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10399705-HB-s1/assignmentML.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "V-JHCfJ88QL4"
},
"source": [
"# Classification problem using Portuguese Bank Marketing Data Set\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"id": "TU_YtRqj8ibq"
},
"outputs": [],
"source": [
"# These imports will be useful for this problem of classification\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import sklearn\n",
"from scipy.stats import zscore\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import confusion_matrix, classification_report, f1_score, recall_score\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.model_selection import StratifiedKFold\n",
"from sklearn.metrics import accuracy_score, mean_squared_error, r2_score\n",
"from sklearn import preprocessing\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import cross_val_score, GridSearchCV\n",
"from imblearn.over_sampling import SMOTE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pre-Proecessing"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"id": "0dPGcFXk8uMs"
},
"outputs": [],
"source": [
"path = \"bank-full.csv\" \n",
"df = pd.read_csv(path,sep=\";\") # reading the csv file and removing ';'"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"id": "UjToIE2E9AsX"
},
"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>age</th>\n",
" <th>job</th>\n",
" <th>marital</th>\n",
" <th>education</th>\n",
" <th>default</th>\n",
" <th>balance</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
" <th>contact</th>\n",
" <th>day</th>\n",
" <th>month</th>\n",
" <th>duration</th>\n",
" <th>campaign</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" <th>poutcome</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>58</td>\n",
" <td>management</td>\n",
" <td>married</td>\n",
" <td>tertiary</td>\n",
" <td>no</td>\n",
" <td>2143</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>unknown</td>\n",
" <td>5</td>\n",
" <td>may</td>\n",
" <td>261</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>44</td>\n",
" <td>technician</td>\n",
" <td>single</td>\n",
" <td>secondary</td>\n",
" <td>no</td>\n",
" <td>29</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>unknown</td>\n",
" <td>5</td>\n",
" <td>may</td>\n",
" <td>151</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>33</td>\n",
" <td>entrepreneur</td>\n",
" <td>married</td>\n",
" <td>secondary</td>\n",
" <td>no</td>\n",
" <td>2</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>unknown</td>\n",
" <td>5</td>\n",
" <td>may</td>\n",
" <td>76</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>47</td>\n",
" <td>blue-collar</td>\n",
" <td>married</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>1506</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>unknown</td>\n",
" <td>5</td>\n",
" <td>may</td>\n",
" <td>92</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>33</td>\n",
" <td>unknown</td>\n",
" <td>single</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>unknown</td>\n",
" <td>5</td>\n",
" <td>may</td>\n",
" <td>198</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age job marital education default balance housing loan \\\n",
"0 58 management married tertiary no 2143 yes no \n",
"1 44 technician single secondary no 29 yes no \n",
"2 33 entrepreneur married secondary no 2 yes yes \n",
"3 47 blue-collar married unknown no 1506 yes no \n",
"4 33 unknown single unknown no 1 no no \n",
"\n",
" contact day month duration campaign pdays previous poutcome y \n",
"0 unknown 5 may 261 1 -1 0 unknown no \n",
"1 unknown 5 may 151 1 -1 0 unknown no \n",
"2 unknown 5 may 76 1 -1 0 unknown no \n",
"3 unknown 5 may 92 1 -1 0 unknown no \n",
"4 unknown 5 may 198 1 -1 0 unknown no "
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head() # first 5 rows of data"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"id": "COZMa8cq9S2n"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 45211 entries, 0 to 45210\n",
"Data columns (total 17 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 age 45211 non-null int64 \n",
" 1 job 45211 non-null object\n",
" 2 marital 45211 non-null object\n",
" 3 education 45211 non-null object\n",
" 4 default 45211 non-null object\n",
" 5 balance 45211 non-null int64 \n",
" 6 housing 45211 non-null object\n",
" 7 loan 45211 non-null object\n",
" 8 contact 45211 non-null object\n",
" 9 day 45211 non-null int64 \n",
" 10 month 45211 non-null object\n",
" 11 duration 45211 non-null int64 \n",
" 12 campaign 45211 non-null int64 \n",
" 13 pdays 45211 non-null int64 \n",
" 14 previous 45211 non-null int64 \n",
" 15 poutcome 45211 non-null object\n",
" 16 y 45211 non-null object\n",
"dtypes: int64(7), object(10)\n",
"memory usage: 5.9+ MB\n"
]
}
],
"source": [
"df.info() # Displaying all the columns with their data types and null values status"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"id": "xv9_LEyo9dDf"
},
"outputs": [
{
"data": {
"text/plain": [
"no 39922\n",
"yes 5289\n",
"Name: y, dtype: int64"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['y'].value_counts() # counting the 'yes' and 'no' for the label"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"id": "Vx33HGKC9zDN"
},
"outputs": [
{
"data": {
"text/plain": [
"(45211, 17)"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape # rows and columns"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"id": "YZDyPZJC-lgu"
},
"outputs": [
{
"data": {
"text/plain": [
"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": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"df.isnull().sum() # show total number of null values for each column"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"id": "GKCgzVRp-rOE"
},
"outputs": [
{
"data": {
"text/plain": [
"array(['no', 'yes'], dtype=object)"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['y'].unique() # unique values present in label of the dataset"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"id": "ZIKfvfrt_Qfm"
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.duplicated().sum() # check for duplication"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"id": "ifznTSqk_kJz"
},
"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>age</th>\n",
" <th>balance</th>\n",
" <th>day</th>\n",
" <th>duration</th>\n",
" <th>campaign</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>45211.000000</td>\n",
" <td>45211.000000</td>\n",
" <td>45211.000000</td>\n",
" <td>45211.000000</td>\n",
" <td>45211.000000</td>\n",
" <td>45211.000000</td>\n",
" <td>45211.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>40.936210</td>\n",
" <td>1362.272058</td>\n",
" <td>15.806419</td>\n",
" <td>258.163080</td>\n",
" <td>2.763841</td>\n",
" <td>40.197828</td>\n",
" <td>0.580323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>10.618762</td>\n",
" <td>3044.765829</td>\n",
" <td>8.322476</td>\n",
" <td>257.527812</td>\n",
" <td>3.098021</td>\n",
" <td>100.128746</td>\n",
" <td>2.303441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>18.000000</td>\n",
" <td>-8019.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>33.000000</td>\n",
" <td>72.000000</td>\n",
" <td>8.000000</td>\n",
" <td>103.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>39.000000</td>\n",
" <td>448.000000</td>\n",
" <td>16.000000</td>\n",
" <td>180.000000</td>\n",
" <td>2.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>48.000000</td>\n",
" <td>1428.000000</td>\n",
" <td>21.000000</td>\n",
" <td>319.000000</td>\n",
" <td>3.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>95.000000</td>\n",
" <td>102127.000000</td>\n",
" <td>31.000000</td>\n",
" <td>4918.000000</td>\n",
" <td>63.000000</td>\n",
" <td>871.000000</td>\n",
" <td>275.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age balance day duration campaign \\\n",
"count 45211.000000 45211.000000 45211.000000 45211.000000 45211.000000 \n",
"mean 40.936210 1362.272058 15.806419 258.163080 2.763841 \n",
"std 10.618762 3044.765829 8.322476 257.527812 3.098021 \n",
"min 18.000000 -8019.000000 1.000000 0.000000 1.000000 \n",
"25% 33.000000 72.000000 8.000000 103.000000 1.000000 \n",
"50% 39.000000 448.000000 16.000000 180.000000 2.000000 \n",
"75% 48.000000 1428.000000 21.000000 319.000000 3.000000 \n",
"max 95.000000 102127.000000 31.000000 4918.000000 63.000000 \n",
"\n",
" pdays previous \n",
"count 45211.000000 45211.000000 \n",
"mean 40.197828 0.580323 \n",
"std 100.128746 2.303441 \n",
"min -1.000000 0.000000 \n",
"25% -1.000000 0.000000 \n",
"50% -1.000000 0.000000 \n",
"75% -1.000000 0.000000 \n",
"max 871.000000 275.000000 "
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe() # statistical summary of the nummerical features"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"df['y'] = df['y'].replace(to_replace = ['yes','no'],value = [1,0]) # changing target values from 'yes' and 'no' to 1 and 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"id": "Ch_oacf2_Lgx",
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Using seaborn to make statistical graphs\n",
"\n",
"sns.set(font_scale=1)\n",
"perc_age = df['y'].agg('value_counts', normalize=True) * 100 # creating percentage distribution\n",
"countplt=sns.countplot(x='y', data=df, palette ='Set1') # creating graph\n",
"plt.text(0, 18000, \"{:.2g}%\".format(perc_age[0]), ha='center', va='bottom', size=15, color='w') # applying percentage distribution to '0'\n",
"plt.text(1, 500, \"{:.2g}%\".format(perc_age[1]), ha='center', va='bottom', size=15, color='w') # applying percentage distribution to '1' \n",
"plt.tight_layout()\n",
"plt.show() # display graph"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"id": "recauKpx_2jc",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# creating boxplot for df\n",
"\n",
"plt.figure(figsize=(20,10))\n",
"df.boxplot()\n",
"plt.title(\"Boxplot of the dataframe\", fontsize = 15)\n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qLq5VAtMGu5l"
},
"source": [
"#### Creating correlation heat map"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"df['normal_balance']=zscore(df['balance']) # adding new column with zscore values of 'balance'"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"df=df.drop(df[(df['normal_balance']>3)|(df['normal_balance']<-3)].index, axis=0, inplace=False) # step to remove outliers"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# showing boxplot with removed outliers\n",
"\n",
"plt.figure(figsize=(20,10))\n",
"df.boxplot()\n",
"plt.title(\"Boxplot of the dataframe\", fontsize = 15)\n",
"plt.tight_layout()\n",
"print()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='age', ylabel='balance'>"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Scatter plot with age and balance on x and y-axis respectively \n",
"\n",
"sns.scatterplot(x='age',y='balance',data=df, hue='y')"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"id": "Qysq9fbAAKIp"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# correlation matrix for df\n",
"\n",
"corr_matrix = df.corr()\n",
"plt.figure(figsize=(16,10))\n",
"sns.heatmap(corr_matrix, annot=True, vmin=-1, vmax=1,fmt='.2f')\n",
"plt.title(\"Correlation Heatmap\", fontsize = 15)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Histogram chart showing age distribution for df\n",
"\n",
"age_dist = plt.figure(figsize = (10,5))\n",
"\n",
"chart = age_dist.add_subplot(1,2,1)\n",
"\n",
"chart.hist(df['age'])\n",
"\n",
"chart.set_title('Age Distribution')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [],
"source": [
"df2 = df.copy() # creating a copy of df called df2"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"del df2[\"normal_balance\"] # removing the normal_balance column from df2"
]
},
{
"cell_type": "code",
"execution_count": 115,
"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>age</th>\n",
" <th>job</th>\n",
" <th>marital</th>\n",
" <th>education</th>\n",
" <th>default</th>\n",
" <th>balance</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
" <th>contact</th>\n",
" <th>day</th>\n",
" <th>month</th>\n",
" <th>duration</th>\n",
" <th>campaign</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" <th>poutcome</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>58</td>\n",
" <td>management</td>\n",
" <td>married</td>\n",
" <td>tertiary</td>\n",
" <td>no</td>\n",
" <td>2143</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>unknown</td>\n",
" <td>5</td>\n",
" <td>may</td>\n",
" <td>261</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>44</td>\n",
" <td>technician</td>\n",
" <td>single</td>\n",
" <td>secondary</td>\n",
" <td>no</td>\n",
" <td>29</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>unknown</td>\n",
" <td>5</td>\n",
" <td>may</td>\n",
" <td>151</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>33</td>\n",
" <td>entrepreneur</td>\n",
" <td>married</td>\n",
" <td>secondary</td>\n",
" <td>no</td>\n",
" <td>2</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>unknown</td>\n",
" <td>5</td>\n",
" <td>may</td>\n",
" <td>76</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>47</td>\n",
" <td>blue-collar</td>\n",
" <td>married</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>1506</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>unknown</td>\n",
" <td>5</td>\n",
" <td>may</td>\n",
" <td>92</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>33</td>\n",
" <td>unknown</td>\n",
" <td>single</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>unknown</td>\n",
" <td>5</td>\n",
" <td>may</td>\n",
" <td>198</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45206</th>\n",
" <td>51</td>\n",
" <td>technician</td>\n",
" <td>married</td>\n",
" <td>tertiary</td>\n",
" <td>no</td>\n",
" <td>825</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>cellular</td>\n",
" <td>17</td>\n",
" <td>nov</td>\n",
" <td>977</td>\n",
" <td>3</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45207</th>\n",
" <td>71</td>\n",
" <td>retired</td>\n",
" <td>divorced</td>\n",
" <td>primary</td>\n",
" <td>no</td>\n",
" <td>1729</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>cellular</td>\n",
" <td>17</td>\n",
" <td>nov</td>\n",
" <td>456</td>\n",
" <td>2</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45208</th>\n",
" <td>72</td>\n",
" <td>retired</td>\n",
" <td>married</td>\n",
" <td>secondary</td>\n",
" <td>no</td>\n",
" <td>5715</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>cellular</td>\n",
" <td>17</td>\n",
" <td>nov</td>\n",
" <td>1127</td>\n",
" <td>5</td>\n",
" <td>184</td>\n",
" <td>3</td>\n",
" <td>success</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45209</th>\n",
" <td>57</td>\n",
" <td>blue-collar</td>\n",
" <td>married</td>\n",
" <td>secondary</td>\n",
" <td>no</td>\n",
" <td>668</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>17</td>\n",
" <td>nov</td>\n",
" <td>508</td>\n",
" <td>4</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>unknown</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45210</th>\n",
" <td>37</td>\n",
" <td>entrepreneur</td>\n",
" <td>married</td>\n",
" <td>secondary</td>\n",
" <td>no</td>\n",
" <td>2971</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>cellular</td>\n",
" <td>17</td>\n",
" <td>nov</td>\n",
" <td>361</td>\n",
" <td>2</td>\n",
" <td>188</td>\n",
" <td>11</td>\n",
" <td>other</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>44466 rows × 17 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default balance housing loan \\\n",
"0 58 management married tertiary no 2143 yes no \n",
"1 44 technician single secondary no 29 yes no \n",
"2 33 entrepreneur married secondary no 2 yes yes \n",
"3 47 blue-collar married unknown no 1506 yes no \n",
"4 33 unknown single unknown no 1 no no \n",
"... ... ... ... ... ... ... ... ... \n",
"45206 51 technician married tertiary no 825 no no \n",
"45207 71 retired divorced primary no 1729 no no \n",
"45208 72 retired married secondary no 5715 no no \n",
"45209 57 blue-collar married secondary no 668 no no \n",
"45210 37 entrepreneur married secondary no 2971 no no \n",
"\n",
" contact day month duration campaign pdays previous poutcome y \n",
"0 unknown 5 may 261 1 -1 0 unknown 0 \n",
"1 unknown 5 may 151 1 -1 0 unknown 0 \n",
"2 unknown 5 may 76 1 -1 0 unknown 0 \n",
"3 unknown 5 may 92 1 -1 0 unknown 0 \n",
"4 unknown 5 may 198 1 -1 0 unknown 0 \n",
"... ... ... ... ... ... ... ... ... .. \n",
"45206 cellular 17 nov 977 3 -1 0 unknown 1 \n",
"45207 cellular 17 nov 456 2 -1 0 unknown 1 \n",
"45208 cellular 17 nov 1127 5 184 3 success 1 \n",
"45209 telephone 17 nov 508 4 -1 0 unknown 0 \n",
"45210 cellular 17 nov 361 2 188 11 other 0 \n",
"\n",
"[44466 rows x 17 columns]"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data preparation for models application"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>balance</th>\n",
" <th>day</th>\n",
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" <th>campaign</th>\n",
" <th>pdays</th>\n",
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" <th>month_may</th>\n",
" <th>month_nov</th>\n",
" <th>month_oct</th>\n",
" <th>month_sep</th>\n",
" <th>poutcome_other</th>\n",
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" <th>poutcome_unknown</th>\n",
" </tr>\n",
" </thead>\n",
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" <td>1</td>\n",
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" <th>2</th>\n",
" <td>33</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>76</td>\n",
" <td>1</td>\n",
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" <td>1506</td>\n",
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" <td>92</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>198</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>51</td>\n",
" <td>825</td>\n",
" <td>17</td>\n",
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" <td>3</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
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" <th>45208</th>\n",
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" <th>45209</th>\n",
" <td>57</td>\n",
" <td>668</td>\n",
" <td>17</td>\n",
" <td>508</td>\n",
" <td>4</td>\n",
" <td>-1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45210</th>\n",
" <td>37</td>\n",
" <td>2971</td>\n",
" <td>17</td>\n",
" <td>361</td>\n",
" <td>2</td>\n",
" <td>188</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>44466 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" age balance day duration campaign pdays previous y \\\n",
"0 58 2143 5 261 1 -1 0 0 \n",
"1 44 29 5 151 1 -1 0 0 \n",
"2 33 2 5 76 1 -1 0 0 \n",
"3 47 1506 5 92 1 -1 0 0 \n",
"4 33 1 5 198 1 -1 0 0 \n",
"... ... ... ... ... ... ... ... .. \n",
"45206 51 825 17 977 3 -1 0 1 \n",
"45207 71 1729 17 456 2 -1 0 1 \n",
"45208 72 5715 17 1127 5 184 3 1 \n",
"45209 57 668 17 508 4 -1 0 0 \n",
"45210 37 2971 17 361 2 188 11 0 \n",
"\n",
" job_blue-collar job_entrepreneur ... month_jul month_jun \\\n",
"0 0 0 ... 0 0 \n",
"1 0 0 ... 0 0 \n",
"2 0 1 ... 0 0 \n",
"3 1 0 ... 0 0 \n",
"4 0 0 ... 0 0 \n",
"... ... ... ... ... ... \n",
"45206 0 0 ... 0 0 \n",
"45207 0 0 ... 0 0 \n",
"45208 0 0 ... 0 0 \n",
"45209 1 0 ... 0 0 \n",
"45210 0 1 ... 0 0 \n",
"\n",
" month_mar month_may month_nov month_oct month_sep poutcome_other \\\n",
"0 0 1 0 0 0 0 \n",
"1 0 1 0 0 0 0 \n",
"2 0 1 0 0 0 0 \n",
"3 0 1 0 0 0 0 \n",
"4 0 1 0 0 0 0 \n",
"... ... ... ... ... ... ... \n",
"45206 0 0 1 0 0 0 \n",
"45207 0 0 1 0 0 0 \n",
"45208 0 0 1 0 0 0 \n",
"45209 0 0 1 0 0 0 \n",
"45210 0 0 1 0 0 1 \n",
"\n",
" poutcome_success poutcome_unknown \n",
"0 0 1 \n",
"1 0 1 \n",
"2 0 1 \n",
"3 0 1 \n",
"4 0 1 \n",
"... ... ... \n",
"45206 0 1 \n",
"45207 0 1 \n",
"45208 1 0 \n",
"45209 0 1 \n",
"45210 0 0 \n",
"\n",
"[44466 rows x 43 columns]"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# converting non-numerical values to numerical\n",
"\n",
"data = pd.get_dummies(df2, columns=[ \"job\", \"marital\", \"education\", \"default\", \"housing\", \"loan\", \"contact\", \"month\", \"poutcome\"], drop_first=True)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [],
"source": [
"# moving target column to the end\n",
"\n",
"column_to_move = data.pop('y')\n",
"data.insert(42, \"y\", column_to_move)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [],
"source": [
"# seperating feature columns and target column\n",
"\n",
"X = data.drop('y',axis='columns')\n",
"y = data['y']"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [],
"source": [
"# fixing the imbalanced data using Synthetic Minority Oversampling technique (SMOTE)\n",
"\n",
"smote = SMOTE(sampling_strategy='minority')\n",
"X_smote, y_smote = smote.fit_resample(X,y)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [],
"source": [
"# creating train test split\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X_smote, y_smote, test_size= 0.30, stratify=y_smote)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"# feature scaling\n",
"\n",
"from sklearn.preprocessing import StandardScaler \n",
"st_x= StandardScaler() \n",
"X_train_scale= st_x.fit_transform(X_train) \n",
"X_test_scale= st_x.transform(X_test) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## KNN"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"153.5545505675426"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# finding ideal number of neighbors for KNN\n",
"\n",
"import math\n",
"math.sqrt(len(y_test))"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [],
"source": [
"clf_knn = KNeighborsClassifier(n_neighbors=153) # creating instance of KNN"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [],
"source": [
"clf_knn.fit(X_train_scale, y_train) # model fitting KNN\n",
"pred_knn = clf_knn.predict(X_test_scale) # prediction of test set results"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[10917 873]\n",
" [ 1271 10518]]\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# creating confusion matrx for KNN\n",
"\n",
"cm_knn = confusion_matrix(y_test, pred_knn)\n",
"print(cm_knn)\n",
"sns.heatmap(cm_knn, annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.90 0.93 0.91 11790\n",
" 1 0.92 0.89 0.91 11789\n",
"\n",
" accuracy 0.91 23579\n",
" macro avg 0.91 0.91 0.91 23579\n",
"weighted avg 0.91 0.91 0.91 23579\n",
"\n",
"0.9075064710957722\n",
"0.9090716315365367\n"
]
}
],
"source": [
"# classification report,f1 score, accuracy score KNN\n",
"\n",
"print(classification_report(y_test,pred_knn))\n",
"print(f1_score(y_test,pred_knn))\n",
"print(accuracy_score(y_test,pred_knn))"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {},
"outputs": [],
"source": [
"grid_knn = KNeighborsClassifier() # new instance of KNN for grid search"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 14 candidates, totalling 42 fits\n",
"[CV 1/3; 1/14] START n_neighbors=100, weights=uniform...........................\n",
"[CV 1/3; 1/14] END n_neighbors=100, weights=uniform;, score=(train=0.907, test=0.908) total time= 2.6s\n",
"[CV 2/3; 1/14] START n_neighbors=100, weights=uniform...........................\n",
"[CV 2/3; 1/14] END n_neighbors=100, weights=uniform;, score=(train=0.907, test=0.906) total time= 1.9s\n",
"[CV 3/3; 1/14] START n_neighbors=100, weights=uniform...........................\n",
"[CV 3/3; 1/14] END n_neighbors=100, weights=uniform;, score=(train=0.909, test=0.906) total time= 2.5s\n",
"[CV 1/3; 2/14] START n_neighbors=100, weights=distance..........................\n",
"[CV 1/3; 2/14] END n_neighbors=100, weights=distance;, score=(train=1.000, test=0.914) total time= 1.5s\n",
"[CV 2/3; 2/14] START n_neighbors=100, weights=distance..........................\n",
"[CV 2/3; 2/14] END n_neighbors=100, weights=distance;, score=(train=1.000, test=0.913) total time= 2.4s\n",
"[CV 3/3; 2/14] START n_neighbors=100, weights=distance..........................\n",
"[CV 3/3; 2/14] END n_neighbors=100, weights=distance;, score=(train=1.000, test=0.913) total time= 1.9s\n",
"[CV 1/3; 3/14] START n_neighbors=110, weights=uniform...........................\n",
"[CV 1/3; 3/14] END n_neighbors=110, weights=uniform;, score=(train=0.907, test=0.907) total time= 3.5s\n",
"[CV 2/3; 3/14] START n_neighbors=110, weights=uniform...........................\n",
"[CV 2/3; 3/14] END n_neighbors=110, weights=uniform;, score=(train=0.906, test=0.905) total time= 3.0s\n",
"[CV 3/3; 3/14] START n_neighbors=110, weights=uniform...........................\n",
"[CV 3/3; 3/14] END n_neighbors=110, weights=uniform;, score=(train=0.908, test=0.904) total time= 2.7s\n",
"[CV 1/3; 4/14] START n_neighbors=110, weights=distance..........................\n",
"[CV 1/3; 4/14] END n_neighbors=110, weights=distance;, score=(train=1.000, test=0.913) total time= 1.9s\n",
"[CV 2/3; 4/14] START n_neighbors=110, weights=distance..........................\n",
"[CV 2/3; 4/14] END n_neighbors=110, weights=distance;, score=(train=1.000, test=0.912) total time= 2.8s\n",
"[CV 3/3; 4/14] START n_neighbors=110, weights=distance..........................\n",
"[CV 3/3; 4/14] END n_neighbors=110, weights=distance;, score=(train=1.000, test=0.912) total time= 2.4s\n",
"[CV 1/3; 5/14] START n_neighbors=115, weights=uniform...........................\n",
"[CV 1/3; 5/14] END n_neighbors=115, weights=uniform;, score=(train=0.906, test=0.906) total time= 3.0s\n",
"[CV 2/3; 5/14] START n_neighbors=115, weights=uniform...........................\n",
"[CV 2/3; 5/14] END n_neighbors=115, weights=uniform;, score=(train=0.904, test=0.904) total time= 2.9s\n",
"[CV 3/3; 5/14] START n_neighbors=115, weights=uniform...........................\n",
"[CV 3/3; 5/14] END n_neighbors=115, weights=uniform;, score=(train=0.907, test=0.903) total time= 2.9s\n",
"[CV 1/3; 6/14] START n_neighbors=115, weights=distance..........................\n",
"[CV 1/3; 6/14] END n_neighbors=115, weights=distance;, score=(train=1.000, test=0.912) total time= 2.5s\n",
"[CV 2/3; 6/14] START n_neighbors=115, weights=distance..........................\n",
"[CV 2/3; 6/14] END n_neighbors=115, weights=distance;, score=(train=1.000, test=0.912) total time= 2.4s\n",
"[CV 3/3; 6/14] START n_neighbors=115, weights=distance..........................\n",
"[CV 3/3; 6/14] END n_neighbors=115, weights=distance;, score=(train=1.000, test=0.911) total time= 2.5s\n",
"[CV 1/3; 7/14] START n_neighbors=120, weights=uniform...........................\n",
"[CV 1/3; 7/14] END n_neighbors=120, weights=uniform;, score=(train=0.905, test=0.906) total time= 3.3s\n",
"[CV 2/3; 7/14] START n_neighbors=120, weights=uniform...........................\n",
"[CV 2/3; 7/14] END n_neighbors=120, weights=uniform;, score=(train=0.904, test=0.904) total time= 1.9s\n",
"[CV 3/3; 7/14] START n_neighbors=120, weights=uniform...........................\n",
"[CV 3/3; 7/14] END n_neighbors=120, weights=uniform;, score=(train=0.906, test=0.903) total time= 2.9s\n",
"[CV 1/3; 8/14] START n_neighbors=120, weights=distance..........................\n",
"[CV 1/3; 8/14] END n_neighbors=120, weights=distance;, score=(train=1.000, test=0.912) total time= 2.2s\n",
"[CV 2/3; 8/14] START n_neighbors=120, weights=distance..........................\n",
"[CV 2/3; 8/14] END n_neighbors=120, weights=distance;, score=(train=1.000, test=0.912) total time= 3.6s\n",
"[CV 3/3; 8/14] START n_neighbors=120, weights=distance..........................\n",
"[CV 3/3; 8/14] END n_neighbors=120, weights=distance;, score=(train=1.000, test=0.911) total time= 2.7s\n",
"[CV 1/3; 9/14] START n_neighbors=130, weights=uniform...........................\n",
"[CV 1/3; 9/14] END n_neighbors=130, weights=uniform;, score=(train=0.904, test=0.905) total time= 3.7s\n",
"[CV 2/3; 9/14] START n_neighbors=130, weights=uniform...........................\n",
"[CV 2/3; 9/14] END n_neighbors=130, weights=uniform;, score=(train=0.903, test=0.903) total time= 4.6s\n",
"[CV 3/3; 9/14] START n_neighbors=130, weights=uniform...........................\n",
"[CV 3/3; 9/14] END n_neighbors=130, weights=uniform;, score=(train=0.905, test=0.902) total time= 3.3s\n",
"[CV 1/3; 10/14] START n_neighbors=130, weights=distance.........................\n",
"[CV 1/3; 10/14] END n_neighbors=130, weights=distance;, score=(train=1.000, test=0.911) total time= 2.2s\n",
"[CV 2/3; 10/14] START n_neighbors=130, weights=distance.........................\n",
"[CV 2/3; 10/14] END n_neighbors=130, weights=distance;, score=(train=1.000, test=0.910) total time= 4.0s\n",
"[CV 3/3; 10/14] START n_neighbors=130, weights=distance.........................\n",
"[CV 3/3; 10/14] END n_neighbors=130, weights=distance;, score=(train=1.000, test=0.909) total time= 4.6s\n",
"[CV 1/3; 11/14] START n_neighbors=140, weights=uniform..........................\n",
"[CV 1/3; 11/14] END n_neighbors=140, weights=uniform;, score=(train=0.904, test=0.904) total time= 4.6s\n",
"[CV 2/3; 11/14] START n_neighbors=140, weights=uniform..........................\n",
"[CV 2/3; 11/14] END n_neighbors=140, weights=uniform;, score=(train=0.902, test=0.903) total time= 4.0s\n",
"[CV 3/3; 11/14] START n_neighbors=140, weights=uniform..........................\n",
"[CV 3/3; 11/14] END n_neighbors=140, weights=uniform;, score=(train=0.904, test=0.902) total time= 3.0s\n",
"[CV 1/3; 12/14] START n_neighbors=140, weights=distance.........................\n",
"[CV 1/3; 12/14] END n_neighbors=140, weights=distance;, score=(train=1.000, test=0.911) total time= 2.6s\n",
"[CV 2/3; 12/14] START n_neighbors=140, weights=distance.........................\n",
"[CV 2/3; 12/14] END n_neighbors=140, weights=distance;, score=(train=1.000, test=0.910) total time= 3.2s\n",
"[CV 3/3; 12/14] START n_neighbors=140, weights=distance.........................\n",
"[CV 3/3; 12/14] END n_neighbors=140, weights=distance;, score=(train=1.000, test=0.909) total time= 3.0s\n",
"[CV 1/3; 13/14] START n_neighbors=180, weights=uniform..........................\n",
"[CV 1/3; 13/14] END n_neighbors=180, weights=uniform;, score=(train=0.902, test=0.903) total time= 4.0s\n",
"[CV 2/3; 13/14] START n_neighbors=180, weights=uniform..........................\n",
"[CV 2/3; 13/14] END n_neighbors=180, weights=uniform;, score=(train=0.902, test=0.902) total time= 3.9s\n",
"[CV 3/3; 13/14] START n_neighbors=180, weights=uniform..........................\n",
"[CV 3/3; 13/14] END n_neighbors=180, weights=uniform;, score=(train=0.902, test=0.900) total time= 3.6s\n",
"[CV 1/3; 14/14] START n_neighbors=180, weights=distance.........................\n",
"[CV 1/3; 14/14] END n_neighbors=180, weights=distance;, score=(train=1.000, test=0.910) total time= 2.7s\n",
"[CV 2/3; 14/14] START n_neighbors=180, weights=distance.........................\n",
"[CV 2/3; 14/14] END n_neighbors=180, weights=distance;, score=(train=1.000, test=0.909) total time= 2.8s\n",
"[CV 3/3; 14/14] START n_neighbors=180, weights=distance.........................\n",
"[CV 3/3; 14/14] END n_neighbors=180, weights=distance;, score=(train=1.000, test=0.907) total time= 3.4s\n"
]
},
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=KNeighborsClassifier(),\n",
" param_grid=[{&#x27;n_neighbors&#x27;: [100, 110, 115, 120, 130, 140, 180],\n",
" &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]}],\n",
" return_train_score=True, scoring=&#x27;accuracy&#x27;, verbose=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=KNeighborsClassifier(),\n",
" param_grid=[{&#x27;n_neighbors&#x27;: [100, 110, 115, 120, 130, 140, 180],\n",
" &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]}],\n",
" return_train_score=True, scoring=&#x27;accuracy&#x27;, verbose=10)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
],
"text/plain": [
"GridSearchCV(cv=3, estimator=KNeighborsClassifier(),\n",
" param_grid=[{'n_neighbors': [100, 110, 115, 120, 130, 140, 180],\n",
" 'weights': ['uniform', 'distance']}],\n",
" return_train_score=True, scoring='accuracy', verbose=10)"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# grid search execution KNN\n",
"\n",
"param_grid = [{\n",
" \n",
" \"n_neighbors\":[100,110,115,120,130,140,180], \"weights\": [\"uniform\", \"distance\"]\n",
" \n",
"}]\n",
"\n",
"grid_search = GridSearchCV(grid_knn, param_grid, cv=3, scoring=\"accuracy\", return_train_score=True, verbose=10)\n",
"\n",
"grid_search.fit(X_train_scale, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9201832138767547"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# grid search score KNN\n",
"\n",
"clf_est = grid_search.best_estimator_\n",
"clf_est.score(X_test_scale, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic Regression"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [],
"source": [
"clf_log = LogisticRegression().fit(X_train_scale, y_train) # creating instance and model fitting LogisticRegression\n",
"pred_log = clf_log.predict(X_test_scale) # prediction of test set LogisticRegression"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[11099 691]\n",
" [ 965 10824]]\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# creating confusion matrix for LogisticRegression\n",
"\n",
"cm_log = confusion_matrix(y_test,pred_log)\n",
"print(cm_log)\n",
"sns.heatmap(cm_log, annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.92 0.94 0.93 11790\n",
" 1 0.94 0.92 0.93 11789\n",
"\n",
" accuracy 0.93 23579\n",
" macro avg 0.93 0.93 0.93 23579\n",
"weighted avg 0.93 0.93 0.93 23579\n",
"\n",
"0.9289392378990731\n",
"0.9297680139106832\n"
]
}
],
"source": [
"# classification report,f1 score, accuracy score LogisticRegression\n",
"\n",
"print(classification_report(y_test,pred_log))\n",
"print(f1_score(y_test,pred_log))\n",
"print(accuracy_score(y_test,pred_log))"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [],
"source": [
"# Hyperparameter tuning LogisticRegression\n",
"\n",
"hyp_log = LogisticRegression(solver='newton-cg',multi_class='multinomial',\n",
" penalty='none',C=1.0, fit_intercept = True).fit(X_train_scale, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [],
"source": [
"hyp_pred_log = hyp_log.predict(X_test_scale)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.92 0.94 0.93 11790\n",
" 1 0.94 0.92 0.93 11789\n",
"\n",
" accuracy 0.93 23579\n",
" macro avg 0.93 0.93 0.93 23579\n",
"weighted avg 0.93 0.93 0.93 23579\n",
"\n",
"0.9289851963098047\n",
"0.9298104245303024\n"
]
}
],
"source": [
"# classification report,f1 score, accuracy score LogisticRegression (hyperparameter tuning)\n",
"\n",
"print(classification_report(y_test,hyp_pred_log))\n",
"print(f1_score(y_test,hyp_pred_log))\n",
"print(accuracy_score(y_test,hyp_pred_log))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Forest Classifier"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [],
"source": [
"clf_rfc = RandomForestClassifier().fit(X_train_scale, y_train) # creating instance and model fitting RFC\n",
"pred_rfc = clf_rfc.predict(X_test_scale) # prediction of test set RFC"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[11205 585]\n",
" [ 662 11127]]\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# creating confusion matrix for RFC\n",
"\n",
"cm_rfc = confusion_matrix(y_test, pred_rfc)\n",
"print(cm_rfc)\n",
"sns.heatmap(cm_rfc, annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.95 0.95 11790\n",
" 1 0.95 0.94 0.95 11789\n",
"\n",
" accuracy 0.95 23579\n",
" macro avg 0.95 0.95 0.95 23579\n",
"weighted avg 0.95 0.95 0.95 23579\n",
"\n",
"0.9469384281519936\n",
"0.9471139573349167\n"
]
}
],
"source": [
"# classification report, f1 score, accuracy score RFC\n",
"\n",
"print(classification_report(y_test,pred_rfc))\n",
"print(f1_score(y_test,pred_rfc))\n",
"print(accuracy_score(y_test,pred_rfc))"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [],
"source": [
"# Hyperparameter tuning RFC\n",
"\n",
"hyp_clf_rfc = RandomForestClassifier(criterion=\"gini\",bootstrap=False).fit(X_train_scale,y_train)\n",
"hyp_pred_rfc = hyp_clf_rfc.predict(X_test_scale)"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.95 0.95 11790\n",
" 1 0.95 0.94 0.95 11789\n",
"\n",
" accuracy 0.95 23579\n",
" macro avg 0.95 0.95 0.95 23579\n",
"weighted avg 0.95 0.95 0.95 23579\n",
"\n",
"0.9469384281519936\n",
"0.9471139573349167\n"
]
}
],
"source": [
"# classification report, f1 score, accuracy score RFC\n",
"\n",
"print(classification_report(y_test,pred_rfc))\n",
"print(f1_score(y_test,pred_rfc))\n",
"print(accuracy_score(y_test,pred_rfc))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"private_outputs": true,
"provenance": []
},
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}