Comparing Stroke Disease Classification Models

This project aims to compare various machine learning classification models to identify the most accurate model for stroke disease classification. Through rigorous analysis and evaluation, the study endeavors to determine the model that achieves a remarkable accuracy level of 98 percent. Ultimately,

Machine Learning-Based Cardiovascular Disease Detection System Development

This portfolio highlights the development of a machine learning-based system for early detection of cardiovascular diseases. Using advanced algorithms and techniques, the project involved data collection, preprocessing, model training, and evaluation to create an accurate detection system.

Transaction Report in Google Looker Studio

This project entailed creating a transaction report dashboard using Google Looker Studio to offer stakeholders insights into transactional data encompassing sales performance, customer behavior, and product trends.

Laptop Sales Prediction Using Machine Learning Regression Models

Developed and tested various machine learning regression models to predict laptop sales. The project involved data collection, preprocessing, and training multiple models to identify the best-performing one.

Twitter Sentiment Analysis

This group project involved building and training deep learning models to analyze tweet sentiments using the Sentimen140 dataset. We developed and trained LSTM and GRU models to automatically identify tweet sentiments, providing valuable insights into public opinions on social media platforms and ai

Analysis of Incidents on the London Bus Network Using Power BI

Created an interactive dashboard using Power BI to analyze incidents on the London bus network. Provided detailed insights into incident nature, frequency, and distribution, facilitating pattern identification and areas for improvement in network safety and efficiency.