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Project 2 - EDA on Vehicle Insurance Customer Data

AAbdul Savith23 Oct 2022
CAEFEA

{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "e581ac2f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2 3 4 5 6 7\n",
"0 1.0 Male 44.0 1.0 28.0 0.0 > 2 Years Yes\n",
"1 2.0 Male 76.0 1.0 3.0 0.0 1-2 Year No\n",
"2 3.0 Male 47.0 1.0 28.0 0.0 > 2 Years Yes\n",
"3 4.0 Male 21.0 1.0 11.0 1.0 < 1 Year No\n",
"4 5.0 Female 29.0 1.0 41.0 1.0 < 1 Year No\n",
" 0 1 2 3 4\n",
"0 1.0 40454.0 26.0 217.0 1.0\n",
"1 2.0 33536.0 26.0 183.0 0.0\n",
"2 3.0 38294.0 26.0 27.0 1.0\n",
"3 4.0 28619.0 152.0 203.0 0.0\n",
"4 5.0 27496.0 152.0 39.0 0.0\n"
]
}
],
"source": [
"import pandas\n",
"table1 = pandas.read_csv(\"customer_details.csv\")\n",
"table2 = pandas.read_csv(\"customer_policy_details.csv\")\n",
"print(table1.head())\n",
"print(table2.head())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6bdcd0b8",
"metadata": {},
"outputs": [],
"source": [
"table1_labels={'0':'customer_id', '1':'gender', '2':'age', '3':'driving_licence_presence',\n",
"'4':'region_code', '5':'previously_insured', '6':'vehicle_age', '7':'vehicle_damage'}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c361c0ee",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of cells of customer_id with null values = 386\n",
"number of cells of gender with null values = 368\n",
"number of cells of age with null values = 368\n",
"number of cells of driving_licence_presence with null values = 393\n",
"number of cells of region_code with null values = 392\n",
"number of cells of previously_insured with null values = 381\n",
"number of cells of vehicle_age with null values = 381\n",
"number of cells of vehicle_damage with null values = 407\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 381109 entries, 0 to 381108\n",
"Data columns (total 8 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 0 380723 non-null float64\n",
" 1 1 380741 non-null object \n",
" 2 2 380741 non-null float64\n",
" 3 3 380716 non-null float64\n",
" 4 4 380717 non-null float64\n",
" 5 5 380728 non-null float64\n",
" 6 6 380728 non-null object \n",
" 7 7 380702 non-null object \n",
"dtypes: float64(5), object(3)\n",
"memory usage: 23.3+ MB\n"
]
}
],
"source": [
"for i in range(table1.shape[1]):\n",
" print(f\"number of cells of {table1_labels[str(i)]} with null values = {table1[str(i)].isnull().sum()}\")\n",
" \n",
"table1.info() "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4b19a093",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of cells of customer_id with null values = 0\n"
]
}
],
"source": [
"table1.dropna(subset=['0'],inplace=True)\n",
"print(f\"number of cells of {table1_labels[str(0)]} with null values = {table1[str(0)].isnull().sum()}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1435254b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of cells of age with null values = 0\n",
"number of cells of driving_licence_presence with null values = 0\n",
"number of cells of region_code with null values = 0\n",
"number of cells of previously_insured with null values = 0\n"
]
}
],
"source": [
"for i in range(2,6):\n",
" table1[str(i)].fillna(table1[str(i)].mean(),inplace=True)\n",
" print(f\"number of cells of {table1_labels[str(i)]} with null values = {table1[str(i)].isnull().sum()}\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a07b0f33",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of cells of gender with null values = 0\n",
"number of cells of vehicle_age with null values = 0\n",
"number of cells of vehicle_damage with null values = 0\n"
]
}
],
"source": [
"for i in [1,6,7]:\n",
" table1[str(i)].fillna(table1[str(i)].mode()[0],inplace=True)\n",
" print(f\"number of cells of {table1_labels[str(i)]} with null values = {table1[str(i)].isnull().sum()}\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6e926e94",
"metadata": {},
"outputs": [],
"source": [
"table2_labels={'0':'customer_id', '1':'annual_premium_INR', '2':'sales_channel_code', '3':'vintage',\n",
"'4':'response'}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "89b4af45",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of cells of customer_id with null values is 387\n",
"number of cells of annual_premium_INR with null values is 346\n",
"number of cells of sales_channel_code with null values is 400\n",
"number of cells of vintage with null values is 388\n",
"number of cells of response with null values is 361\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 381109 entries, 0 to 381108\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 0 380722 non-null float64\n",
" 1 1 380763 non-null float64\n",
" 2 2 380709 non-null float64\n",
" 3 3 380721 non-null float64\n",
" 4 4 380748 non-null float64\n",
"dtypes: float64(5)\n",
"memory usage: 14.5 MB\n"
]
}
],
"source": [
"for i in range(table2.shape[1]):\n",
" print(f\"number of cells of {table2_labels[str(i)]} with null values is {table2[str(i)].isnull().sum()}\")\n",
"\n",
"table2.info() "
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2cc5d4f8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of cells of customer_id with null values = 0\n"
]
}
],
"source": [
"table2.dropna(subset=['0'],inplace=True)\n",
"print(f\"number of cells of {table1_labels[str(0)]} with null values = {table2[str(0)].isnull().sum()}\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e004fe2e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of cells of annual_premium_INR with null values = 0\n",
"number of cells of sales_channel_code with null values = 0\n",
"number of cells of vintage with null values = 0\n",
"number of cells of response with null values = 0\n"
]
}
],
"source": [
"for i in range(1,5):\n",
" table2[str(i)].fillna(table2[str(i)].mean(),inplace=True)\n",
" print(f\"number of cells of {table2_labels[str(i)]} with null values = {table2[str(i)].isnull().sum()}\")\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "948a770f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'2': (-11.0, 85.0), '3': (1.0, 1.0), '4': (-15.0, 65.0), '5': (-1.5, 2.5)}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table1_limits={}\n",
"for i in range(2,6):\n",
" computations=table1[str(i)].describe(percentiles=[.25,.75])\n",
" mean=computations.values[1]\n",
" Q1=computations.values[4]\n",
" Q3=computations.values[6]\n",
" IQR=Q3-Q1\n",
" II=Q1-1.5*IQR\n",
" hI=Q3+1.5*IQR\n",
" table1_limits[str(i)]=(II,hI)\n",
"table1_limits "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "618da5b9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'2': 0, '3': 0, '4': 0, '5': 0, '6': 0}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table1_outliers={'2':0,'3':0,'4':0,'5':0,'6':0}\n",
"\n",
"for j in table1.index:\n",
" for i in range(2,6):\n",
" if(table1_limits[str(i)][0]!=table1_limits[str(i)][1])and(table1.loc[j,str(i)]>table1_limits[str(i)][1]or table1.loc[j,str(i)]<table1_limits[str(i)][0]):\n",
" table1_outliers[str(i)]+=1\n",
" \n",
"table1_outliers "
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "43da47fa",
"metadata": {},
"outputs": [],
"source": [
"for j in table1.index:\n",
" for i in range(2,6):\n",
" if table1.loc[j, str(i)]<table1_limits[str(i)][0]:\n",
" table1.loc[j, str(i)]=table1[str(i)].mean()\n",
" if table1.loc[j, str(i)]>table1_limits[str(i)][1]:\n",
" table1.loc[j, str(i)]=table1[str(i)].mean()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "87c9cc1b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'1': (1952.375, 61855.375),\n",
" '2': (-155.5, 336.5),\n",
" '3': (-135.5, 444.5),\n",
" '4': (0.0, 0.0)}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table2_limits={}\n",
"for i in range(1,5):\n",
" computations=table2[str(i)].describe(percentiles=[.25,.75])\n",
" mean=computations.values[1]\n",
" Q1=computations.values[4]\n",
" Q3=computations.values[6]\n",
" IQR=Q3-Q1\n",
" II=Q1-1.5*IQR\n",
" hI=Q3+1.5*IQR\n",
" table2_limits[str(i)]=(II,hI)\n",
"table2_limits "
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "5821c3e9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'1': 10332, '2': 0, '3': 0, '4': 0, '5': 0}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table2_outliers={'1':0,'2':0,'3':0,'4':0,'5':0}\n",
"\n",
"for j in table2.index:\n",
" for i in range(1,5):\n",
" if(table2_limits[str(i)][0]!=table2_limits[str(i)][1])and(table2.loc[j,str(i)]>table2_limits[str(i)][1]or table2.loc[j,str(i)]<table2_limits[str(i)][0]):\n",
" table2_outliers[str(i)]+=1\n",
" \n",
"table2_outliers "
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9fe1947e",
"metadata": {},
"outputs": [],
"source": [
"for j in table2.index:\n",
" for i in range(1,5):\n",
" if table2.loc[j,str(i)]<table2_limits[str(i)][0]:\n",
" table2.loc[j,str(i)]=table2[str(i)].mean()\n",
" if table2.loc[j,str(i)]>table2_limits[str(i)][1]:\n",
" table2.loc[j,str(i)]=table2[str(i)].mean()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "7fc7a34d",
"metadata": {},
"outputs": [
{
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" <td>Male</td>\n",
" <td>21.0</td>\n",
" <td>1.0</td>\n",
" <td>11.0</td>\n",
" <td>1.0</td>\n",
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" <td>1.0</td>\n",
" <td>37.0</td>\n",
" <td>1.0</td>\n",
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" <td>381107.0</td>\n",
" <td>Male</td>\n",
" <td>21.0</td>\n",
" <td>1.0</td>\n",
" <td>30.0</td>\n",
" <td>1.0</td>\n",
" <td>&lt; 1 Year</td>\n",
" <td>No</td>\n",
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" <tr>\n",
" <th>381107</th>\n",
" <td>381108.0</td>\n",
" <td>Female</td>\n",
" <td>68.0</td>\n",
" <td>1.0</td>\n",
" <td>14.0</td>\n",
" <td>0.0</td>\n",
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" 0 1 2 3 4 5 6 7\n",
"0 1.0 Male 44.0 1.0 28.0 0.0 > 2 Years Yes\n",
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"... ... ... ... ... ... ... ... ...\n",
"381104 381105.0 Male 74.0 1.0 26.0 1.0 1-2 Year No\n",
"381105 381106.0 Male 30.0 1.0 37.0 1.0 < 1 Year No\n",
"381106 381107.0 Male 21.0 1.0 30.0 1.0 < 1 Year No\n",
"381107 381108.0 Female 68.0 1.0 14.0 0.0 > 2 Years Yes\n",
"381108 381109.0 Male 46.0 1.0 29.0 0.0 1-2 Year No\n",
"\n",
"[380723 rows x 8 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table1.apply(lambda x:x.str.strip() if x.dtype=='object' else x)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "6ecc4e5b",
"metadata": {},
"outputs": [
{
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"380331 26.0 88.0 0.000000 \n",
"380332 152.0 131.0 0.000000 \n",
"380333 160.0 161.0 0.000000 \n",
"380334 124.0 74.0 0.000000 \n",
"380335 26.0 237.0 0.000000 \n",
"\n",
"[380336 rows x 12 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data=pandas.merge(table1,table2,on='0')\n",
"labels={'0':'customer_id','1_x':'gender','2_x':'age','3_x':'driving_license_presence','4_x':'region_code','5':'previously_insured','6':'vehicle_age','7':'vehicle_damage','1_y':'annual_premium_INR','2_y':'sales_channel_code','3_y':'vintage','4_y':'response'}\n",
"data.rename(columns=labels,inplace=True)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "45f5f514",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"result4_1=data.groupby('gender')['annual_premium_INR'].mean()\n",
"import matplotlib.pyplot as pyplot\n",
"result4_1.plot()\n",
"pyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "b42bd60f",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"result4_2 = data.groupby('age')['annual_premium_INR'].mean()\n",
"import matplotlib.pyplot as pyplot\n",
"result4_2.plot()\n",
"pyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "c0e673a2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"male to female ration is 1.18\n",
"generally, the standard is: \n",
" balanced data ratio: 1.0\n",
" slightly balanced data ratio: 1.22-1.5 \n",
" imbalanced data ratio: 4.0-9.0\n"
]
}
],
"source": [
"print(f\"male to female ration is {round(data['gender'].value_counts()[0]/data['gender'].value_counts()[1],2)}\")\n",
"print(f\"generally, the standard is: \\n balanced data ratio: {50/50}\\n slightly balanced data ratio: {round(55/45,2)}-{60/40} \\n imbalanced data ratio: {80/20}-{90/10}\")\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "718a7c3a",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"result4_4=data.groupby('vehicle_age')['annual_premium_INR'].mean()\n",
"import matplotlib.pyplot as pyplot\n",
"result4_4.plot()\n",
"pyplot.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "60aba423",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There is no relationship!\n"
]
}
],
"source": [
"n = data['age'].corr(data['annual_premium_INR'])\n",
"if n<-0.5:\n",
" print(\"Strong negative relationship\")\n",
"if n>0.5:\n",
" print(\"Strong positive relationship\")\n",
"if n>-0.5 and n<0.5:\n",
" print(\"There is no relationship!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3cf2b0b3",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "05908ded",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f123ab0",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": 5
}

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