Analyze Banking Customer data and predict churn using Random Forest, AdaBoost and SVM. In order to improve the accuracy the prediction models I implemented Unsampling through SMOTE.

This dashboard sheds some light on the effects Covid-19 has had on mental health. The data consists of survey from CDC of people showing signs of Anxiety or Depression.

This project comprises of using user data from a german bank to conduct customer segmentation of loans given to people. By conducting customer segmentation we are able to see the average characteristics of customers issued loans.

The data is taken from a mobile game called Cookie Cat. It represnts players progress throughout the game in which they encounter obstacles represented as gates that players have to cross either by waiting or doing in-app purchases. In this project we conduct an A/B test to study players retention rate if the first gate in the game is moved from level 30 to level 40.

We will analyze the similarities between songs that came out in a span of four decades. This will be done by collecting the lyrics of top five songs from each decade using the Genius api and applying topic modeling on them to see if we are able to categorize them by their genres.

Made an interactive dashboard using plotly, leaflet and flexdashboard with geolocation of all Lyft Bike stations in the Bayarea and some characteristics of users.This was a project for my Data Visualization class.

Predicitng heart disease in patients using Logisitic Regression and KNN modeling.

Applying various machine learning Algorithms such as Logistic Regression, Naive Bayes, Decision Trees and Random Forest. Accuracy varied from 75% to 85% for all models. This was a project for my Statistical Learning class.