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Understanding the problem statement
Importing the Dataset and understanding "masked data"
Performing basic EDA and checking for null values
What is a pivot table and its interpretation
Using pivot tables for filling the null values
Plotting frequency graphs for the target variable
Using the pie chart for visualizing categorical values
Encoding categorical variables
Using Recursive Feature Selection for selecting the best feature
Performing train_test_split on the Dataset
Applying Extra Tree Regressor along with feature_importance function for training and potting it for visualization
Performing Feature Engineering to add new meaningful feature
Applying Random Forest Regressor along with Cross-Folds CV for training the model
Using RMSE for measuring the accuracy
Making final predictions and saving it in CSV format
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 summary of various customers for selected high volume products from last month.
The black friday data hack dataset also contains customer demographics (age, gender, marital status, city_type, stay_in_current_city), product details (product_id and product category) and Total purchase_amount from last month.
Now, they want to build a machine learning model to predict the purchase amount of customer against various products which will help them to create personalized offer for customers against different products.