Disease Prediction System

The Disease Prediction System is a machine learning–based healthcare application developed using Python, Streamlit, Pandas, and Scikit-learn that predicts diseases based on user symptoms using Decision Tree and Random Forest algorithms. It supports 133 symptoms and provides accurate predictions

Credit card analysis for Mitron Bank

The Mitron Bank Data Analysis project aims to tailor credit card offerings to customer needs and market trends through thorough data analysis. Utilizing Tableau for data visualization and PowerPoint for reporting, the project will provide actionable insights to enhance customer satisfaction.

Covid Data Insights

The purpose of this project is to explore and gain insights from COVID-19 data using SQL Server Management Studio. The dataset, which includes information on COVID deaths and vaccination, was collected in CSV format from Our World in Data. Following that, the data was explore using advance SQL skill

Amazon web Scraper

What is Web Scraping and How Does it Work? Using Python Libraries & BeautifulSoup for Amazon Data Extraction Creating a Script to Extract Price and Product Information from AmazonUsing Data Extracted from Amazon to Improve Your Business or Website Best Practices for Scraping Data from Websites

Google Play Store Analysis

this project is to analyse the Google Play Store Apps dataset to derive insights into the app market. We aim to understand the factors that contribute to an app's success, including user ratings, reviews, and category. Exploring user sentiments towards apps through the analysis of user reviews

Athlete in Olympic 2021 Data Analysis

Using Python, this project analyzes Olympic athletes' performance and trends from 1928 to 2016 through data cleaning and visualization. It seeks to uncover insights into athletes' achievements over time, enhancing understanding of historical trends in Olympic sports.

T20 data Analysis

project focuses on performing T20 data analysis using data obtained from ESPN Cricinfo website through web scraping. The data was available in JSON format, which was cleaned and processed using Python. The cleaned data was then converted to CSV format for further analysis & visualization in Power BI