##Dataset History

A retail company “ABC Private Limited” wants to understand the customer purchase behavior (specifically, purchase amount) against various products of different categories. They have shared purchase summaries of various customers for selected high-volume products from last month. The data set also contains customer demographics (age, gender, marital status, city type, stayincurrentcity), product details (productid and product category), and Total purchase amount from last month.

Now, they want to build a model to predict the purchase amount of customers against various products which will help them to create a personalized offer for customers against different products.

Tasks to perform

The purchase col column is the Target Variable, perform Univariate Analysis and Bivariate Analysis w.r.t the Purchase.

Masked in the column description means already converted from categorical value to numerical column.

Below mentioned points are just given to get you started with the dataset, not mandatory to follow the same sequence.

DATA PREPROCESSING

Check basic statistics of the dataset

Check for missing values in the data

Check for unique values in data

Perform EDA

Purchase Distribution

Check for outliers

Analysis by Gender, Marital Status, occupation, occupation vs purchase, purchase by city, purchase by age group, etc

Drop unnecessary fields

Convert categorical data into integer using map function (e.g 'Gender' column)

Missing value treatment

Rename columns

Fill nan values

Map range variables into integers (e.g 'Age' column)

Data Visualisation

visualize an individual column
Age vs Purchased
Occupation vs Purchased
Productcategory1 vs Purchased
Productcategory2 vs Purchased
Productcategory3 vs Purchased
City category pie chart
Check for more possible plots

All the Best!!