MYSQL:
A MySQL database was created to load the datasets and run queries, A view was finally created to join various tables. This view was consequently downloaded and used as a dataset in Jupyter Notebook to do descriptive statistics and Machine Learning
Python:
Propensity calculator to maximize Sales revenue using Machine Learning (Python) This repository uses Machine Learning techniques such as Logistic Regression, Random Forest and Naive Bayes Gaussian algorithms to determine the propensity scores of a customer to buy a target product
The various questions answered in this analysis are: - • Which clients have higher propensity to buy consumer loan? • Which clients have higher propensity to buy credit card? • Which clients have higher propensity to buy mutual fund? • Which clients should be targeted with which offer? • What would be the expected revenue based on the analysis?
Code: Propensity Prediction.ipynb Technical Report and Executive summary are attached in this repositories which details the process.