Project 2 - EDA on Vehicle Insurance Customer Data

# to avoid warnings from python
import warnings
warnings.filterwarnings('ignore')

A company has customer data that contains 8 columns of customer details and another table having name customer_policy data contains the policy details of the customer.

### The company intends to offer some discount in premium for certain customers. To do that they ask their Data scientist team to get some information. Hence, following tasks DS team decided to perform:

#### 1. Add the column names to both datasets:

##### i. Column Name for customer details table:

customer_id,
Gender,
age,
driving licence present,
region code,
previously insured,
vehicle age
and vehicle damage, in respective order.

##### ii. Column Name for customer_policy table:customer_id,
annual premium (in Rs),
sales channel code,
vintage and response.

import pandas as pd

customer_detail_df=pd.read_csv(".\customer_details.csv")
 # ' .\ ' represent the current directory
customer_detail_df

customer_detail_df.columns=["customer_id","Gender","age","driving licence present","region code","previously insured","vehicle age","vehicle damage"]
customer_detail_df

customer_policy_detail_df=pd.read_csv(".\customer_policy_details.csv")
customer_policy_detail_df

customer_policy_detail_df.columns=["customer_id","annual premium (in Rs)","sales channel code","vintage","response"]
customer_policy_detail_df

#### 2. Checking and Cleaning Data Quality:

##### i. Null values
Generate a summary of count of all the null values column wise
Drop Null values for customer_id because central tendencies for id’s is not feasible.
Replace all null values for numeric columns by mean.
Replace all null values for Categorical value by mode.

##### ii. Outliers
Generate a summary of count of all the outliers column wise
Replace all outlier values for numeric columns by mean.
(Hint1: for outlier treatment use IQR method as follows:
For example: for a column X calculate Q1 = 25th percentile and Q3 = 75th percentile then IQR = Q3 – Q1 ) then to check outlier, anything lower than a Q1 – 1.5IQR or greater than Q3 + 1.5 IQR would be an outlier
Hint2: For getting percentile value, explore pd.describe() function)

##### iii. White spaces
Remove white spaces

##### iv. case correction(lower or upper, any one)

##### v. Convert nominal data (categorical) into dummies
for future modeling use if required

##### vi. Drop Duplicates (duplicated rows)

customer_detail_df.shape

## Null values for customer_detail

print("Null value in 'customer_id' : ",customer_detail_df["customer_id"].isnull().sum())
customer_detail_df=customer_detail_df.dropna(subset=["customer_id"])
print("Null value in 'customer_id' after droping : ",customer_detail_df["customer_id"].isnull().sum())

print("Null value in 'Gender' : ",customer_detail_df["Gender"].isnull().sum())
customer_detail_df["Gender"]=customer_detail_df["Gender"].fillna(customer_detail_df["Gender"].mode()[0])
print("Null value in 'Gender' after replacing : ",customer_detail_df["Gender"].isnull().sum())

print("Null value in 'age' : ",customer_detail_df["age"].isnull().sum())
customer_detail_df["age"]=customer_detail_df["age"].fillna(customer_detail_df["age"].mean())
print("Null value in 'age' after replacing : ",customer_detail_df["age"].isnull().sum())

print("Null value in 'driving licence present' : ",customer_detail_df["driving licence present"].isnull().sum())
customer_detail_df["driving licence present"]=customer_detail_df["driving licence present"].fillna(customer_detail_df["driving licence present"].mode()[0])
print("Null value in 'driving licence present' after replacing : ",customer_detail_df["driving licence present"].isnull().sum())

print("Null value in 'region code' : ",customer_detail_df["region code"].isnull().sum())
customer_detail_df["region code"]=customer_detail_df["region code"].fillna(customer_detail_df["region code"].mode()[0])
print("Null value in 'region code' after replacing : ",customer_detail_df["region code"].isnull().sum())

print("Null value in 'previously insured' : ",customer_detail_df["previously insured"].isnull().sum())
customer_detail_df["previously insured"]=customer_detail_df["previously insured"].fillna(customer_detail_df["previously insured"].mode()[0])
print("Null value in 'previously insured' after replacing : ",customer_detail_df["previously insured"].isnull().sum())

print("Null value in 'vehicle age' : ",customer_detail_df["vehicle age"].isnull().sum())
customer_detail_df["vehicle age"]=customer_detail_df["vehicle age"].fillna(customer_detail_df["vehicle age"].mode()[0])
print("Null value in 'vehicle age' after replacing : ",customer_detail_df["vehicle age"].isnull().sum())

print("Null value in 'vehicle damage' : ",customer_detail_df["vehicle damage"].isnull().sum())
customer_detail_df["vehicle damage"]=customer_detail_df["vehicle damage"].fillna(customer_detail_df["vehicle damage"].mode()[0])
print("Null value in 'vehicle damage' after replacing : ",customer_detail_df["vehicle damage"].isnull().sum())

## Null values for customer_policy_detail

print("Null value in 'customer_id' : ",customer_policy_detail_df["customer_id"].isnull().sum())
customer_policy_detail_df=customer_policy_detail_df.dropna(subset=["customer_id"])
print("Null value in 'customer_id' after droping : ",customer_policy_detail_df["customer_id"].isnull().sum())

print("Null value in 'annual premium (in Rs)' : ",customer_policy_detail_df["annual premium (in Rs)"].isnull().sum())
customer_policy_detail_df["annual premium (in Rs)"]=customer_policy_detail_df["annual premium (in Rs)"].fillna(customer_policy_detail_df["annual premium (in Rs)"].mean())
print("Null value in 'annual premium (in Rs)' after replacing : ",customer_policy_detail_df["annual premium (in Rs)"].isnull().sum())

print("Null value in 'sales channel code' : ",customer_policy_detail_df["sales channel code"].isnull().sum())
customer_policy_detail_df["sales channel code"]=customer_policy_detail_df["sales channel code"].fillna(customer_policy_detail_df["sales channel code"].mode()[0])
print("Null value in 'sales channel code' after replacing : ",customer_policy_detail_df["sales channel code"].isnull().sum())

print("Null value in 'vintage' : ",customer_policy_detail_df["vintage"].isnull().sum())
customer_policy_detail_df["vintage"]=customer_policy_detail_df["vintage"].fillna(customer_policy_detail_df["vintage"].mean())
print("Null value in 'vintage' after replacing : ",customer_policy_detail_df["vintage"].isnull().sum())

print("Null value in 'response' : ",customer_policy_detail_df["response"].isnull().sum())
customer_policy_detail_df["response"]=customer_policy_detail_df["response"].fillna(customer_policy_detail_df["response"].mode()[0])
print("Null value in 'response' after replacing : ",customer_policy_detail_df["response"].isnull().sum())

## Outliers

customer_detail_df.describe()

customer_policy_detail_df.describe()

Q1=customer_detail_df.describe().loc["25%","age"]
Q3=customer_detail_df.describe().loc["75%","age"]
IQR=Q3-Q1
print(customer_detail_df.loc[customer_detail_df["age"]<(Q1-IQR*1.5),"age"].count())
print(customer_detail_df.loc[customer_detail_df["age"]>(Q3+IQR*1.5),"age"].count())

##### there are no outliers in 'age' column

Q1=customer_policy_detail_df.describe().loc["25%","annual premium (in Rs)"]
Q3=customer_policy_detail_df.describe().loc["75%","annual premium (in Rs)"]
IQR=Q3-Q1
print(customer_policy_detail_df.loc[customer_policy_detail_df["annual premium (in Rs)"]<(Q1-IQR*1.5),"annual premium (in Rs)"].count())
print(customer_policy_detail_df.loc[customer_policy_detail_df["annual premium (in Rs)"]>(Q3+IQR*1.5),"annual premium (in Rs)"].count())

customer_policy_detail_df.loc[customer_policy_detail_df["annual premium (in Rs)"]>(Q3+IQR*1.5),"annual premium (in Rs)"]=customer_policy_detail_df["annual premium (in Rs)"].mean()
print(customer_policy_detail_df.loc[customer_policy_detail_df["annual premium (in Rs)"]>(Q3+IQR*1.5),"annual premium (in Rs)"].count())

Q1=customer_policy_detail_df.describe().loc["25%","vintage"]
Q3=customer_policy_detail_df.describe().loc["75%","vintage"]
IQR=Q3-Q1
print(customer_policy_detail_df.loc[customer_policy_detail_df["vintage"]<(Q1-IQR*1.5),"vintage"].count())
print(customer_policy_detail_df.loc[customer_policy_detail_df["vintage"]>(Q3+IQR*1.5),"vintage"].count())

##### there are no outliers in 'vintage' column

### White spaces

customer_detail_df["Gender"]=customer_detail_df["Gender"].str.strip()
customer_detail_df["vehicle age"]=customer_detail_df["vehicle age"].str.strip()
customer_detail_df["vehicle damage"]=customer_detail_df["vehicle damage"].str.strip()

### case change

customer_detail_df["Gender"]=customer_detail_df["Gender"].str.lower()
customer_detail_df["vehicle age"]=customer_detail_df["vehicle age"].str.lower()
customer_detail_df["vehicle damage"]=customer_detail_df["vehicle damage"].str.lower()

### creating dummies

dummies_gender=pd.get_dummies(customer_detail_df["Gender"])
dummies_vehicle_age=pd.get_dummies(customer_detail_df["vehicle age"])
dummies_vehicle_damage=pd.get_dummies(customer_detail_df["vehicle damage"])
dummies_d_l_p=pd.get_dummies(customer_detail_df["driving licence present"])
dummies_p_i=pd.get_dummies(customer_detail_df["previously insured"])
dummies_response=pd.get_dummies(customer_policy_detail_df["response"])

### checking for any duplicates

print(customer_detail_df.duplicated().sum())
print(customer_policy_detail_df.duplicated().sum())

### 3. Create a Master table for future use. Join the customer table and customer_policy table to get a master table using customer_id in both tables.

(Hint: use pd.merge() function)

master_df=pd.merge(customer_detail_df,customer_policy_detail_df,on='customer_id')
master_df

#### 4. Company needs some important information from the master table to make decisions for future growth.They needs following information:

##### i. Gender wise average annual premium

##### ii. Age wise average annual premium

##### iii. Is your data balanced between the genders?
(Hint: Data is balanced if number of counts in each group is approximately same)

##### iv. Vehicle age wise average annual premium.

#Gender wise average annual premium 
aap_gender_df=master_df.groupby(["Gender"])["annual premium (in Rs)"].mean()
aap_gender_df

#data is balanced between the two genders because the avereages are approximately equal
aap_gender_df

## Data is balanced between the two genders
#### data is balanced between the two genders because the avereages are approximately equal

# age wise average annual premium 
aap_age_df=master_df.groupby(["age"])["annual premium (in Rs)"].mean()
aap_age_df

#vehicle age wise average annual premium 
aap_v_age_df=master_df.groupby(["vehicle age"])["annual premium (in Rs)"].mean()
aap_v_age_df

#### 5. Is there any relation between Person Age and annual premium?
Hint: use correlation function (Correlation describes the relationship between two variables).

Correlation coefficient < -0.5 - Strong negative relationship

Correlation coefficient > 0.5 - Strong positive relationship

0.5 < Correlation coefficient < 0.5 - There is no relationship.

Correlation_coefficient=master_df["age"].corr(master_df["annual premium (in Rs)"])
print(Correlation_coefficient)
# there is no relationship between Person age and annual premium because Correlation coefficient<0.5

## There is no relationship between Person age and annual premium
### because Correlation coefficient < 0.5