{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bank marketing analysis"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# chargement des libraries\n",
"\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Unnamed: 0 | \n",
" age | \n",
" job | \n",
" marital | \n",
" education | \n",
" default | \n",
" balance | \n",
" housing | \n",
" loan | \n",
" contact | \n",
" day | \n",
" month | \n",
" duration | \n",
" campaign | \n",
" pdays | \n",
" previous | \n",
" poutcome | \n",
" y | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 0 | \n",
" 30 | \n",
" unemployed | \n",
" married | \n",
" primary | \n",
" no | \n",
" 1787 | \n",
" no | \n",
" no | \n",
" cellular | \n",
" 19 | \n",
" oct | \n",
" 79 | \n",
" 1 | \n",
" -1 | \n",
" 0 | \n",
" unknown | \n",
" no | \n",
"
\n",
" \n",
" 1 | \n",
" 1 | \n",
" 33 | \n",
" services | \n",
" married | \n",
" secondary | \n",
" no | \n",
" 4789 | \n",
" yes | \n",
" yes | \n",
" cellular | \n",
" 11 | \n",
" may | \n",
" 220 | \n",
" 1 | \n",
" 339 | \n",
" 4 | \n",
" failure | \n",
" no | \n",
"
\n",
" \n",
" 2 | \n",
" 2 | \n",
" 35 | \n",
" management | \n",
" single | \n",
" tertiary | \n",
" no | \n",
" 1350 | \n",
" yes | \n",
" no | \n",
" cellular | \n",
" 16 | \n",
" apr | \n",
" 185 | \n",
" 1 | \n",
" 330 | \n",
" 1 | \n",
" failure | \n",
" no | \n",
"
\n",
" \n",
" 3 | \n",
" 3 | \n",
" 30 | \n",
" management | \n",
" married | \n",
" tertiary | \n",
" no | \n",
" 1476 | \n",
" yes | \n",
" yes | \n",
" unknown | \n",
" 3 | \n",
" jun | \n",
" 199 | \n",
" 4 | \n",
" -1 | \n",
" 0 | \n",
" unknown | \n",
" no | \n",
"
\n",
" \n",
" 4 | \n",
" 4 | \n",
" 59 | \n",
" blue-collar | \n",
" married | \n",
" secondary | \n",
" no | \n",
" 0 | \n",
" yes | \n",
" no | \n",
" unknown | \n",
" 5 | \n",
" may | \n",
" 226 | \n",
" 1 | \n",
" -1 | \n",
" 0 | \n",
" unknown | \n",
" no | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Unnamed: 0 age job marital education default balance housing \\\n",
"0 0 30 unemployed married primary no 1787 no \n",
"1 1 33 services married secondary no 4789 yes \n",
"2 2 35 management single tertiary no 1350 yes \n",
"3 3 30 management married tertiary no 1476 yes \n",
"4 4 59 blue-collar married secondary no 0 yes \n",
"\n",
" loan contact day month duration campaign pdays previous poutcome y \n",
"0 no cellular 19 oct 79 1 -1 0 unknown no \n",
"1 yes cellular 11 may 220 1 339 4 failure no \n",
"2 no cellular 16 apr 185 1 330 1 failure no \n",
"3 yes unknown 3 jun 199 4 -1 0 unknown no \n",
"4 no unknown 5 may 226 1 -1 0 unknown no "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('./bank_data.csv')\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 0\n",
"age 0\n",
"job 0\n",
"marital 0\n",
"education 0\n",
"default 0\n",
"balance 0\n",
"housing 0\n",
"loan 0\n",
"contact 0\n",
"day 0\n",
"month 0\n",
"duration 0\n",
"campaign 0\n",
"pdays 0\n",
"previous 0\n",
"poutcome 0\n",
"y 0\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Unnamed: 0 | \n",
" age | \n",
" balance | \n",
" day | \n",
" duration | \n",
" campaign | \n",
" pdays | \n",
" previous | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 4521.000000 | \n",
" 4521.000000 | \n",
" 4521.000000 | \n",
" 4521.000000 | \n",
" 4521.000000 | \n",
" 4521.000000 | \n",
" 4521.000000 | \n",
" 4521.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 2260.000000 | \n",
" 41.170095 | \n",
" 1422.657819 | \n",
" 15.915284 | \n",
" 263.961292 | \n",
" 2.793630 | \n",
" 39.766645 | \n",
" 0.542579 | \n",
"
\n",
" \n",
" std | \n",
" 1305.244613 | \n",
" 10.576211 | \n",
" 3009.638142 | \n",
" 8.247667 | \n",
" 259.856633 | \n",
" 3.109807 | \n",
" 100.121124 | \n",
" 1.693562 | \n",
"
\n",
" \n",
" min | \n",
" 0.000000 | \n",
" 19.000000 | \n",
" -3313.000000 | \n",
" 1.000000 | \n",
" 4.000000 | \n",
" 1.000000 | \n",
" -1.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 1130.000000 | \n",
" 33.000000 | \n",
" 69.000000 | \n",
" 9.000000 | \n",
" 104.000000 | \n",
" 1.000000 | \n",
" -1.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 50% | \n",
" 2260.000000 | \n",
" 39.000000 | \n",
" 444.000000 | \n",
" 16.000000 | \n",
" 185.000000 | \n",
" 2.000000 | \n",
" -1.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 75% | \n",
" 3390.000000 | \n",
" 49.000000 | \n",
" 1480.000000 | \n",
" 21.000000 | \n",
" 329.000000 | \n",
" 3.000000 | \n",
" -1.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" max | \n",
" 4520.000000 | \n",
" 87.000000 | \n",
" 71188.000000 | \n",
" 31.000000 | \n",
" 3025.000000 | \n",
" 50.000000 | \n",
" 871.000000 | \n",
" 25.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Unnamed: 0 age balance day duration \\\n",
"count 4521.000000 4521.000000 4521.000000 4521.000000 4521.000000 \n",
"mean 2260.000000 41.170095 1422.657819 15.915284 263.961292 \n",
"std 1305.244613 10.576211 3009.638142 8.247667 259.856633 \n",
"min 0.000000 19.000000 -3313.000000 1.000000 4.000000 \n",
"25% 1130.000000 33.000000 69.000000 9.000000 104.000000 \n",
"50% 2260.000000 39.000000 444.000000 16.000000 185.000000 \n",
"75% 3390.000000 49.000000 1480.000000 21.000000 329.000000 \n",
"max 4520.000000 87.000000 71188.000000 31.000000 3025.000000 \n",
"\n",
" campaign pdays previous \n",
"count 4521.000000 4521.000000 4521.000000 \n",
"mean 2.793630 39.766645 0.542579 \n",
"std 3.109807 100.121124 1.693562 \n",
"min 1.000000 -1.000000 0.000000 \n",
"25% 1.000000 -1.000000 0.000000 \n",
"50% 2.000000 -1.000000 0.000000 \n",
"75% 3.000000 -1.000000 0.000000 \n",
"max 50.000000 871.000000 25.000000 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"data = data.drop(columns=['Unnamed: 0'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"management 969\n",
"blue-collar 946\n",
"technician 768\n",
"admin. 478\n",
"services 417\n",
"retired 230\n",
"self-employed 183\n",
"entrepreneur 168\n",
"unemployed 128\n",
"housemaid 112\n",
"student 84\n",
"unknown 38\n",
"Name: job, dtype: int64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['job'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"management 969\n",
"blue-collar 946\n",
"technician 768\n",
"admin. 478\n",
"jobless 442\n",
"services 417\n",
"self-employed 183\n",
"entrepreneur 168\n",
"housemaid 112\n",
"unknown 38\n",
"Name: job, dtype: int64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jobless_categories = ['student', 'unemployed', 'retired']\n",
"data['job'] = data['job'].replace(jobless_categories, 'jobless')\n",
"data['job'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"secondary 2306\n",
"tertiary 1350\n",
"primary 678\n",
"unknown 187\n",
"Name: education, dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['education'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 4521 entries, 0 to 4520\n",
"Data columns (total 17 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 age 4521 non-null int64 \n",
" 1 job 4521 non-null object\n",
" 2 marital 4521 non-null object\n",
" 3 education 4521 non-null object\n",
" 4 default 4521 non-null object\n",
" 5 balance 4521 non-null int64 \n",
" 6 housing 4521 non-null object\n",
" 7 loan 4521 non-null object\n",
" 8 contact 4521 non-null object\n",
" 9 day 4521 non-null int64 \n",
" 10 month 4521 non-null object\n",
" 11 duration 4521 non-null int64 \n",
" 12 campaign 4521 non-null int64 \n",
" 13 pdays 4521 non-null int64 \n",
" 14 previous 4521 non-null int64 \n",
" 15 poutcome 4521 non-null object\n",
" 16 y 4521 non-null object\n",
"dtypes: int64(7), object(10)\n",
"memory usage: 600.6+ KB\n"
]
}
],
"source": [
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
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" | \n",
" age | \n",
" job | \n",
" marital | \n",
" education | \n",
" default | \n",
" balance | \n",
" housing | \n",
" loan | \n",
" contact | \n",
" day | \n",
" month | \n",
" duration | \n",
" campaign | \n",
" pdays | \n",
" previous | \n",
" poutcome | \n",
" y | \n",
"
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" 0 | \n",
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" 1787 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 19 | \n",
" 10 | \n",
" 79 | \n",
" 1 | \n",
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" 3 | \n",
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"
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" 1 | \n",
" 33 | \n",
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" 1 | \n",
" 0 | \n",
" 4789 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
" 11 | \n",
" 8 | \n",
" 220 | \n",
" 1 | \n",
" 339 | \n",
" 4 | \n",
" 0 | \n",
" 0 | \n",
"
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" 2 | \n",
" 35 | \n",
" 5 | \n",
" 2 | \n",
" 2 | \n",
" 0 | \n",
" 1350 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 16 | \n",
" 0 | \n",
" 185 | \n",
" 1 | \n",
" 330 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
"
\n",
" \n",
" 3 | \n",
" 30 | \n",
" 5 | \n",
" 1 | \n",
" 2 | \n",
" 0 | \n",
" 1476 | \n",
" 1 | \n",
" 1 | \n",
" 2 | \n",
" 3 | \n",
" 6 | \n",
" 199 | \n",
" 4 | \n",
" -1 | \n",
" 0 | \n",
" 3 | \n",
" 0 | \n",
"
\n",
" \n",
" 4 | \n",
" 59 | \n",
" 1 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
" 2 | \n",
" 5 | \n",
" 8 | \n",
" 226 | \n",
" 1 | \n",
" -1 | \n",
" 0 | \n",
" 3 | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" age job marital education default balance housing loan contact \\\n",
"0 30 4 1 0 0 1787 0 0 0 \n",
"1 33 7 1 1 0 4789 1 1 0 \n",
"2 35 5 2 2 0 1350 1 0 0 \n",
"3 30 5 1 2 0 1476 1 1 2 \n",
"4 59 1 1 1 0 0 1 0 2 \n",
"\n",
" day month duration campaign pdays previous poutcome y \n",
"0 19 10 79 1 -1 0 3 0 \n",
"1 11 8 220 1 339 4 0 0 \n",
"2 16 0 185 1 330 1 0 0 \n",
"3 3 6 199 4 -1 0 3 0 \n",
"4 5 8 226 1 -1 0 3 0 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"# Select categorical columns for label encoding\n",
"categorical_columns = ['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'poutcome','y']\n",
"\n",
"# Initialize LabelEncoder\n",
"label_encoder = LabelEncoder()\n",
"\n",
"# Apply label encoding to each categorical column\n",
"for col in categorical_columns:\n",
" data[col] = label_encoder.fit_transform(data[col])\n",
"\n",
"# Display the modified DataFrame\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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