Bike-sharing-demand-analysis

Bike-sharing Analysis is a data science project focused on exploring and modeling rental behavior within urban bike-sharing systems. By leveraging the UCI Bike Sharing Dataset, the analysis employs EDA, inferential statistics, and regression models to identify key factors influencing bike rentals.

Finance Insights App

A Streamlit-based web application that allows users to upload financial documents in Excel or PDF format and query metrics like Revenue, Expenses, and Profit by year. The app extracts tables automatically and provides instant answers through a simple interface.

Proportional Species Richness Analysis

Analyzed biodiversity patterns through proportional species richness across multiple taxonomic groups using R. Applied statistical analyses, hypothesis testing, and regression modeling to study ecological changes over time and visualized results with advanced plots.

IMDB Movie Data Analysis

Analyzed IMDB dataset of 5,000+ movies through data cleaning, EDA, and visualization to identify top profitable films, best directors, popular genres, and actor influence on reviews, while uncovering audience voting trends across decades.

Football result prediction using machine learning

This project is a machine learning-based football prediction model that analyzes historical match data to predict the outcomes of future matches. The script is designed to process match statistics, train predictive models, and generate insights based on key performance indicators.

Game of Thrones Network Analysis

This project explores the social structure of the Game of Thrones universe using Network Analysis techniques. By treating characters as nodes and their interactions as edges, the analysis identifies the most central and influential figures across all five books.

H1N1 and Seasonal Flu Vaccines

Developed ML models to predict H1N1 and seasonal flu vaccination likelihood using demographic, behavioral, and health data. Applied KNN imputation, SMOTE, and hypothesis testing; compared models including Logistic Regression, Random Forest, and Gradient Boosting, with Gradient Boosting best results.

Instagram User Analytics

This project involved building and analyzing a simulated Instagram database to extract actionable business insights for marketing and investor decision-making.