April 29, 2021

Hybrid Recommendation System Web Application

Github link Deployed live application here Overview The Regression-based Movie Recommender system that’s a hybrid of content-based and collaborative filtering Recommender system. Simply rate some movies and get immediate recommendations tailored for you Table of contents Live Applicaiton Live Applicaiton Accompanying Medium articles Prerequisites Project Structure Dataset Roadmap Roadmap Step Notebooks About Hybrid Algorithm Step 1. Collaborative filtering Step 2. Content-based filtering Step 3. Prediction Usage Contributing Contact License References Live Applicaiton Deployed live application here Accompanying Medium articles Hybrid Recommendation System Web-Application Part 1: Exploratory Data Analysis with PostgreSQL Prerequisites The following open source packages are used in this project:

February 9, 2021

Myers–Briggs Type Indicator (MBTI) classification Web Application

Github link Web Application web application screenshot Overview During my sophomore year of bachelors, I stumbled upon a book titled “Gifts differing: understanding personality type” by Isabel Briggs Myers and Peter B. Myers through a friend I met on Reddit “This book distinguishes four categories of personality styles and shows how these qualities determine the way you perceive the world and come to conclusions about what you’ve seen”

January 22, 2021

Politician-Face-Classifier Web Application

GitHub link Web Application Web Scraping Collected images from google through web-scraping using Selenium with ChromeDriver Code Link Data Cleaning Performed data cleaning through face detection using OpenCV pre-trained feature-based Haar cascade classifiers to discard images from the dataset without face and two eyes visible Feature engineering Performed feature engineering through extraction by wavelet transformation of images using PyWavelets and then vertically stacking raw and wavelet transformed images Model Building trained machine learning models such as Logistic Regression, SVM(Support vector machine), and Ensembling bagging Random Forest