Customer based predictive analytics to find the next best offer

In this machine learning project, you will build a model to predict the purchase amount of customer against various products which will help the company create personalized offer for customers against different products.

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What will you learn

  • 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

Project Description

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.

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Curriculum For This Mini Project

 
  14-Oct-2016
02h 36m
  15-Oct-2016
01h 56m