36T5JPuPYbPzVuk6stlfdNu9G4FuVz++m3BPdJLZ6Jc+y5IO5zcqtxh5DXu+bPgis3VdHdsz78kU3Z2589eq8rt3zVes8xlNu2mwRW7s/bEPyJR0j24j9CPkIxYg2YieTNxqtyq2ddl6Z5g+7qC93V/IRtZHdzIPa0fJhbZS5IvlMYqv5tWz5vDW545+njXK3KB3WiLq46v1325f3X2PmS3waxZ1E3ljVn6VqFDuVfLSvnfF/kZx4D28RWzuKdgqNLxtqVO6XyjLWqtztK+W2Mw9Oq49rEftlKkfiu4n9Cjk5q15O0OgeyGNp8HjYJrFfoMHjmpvEHgMcVN6PaKPc2pnYedqIPbrNutbKHN5N7LHU3a/ZTbnHthl7HA2uOmhRbu3McfXscv19qNOAU+t/3yT2W3Q9Oar+qZP1sWeTL0McwczrWH3sWWX5moe6vqMu9oe0vm+2Wblz17VZfdw51F0K3c08+EabsdMol7e2WdeTyAc7h9XFTqTyUIwyX5s9Mrs+9li61rH6frE+9jjg4Pr1pknsVMrVBszcfzWqwxfaqOtUutax+oM1jeo60zrWYvzN7suuj/0STS7l///t3W+MHVUZx/HvAxQLBWo1iIkRigaDUUOMUYjGUmitYCxqJL4zxsSYmPhCJEZBjH/e8I7UIGo0GlQSXqgJQqREQ1FANBabmNCklERKQaymFSRQ2+3C44tzNs5O5947d7fbu3++n+Sm98789jmnszuz9+yZmTugvzNnRjSPX13XZm+hcYr/kNwm6ht0Zp+y3pX9AGWgeBqzj3Nd2c2U3w3R3LZD+vCDjv//oLrbetbdwvE3kxjU/g97tn8V3demD+rrre3vV33dvj7+Te2aQ7IXNNa197F2duaOy6vpeJ80l8eS/6DYtsw8Nuk+rBSZ+UvKm8vtEfE45Q3hVGZOD8n+pmYvzsz7MvPIiOxe4MXM/GhmHu2RPSUzvzii7m8jYg+wIese1aPu6sy8oV23tQ32UD+ALTNfHlFzD2X28OUR22tm256fmfsyc2pE3ccpA6WftbdXo51ngMuifBjy2Zm5q0dubWb+ub29BmTfkJkP9Wz/9Zn5h/Z+28peWrNrMnNnx/+/q+7rMvOREXUva9Ttsw3OaOeGZF+bmY+Okf1TNj6IL1sfyhcR51F+uT3bUbMru4UyI0CPupspnxNC83s2pO7zdf1Uj+z+nn2dqXlsRHYTZdv13Qb7emavoJyW0bfuwbq+uZ/vpsw+PJCZ11DeRDzWrjkg+z3K6XLHtdmRvQ14OjOPdhwT29nvAn+rx+X2saOZ3Vr78Nee/Z2pO93ax9q571Bmkvtug+P2rwHZWykzVH37ujeLWcflzNyRmS/BrJ+Dv3cV7chuppyqTcdxsZ29knLaJO3vQ0d2E+X6pOPezwzow6x9bEjNf9d10yOyV3bVHNL+Uz2zl1OPST3723X8OkwZPD0K3BER76ScjnegVa8rdxPllP5Z368B2RuBw/Xne2pE9qvAC/Xna3pE9iY6fr6G1P1Pz7o30PrdMKT9ruNnV/bLlNML+/b1UF3/Sis/s88dpMwKn9OuOSS7rrGuvY+1s2vr8iMdx7q5yRMwyvKxch/0vOarlR14Q4iTmD1h/Z1jzYXK9tkGfa+/65VbztlF0H6vayCXWnbS7S9EljGuQ12u2Um3P262ke97PfCSyU66/YXM1nyva7P75pZzdtLtN/JDr48/GdlxHjPTY9LYImIV5YLYn2TmXSsxO+n2x83W/NUAmbn9ROSWc3YRtB+UUwjOzMwdyyU76fYXMlvzp+TxM0ErKjvp9sfJRsQ7KLPO9y+n7KTbX8istNAcIGleImJV9jytcblmJ93+uFlJkiQN5gBJkiRJkqpld5MGSZIkSZorB0iSJEmSVDlAkiRJkqTKAZIkSZIkVadNugOSpJUpIjYCG+vLbZn5/OR6I0lS4QBJkjQpG4Gv1+e3Aw6QJEkT5yl2kiSNEBGnR4S/MyVpBfBgL0k66SLid/x/9gjgyYjIiNhX118cEXdGxIGImIqIpyLilog4u1FjY/2ajIjPRcS3I+Jgffw4ItY022vWr8vWN77+G43lM8tuj4jrI2I/cAQ4p66/NCLujohDtW97I+JrEbFqYbaWJOlk8hQ7SdKiEhGXAA8DZzUWnw9cB2yIiPdl5tHWl90MrG28/jTwL+Ar8+jKNcCnWn37IHAP0BwMXQR8C3gPsHUe7UmSFgFnkCRJJ11mbgS+2Vh0YWZGZq4HbqEMjp4A3gasBj5ec++iDH7apikDlAuBA3XZtfPs5jrgRsrA663AS8BtlMHRI8B64AzKwA3gwxFx1TzblCRNmDNIkqRFIyLOBC6vLy8CdnfErgC+31r2o8zcWWs8CHwCeOM8u7M7M2+uz1+IiLcAb66v3wvsG9C3++bZriRpgpxBkiQtJuuAU0dkXtOx7InG8yP139NH1BnVzmOt1+eOyEN33yRJS4gzSJKkScmOZc8Br1D+gHdXZn6sHYiI6Pi66RF1Z65ZWt1Ytn5E/460Xh9sPN+Wmde11g/qmyRpCXEGSZI0Kc81nr8dIDMPAw/WZVsj4pMRsSYiXh0RH4qIXwEb5tDWM/Xf8yLikoh4FfClMWvsBZ6szz8TEVdHxOqIODciro2I3wMXzKFvkqRFxAGSJGlSdjae31NvrX0HcD3lhginAj8FXqQMpn5NubPcXGZpftF4/pdab6yBVmYm8HnKbNVZwL3Afyl3y/v5uPUkSYuTAyRJ0kRk5h8pd4l7mnJa3czyXcC7gTuBfwLHgH8ADwBfAHbNoa3tlBmj/ZTT7XYAH5lDnXuB9wN3A4eAqdr/7cBngWfHrSlJWlyi/EFMkiRJkuQMkiRJkiRVDpAkSZIkqXKAJEmSJEmVAyRJkiRJqhwgSZIkSVLlAEmSJEmSKgdIkiRJklQ5QJIkSZKkygGSJEmSJFX/A3Ir8bP4hPk6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_w_hue(df1, col='tenure', hue_col='Churn',rot=40,figsize=(14,5))"
   ]
  },
  {
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      "text/plain": [
       "       count       per      %\n",
       "20.05     61  0.008675  0.87%\n",
       "19.90     44  0.006257  0.63%\n",
       "19.95     44  0.006257  0.63%\n",
       "19.85     44  0.006257  0.63%\n",
       "19.65     43  0.006115  0.61%\n",
       "20.00     42  0.005973   0.6%\n",
       "19.70     42  0.005973   0.6%\n",
       "20.15     40  0.005688  0.57%\n",
       "19.55     40  0.005688  0.57%\n",
       "19.75     39  0.005546  0.55%\n",
       "20.35     38  0.005404  0.54%\n",
       "20.25     38  0.005404  0.54%\n",
       "19.80     38  0.005404  0.54%\n",
       "20.10     37  0.005262  0.53%\n",
       "19.60     37  0.005262  0.53%\n",
       "20.20     35  0.004977   0.5%\n",
       "19.50     32  0.004551  0.46%\n",
       "19.40     31  0.004408  0.44%\n",
       "20.45     31  0.004408  0.44%\n",
       "20.40     30  0.004266  0.43%"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_value_status(df1, 'MonthlyCharges').head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cGfVdScVMtSL"
   },
   "outputs": [],
   "source": [
    "monthly_churn_no = df1[df1.Churn == 'No'].MonthlyCharges\n",
    "monthly_churn_yes = df1[df1.Churn == 'Yes'].MonthlyCharges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FakZwGwO2ZO1"
   },
   "source": [
    "Reagrding feature 'MonthlyCharges', the largest categories are those paying $20.05, $19.90, $19.95 and 19.85 per month.  The below plot shows that the vast majority in these groups didn't churn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "im9vLTqTMtV9",
    "outputId": "75c8a97f-6db4-4491-c8cb-a2f9ab5cc614"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return asarray(a).size\n",
      "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([monthly_churn_yes,monthly_churn_no], color=['green','red'], label=['Churn=Yes','Churn=No'])\n",
    "plt.xlabel('Monthly')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qbnJy5EFRGvE",
    "outputId": "c47cd111-a5c5-4e08-ac4e-d418ef80b691"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender : ['Female' 'Male']\n",
      "SeniorCitizen : [0 1]\n",
      "Partner : ['Yes' 'No']\n",
      "Dependents : ['No' 'Yes']\n",
      "tenure : [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "PhoneService : ['No' 'Yes']\n",
      "MultipleLines : ['No phone service' 'No' 'Yes']\n",
      "InternetService : ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity : ['No' 'Yes' 'No internet service']\n",
      "OnlineBackup : ['Yes' 'No' 'No internet service']\n",
      "DeviceProtection : ['No' 'Yes' 'No internet service']\n",
      "TechSupport : ['No' 'Yes' 'No internet service']\n",
      "StreamingTV : ['No' 'Yes' 'No internet service']\n",
      "StreamingMovies : ['No' 'Yes' 'No internet service']\n",
      "Contract : ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling : ['Yes' 'No']\n",
      "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "MonthlyCharges : [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "TotalCharges : ['29.85' '1889.5' '108.15' ... '346.45' '306.6' '6844.5']\n",
      "Churn : ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "yes/no columns\n",
    "find unique values in each column\n",
    "find yes/no columns to do label encoding\n",
    "\n",
    "\"\"\"\n",
    "for col in df1:\n",
    "  print(f'{col} : {df1[col].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "t5y-J3lkRGzJ"
   },
   "outputs": [],
   "source": [
    "#print columns where data type is an object \n",
    "\n",
    "def unique_obj_col_values(df):\n",
    "  for col in df:\n",
    "    if df[col].dtypes== 'object':\n",
    "      print(f'{col} : {df[col].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZffKwXfrRG3L",
    "outputId": "b761c53c-c0b7-45c5-dabc-1827f235c591"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender : ['Female' 'Male']\n",
      "Partner : ['Yes' 'No']\n",
      "Dependents : ['No' 'Yes']\n",
      "PhoneService : ['No' 'Yes']\n",
      "MultipleLines : ['No phone service' 'No' 'Yes']\n",
      "InternetService : ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity : ['No' 'Yes' 'No internet service']\n",
      "OnlineBackup : ['Yes' 'No' 'No internet service']\n",
      "DeviceProtection : ['No' 'Yes' 'No internet service']\n",
      "TechSupport : ['No' 'Yes' 'No internet service']\n",
      "StreamingTV : ['No' 'Yes' 'No internet service']\n",
      "StreamingMovies : ['No' 'Yes' 'No internet service']\n",
      "Contract : ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling : ['Yes' 'No']\n",
      "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "TotalCharges : ['29.85' '1889.5' '108.15' ... '346.45' '306.6' '6844.5']\n",
      "Churn : ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "unique_obj_col_values(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_CfTJX2y8jjI"
   },
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "B_RNeguhRG6F",
    "outputId": "c341ebbd-7622-4244-b422-870d6a78d6df"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:5244: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  method=method,\n"
     ]
    }
   ],
   "source": [
    "df1.replace('No internet service','No',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jOYZqTFGRG9b",
    "outputId": "84760012-5b19-49d2-e839-98fad2075a36"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender : ['Female' 'Male']\n",
      "Partner : ['Yes' 'No']\n",
      "Dependents : ['No' 'Yes']\n",
      "PhoneService : ['No' 'Yes']\n",
      "MultipleLines : ['No phone service' 'No' 'Yes']\n",
      "InternetService : ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity : ['No' 'Yes']\n",
      "OnlineBackup : ['Yes' 'No']\n",
      "DeviceProtection : ['No' 'Yes']\n",
      "TechSupport : ['No' 'Yes']\n",
      "StreamingTV : ['No' 'Yes']\n",
      "StreamingMovies : ['No' 'Yes']\n",
      "Contract : ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling : ['Yes' 'No']\n",
      "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "TotalCharges : ['29.85' '1889.5' '108.15' ... '346.45' '306.6' '6844.5']\n",
      "Churn : ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "unique_obj_col_values(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rW4GS7ynRHA8",
    "outputId": "a4ad5f32-2339-4ef5-b6ed-5d24b547bfbb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:5244: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  method=method,\n"
     ]
    }
   ],
   "source": [
    "df1.replace('No phone service','No',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sT2hMwaORHE2",
    "outputId": "835f1de0-8b08-4177-9160-1ddcb1f09aec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender : ['Female' 'Male']\n",
      "Partner : ['Yes' 'No']\n",
      "Dependents : ['No' 'Yes']\n",
      "PhoneService : ['No' 'Yes']\n",
      "MultipleLines : ['No' 'Yes']\n",
      "InternetService : ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity : ['No' 'Yes']\n",
      "OnlineBackup : ['Yes' 'No']\n",
      "DeviceProtection : ['No' 'Yes']\n",
      "TechSupport : ['No' 'Yes']\n",
      "StreamingTV : ['No' 'Yes']\n",
      "StreamingMovies : ['No' 'Yes']\n",
      "Contract : ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling : ['Yes' 'No']\n",
      "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "TotalCharges : ['29.85' '1889.5' '108.15' ... '346.45' '306.6' '6844.5']\n",
      "Churn : ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "unique_obj_col_values(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4P6j1FA9RHIP",
    "outputId": "18a2369c-6e4d-41e6-99af-910d60462b05"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6619: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return self._update_inplace(result)\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "replace yes/no with 1 and 0\n",
    "have to convert all strings to numbers\n",
    "\"\"\"\n",
    "yes_no_cols = ['Partner','Dependents','PhoneService','MultipleLines','OnlineSecurity','OnlineBackup','DeviceProtection',\n",
    "               'TechSupport','StreamingTV','StreamingMovies','PaperlessBilling','Churn']\n",
    "for cols in yes_no_cols: \n",
    "  df1[cols].replace({'Yes':1,'No':0},inplace=True)              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IWS6QUSsY4qR",
    "outputId": "17883c51-a8dc-4dde-e5c5-12df9e4bd478"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender : ['Female' 'Male']\n",
      "SeniorCitizen : [0 1]\n",
      "Partner : [1 0]\n",
      "Dependents : [0 1]\n",
      "tenure : [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "PhoneService : [0 1]\n",
      "MultipleLines : [0 1]\n",
      "InternetService : ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity : [0 1]\n",
      "OnlineBackup : [1 0]\n",
      "DeviceProtection : [0 1]\n",
      "TechSupport : [0 1]\n",
      "StreamingTV : [0 1]\n",
      "StreamingMovies : [0 1]\n",
      "Contract : ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling : [1 0]\n",
      "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "MonthlyCharges : [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "TotalCharges : ['29.85' '1889.5' '108.15' ... '346.45' '306.6' '6844.5']\n",
      "Churn : [0 1]\n"
     ]
    }
   ],
   "source": [
    "for col in df1:\n",
    "  print(f'{col} : {df1[col].unique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d51EUlxcafrt",
    "outputId": "a920990d-9e85-4250-ee46-07d1cbf2ba57"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6619: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return self._update_inplace(result)\n"
     ]
    }
   ],
   "source": [
    "df1['gender'].replace({'Female':1,'Male':0},inplace=True)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qh7z-tZoafwr",
    "outputId": "1971760e-8d0f-49c0-c7a1-40842e4f24d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender : [1 0]\n",
      "SeniorCitizen : [0 1]\n",
      "Partner : [1 0]\n",
      "Dependents : [0 1]\n",
      "tenure : [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
      "PhoneService : [0 1]\n",
      "MultipleLines : [0 1]\n",
      "InternetService : ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity : [0 1]\n",
      "OnlineBackup : [1 0]\n",
      "DeviceProtection : [0 1]\n",
      "TechSupport : [0 1]\n",
      "StreamingTV : [0 1]\n",
      "StreamingMovies : [0 1]\n",
      "Contract : ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling : [1 0]\n",
      "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "MonthlyCharges : [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "TotalCharges : ['29.85' '1889.5' '108.15' ... '346.45' '306.6' '6844.5']\n",
      "Churn : [0 1]\n"
     ]
    }
   ],
   "source": [
    "for col in df1:\n",
    "  print(f'{col} : {df1[col].unique()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5KfsLrtv8rAB"
   },
   "source": [
    "## One-Hot Encoding\n",
    "The features 'InternetService','Contract','PaymentMethod' are objects and have more than 2 categories so I'm not going to replace their values but rather One-Hot Encode them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MMZMVYCvaf1F"
   },
   "outputs": [],
   "source": [
    "df2 = pd.get_dummies(data=df1, columns=['InternetService','Contract','PaymentMethod'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5WIs87UcboEY",
    "outputId": "27b48569-0142-40b9-fe93-466992bd740d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
       "       'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\n",
       "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
       "       'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'Churn',\n",
       "       'InternetService_DSL', 'InternetService_Fiber optic',\n",
       "       'InternetService_No', 'Contract_Month-to-month', 'Contract_One year',\n",
       "       'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
       "       'PaymentMethod_Credit card (automatic)',\n",
       "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "hxQ8gFUTboLP",
    "outputId": "609548eb-238c-4334-d45c-3c1c34e6986f"
   },
   "outputs": [
    {
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       "\n",
       "  <div id=\"df-e4e819e1-5c2a-4a6c-ae9d-9ef0f3b2cc93\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "        buttonEl.style.display =\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       1              0        1           0       1             0   \n",
       "1       0              0        0           0      34             1   \n",
       "2       0              0        0           0       2             1   \n",
       "3       0              0        0           0      45             0   \n",
       "4       1              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "0              0               0             1                 0  ...   \n",
       "1              0               1             0                 1  ...   \n",
       "2              0               1             1                 0  ...   \n",
       "3              0               1             0                 1  ...   \n",
       "4              0               0             0                 0  ...   \n",
       "\n",
       "   InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "0                    1                            0                   0   \n",
       "1                    1                            0                   0   \n",
       "2                    1                            0                   0   \n",
       "3                    1                            0                   0   \n",
       "4                    0                            1                   0   \n",
       "\n",
       "   Contract_Month-to-month  Contract_One year Contract_Two year  \\\n",
       "0                        1                  0                 0   \n",
       "1                        0                  1                 0   \n",
       "2                        1                  0                 0   \n",
       "3                        0                  1                 0   \n",
       "4                        1                  0                 0   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        1   \n",
       "4                                        0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               1   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WVqzxktaboPp",
    "outputId": "b42fbe4d-6f38-4e32-8e8f-1fba90e71cbf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                                       int64\n",
       "SeniorCitizen                                int64\n",
       "Partner                                      int64\n",
       "Dependents                                   int64\n",
       "tenure                                       int64\n",
       "PhoneService                                 int64\n",
       "MultipleLines                                int64\n",
       "OnlineSecurity                               int64\n",
       "OnlineBackup                                 int64\n",
       "DeviceProtection                             int64\n",
       "TechSupport                                  int64\n",
       "StreamingTV                                  int64\n",
       "StreamingMovies                              int64\n",
       "PaperlessBilling                             int64\n",
       "MonthlyCharges                             float64\n",
       "TotalCharges                                object\n",
       "Churn                                        int64\n",
       "InternetService_DSL                          uint8\n",
       "InternetService_Fiber optic                  uint8\n",
       "InternetService_No                           uint8\n",
       "Contract_Month-to-month                      uint8\n",
       "Contract_One year                            uint8\n",
       "Contract_Two year                            uint8\n",
       "PaymentMethod_Bank transfer (automatic)      uint8\n",
       "PaymentMethod_Credit card (automatic)        uint8\n",
       "PaymentMethod_Electronic check               uint8\n",
       "PaymentMethod_Mailed check                   uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#all data types are now numbers\n",
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J8BtmWtD802P"
   },
   "source": [
    "## Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rr1ja_Qgc6Ym"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8XgS6c7QcPae"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "scaling data\n",
    "all values in 0 to 1 range\n",
    "use minmax scaler\n",
    "\"\"\"\n",
    "cols_to_scale = ['tenure','MonthlyCharges','TotalCharges']\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "df2[cols_to_scale] = scaler.fit_transform(df2[cols_to_scale])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "rvSrnj8ocPfX",
    "outputId": "dc699266-0a9d-439a-ee90-d472029be282"
   },
   "outputs": [
    {
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      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
       "0       1              0        1           0  0.000000             0   \n",
       "1       0              0        0           0  0.464789             1   \n",
       "2       0              0        0           0  0.014085             1   \n",
       "3       0              0        0           0  0.619718             0   \n",
       "4       1              0        0           0  0.014085             1   \n",
       "\n",
       "   MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
       "0              0               0             1                 0  ...   \n",
       "1              0               1             0                 1  ...   \n",
       "2              0               1             1                 0  ...   \n",
       "3              0               1             0                 1  ...   \n",
       "4              0               0             0                 0  ...   \n",
       "\n",
       "   InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
       "0                    1                            0                   0   \n",
       "1                    1                            0                   0   \n",
       "2                    1                            0                   0   \n",
       "3                    1                            0                   0   \n",
       "4                    0                            1                   0   \n",
       "\n",
       "   Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
       "0                        1                  0                  0   \n",
       "1                        0                  1                  0   \n",
       "2                        1                  0                  0   \n",
       "3                        0                  1                  0   \n",
       "4                        1                  0                  0   \n",
       "\n",
       "   PaymentMethod_Bank transfer (automatic)  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        1   \n",
       "4                                        0   \n",
       "\n",
       "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
       "0                                      0                               1   \n",
       "1                                      0                               0   \n",
       "2                                      0                               0   \n",
       "3                                      0                               0   \n",
       "4                                      0                               1   \n",
       "\n",
       "   PaymentMethod_Mailed check  \n",
       "0                           0  \n",
       "1                           1  \n",
       "2                           1  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yms-qiY1cPit",
    "outputId": "2f019f5f-2771-4496-b31d-76e0b446f8c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender : [1 0]\n",
      "SeniorCitizen : [0 1]\n",
      "Partner : [1 0]\n",
      "Dependents : [0 1]\n",
      "tenure : [0.         0.46478873 0.01408451 0.61971831 0.09859155 0.29577465\n",
      " 0.12676056 0.38028169 0.85915493 0.16901408 0.21126761 0.8028169\n",
      " 0.67605634 0.33802817 0.95774648 0.71830986 0.98591549 0.28169014\n",
      " 0.15492958 0.4084507  0.64788732 1.         0.22535211 0.36619718\n",
      " 0.05633803 0.63380282 0.14084507 0.97183099 0.87323944 0.5915493\n",
      " 0.1971831  0.83098592 0.23943662 0.91549296 0.11267606 0.02816901\n",
      " 0.42253521 0.69014085 0.88732394 0.77464789 0.08450704 0.57746479\n",
      " 0.47887324 0.66197183 0.3943662  0.90140845 0.52112676 0.94366197\n",
      " 0.43661972 0.76056338 0.50704225 0.49295775 0.56338028 0.07042254\n",
      " 0.04225352 0.45070423 0.92957746 0.30985915 0.78873239 0.84507042\n",
      " 0.18309859 0.26760563 0.73239437 0.54929577 0.81690141 0.32394366\n",
      " 0.6056338  0.25352113 0.74647887 0.70422535 0.35211268 0.53521127]\n",
      "PhoneService : [0 1]\n",
      "MultipleLines : [0 1]\n",
      "OnlineSecurity : [0 1]\n",
      "OnlineBackup : [1 0]\n",
      "DeviceProtection : [0 1]\n",
      "TechSupport : [0 1]\n",
      "StreamingTV : [0 1]\n",
      "StreamingMovies : [0 1]\n",
      "PaperlessBilling : [1 0]\n",
      "MonthlyCharges : [0.11542289 0.38507463 0.35422886 ... 0.44626866 0.25820896 0.60149254]\n",
      "TotalCharges : [0.0012751  0.21586661 0.01031041 ... 0.03780868 0.03321025 0.78764136]\n",
      "Churn : [0 1]\n",
      "InternetService_DSL : [1 0]\n",
      "InternetService_Fiber optic : [0 1]\n",
      "InternetService_No : [0 1]\n",
      "Contract_Month-to-month : [1 0]\n",
      "Contract_One year : [0 1]\n",
      "Contract_Two year : [0 1]\n",
      "PaymentMethod_Bank transfer (automatic) : [0 1]\n",
      "PaymentMethod_Credit card (automatic) : [0 1]\n",
      "PaymentMethod_Electronic check : [1 0]\n",
      "PaymentMethod_Mailed check : [0 1]\n"
     ]
    }
   ],
   "source": [
    "#the values are now between 0 and 1\n",
    "for col in df2:\n",
    "  print(f'{col} : {df2[col].unique()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MiJ2f2BD9Iec"
   },
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p71NUxm7cPlo"
   },
   "outputs": [],
   "source": [
    "X = df2.drop('Churn',axis=1)\n",
    "y=df2.Churn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gbFBBejFd8yc"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L6-mhlNKd85J",
    "outputId": "35839740-dab8-40f4-a0b9-6f8ccc35f379"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4711, 26)\n",
      "(4711,)\n",
      "(2321, 26)\n",
      "(2321,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BIwquN7n9V33"
   },
   "source": [
    "## Build the Model and Train the Data with the Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ns13tENLcPrX",
    "outputId": "41ee41b9-800d-4eb0-de95-f1f6a8fe6df8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.5409 - accuracy: 0.7192\n",
      "Epoch 2/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4400 - accuracy: 0.7860\n",
      "Epoch 3/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4270 - accuracy: 0.7956\n",
      "Epoch 4/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4217 - accuracy: 0.7983\n",
      "Epoch 5/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4182 - accuracy: 0.8000\n",
      "Epoch 6/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4164 - accuracy: 0.8030\n",
      "Epoch 7/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4146 - accuracy: 0.8015\n",
      "Epoch 8/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4130 - accuracy: 0.8045\n",
      "Epoch 9/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4119 - accuracy: 0.8053\n",
      "Epoch 10/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4111 - accuracy: 0.8047\n",
      "Epoch 11/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4090 - accuracy: 0.8081\n",
      "Epoch 12/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4077 - accuracy: 0.8102\n",
      "Epoch 13/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4062 - accuracy: 0.8096\n",
      "Epoch 14/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4054 - accuracy: 0.8090\n",
      "Epoch 15/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4038 - accuracy: 0.8098\n",
      "Epoch 16/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4029 - accuracy: 0.8107\n",
      "Epoch 17/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4032 - accuracy: 0.8124\n",
      "Epoch 18/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.4011 - accuracy: 0.8104\n",
      "Epoch 19/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3997 - accuracy: 0.8121\n",
      "Epoch 20/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3987 - accuracy: 0.8155\n",
      "Epoch 21/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3978 - accuracy: 0.8168\n",
      "Epoch 22/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3963 - accuracy: 0.8168\n",
      "Epoch 23/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3951 - accuracy: 0.8132\n",
      "Epoch 24/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3935 - accuracy: 0.8143\n",
      "Epoch 25/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3930 - accuracy: 0.8155\n",
      "Epoch 26/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3934 - accuracy: 0.8162\n",
      "Epoch 27/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3907 - accuracy: 0.8168\n",
      "Epoch 28/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3910 - accuracy: 0.8174\n",
      "Epoch 29/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3903 - accuracy: 0.8202\n",
      "Epoch 30/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3883 - accuracy: 0.8160\n",
      "Epoch 31/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3888 - accuracy: 0.8158\n",
      "Epoch 32/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3868 - accuracy: 0.8183\n",
      "Epoch 33/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3861 - accuracy: 0.8189\n",
      "Epoch 34/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3855 - accuracy: 0.8204\n",
      "Epoch 35/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3848 - accuracy: 0.8211\n",
      "Epoch 36/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3842 - accuracy: 0.8198\n",
      "Epoch 37/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3824 - accuracy: 0.8223\n",
      "Epoch 38/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3825 - accuracy: 0.8208\n",
      "Epoch 39/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3812 - accuracy: 0.8206\n",
      "Epoch 40/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3820 - accuracy: 0.8215\n",
      "Epoch 41/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3807 - accuracy: 0.8219\n",
      "Epoch 42/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3801 - accuracy: 0.8185\n",
      "Epoch 43/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3817 - accuracy: 0.8179\n",
      "Epoch 44/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3792 - accuracy: 0.8238\n",
      "Epoch 45/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3793 - accuracy: 0.8236\n",
      "Epoch 46/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3785 - accuracy: 0.8179\n",
      "Epoch 47/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3786 - accuracy: 0.8221\n",
      "Epoch 48/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3773 - accuracy: 0.8225\n",
      "Epoch 49/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3761 - accuracy: 0.8257\n",
      "Epoch 50/50\n",
      "148/148 [==============================] - 1s 4ms/step - loss: 0.3754 - accuracy: 0.8247\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fb8b677cc10>"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "        keras.layers.Dense(20, input_shape=(26,),activation='relu'),\n",
    "        keras.layers.Dense(15, activation='relu'),   \n",
    "        keras.layers.Dense(1, activation='sigmoid')                \n",
    "])\n",
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(X_train, y_train, epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LF20ekY_jHaI",
    "outputId": "460e6a6d-8584-43e2-f9f9-bd86a9bd8644"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73/73 [==============================] - 0s 3ms/step - loss: 0.4433 - accuracy: 0.7945\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.44326990842819214, 0.7944851517677307]"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "evaluate model\n",
    "\"\"\"\n",
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VcvqbaDt9g4Q"
   },
   "source": [
    "## Model Makes Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pOTBastOjHhW",
    "outputId": "934a18da-7e1c-4e62-cd04-791e1fcdd226"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00444188],\n",
       "       [0.06519017],\n",
       "       [0.6879966 ],\n",
       "       [0.05169176],\n",
       "       [0.37124437]], dtype=float32)"
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yp=model.predict(X_test)\n",
    "yp[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Nbtoi5VhjHoA"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "need to convert 2D array to 1D and convert all values to 0 and 1\n",
    "\"\"\"\n",
    "y_pred = []\n",
    "for element in yp:\n",
    "  if element > 0.5:\n",
    "    y_pred.append(1)\n",
    "  else: \n",
    "    y_pred.append(0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "42kDoEvWjHri",
    "outputId": "b0622ab9-dff4-445a-c54c-b387a9271acd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 1, 0, 0]"
      ]
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zKwJ61Z69trj"
   },
   "source": [
    "## Evaluate Model's Performance\n",
    "### Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rkvFkhj6jHvU",
    "outputId": "c598b5c8-0add-4032-94be-7dedc7452deb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.89      0.86      1711\n",
      "           1       0.63      0.53      0.58       610\n",
      "\n",
      "    accuracy                           0.79      2321\n",
      "   macro avg       0.74      0.71      0.72      2321\n",
      "weighted avg       0.79      0.79      0.79      2321\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "44EAMFKo97VC"
   },
   "source": [
    "### Confusion Matrix\n",
    "<br>\n",
    "When the True value is 1, which means customer is leaving, the model 324 times accurately predicted 1.  The model incorrectly predicted 286 times 0 when True values was 1.<br>\n",
    "When true value was 0 the model 1520 times correctly predicted 0 and 191 times incorrectly predcited 1. \n",
    "Any number on on diagonal is a correct prediction.\n",
    "Any number not on diagonal is an error.\n",
    "\n",
    "our model madse 256 + 232 total errors\n",
    "total correct predictions 1479 + 354"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OA4p-q8VplML"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 515
    },
    "id": "m0DuvAQ3ptJs",
    "outputId": "0ca11f77-c74b-4de0-a9db-7435551f92de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'True')"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = tf.math.confusion_matrix(labels=y_test, predictions=y_pred)\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(cm,annot=True, fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ljy65l8wptNL",
    "outputId": "5f49df96-b809-4c50-87ca-5bba53f17086"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79"
      ]
     },
     "execution_count": 546,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\"\n",
    "accuracy\n",
    "\"\"\"\n",
    "round((1520 + 324) / (1520 + 286 + 191 + 324),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zZ_68CNaptQd",
    "outputId": "8bf6a5f4-6457-47c4-a542-5962cbbdf69f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8416389811738649"
      ]
     },
     "execution_count": 545,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Precision for class 0\n",
    "number of correct predictions made for class 0\n",
    "  How many corect predcitons were made for class 0? 1520\n",
    "  How many samples it predcited to be 0?\n",
    "What is 0.84?\n",
    "out of predictions that model made it was correct 84% of the time \n",
    "\"\"\"\n",
    "(1520) / (1520 + 286)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VnLBtXRTptSx",
    "outputId": "bd96cc31-50f2-4d1e-8414-0d4f044a8654"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.629126213592233"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "precision for class 1\n",
    "How many correct predcitions for class 1\n",
    "#lower right / #how many predictions made(lower right + upper right)\n",
    "  upper right was predcited to be 1, but ut was actually 0\n",
    "  upper right were error, but totally uppoer right + lower right were total predictions\n",
    "\n",
    "\"\"\"\n",
    "(324) / (324 + 191)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ch4FPFF6ptVl",
    "outputId": "4e599a3b-006a-4198-e477-c8b83bf70422"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8883693746347165"
      ]
     },
     "execution_count": 549,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "recall\n",
    "recall for 0 is 0.86\n",
    "upper left / upper left + upper right\n",
    "\n",
    "total correct prediciton for 0\n",
    "\"\"\"\n",
    "(1520)/(1520 + 191)"
   ]
  }
 ],
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