This is an end-to-end ML project, which aims at developing a classification model for the problem of classifying a given customer profile into either of the risk category (Good or Bad). The final classifier used for this project is XGBoost classifier. Deployed in Heroku.

Link for the webapp : Credit risk Classification

This is an end-to-end ML project, which aims at developing a classification model for the problem of predicting credit card frauds using a given labeled dataset. The classifier used for this project is RandomForestClassifier. Deployed in Heroku.

Link for the webapp : Credit Card Defaulter Prediction

This project looks at the sales pattern of a product category in a retail store, using the store’s transaction dataset and identifying customer purchase behavior, to generate insights and recommendations.

This project comprises of 3 tasks:

1. Data preparation and customer analytics
2. Experimentation and uplift testing
3. Analytics and commercial application

This is an end-to-end ML project, which aims at developing a classification model for predicting if a customer for an ecommerce business will churn or not in the following month Depoyed in Heroku and Railway app.

Link for the web app: E-commerce Customer Churn Prediction

This project aims to accomplish the following:

• Identify and visualize which factors contribute to customer churn.
• Build a prediction model that will perform the function of classifying if a customer is going to churn or not.

An end-to-end classification problem to predict whether the stock price will increase or decrease, based on the news headlines. Deployed in Railway app using Flask.

Link for the web app: Stock Sentiment Analysis