Predict Employee Retention

This project employs data analytics and machine learning to predict employee retention. Through the analysis of employee data, predictive models are developed to identify potential departures, aiding organizations in proactive retention strategies.

Ride Fare Predictor

This project aims to develop a powerful regression model capable or predicting taxi fares in advance,, utilizing extensive data analysis, hypothesis testing, and machine learning techniques.

Movie Recommender

This Movie Recommendation System employs various algorithms, including Content-Based and Collaborative Filtering, Genre-Based Recommendations, and techniques like Pearson Correlation and K-Nearest Neighbors, to offer personalized movie suggestions based on user preferences and history.

User Churn Prediction

In this project, we tackle the challenge of user churn prevention within the Waze app. By harnessing data analytics and machine learning, we aim to develop a predictive model that identifies potential churners—users who may uninstall or discontinue app usage.

Membership Conversion Cyclistic Bike Share

This project maximizes annual memberships in the Cyclistic Bike Share program by converting casual riders into long-term members. We analyze user behavior, pricing plans, and ridership patterns to enhance membership conversion rates and drive profits.

Fitness Tracker Trends Analysis

Explore trends in smart fitness tracker usage to drive growth and marketing strategies for Bellabeat. Uncover insights from health data collected by wearable devices, enabling data-driven decisions for a healthier future.

Video Claim Classification

Created a predictive model that can determine whether a video contains a claim or offers an opinion. With this model, we can efficiently prioritize and review user reports, reducing the backlog and enhancing user experience.