Mobile App Success Predictor

Collaboratively developed a predictive model to estimate the success of mobile apps based on features such as price, category, and content rating. The project used machine learning techniques to classify apps into success categories, offering insights for app developers.

Twitter Sentiment Analysis

Implemented a natural language processing (NLP) model to classify sentiments in tweets as positive, negative, or neutral. This project analyzed public opinion on Apple and Google products and provided insights into social media trends.

Air Quality Health Impact Prediction

This project develops a machine learning model to predict the health impacts of air quality based on pollutant levels,e.g. PM2.5 and NO2. Analyzing the relationship between air quality data and respiratory health outcomes provides insights for public health initiatives and environmental policies.

Film Analysis

This project explored trends in movie data to analyze box office performance, audience preferences, and the factors contributing to a film's success. The project was conducted collaboratively, leveraging data visualization and statistical methods to identify patterns and trends in the film industry.

Aircraft Risk Assessment Analysis

This project analyzes risk factors associated with aircraft incidents using machine-learning classification models. By examining historical data on flight incidents, the analysis aims to identify patterns and predict potential risks, contributing to improved safety measures in the aviation industry.

Vehicle Co2 Emissions Prediction

I developed a predictive model to estimate CO2 emissions from vehicles based on various features such as engine size and fuel type. This project aimed to provide insights into factors influencing CO2 emissions and contribute to the efforts for more sustainable transportation solutions.