Customer-Churn-Prediction-ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\yasha\\Documents\\Projects\\Customer-Churn-Prediction-ML\\Churn_Modelling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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",
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       "      <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",
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       "      <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",
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       "      <td>Boni</td>\n",
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       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
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       "      <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",
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      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
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     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    0\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.iloc[:,13]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
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       "      <td>42</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
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       "      <th>2</th>\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",
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       "      <td>1</td>\n",
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       "      <td>113931.57</td>\n",
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       "      <th>3</th>\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",
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       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
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      ],
      "text/plain": [
       "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619    France  Female   42       2       0.00              1   \n",
       "1          608     Spain  Female   41       1   83807.86              1   \n",
       "2          502    France  Female   42       8  159660.80              3   \n",
       "3          699    France  Female   39       1       0.00              2   \n",
       "4          850     Spain  Female   43       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "0          1               1        101348.88  \n",
       "1          0               1        112542.58  \n",
       "2          1               0        113931.57  \n",
       "3          0               0         93826.63  \n",
       "4          1               1         79084.10  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.iloc[:,3:13]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cc048aafd0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cc048a78d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(X['Geography'],palette='pastel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male      5457\n",
      "Female    4543\n",
      "Name: Gender, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Male','Female']\n",
    "sizes = X['Gender'].value_counts()\n",
    "print(sizes)\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%', shadow=True)\n",
    "ax1.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cc04aa12e8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Geography', y='Exited', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cc048e1d30>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Gender', y='Exited', data=df,palette='rocket')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAKtCAYAAADl3OKAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3de7Dnd13f8dfbLMjFS7gEmubigZpBHEcgrpAWSxXECosEHVNxrGaYaDpTqlDsyMo4VTt1Zu1YuYwdayS2gaoIKJK6VI1c1P7BJRHkFpxEXMmalES5qSgIvvvH+e66hE1yzjvnt79zdh+PmZ3z/X7Pb0/efOecs08+53N+v+ruAAAA2/cF6x4AAAD2KjENAABDYhoAAIbENAAADIlpAAAYEtMAADC00piuqiNV9Z6qeldVXb9ce3BVXVdVNy1vH7Rcr6p6WVXdXFXvrqqLVzkbAADcW7XK55muqiNJ9nf3n59w7b8k+Uh3H6qqg0ke1N0vrKqnJ/n+JE9P8oQkL+3uJ9zdx3/oQx/aGxsbK5sfAACS5IYbbvjz7j7nztf3rWGWS5N8/XJ8TZK3JHnhcv0VvVn3b62qs6vq3O6+7a4+0MbGRq6//voVjwsAwJmuqv70ZNdXvWe6k/x2Vd1QVVcu1x5+LJCXtw9brp+X5JYT/u7R5RoAAOxKq16ZfmJ331pVD0tyXVV94G4eWye59nl7UJYovzJJLrzwwp2ZEgAABla6Mt3dty5vb0/yuiSPT/Lhqjo3SZa3ty8PP5rkghP++vlJbj3Jx7yqu/d39/5zzvm8bSsAAHDKrCymq+qBVfXFx46TfFOS9ya5Nsnly8MuT/L65fjaJN+zPKvHJUk+fnf7pQEAYN1Wuc3j4UleV1XH/ju/1N2/WVXvSPLqqroiyYeSXLY8/g3ZfCaPm5N8MslzVjgbAADcayuL6e7+YJLHnOT6XyR5ykmud5LnrmoeAADYaV4BEQAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAEP71j0AcPc2Dh4+fnzk0IE1TgIA3JmYhtOA4AaA9bDNAwAAhsQ0AAAMiWkAABiyZxpW4MQ9zIl9zABwurIyDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgaN+6B4Az2cbBw8ePjxw6sMZJAIAJK9MAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMDQvnUPAHvZxsHDx4+PHDqwxkkAgHWwMg0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMOTZPOAueKYOAOCeWJkGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAztW/cAwHptHDx8/PjIoQNrnAQA9h4r0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwNC+dQ8ArM7GwcPHj48cOrDGSQDg9GRlGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQp8bjjOQp4wCAnWBlGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwJCYBgCAITENAABDYhoAAIb2rXsAWKWNg4ePHx85dGCNkwAAp6OVr0xX1VlV9c6q+o3l/BFV9baquqmqfqWq7rtc/8Ll/Obl/Rurng0AAO6NU7HN43lJbjzh/CeTvLi7L0ry0SRXLNevSPLR7v7yJC9eHgcAALvWSmO6qs5PciDJy5fzSvLkJK9dHnJNkmctx5cu51ne/5Tl8QAAsCutemX6JUl+KMnfL+cPSfKx7v7Mcn40yXnL8XlJbkmS5f0fXx4PAAC70spiuqqekeT27r7hxMsneWhv4X0nftwrq+r6qrr+jjvu2IFJAQBgZpUr009M8syqOpLkVdnc3vGSJGdX1bFnETk/ya3L8dEkFyTJ8v4vTfKRO3/Q7r6qu/d39/5zzjlnheMDAMDdW1lMd/cPd/f53b2R5NlJ3tTd35XkzUm+fXnY5Ulevxxfu5xnef+buvvzVqYBAGC3WMeLtrwwyQuq6uZs7om+erl+dZKHLNdfkOTgGmYDAIAtOyUv2tLdb0nyluX4g0kef5LH/G2Sy07FPAAAsBO8nDgAAAyJaQAAGBLTAAAwJKYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAEP71j0AsPttHDx8/PjIoQNrnAQAdhcr0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAM7Vv3AHAyGwcPHz8+cujAGicBALhrVqYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwNC+dQ8AUxsHDx8/PnLowBonAQDOVFamAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGPI808CY5/oG4ExnZRoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwNDKYrqq7ldVb6+qP6yq91XVjy/XH1FVb6uqm6rqV6rqvsv1L1zOb17ev7Gq2QAAYCescmX6U0me3N2PSfLYJN9cVZck+ckkL+7ui5J8NMkVy+OvSPLR7v7yJC9eHgcAALvWymK6N/3Vcnqf5U8neXKS1y7Xr0nyrOX40uU8y/ufUlW1qvkAAODeWume6ao6q6releT2JNcl+eMkH+vuzywPOZrkvOX4vCS3JMny/o8necgq5wMAgHtjpTHd3Z/t7scmOT/J45M8+mQPW96ebBW673yhqq6squur6vo77rhj54YFAIBtOiXP5tHdH0vyliSXJDm7qvYt7zo/ya3L8dEkFyTJ8v4vTfKRk3ysq7p7f3fvP+ecc1Y9OgAA3KVVPpvHOVV19nJ8/yTfmOTGJG9O8u3Lwy5P8vrl+NrlPMv739Tdn7cyDQAAu8W+e37I2LlJrqmqs7IZ7a/u7t+oqvcneVVV/eck70xy9fL4q5O8sqpuzuaK9LNXOBsAANxrK4vp7n53ksed5PoHs7l/+s7X/zbJZauaB1iPjYOHjx8fOXRgjZMAwM7zCogAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhvatewDObBsHDx8/PnLowBonAQDYPivTAAAwJKYBAGBITAMAwNCWYrqqvmrVgwAAwF6z1ZXp/15Vb6+qf1tVZ690IgAA2CO2FNPd/XVJvivJBUmur6pfqqqnrnQyAADY5ba8Z7q7b0ryI0lemORfJHlZVX2gqr5tVcMBAMButtU9019dVS9OcmOSJyf5lu5+9HL84hXOBwAAu9ZWX7TlZ5L8fJIXdfffHLvY3bdW1Y+sZDIAANjlthrTT0/yN9392SSpqi9Icr/u/mR3v3Jl0wEAwC621T3Tv5Pk/iecP2C5BgAAZ6ytxvT9uvuvjp0sxw9YzUgAALA3bDWm/7qqLj52UlVfk+Rv7ubxAABw2tvqnunnJ3lNVd26nJ+b5DtWMxIAAOwNW4rp7n5HVX1FkkclqSQf6O6/W+lkAACwy211ZTpJvjbJxvJ3HldV6e5XrGQqAADYA7YU01X1yiT/JMm7knx2udxJxDQAAGesra5M70/yld3dqxwGAAD2kq0+m8d7k/yjVQ4CAAB7zVZXph+a5P1V9fYknzp2sbufuZKpAABgD9hqTP/YKocA2Dh4+PjxkUMH1jgJAGzdVp8a73er6suSXNTdv1NVD0hy1mpHAwCA3W1Le6ar6vuSvDbJzy2Xzkvy66saCgAA9oKt/gLic5M8McknkqS7b0rysFUNBQAAe8FWY/pT3f3pYydVtS+bzzMNAABnrK3G9O9W1YuS3L+qnprkNUn+9+rGAgCA3W+rMX0wyR1J3pPk3yR5Q5IfWdVQAACwF2z12Tz+PsnPL38AAIBsMaar6k9ykj3S3f3IHZ8IAAD2iK2+aMv+E47vl+SyJA/e+XEAAGDv2NKe6e7+ixP+/Fl3vyTJk1c8GwAA7Gpb3eZx8QmnX5DNleovXslEAACwR2x1m8d/PeH4M0mOJPlXOz4NAADsIVt9No9vWPUgAACw12x1m8cL7u793f3TOzMOAADsHdt5No+vTXLtcv4tSX4vyS2rGAoAAPaCrcb0Q5Nc3N1/mSRV9WNJXtPd37uqwQAAYLfb6suJX5jk0yecfzrJxo5PAwAAe8hWV6ZfmeTtVfW6bL4S4rcmecXKpgIAgD1gq8/m8RNV9X+S/PPl0nO6+52rGwsAAHa/rW7zSJIHJPlEd780ydGqesSKZgIAgD1hSzFdVT+a5IVJfni5dJ8k/2tVQwEAwF6w1ZXpb03yzCR/nSTdfWu8nDgAAGe4rcb0p7u7s/nLh6mqB65uJAAA2Bu2GtOvrqqfS3J2VX1fkt9J8vOrGwsAAHa/rT6bx09V1VOTfCLJo5L8x+6+bqWTAQDALnePMV1VZyX5re7+xiQCGgAAFve4zaO7P5vkk1X1padgHgAA2DO2+gqIf5vkPVV1XZZn9EiS7v6BlUwFAAB7wFZj+vDyBwAAWNxtTFfVhd39oe6+5lQNBAAAe8U97Zn+9WMHVfWrK54FAAD2lHuK6Trh+JGrHAQAAPaae4rpvotjAAA4493TLyA+pqo+kc0V6vsvx1nOu7u/ZKXTAQDALna3Md3dZ52qQQAAYK/Z6lPjAazFxsF/eFbOI4cOrHESAPh89/gKiAAAwMmJaQAAGBLTAAAwZM80sOfZVw3AuliZBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGNq37gE4fW0cPHz8+MihA2ucBABgNaxMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMLSymK6qC6rqzVV1Y1W9r6qet1x/cFVdV1U3LW8ftFyvqnpZVd1cVe+uqotXNRsAAOyEVa5MfybJD3b3o5NckuS5VfWVSQ4meWN3X5Tkjct5kjwtyUXLnyuT/OwKZwMAgHttZTHd3bd19x8sx3+Z5MYk5yW5NMk1y8OuSfKs5fjSJK/oTW9NcnZVnbuq+QAA4N46JXumq2ojyeOSvC3Jw7v7tmQzuJM8bHnYeUluOeGvHV2uAQDArrRv1f+BqvqiJL+a5Pnd/YmqusuHnuRan+TjXZnNbSC58MILd2pMtmHj4OHjx0cOHVjjJAAA67XSlemquk82Q/oXu/vXlssfPrZ9Y3l7+3L9aJILTvjr5ye59c4fs7uv6u793b3/nHPOWd3wAABwD1b5bB6V5OokN3b3T5/wrmuTXL4cX57k9Sdc/57lWT0uSfLxY9tBAABgN1rlNo8nJvnuJO+pqnct116U5FCSV1fVFUk+lOSy5X1vSPL0JDcn+WSS56xwNgAAuNdWFtPd/X9z8n3QSfKUkzy+kzx3VfMAAMBO8wqIAAAwJKYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwNC+dQ8AcCpsHDx8/PjIoQNrnASA04mVaQAAGBLTAAAwJKYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADHnRFoCFF3YBYLusTAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgKF96x6A3WXj4OHjx0cOHVjjJAAAu5+VaQAAGBLTAAAwJKYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADHnRFoB74MWMALgrVqYBAGBITAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMLRv3QMAnA42Dh4+fnzk0IE1TgLAqWRlGgAAhqxMn6GsogEA3HtWpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwtG/dAwCcKTYOHj5+fOTQgTVOAsBOsTINAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGFpZTFfVL1TV7VX13hOuPbiqrquqm5a3D1quV1W9rKpurqp3V9XFq5oLAAB2yr4Vfuz/meRnkrzihGsHk7yxuw9V1cHl/IVJnpbkouXPE5L87PIW4LS3cfDw55wfOXRgTZMAsF0rW5nu7t9L8pE7Xb40yTXL8TVJnnXC9Vf0prcmObuqzl3VbAAAsBNO9Z7ph3f3bUmyvH3Ycv28JLec8LijyzUAANi1dssvINZJrvVJH1h1ZVVdX1XX33HHHSseCwAA7toq90yfzIer6tzuvm3ZxnH7cv1okgtOeNz5SW492Qfo7quSXJUk+/fvP2lwn+lO3H9p7yUAwOqc6pXpa5NcvhxfnuT1J1z/nuVZPS5J8vFj20EAAGC3WtnKdFX9cpKvT/LQqjqa5EeTHEry6qq6IsmHkly2PPwNSZ6e5OYkn0zynFXNBQAAO2VlMd3d33kX73rKSR7bSZ67qlkAAGAVdssvIAIAwJ4jpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGNq37gHYno2Dh48fHzl0YI2TALuR7xEAp5aVaQAAGLIyDXCGsXoNsHOsTAMAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYMiLtgCwZV7wBeBzWZkGAIAhK9MAe4RVYYDdx8o0AAAMiWkAABgS0wAAMCSmAQBgyC8gApDELzgCTFiZBgCAITENAABDYhoAAIbsmQZgR9l7DZxJrEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAx5Ng8ATomtPMuHZwIB9hor0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwNC+dQ/AyW0cPHz8+MihA2ucBGD38T0S2C3ENACnDZENnGq2eQAAwJCYBgCAITENAABDYhoAAIbENAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABgS0wAAMLRv3QOciTYOHj5+fOTQgTVOAnDm8T0Y2ElWpgEAYEhMAwDAkJgGAIAhe6YB4E5Wva/avm04fViZBgCAISvTALCDrDrDmcXKNAAADIlpAAAYEtMAADAkpgEAYEhMAwDAkJgGAIAhMQ0AAENiGgAAhsQ0AAAMiWkAABjycuI7zMvIArBX+DcL7j0r0wAAMCSmAQBgSEwDAMCQmAYAgCExDQAAQ57NY8hvQAOwDif++5P4NwjWzco0AAAMWZkGAD6Hn77C1lmZBgCAISvTALALWR2GvUFMA8DAbo/d3TDfbpgBVs02DwAAGBLTAAAwJKYBAGBITAMAwJCYBgCAITENAABDnhoPANg2T3sHm6xMAwDAkJgGAIAh2zwAgF3HNhL2CjENAOxJgpvdQEwDAGsjiNnr7JkGAIAhMQ0AAENiGgAAhsQ0AAAM+QVEAOC05pccWSUr0wAAMGRlGgA4I+3UirWV7zPbrorpqvrmJC9NclaSl3f3oTWPBACcwYQy92TXxHRVnZXkvyV5apKjSd5RVdd29/vXOxkAwPqc6UG/2//376Y9049PcnN3f7C7P53kVUkuXfNMAABwl3bNynSS85LccsL50SRPWNMsAAD3ylZWVPfSvu3dvkK8LtXd654hSVJVlyX5l939vcv5dyd5fHd//50ed2WSK5fTRyX5o1M66M55aJI/X/cQpwH3cWe4jzvHvdwZ7uPOcB93jnu5M/byffyy7j7nzhd308r00SQXnHB+fpJb7/yg7r4qyVWnaqhVqarru3v/uufY69zHneE+7hz3cme4jzvDfdw57uXOOB3v427aM/2OJBdV1SOq6r5Jnp3k2jXPBAAAd2nXrEx392eq6t8l+a1sPjXeL3T3+9Y8FgAA3KVdE9NJ0t1vSPKGdc9xiuz5rSq7hPu4M9zHneNe7gz3cWe4jzvHvdwZp9193DW/gAgAAHvNbtozDQAAe4qYXrGquqCq3lxVN1bV+6rqecv1B1fVdVV10/L2QeuedberqvtV1dur6g+Xe/njy/VHVNXblnv5K8svsHIPquqsqnpnVf3Gcu4+blNVHamq91TVu6rq+uWar+1tqqqzq+q1VfWB5XvlP3Uft6+qHrV8Lh7784mqer57uX1V9e+Xf2feW1W/vPz743vkNlXV85Z7+L6qev5y7bT7fBTTq/eZJD/Y3Y9OckmS51bVVyY5mOSN3X1Rkjcu59y9TyV5cnc/Jsljk3xzVV2S5CeTvHi5lx9NcsUaZ9xLnpfkxhPO3ceZb+jux57wVE++trfvpUl+s7u/Isljsvl56T5uU3f/0fK5+NgkX5Pkk0leF/dyW6rqvCQ/kGR/d39VNp8U4dnxPXJbquqrknxfNl/h+jFJnlFVF+U0/HwU0yvW3bd19x8sx3+ZzX8kzsvmS6VfszzsmiTPWs+Ee0dv+qvl9D7Ln07y5CSvXa67l1tQVecnOZDk5ct5xX3cKb62t6GqviTJk5JcnSTd/enu/ljcx3vrKUn+uLv/NO7lxL4k96+qfUkekOS2+B65XY9O8tbu/mR3fybJ7yb51pyGn49i+hSqqo0kj0vytiQP7+7bks3gTvKw9U22dyxbE96V5PYk1yX54yQfW75Qk80X/zlvXfPtIS9J8kNJ/n45f0jcx4lO8ttVdcPy6qyJr+3temSSO5L8j2Xb0cur6oFxH++tZyf55eXYvdyG7v6zJD+V5EPZjOiPJ7khvkdu13uTPKmqHlJVD0jy9Gy+ON9p9/kopk+RqvqiJL+a5Pnd/Yl1z7NXdfdnlx9hnp/NHx09+mQPO7VT7S1V9Ywkt3f3DSdePslD3cd79sTuvjjJ07K5hetJ6x5oD9qX5OIkP9vdj0vy1zkNfuy7Tste3mcmec26Z9mLlj28lyZ5RJJ/nOSB2fwavzPfI+9Gd9+Yza0x1yX5zSR/mM2tr6cdMX0KVNV9shnSv9jdv7Zc/nBVnbu8/9xsrrSyRcuPgd+SzX3oZy8/ikvu4mXo+RxPTPLMqjqS5FXZ/NHlS+I+blt337q8vT2be1MfH1/b23U0ydHuftty/tpsxrX7OPe0JH/Q3R9ezt3L7fnGJH/S3Xd0998l+bUk/yy+R25bd1/d3Rd395OSfCTJTTkNPx/F9Iote1GvTnJjd//0Ce+6Nsnly/HlSV5/qmfba6rqnKo6ezm+fza/4d2Y5M1Jvn15mHt5D7r7h7v7/O7eyOaPgt/U3d8V93FbquqBVfXFx46TfFM2f6zpa3sbuvv/Jbmlqh61XHpKkvfHfbw3vjP/sMUjcS+360NJLqmqByz/hh/7nPQ9cpuq6mHL2wuTfFs2Py9Pu89HL9qyYlX1dUl+P8l78g/7U1+UzX3Tr05yYTa/cC/r7o+sZcg9oqq+Opu/rHBWNv+P4Ku7+z9V1SOzucL64CTvTPKvu/tT65t076iqr0/yH7r7Ge7j9iz363XL6b4kv9TdP1FVD4mv7W2pqsdm85dh75vkg0mek+VrPO7jtix7U29J8sju/vhyzefkNtXmU69+Rza3Jbwzyfdmc4+075HbUFW/n83fyfm7JC/o7jeejp+PYhoAABezODIAAAAxSURBVIZs8wAAgCExDQAAQ2IaAACGxDQAAAyJaQAAGBLTAAAwJKYBAGBITAMAwND/B3AZdlu4MbgJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Age.plot(kind = 'hist', bins = 200, figsize = (12,12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 584x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, row='Gender', col='Exited', height=4)\n",
    "g.map(plt.hist,'Age', alpha=0.5, bins=20)\n",
    "g.add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 584x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, row='IsActiveMember', col='Exited', height=4)\n",
    "g.map(plt.hist,'Age', alpha=0.5, bins=20)\n",
    "g.add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "4    0\n",
      "5    1\n",
      "6    1\n",
      "Name: Gender, dtype: int32\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
    "label = LabelEncoder()\n",
    "X['Gender'] = label.fit_transform(X['Gender'])\n",
    "print(X['Gender'].head(7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    2\n",
      "2    0\n",
      "3    0\n",
      "4    2\n",
      "Name: Geography, dtype: int32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    5014\n",
       "1    2509\n",
       "2    2477\n",
       "Name: Geography, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Geography']=label.fit_transform(X['Geography'])\n",
    "print(X['Geography'].head())\n",
    "X['Geography'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.0000000e+00 0.0000000e+00 0.0000000e+00 ... 1.0000000e+00\n",
      "  1.0000000e+00 1.0134888e+05]\n",
      " [0.0000000e+00 0.0000000e+00 1.0000000e+00 ... 0.0000000e+00\n",
      "  1.0000000e+00 1.1254258e+05]\n",
      " [1.0000000e+00 0.0000000e+00 0.0000000e+00 ... 1.0000000e+00\n",
      "  0.0000000e+00 1.1393157e+05]\n",
      " ...\n",
      " [1.0000000e+00 0.0000000e+00 0.0000000e+00 ... 0.0000000e+00\n",
      "  1.0000000e+00 4.2085580e+04]\n",
      " [0.0000000e+00 1.0000000e+00 0.0000000e+00 ... 1.0000000e+00\n",
      "  0.0000000e+00 9.2888520e+04]\n",
      " [1.0000000e+00 0.0000000e+00 0.0000000e+00 ... 1.0000000e+00\n",
      "  0.0000000e+00 3.8190780e+04]]\n"
     ]
    }
   ],
   "source": [
    "onehotencoding = OneHotEncoder(categorical_features  = [1])\n",
    "X = onehotencoding.fit_transform(X).toarray()\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.35,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_X=StandardScaler()\n",
    "X_train=sc_X.fit_transform(X_train)\n",
    "X_test=sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.00462608, -0.56905968,  1.71795453, ...,  0.64722807,\n",
       "         0.96581473,  0.56276126],\n",
       "       [-1.00462608, -0.56905968,  1.71795453, ...,  0.64722807,\n",
       "         0.96581473, -1.58504165],\n",
       "       [ 0.99539522, -0.56905968, -0.58208758, ...,  0.64722807,\n",
       "        -1.03539526, -1.07751299],\n",
       "       ...,\n",
       "       [ 0.99539522, -0.56905968, -0.58208758, ..., -1.54505043,\n",
       "        -1.03539526, -0.1530867 ],\n",
       "       [ 0.99539522, -0.56905968, -0.58208758, ...,  0.64722807,\n",
       "        -1.03539526, -0.06146547],\n",
       "       [-1.00462608,  1.75728493, -0.58208758, ...,  0.64722807,\n",
       "         0.96581473, -0.82256561]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.00462608,  1.75728493, -0.58208758, ..., -1.54505043,\n",
       "        -1.03539526, -1.02689323],\n",
       "       [ 0.99539522, -0.56905968, -0.58208758, ...,  0.64722807,\n",
       "         0.96581473,  0.78530321],\n",
       "       [-1.00462608, -0.56905968,  1.71795453, ...,  0.64722807,\n",
       "        -1.03539526, -0.73627661],\n",
       "       ...,\n",
       "       [ 0.99539522, -0.56905968, -0.58208758, ..., -1.54505043,\n",
       "        -1.03539526, -0.86130337],\n",
       "       [-1.00462608, -0.56905968,  1.71795453, ...,  0.64722807,\n",
       "        -1.03539526,  0.15695849],\n",
       "       [-1.00462608,  1.75728493, -0.58208758, ...,  0.64722807,\n",
       "        -1.03539526,  0.69271637]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (6500, 12) (6500,)\n",
      "Test set: (3500, 12) (3500,)\n"
     ]
    }
   ],
   "source": [
    "#Shape of train and test data\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using KNeighborsClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Model  \n",
    "neigh = KNeighborsClassifier(n_neighbors = 4).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction\n",
    "prediction = neigh.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 69,
     "metadata": {},
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   ],
   "source": [
    "prediction1=pd.DataFrame(prediction)\n",
    "prediction1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8308571428571428"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    "from sklearn import metrics\n",
    "percent1 = metrics.accuracy_score(y_test, prediction)\n",
    "percent1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "    kernel='rbf', max_iter=-1, probability=False, random_state=0,\n",
       "    shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now Using SVM Algorithm \n",
    "from sklearn.svm import SVC\n",
    "classifier=SVC(kernel='rbf',random_state=0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict\n",
    "y_pred=classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "prediction2=pd.DataFrame(y_pred)\n",
    "prediction2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8577142857142858"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    "percent2 =metrics.accuracy_score(y_test, prediction2)\n",
    "percent2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier_4 = RandomForestClassifier(n_estimators=100) #warning 10 to 100\n",
    "classifier_4.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict\n",
    "y_randomfor=classifier_4.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "prediction3=pd.DataFrame(y_randomfor)\n",
    "prediction3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8674285714285714"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    "percent3 = metrics.accuracy_score(y_test, prediction3)\n",
    "percent3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using DecisionTreeClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "TeleTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TeleTree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predtree = TeleTree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 82,
     "metadata": {},
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   ],
   "source": [
    "prediction4=pd.DataFrame(y_pred)\n",
    "prediction4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8577142857142858"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    "percent4 = metrics.accuracy_score(y_test,prediction4)\n",
    "percent4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = pd.DataFrame({'name_model':[\"KNN\",\"SVM\",\"Random Forest\",\"Decision Trees\"],\\\n",
    "                        'accuracy_percentage':[percent1,percent2,percent3,percent4]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>name_model</th>\n",
       "      <th>accuracy_percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.830857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.857714</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.867429</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Trees</td>\n",
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      "text/plain": [
       "       name_model  accuracy_percentage\n",
       "0             KNN             0.830857\n",
       "1             SVM             0.857714\n",
       "2   Random Forest             0.867429\n",
       "3  Decision Trees             0.857714"
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    "models"
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