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moaminsharifi/the-case-of-the-migrating-customers-unveiling-churn-with-data-at-a-western-eu-bank.ipynb

Created February 15, 2024 14:11
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the-case-of-the-migrating-customers-unveiling-churn-with-data-at-a-western-eu-bank.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "lP6JLo1tGNBg"
},
"source": [
"# The Case of the Migrating Customers: Unveiling Churn with Data at a Western EU Bank\n",
"\n",
"medium article: \n",
"\n",
"dataset: https://huggingface.co/datasets/moaminsharifi/Churn_Modelling"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gWZyYmS_UE_L"
},
"source": [
"### Importing the libraries"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-02-15 16:52:50.777393: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensorflow version: 2.15.0\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"from sklearn.preprocessing import LabelEncoder\n",
"print('tensorflow version: ',tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1E0Q3aoKUCRX"
},
"source": [
"## Part 1 - Data Preprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "cKWAkFVGUU0Z"
},
"source": [
"### Importing the dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"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",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 RowNumber 10000 non-null int64 \n",
" 1 CustomerId 10000 non-null int64 \n",
" 2 Surname 10000 non-null object \n",
" 3 CreditScore 10000 non-null int64 \n",
" 4 Geography 10000 non-null object \n",
" 5 Gender 10000 non-null object \n",
" 6 Age 10000 non-null int64 \n",
" 7 Tenure 10000 non-null int64 \n",
" 8 Balance 10000 non-null float64\n",
" 9 NumOfProducts 10000 non-null int64 \n",
" 10 HasCrCard 10000 non-null int64 \n",
" 11 IsActiveMember 10000 non-null int64 \n",
" 12 EstimatedSalary 10000 non-null float64\n",
" 13 Exited 10000 non-null int64 \n",
"dtypes: float64(2), int64(9), object(3)\n",
"memory usage: 1.1+ MB\n"
]
}
],
"source": [
"dataset_file = 'Churn_Modelling.csv'\n",
"if not os.path.exists(dataset_file):\n",
" print(\"can not find dataset file\")\n",
"\n",
"df = pd.read_csv(dataset_file)\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"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": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"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 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 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Overal Distibution:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exploring Dataset Characteristics"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"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>CreditScore</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>count</th>\n",
" <td>10000.00000</td>\n",
" <td>1.000000e+04</td>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" <td>10000.00000</td>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" <td>10000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5000.50000</td>\n",
" <td>1.569094e+07</td>\n",
" <td>650.528800</td>\n",
" <td>38.921800</td>\n",
" <td>5.012800</td>\n",
" <td>76485.889288</td>\n",
" <td>1.530200</td>\n",
" <td>0.70550</td>\n",
" <td>0.515100</td>\n",
" <td>100090.239881</td>\n",
" <td>0.203700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2886.89568</td>\n",
" <td>7.193619e+04</td>\n",
" <td>96.653299</td>\n",
" <td>10.487806</td>\n",
" <td>2.892174</td>\n",
" <td>62397.405202</td>\n",
" <td>0.581654</td>\n",
" <td>0.45584</td>\n",
" <td>0.499797</td>\n",
" <td>57510.492818</td>\n",
" <td>0.402769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.00000</td>\n",
" <td>1.556570e+07</td>\n",
" <td>350.000000</td>\n",
" <td>18.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>11.580000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2500.75000</td>\n",
" <td>1.562853e+07</td>\n",
" <td>584.000000</td>\n",
" <td>32.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>51002.110000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5000.50000</td>\n",
" <td>1.569074e+07</td>\n",
" <td>652.000000</td>\n",
" <td>37.000000</td>\n",
" <td>5.000000</td>\n",
" <td>97198.540000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" <td>1.000000</td>\n",
" <td>100193.915000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>7500.25000</td>\n",
" <td>1.575323e+07</td>\n",
" <td>718.000000</td>\n",
" <td>44.000000</td>\n",
" <td>7.000000</td>\n",
" <td>127644.240000</td>\n",
" <td>2.000000</td>\n",
" <td>1.00000</td>\n",
" <td>1.000000</td>\n",
" <td>149388.247500</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>10000.00000</td>\n",
" <td>1.581569e+07</td>\n",
" <td>850.000000</td>\n",
" <td>92.000000</td>\n",
" <td>10.000000</td>\n",
" <td>250898.090000</td>\n",
" <td>4.000000</td>\n",
" <td>1.00000</td>\n",
" <td>1.000000</td>\n",
" <td>199992.480000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RowNumber CustomerId CreditScore Age Tenure \\\n",
"count 10000.00000 1.000000e+04 10000.000000 10000.000000 10000.000000 \n",
"mean 5000.50000 1.569094e+07 650.528800 38.921800 5.012800 \n",
"std 2886.89568 7.193619e+04 96.653299 10.487806 2.892174 \n",
"min 1.00000 1.556570e+07 350.000000 18.000000 0.000000 \n",
"25% 2500.75000 1.562853e+07 584.000000 32.000000 3.000000 \n",
"50% 5000.50000 1.569074e+07 652.000000 37.000000 5.000000 \n",
"75% 7500.25000 1.575323e+07 718.000000 44.000000 7.000000 \n",
"max 10000.00000 1.581569e+07 850.000000 92.000000 10.000000 \n",
"\n",
" Balance NumOfProducts HasCrCard IsActiveMember \\\n",
"count 10000.000000 10000.000000 10000.00000 10000.000000 \n",
"mean 76485.889288 1.530200 0.70550 0.515100 \n",
"std 62397.405202 0.581654 0.45584 0.499797 \n",
"min 0.000000 1.000000 0.00000 0.000000 \n",
"25% 0.000000 1.000000 0.00000 0.000000 \n",
"50% 97198.540000 1.000000 1.00000 1.000000 \n",
"75% 127644.240000 2.000000 1.00000 1.000000 \n",
"max 250898.090000 4.000000 1.00000 1.000000 \n",
"\n",
" EstimatedSalary Exited \n",
"count 10000.000000 10000.000000 \n",
"mean 100090.239881 0.203700 \n",
"std 57510.492818 0.402769 \n",
"min 11.580000 0.000000 \n",
"25% 51002.110000 0.000000 \n",
"50% 100193.915000 0.000000 \n",
"75% 149388.247500 0.000000 \n",
"max 199992.480000 1.000000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Distribution:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Country:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Geography\n",
"France 5014\n",
"Germany 2509\n",
"Spain 2477\n",
"Name: RowNumber, dtype: int64\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.groupby('Geography')['RowNumber'].count().plot.bar()\n",
"print(df.groupby('Geography')['RowNumber'].count())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Geography'>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[df[\"Exited\"] == 0].groupby('Geography')['RowNumber'].count().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Geography'>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[df[\"Exited\"] == 1].groupby('Geography')['RowNumber'].count().plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Gender:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Gender'>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.groupby('Gender')['RowNumber'].count().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Gender'>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[df[\"Exited\"] == 0].groupby('Gender')['RowNumber'].count().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Gender'>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[df[\"Exited\"] == 1].groupby('Gender')['RowNumber'].count().plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Distribution of Salary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"EstimatedSalary Distribution"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='Frequency'>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['EstimatedSalary'].plot.hist()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='Frequency'>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['Age'].plot.hist()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: ylabel='Frequency'>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df['Balance'].plot.hist()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 250898.09, 76485.889288)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Balance'].min(), df['Balance'].max(), df['Balance'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 25000, 50000, 75000, 100000, 125000, 150000, 175000, 200000, 225000]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(range(0,250_000, 25_000))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Distribution of Exited Customer"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exited\n",
"0 7963\n",
"1 2037\n",
"Name: RowNumber, dtype: int64\n"
]
},
{
"data": {
"text/plain": [
"<Axes: xlabel='Exited'>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(df.groupby('Exited')['RowNumber'].count())\n",
"df.groupby('Exited')['RowNumber'].count().plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PreProcessing Data:"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"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>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</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>2</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</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>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</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>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</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>2</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RowNumber CustomerId Surname CreditScore Geography Gender Age \\\n",
"0 1 15634602 Hargrave 619 0 0 6 \n",
"1 2 15647311 Hill 608 2 0 6 \n",
"2 3 15619304 Onio 502 0 0 6 \n",
"3 4 15701354 Boni 699 0 0 5 \n",
"4 5 15737888 Mitchell 850 2 0 6 \n",
"\n",
" Tenure Balance NumOfProducts HasCrCard IsActiveMember EstimatedSalary \\\n",
"0 2 1 1 1 1 1 \n",
"1 1 4 1 0 1 1 \n",
"2 8 7 3 1 0 1 \n",
"3 1 1 2 0 0 1 \n",
"4 2 6 1 1 1 1 \n",
"\n",
" Exited \n",
"0 1 \n",
"1 0 \n",
"2 1 \n",
"3 0 \n",
"4 0 "
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"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>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</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>6</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</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>6</td>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</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>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</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>6</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CreditScore Geography Gender Age Tenure Balance NumOfProducts \\\n",
"0 619 0 0 6 2 1 1 \n",
"1 608 2 0 6 1 4 1 \n",
"2 502 0 0 6 8 7 3 \n",
"3 699 0 0 5 1 1 2 \n",
"4 850 2 0 6 2 6 1 \n",
"\n",
" HasCrCard IsActiveMember EstimatedSalary Exited \n",
"0 1 1 1 1 \n",
"1 0 1 1 0 \n",
"2 1 0 1 1 \n",
"3 0 0 1 0 \n",
"4 1 1 1 0 "
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df.drop(columns=['RowNumber', 'CustomerId', 'Surname'])\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bining: convert From Continuous numbers to discrete"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"df['Age'] = np.digitize(df['Age'], bins=[0, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95])\n",
"df['Balance'] = np.digitize(df['Balance'], bins=[0, 25000, 50000, 75000, 100000, 125000, 150000, 175000, 200000, 225000]) # list(range(0,250_000,25_000))\n",
"df['EstimatedSalary'] = np.digitize(df['EstimatedSalary'], bins=[0, 25000, 50000, 75000, 100000, 125000, 150000, 175000, 200000, 225000]) # list(range(0,250_000, 25_000))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Label Encoding: (manual way)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"df['Geography'] = df['Geography'].replace({'France': 0, 'Germany': 1, 'Spain':2})\n",
"df['Gender'] = df['Gender'].replace({'Female': 0, 'Male': 1})"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X.shape, Y.shape : (10000, 10) (10000,)\n"
]
}
],
"source": [
"X = df.iloc[:, :-1].values\n",
"Y = df.iloc[: , -1].values\n",
"\n",
"print('X.shape, Y.shape : ',X.shape, Y.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "vHol938cW8zd"
},
"source": [
"### Splitting the dataset into the Training set and Test set"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0, stratify=Y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RE_FcHyfV3TQ"
},
"source": [
"### Feature Scaling"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "-zfEzkRVXIwF"
},
"source": [
"## Part 2 - Building the ANN"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KvdeScabXtlB"
},
"source": [
"### Initializing the ANN"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"ann = tf.keras.models.Sequential()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "rP6urV6SX7kS"
},
"source": [
"### Adding the input layer and the first hidden layer"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"ann.add(tf.keras.layers.Dense(units=6, activation='relu'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BELWAc_8YJze"
},
"source": [
"### Adding the second hidden layer"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"ann.add(tf.keras.layers.Dense(units=6, activation='relu'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "OyNEe6RXYcU4"
},
"source": [
"### Adding the output layer"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
"ann.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "JT4u2S1_Y4WG"
},
"source": [
"## Part 3 - Training the ANN"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8GWlJChhY_ZI"
},
"source": [
"### Compiling the ANN"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "0QR_G5u7ZLSM"
},
"source": [
"### Training the ANN on the Training set"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"250/250 [==============================] - 1s 951us/step - loss: 0.6313 - accuracy: 0.6846\n",
"Epoch 2/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.5053 - accuracy: 0.7962\n",
"Epoch 3/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4862 - accuracy: 0.7962\n",
"Epoch 4/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4779 - accuracy: 0.7962\n",
"Epoch 5/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4670 - accuracy: 0.7985\n",
"Epoch 6/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4457 - accuracy: 0.8126\n",
"Epoch 7/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4288 - accuracy: 0.8170\n",
"Epoch 8/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4192 - accuracy: 0.8186\n",
"Epoch 9/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4144 - accuracy: 0.8192\n",
"Epoch 10/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4119 - accuracy: 0.8199\n",
"Epoch 11/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4104 - accuracy: 0.8200\n",
"Epoch 12/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4094 - accuracy: 0.8198\n",
"Epoch 13/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4089 - accuracy: 0.8195\n",
"Epoch 14/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4085 - accuracy: 0.8199\n",
"Epoch 15/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4080 - accuracy: 0.8194\n",
"Epoch 16/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4079 - accuracy: 0.8200\n",
"Epoch 17/100\n",
"250/250 [==============================] - 0s 906us/step - loss: 0.4074 - accuracy: 0.8195\n",
"Epoch 18/100\n",
"250/250 [==============================] - 0s 973us/step - loss: 0.4075 - accuracy: 0.8201\n",
"Epoch 19/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4074 - accuracy: 0.8205\n",
"Epoch 20/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4074 - accuracy: 0.8216\n",
"Epoch 21/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4068 - accuracy: 0.8213\n",
"Epoch 22/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4071 - accuracy: 0.8229\n",
"Epoch 23/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4068 - accuracy: 0.8215\n",
"Epoch 24/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4067 - accuracy: 0.8226\n",
"Epoch 25/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4067 - accuracy: 0.8220\n",
"Epoch 26/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4065 - accuracy: 0.8229\n",
"Epoch 27/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4064 - accuracy: 0.8226\n",
"Epoch 28/100\n",
"250/250 [==============================] - 0s 980us/step - loss: 0.4063 - accuracy: 0.8236\n",
"Epoch 29/100\n",
"250/250 [==============================] - 0s 908us/step - loss: 0.4063 - accuracy: 0.8227\n",
"Epoch 30/100\n",
"250/250 [==============================] - 0s 773us/step - loss: 0.4064 - accuracy: 0.8231\n",
"Epoch 31/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4065 - accuracy: 0.8227\n",
"Epoch 32/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4064 - accuracy: 0.8221\n",
"Epoch 33/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4059 - accuracy: 0.8232\n",
"Epoch 34/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4062 - accuracy: 0.8234\n",
"Epoch 35/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4061 - accuracy: 0.8226\n",
"Epoch 36/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4059 - accuracy: 0.8231\n",
"Epoch 37/100\n",
"250/250 [==============================] - 0s 883us/step - loss: 0.4061 - accuracy: 0.8220\n",
"Epoch 38/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4062 - accuracy: 0.8229\n",
"Epoch 39/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4060 - accuracy: 0.8232\n",
"Epoch 40/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4059 - accuracy: 0.8225\n",
"Epoch 41/100\n",
"250/250 [==============================] - 0s 890us/step - loss: 0.4061 - accuracy: 0.8221\n",
"Epoch 42/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4061 - accuracy: 0.8234\n",
"Epoch 43/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4062 - accuracy: 0.8230\n",
"Epoch 44/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4060 - accuracy: 0.8223\n",
"Epoch 45/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4057 - accuracy: 0.8234\n",
"Epoch 46/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4058 - accuracy: 0.8231\n",
"Epoch 47/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4058 - accuracy: 0.8232\n",
"Epoch 48/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4059 - accuracy: 0.8234\n",
"Epoch 49/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4058 - accuracy: 0.8232\n",
"Epoch 50/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4057 - accuracy: 0.8231\n",
"Epoch 51/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4060 - accuracy: 0.8239\n",
"Epoch 52/100\n",
"250/250 [==============================] - 0s 930us/step - loss: 0.4057 - accuracy: 0.8235\n",
"Epoch 53/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4059 - accuracy: 0.8238\n",
"Epoch 54/100\n",
"250/250 [==============================] - 0s 948us/step - loss: 0.4059 - accuracy: 0.8231\n",
"Epoch 55/100\n",
"250/250 [==============================] - 0s 987us/step - loss: 0.4057 - accuracy: 0.8239\n",
"Epoch 56/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4059 - accuracy: 0.8251\n",
"Epoch 57/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4056 - accuracy: 0.8240\n",
"Epoch 58/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4056 - accuracy: 0.8230\n",
"Epoch 59/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4056 - accuracy: 0.8232\n",
"Epoch 60/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4058 - accuracy: 0.8230\n",
"Epoch 61/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4054 - accuracy: 0.8231\n",
"Epoch 62/100\n",
"250/250 [==============================] - 0s 796us/step - loss: 0.4056 - accuracy: 0.8229\n",
"Epoch 63/100\n",
"250/250 [==============================] - 0s 901us/step - loss: 0.4055 - accuracy: 0.8227\n",
"Epoch 64/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4056 - accuracy: 0.8246\n",
"Epoch 65/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4057 - accuracy: 0.8240\n",
"Epoch 66/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4056 - accuracy: 0.8251\n",
"Epoch 67/100\n",
"250/250 [==============================] - 0s 948us/step - loss: 0.4055 - accuracy: 0.8232\n",
"Epoch 68/100\n",
"250/250 [==============================] - 0s 940us/step - loss: 0.4057 - accuracy: 0.8232\n",
"Epoch 69/100\n",
"250/250 [==============================] - 0s 951us/step - loss: 0.4055 - accuracy: 0.8223\n",
"Epoch 70/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4056 - accuracy: 0.8231\n",
"Epoch 71/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4054 - accuracy: 0.8238\n",
"Epoch 72/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4054 - accuracy: 0.8230\n",
"Epoch 73/100\n",
"250/250 [==============================] - 0s 910us/step - loss: 0.4054 - accuracy: 0.8231\n",
"Epoch 74/100\n",
"250/250 [==============================] - 0s 776us/step - loss: 0.4054 - accuracy: 0.8226\n",
"Epoch 75/100\n",
"250/250 [==============================] - 0s 822us/step - loss: 0.4056 - accuracy: 0.8219\n",
"Epoch 76/100\n",
"250/250 [==============================] - 0s 875us/step - loss: 0.4056 - accuracy: 0.8221\n",
"Epoch 77/100\n",
"250/250 [==============================] - 0s 939us/step - loss: 0.4052 - accuracy: 0.8236\n",
"Epoch 78/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4054 - accuracy: 0.8232\n",
"Epoch 79/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4055 - accuracy: 0.8236\n",
"Epoch 80/100\n",
"250/250 [==============================] - 0s 997us/step - loss: 0.4053 - accuracy: 0.8221\n",
"Epoch 81/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4054 - accuracy: 0.8232\n",
"Epoch 82/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4053 - accuracy: 0.8230\n",
"Epoch 83/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4052 - accuracy: 0.8224\n",
"Epoch 84/100\n",
"250/250 [==============================] - 0s 822us/step - loss: 0.4053 - accuracy: 0.8225\n",
"Epoch 85/100\n",
"250/250 [==============================] - 0s 846us/step - loss: 0.4052 - accuracy: 0.8230\n",
"Epoch 86/100\n",
"250/250 [==============================] - 0s 775us/step - loss: 0.4052 - accuracy: 0.8232\n",
"Epoch 87/100\n",
"250/250 [==============================] - 0s 843us/step - loss: 0.4051 - accuracy: 0.8232\n",
"Epoch 88/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4053 - accuracy: 0.8220\n",
"Epoch 89/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4054 - accuracy: 0.8234\n",
"Epoch 90/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4050 - accuracy: 0.8236\n",
"Epoch 91/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4054 - accuracy: 0.8214\n",
"Epoch 92/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4053 - accuracy: 0.8238\n",
"Epoch 93/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4054 - accuracy: 0.8229\n",
"Epoch 94/100\n",
"250/250 [==============================] - 0s 879us/step - loss: 0.4052 - accuracy: 0.8227\n",
"Epoch 95/100\n",
"250/250 [==============================] - 0s 782us/step - loss: 0.4051 - accuracy: 0.8240\n",
"Epoch 96/100\n",
"250/250 [==============================] - 0s 796us/step - loss: 0.4053 - accuracy: 0.8221\n",
"Epoch 97/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4052 - accuracy: 0.8219\n",
"Epoch 98/100\n",
"250/250 [==============================] - 0s 820us/step - loss: 0.4051 - accuracy: 0.8227\n",
"Epoch 99/100\n",
"250/250 [==============================] - 0s 910us/step - loss: 0.4051 - accuracy: 0.8232\n",
"Epoch 100/100\n",
"250/250 [==============================] - 0s 1ms/step - loss: 0.4052 - accuracy: 0.8223\n"
]
},
{
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x7fdf200c2130>"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ann.fit(X_train, y_train, batch_size = 32, epochs = 100)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "tJj5k2MxZga3"
},
"source": [
"## Part 4 - Making the predictions and evaluating the model"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "u7yx47jPZt11"
},
"source": [
"### Predicting the Test set results"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 137
},
"colab_type": "code",
"id": "nIyEeQdRZwgs",
"outputId": "82330ba8-9bdc-4fd1-d3cf-b6d78ee7c2a3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"63/63 [==============================] - 0s 606us/step\n",
"[[0 1]\n",
" [0 0]\n",
" [0 0]\n",
" ...\n",
" [0 0]\n",
" [1 1]\n",
" [0 0]]\n"
]
}
],
"source": [
"y_pred = ann.predict(X_test)\n",
"y_pred = (y_pred > 0.5)\n",
"print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "o0oyfLWoaEGw"
},
"source": [
"### Making the Confusion Matrix"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
},
"colab_type": "code",
"id": "ci6K_r6LaF6P",
"outputId": "4d854e9e-22d5-432f-f6e5-a102fe3ae0bd"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1536 57]\n",
" [ 299 108]]\n"
]
},
{
"data": {
"text/plain": [
"0.822"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import confusion_matrix, accuracy_score\n",
"cm = confusion_matrix(y_test, y_pred)\n",
"print(cm)\n",
"accuracy_score(y_test, y_pred)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "artificial_neural_network.ipynb",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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