Customer_exit_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": "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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "oCxH_Y4P9ETx",
    "outputId": "7d2f8b43-5ed6-4389-f585-cae624f0b3f1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-b1f3d350-89a0-44fe-b15e-7e26b725772d\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-b1f3d350-89a0-44fe-b15e-7e26b725772d\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving BankCustomers.csv to BankCustomers.csv\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "file = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UVTQ8J54-chC"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "u9z6OMe4-rOq",
    "outputId": "f41a5210-3a52-4d53-a1f2-e5635a90d69c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  ...  IsActiveMember EstimatedSalary Exited\n",
       "0          1    15634602  Hargrave  ...               1       101348.88      1\n",
       "1          2    15647311      Hill  ...               1       112542.58      0\n",
       "2          3    15619304      Onio  ...               0       113931.57      1\n",
       "3          4    15701354      Boni  ...               0        93826.63      0\n",
       "4          5    15737888  Mitchell  ...               1        79084.10      0\n",
       "\n",
       "[5 rows x 14 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('BankCustomers.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "LzJNXZDB-12D",
    "outputId": "24db55b0-5442-4331-f42d-f8c93011f2f2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      "RowNumber          10000 non-null int64\n",
      "CustomerId         10000 non-null int64\n",
      "Surname            10000 non-null object\n",
      "CreditScore        10000 non-null int64\n",
      "Geography          10000 non-null object\n",
      "Gender             10000 non-null object\n",
      "Age                10000 non-null int64\n",
      "Tenure             10000 non-null int64\n",
      "Balance            10000 non-null float64\n",
      "NumOfProducts      10000 non-null int64\n",
      "HasCrCard          10000 non-null int64\n",
      "IsActiveMember     10000 non-null int64\n",
      "EstimatedSalary    10000 non-null float64\n",
      "Exited             10000 non-null int64\n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "lDHqZ1K7A9Sm",
    "outputId": "fc875aa4-c8ff-4ebc-9f46-b5f052157d2d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography',\n",
       "       'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
       "       'IsActiveMember', 'EstimatedSalary', 'Exited'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "TOZl5m3IBBJx",
    "outputId": "844fed67-97d6-4256-fe7e-50980d8365ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nTgIsAahBGj8"
   },
   "outputs": [],
   "source": [
    "df.drop(['RowNumber', 'CustomerId', 'Surname'],1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w8ewB3LtFOZU"
   },
   "outputs": [],
   "source": [
    "Gender = {'Female':0, 'Male':1}\n",
    "\n",
    "df.Gender = [Gender[i] for i in df.Gender]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "id": "_fqc39L0FQcA",
    "outputId": "f63712ab-afcf-46f1-d0d2-ba85fe4c4dda"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>645</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>149756.71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>822</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10062.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>376</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>115046.74</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>119346.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>501</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>142051.07</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>74940.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>684</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>134603.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71725.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Geography  Gender  ...  IsActiveMember  EstimatedSalary  Exited\n",
       "0          619          0       0  ...               1        101348.88       1\n",
       "1          608          2       0  ...               1        112542.58       0\n",
       "2          502          0       0  ...               0        113931.57       1\n",
       "3          699          0       0  ...               0         93826.63       0\n",
       "4          850          2       0  ...               1         79084.10       0\n",
       "5          645          2       1  ...               0        149756.71       1\n",
       "6          822          0       1  ...               1         10062.80       0\n",
       "7          376          1       0  ...               0        119346.88       1\n",
       "8          501          0       1  ...               1         74940.50       0\n",
       "9          684          0       1  ...               1         71725.73       0\n",
       "\n",
       "[10 rows x 11 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  convert text to integer\n",
    "\n",
    "def convert_to_int(data):\n",
    "  columns = df.columns.values\n",
    "  \n",
    "  for col in columns: \n",
    "    text_to_list = {}\n",
    "    \n",
    "    def to_list(i):\n",
    "      return text_to_list[i]\n",
    "    \n",
    "    if df[col].dtype!=np.int64 and df[col].dtype!=np.float64:\n",
    "      column_content = df[col].values.tolist()\n",
    "      column_content = set(column_content)\n",
    "      \n",
    "      X=0\n",
    "      for unique in column_content:\n",
    "        if unique not in text_to_list:\n",
    "          text_to_list[unique] = X\n",
    "          X+=1\n",
    "      df[col] = list(map(to_list, df[col]))\n",
    "  return df.head(10)\n",
    "      \n",
    "    \n",
    " \n",
    "convert_to_int(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "colab_type": "code",
    "id": "gX0rlKf3QFLo",
    "outputId": "21a700f1-7def-4e14-9e93-86070ef6b1af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2863975dd8>"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3AAAAHjCAYAAABih/0TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHAAAwBACDgAAYIj/D4CaDljS3EeHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =[15,8])\n",
    "sns.distplot(df['Age'],kde=False,rug=False, color = 'green', label='Age',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 354
    },
    "colab_type": "code",
    "id": "FMEL8zWaRqdO",
    "outputId": "1be34358-9546-441c-bbf3-07a2f2ac71b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f284d7fb048>"
      ]
     },
     "execution_count": 79,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk8AAAFACAYAAACleZgmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPAEAAHjw/wHnlPBAhulgdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(df['Geography'], axlabel='0: France, 1: Spain, 2:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "qLasvpVIl7Hp",
    "outputId": "7c7742e8-9791-4e94-a55b-bbb760a98b3f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CreditScore', 'Geography', 'Gender', 'Age', 'Tenure', 'Balance',\n",
       "       'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary',\n",
       "       'Exited'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 90,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wp6rWS_Z9Lef"
   },
   "outputs": [],
   "source": [
    "X = df.iloc[:, :9]\n",
    "y = df.iloc[:,10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IyAJ0DW38_A0"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bTuSGFwWTRb_"
   },
   "outputs": [],
   "source": [
    "clf = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "2SL23HeSVAGB",
    "outputId": "454a170b-797f-4f24-9c7b-6effaf14f689"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "8000/8000 [==============================] - 2s 201us/step - loss: 0.5228 - acc: 0.7926\n",
      "Epoch 2/100\n",
      "8000/8000 [==============================] - 1s 142us/step - loss: 0.5112 - acc: 0.7929\n",
      "Epoch 3/100\n",
      "8000/8000 [==============================] - 1s 145us/step - loss: 0.5097 - acc: 0.7929\n",
      "Epoch 4/100\n",
      "8000/8000 [==============================] - 1s 138us/step - loss: 0.5084 - acc: 0.7929\n",
      "Epoch 5/100\n",
      "8000/8000 [==============================] - 1s 146us/step - loss: 0.5060 - acc: 0.7929\n",
      "Epoch 6/100\n",
      "8000/8000 [==============================] - 1s 145us/step - loss: 0.5044 - acc: 0.7929\n",
      "Epoch 7/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.5020 - acc: 0.7929\n",
      "Epoch 8/100\n",
      "8000/8000 [==============================] - 1s 130us/step - loss: 0.5001 - acc: 0.7929\n",
      "Epoch 9/100\n",
      "8000/8000 [==============================] - 1s 124us/step - loss: 0.4971 - acc: 0.7929\n",
      "Epoch 10/100\n",
      "8000/8000 [==============================] - 1s 123us/step - loss: 0.4936 - acc: 0.7929\n",
      "Epoch 11/100\n",
      "8000/8000 [==============================] - 1s 131us/step - loss: 0.4927 - acc: 0.7929\n",
      "Epoch 12/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4915 - acc: 0.7929\n",
      "Epoch 13/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4907 - acc: 0.7929\n",
      "Epoch 14/100\n",
      "8000/8000 [==============================] - 1s 129us/step - loss: 0.4909 - acc: 0.7929\n",
      "Epoch 15/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4907 - acc: 0.7929\n",
      "Epoch 16/100\n",
      "8000/8000 [==============================] - 1s 129us/step - loss: 0.4906 - acc: 0.7929\n",
      "Epoch 17/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4896 - acc: 0.7929\n",
      "Epoch 18/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4885 - acc: 0.7929\n",
      "Epoch 19/100\n",
      "8000/8000 [==============================] - 1s 124us/step - loss: 0.4892 - acc: 0.7929\n",
      "Epoch 20/100\n",
      "8000/8000 [==============================] - 1s 122us/step - loss: 0.4880 - acc: 0.7929\n",
      "Epoch 21/100\n",
      "8000/8000 [==============================] - 1s 123us/step - loss: 0.4879 - acc: 0.7929\n",
      "Epoch 22/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4872 - acc: 0.7929\n",
      "Epoch 23/100\n",
      "8000/8000 [==============================] - 1s 129us/step - loss: 0.4865 - acc: 0.7929\n",
      "Epoch 24/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4880 - acc: 0.7929\n",
      "Epoch 25/100\n",
      "8000/8000 [==============================] - 1s 126us/step - loss: 0.4862 - acc: 0.7929\n",
      "Epoch 26/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4873 - acc: 0.7922\n",
      "Epoch 27/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4884 - acc: 0.7929\n",
      "Epoch 28/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4878 - acc: 0.7929\n",
      "Epoch 29/100\n",
      "8000/8000 [==============================] - 1s 126us/step - loss: 0.4875 - acc: 0.7929\n",
      "Epoch 30/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4879 - acc: 0.7929\n",
      "Epoch 31/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4883 - acc: 0.7929\n",
      "Epoch 32/100\n",
      "8000/8000 [==============================] - 1s 126us/step - loss: 0.4876 - acc: 0.7929\n",
      "Epoch 33/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4874 - acc: 0.7929\n",
      "Epoch 34/100\n",
      "8000/8000 [==============================] - 1s 126us/step - loss: 0.4874 - acc: 0.7929\n",
      "Epoch 35/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4848 - acc: 0.7930\n",
      "Epoch 36/100\n",
      "8000/8000 [==============================] - 1s 126us/step - loss: 0.4825 - acc: 0.7940\n",
      "Epoch 37/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4823 - acc: 0.7944\n",
      "Epoch 38/100\n",
      "8000/8000 [==============================] - 1s 124us/step - loss: 0.4819 - acc: 0.7962\n",
      "Epoch 39/100\n",
      "8000/8000 [==============================] - 1s 130us/step - loss: 0.4824 - acc: 0.7946\n",
      "Epoch 40/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4816 - acc: 0.7954\n",
      "Epoch 41/100\n",
      "8000/8000 [==============================] - 1s 133us/step - loss: 0.4811 - acc: 0.7945\n",
      "Epoch 42/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4800 - acc: 0.7950\n",
      "Epoch 43/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4794 - acc: 0.7985\n",
      "Epoch 44/100\n",
      "8000/8000 [==============================] - 1s 126us/step - loss: 0.4808 - acc: 0.7956\n",
      "Epoch 45/100\n",
      "8000/8000 [==============================] - 1s 123us/step - loss: 0.4789 - acc: 0.7969\n",
      "Epoch 46/100\n",
      "8000/8000 [==============================] - 1s 124us/step - loss: 0.4792 - acc: 0.7986\n",
      "Epoch 47/100\n",
      "8000/8000 [==============================] - 1s 122us/step - loss: 0.4796 - acc: 0.7971\n",
      "Epoch 48/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4784 - acc: 0.7970\n",
      "Epoch 49/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4778 - acc: 0.7964\n",
      "Epoch 50/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4766 - acc: 0.7975\n",
      "Epoch 51/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4761 - acc: 0.7987\n",
      "Epoch 52/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4754 - acc: 0.7976\n",
      "Epoch 53/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4759 - acc: 0.7979\n",
      "Epoch 54/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4747 - acc: 0.7970\n",
      "Epoch 55/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4745 - acc: 0.7977\n",
      "Epoch 56/100\n",
      "8000/8000 [==============================] - 1s 124us/step - loss: 0.4742 - acc: 0.7972\n",
      "Epoch 57/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4727 - acc: 0.7979\n",
      "Epoch 58/100\n",
      "8000/8000 [==============================] - 1s 123us/step - loss: 0.4707 - acc: 0.8000\n",
      "Epoch 59/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4729 - acc: 0.7985\n",
      "Epoch 60/100\n",
      "8000/8000 [==============================] - 1s 124us/step - loss: 0.4706 - acc: 0.7987\n",
      "Epoch 61/100\n",
      "8000/8000 [==============================] - 1s 122us/step - loss: 0.4688 - acc: 0.7999\n",
      "Epoch 62/100\n",
      "8000/8000 [==============================] - 1s 126us/step - loss: 0.4727 - acc: 0.7971\n",
      "Epoch 63/100\n",
      "8000/8000 [==============================] - 1s 131us/step - loss: 0.4692 - acc: 0.8016\n",
      "Epoch 64/100\n",
      "8000/8000 [==============================] - 1s 130us/step - loss: 0.4689 - acc: 0.8012\n",
      "Epoch 65/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4690 - acc: 0.7989\n",
      "Epoch 66/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4691 - acc: 0.8004\n",
      "Epoch 67/100\n",
      "8000/8000 [==============================] - 1s 129us/step - loss: 0.4681 - acc: 0.7987\n",
      "Epoch 68/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4691 - acc: 0.8002\n",
      "Epoch 69/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4672 - acc: 0.8009\n",
      "Epoch 70/100\n",
      "8000/8000 [==============================] - 1s 145us/step - loss: 0.4670 - acc: 0.7999\n",
      "Epoch 71/100\n",
      "8000/8000 [==============================] - 1s 164us/step - loss: 0.4683 - acc: 0.7985\n",
      "Epoch 72/100\n",
      "8000/8000 [==============================] - 1s 151us/step - loss: 0.4674 - acc: 0.7980\n",
      "Epoch 73/100\n",
      "8000/8000 [==============================] - 1s 151us/step - loss: 0.4670 - acc: 0.7997\n",
      "Epoch 74/100\n",
      "8000/8000 [==============================] - 1s 148us/step - loss: 0.4670 - acc: 0.8002\n",
      "Epoch 75/100\n",
      "8000/8000 [==============================] - 1s 147us/step - loss: 0.4654 - acc: 0.8000\n",
      "Epoch 76/100\n",
      "8000/8000 [==============================] - 1s 153us/step - loss: 0.4665 - acc: 0.8006\n",
      "Epoch 77/100\n",
      "8000/8000 [==============================] - 1s 146us/step - loss: 0.4675 - acc: 0.8005\n",
      "Epoch 78/100\n",
      "8000/8000 [==============================] - 1s 141us/step - loss: 0.4659 - acc: 0.7989\n",
      "Epoch 79/100\n",
      "8000/8000 [==============================] - 1s 129us/step - loss: 0.4662 - acc: 0.8002\n",
      "Epoch 80/100\n",
      "8000/8000 [==============================] - 1s 129us/step - loss: 0.4655 - acc: 0.7991\n",
      "Epoch 81/100\n",
      "8000/8000 [==============================] - 1s 129us/step - loss: 0.4650 - acc: 0.8000\n",
      "Epoch 82/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4646 - acc: 0.8006\n",
      "Epoch 83/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4664 - acc: 0.8009\n",
      "Epoch 84/100\n",
      "8000/8000 [==============================] - 1s 126us/step - loss: 0.4646 - acc: 0.7999\n",
      "Epoch 85/100\n",
      "8000/8000 [==============================] - 1s 132us/step - loss: 0.4666 - acc: 0.8007\n",
      "Epoch 86/100\n",
      "8000/8000 [==============================] - 1s 126us/step - loss: 0.4650 - acc: 0.7997\n",
      "Epoch 87/100\n",
      "8000/8000 [==============================] - 1s 124us/step - loss: 0.4656 - acc: 0.8011\n",
      "Epoch 88/100\n",
      "8000/8000 [==============================] - 1s 124us/step - loss: 0.4650 - acc: 0.8006\n",
      "Epoch 89/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4637 - acc: 0.8011\n",
      "Epoch 90/100\n",
      "8000/8000 [==============================] - 1s 126us/step - loss: 0.4663 - acc: 0.7996\n",
      "Epoch 91/100\n",
      "8000/8000 [==============================] - 1s 130us/step - loss: 0.4648 - acc: 0.8000\n",
      "Epoch 92/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4644 - acc: 0.7991\n",
      "Epoch 93/100\n",
      "8000/8000 [==============================] - 1s 130us/step - loss: 0.4655 - acc: 0.8012\n",
      "Epoch 94/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4635 - acc: 0.7991\n",
      "Epoch 95/100\n",
      "8000/8000 [==============================] - 1s 124us/step - loss: 0.4645 - acc: 0.7995\n",
      "Epoch 96/100\n",
      "8000/8000 [==============================] - 1s 125us/step - loss: 0.4635 - acc: 0.7997\n",
      "Epoch 97/100\n",
      "8000/8000 [==============================] - 1s 128us/step - loss: 0.4644 - acc: 0.7999\n",
      "Epoch 98/100\n",
      "8000/8000 [==============================] - 1s 122us/step - loss: 0.4623 - acc: 0.8005\n",
      "Epoch 99/100\n",
      "8000/8000 [==============================] - 1s 131us/step - loss: 0.4636 - acc: 0.7990\n",
      "Epoch 100/100\n",
      "8000/8000 [==============================] - 1s 127us/step - loss: 0.4640 - acc: 0.7997\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f284793acc0>"
      ]
     },
     "execution_count": 138,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.add(Dense(activation='relu', input_dim = 9, units=6, kernel_initializer='uniform'))\n",
    "\n",
    "clf.add(Dense(activation = 'relu', units = 6,kernel_initializer='uniform'))\n",
    "\n",
    "clf.add(Dense(activation = 'relu', units = 6,kernel_initializer='uniform'))\n",
    "\n",
    "clf.add(Dense(activation = 'sigmoid', units = 1, kernel_initializer=\"uniform\"))\n",
    "\n",
    "clf.compile(optimizer='adam', loss='binary_crossentropy', metrics =['accuracy'])\n",
    "\n",
    "clf.fit(X_train, y_train, batch_size=10, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136
    },
    "colab_type": "code",
    "id": "DTBv_L_e-7mG",
    "outputId": "f07be914-453e-4f5c-c9b1-f74f446d9880"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       ...,\n",
       "       [False],\n",
       "       [False],\n",
       "       [False]])"
      ]
     },
     "execution_count": 155,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = clf.predict(X_test)\n",
    "score = score>0.5\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "sWaK838IHIig",
    "outputId": "1693cf1d-e39e-4f6b-9dae-9848a8ccc936"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[1600,   20],\n",
       "        [ 350,   30]]), 0.815)"
      ]
     },
     "execution_count": 156,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "cm = confusion_matrix(y_test, score.round())\n",
    "a_s = accuracy_score(y_test, score.round())\n",
    "cm,a_s"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Welcome To Colaboratory",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}