Santander Product Recommendation ML Project in Python

Santander Product Recommendation ML Project in Python

The goal of this machine learning project is to predict which products existing customers will use next month based on their past behaviour and that of similar customers.

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What will you learn

Understanding the problem statement and Importing the dataset
Importing the important libraries
Basic EDA of the dataset
Identifying and imputing the null values using info and groupby function respectively
Feature engineering / Creating new features
Plotting pie chart for categorical variables
Using Groupby function for understanding the combined effect of multiple dependent variables
Using seaborn for plotting stacked bar plot and interpreting them
Plotting chart for median income versus province and categorizing them under different tiers
Preparing the data for training and testing
Selection of evaluation metrics and understanding the metrics
Applying a linear classification model Logistic Regression
Applying ensembling models Random Forest Classifier and Extra Tree Forest Classifier
Applying boosting models XGboost and Gradient Boosting Tree Classifier
Applying Neural Networks using MLPClassifier
Creating function for On spot-checking and selecting the best model for further hyperparameter tuning
Defining parameters for the selected models
Creating a function for Hyper-parameter tuning
Selecting the best model and Visualizing the final output
Predicting for the test and Saving the final output

Project Description

Ready to make a down payment on your first house? Or looking to leverage the equity in the home you have? To support needs for a range of financial decisions, Santander Bank offers a lending hand to their customers through personalized product recommendations

Under their current system, a small number of Santander’s customers receive many recommendations while many others rarely see any resulting in an uneven customer experience. In this machine learning project in Python, Santander is challenging to predict which products their existing customers will use in the next month based on their past behavior and that of similar customers.

With a more effective recommendation system in place, Santander can better meet the individual needs of all customers and ensure their satisfaction no matter where they are in life.

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Curriculum For This Mini Project

17-Dec-2016
02h 21m
18-Dec-2018
02h 09m