Unraveling Helsinki’s Weather Patterns: A Data-Driven Journey

Led a data analysis project investigating historical weather patterns in Helsinki, Finland. Utilized Python and data visualization techniques to analyze meteorological data and uncover insights into climate dynamics.

Sentimental Analysis on Reviews of Protein Supplements

Implement a RNN- LSTM model for suggesting a protein supplement Collect and prepare the dataset for the project. Iterate and improve the results; achieved about 82.8% accuracy It has been published in the IEEE

Loan Eligibility Prediction

Collect and process the dataset Used boxplot for showing outlier Compared two models ; Decision tree and Naive bayes and increased the accuracy result by 13% using naive bayes Algorithm.

Bitcoin Price Prediction Using LSTM (Long Short-Term Memory) Network

This project focuses on predicting the future price of Bitcoin using historical price data and applying a Long Short-Term Memory (LSTM) network. The goal is to demonstrate how machine learning can be applied to financial data, specifically cryptocurrency, to create predictive models that can aid inv