Olympics Data Analysis Web Application using Streamlit. For development, I will be using Python and Pandas. For plotting, I will be using Seaborn and Plotly libraries.

The "kidney-stones-Detector" is an advanced system delivering precise detection and classification of kidney conditions including stones, cysts, tumors, and normal states from medical imaging data. With an impressive accuracy of 98.87%, this machine learning-powered models offer reliable insights for medical professionals.

Learning Python For Data Engineering

A Machine Learning Project that predicts student grade and performance from a Dataset. Copule of Libraries are used for data pre-processing, training model, heatmaps, trees and other Algorithms.

This is a sophisticated app designed to analyze financial datasets and uncover market trends. Leveraging powerful tools like Python, Pandas, and Scikit-learn, this app offers deep insights into stock performance, helping investors and analysts make informed decisions. Explore data-driven strategies and stay ahead in the financial market.

Exploratory data analysis of food order trends using Python and Jupyter Notebook.

This project analyzes Zara’s retail sales data to identify key performance trends across products, seasons, and promotions. Using Python for data processing and visualization, and Power BI for interactive dashboards, it highlights insights that support data-driven decisions in retail strategy and inventory planning.

Bangalore House Price Prediction by KNN

Prediction model for Delivery Time by Simple Linear Regression

Prediction model for Salary Hike by Simple Linear Regression

This project is developed on python language, It scraps the data from a famous website BBC/Urdu and stores it in an CSV file. There are also other methods through them we read the csv file and manuplate its data

Crime Data Analysis by Clustering.

Fraud Check Analysis by Random Forest.

Health Analysis Report based on money spent on health and its impact on life expectancy.

The IPL Data Analysis project focuses on extracting valuable insights from IPL match data using various data analytics techniques. By analyzing historical match outcomes, player performances, team comparisons, and venue statistics, the project visualizes trends and patterns through graphs like bar charts, line graphs, and scatter plots.

Predict salary of new employee by Polynomial Regression.

Prediction model for Insurance Cost dataset by Regression

Prediction model for profit of 50 startups dataset by Multiple Linear Regression

Linea De Enfasis Ciencia de Datos y Machine Learning

This project uses machine learning algorithms to predict earthquake damage based on historical data. It analyzes factors like magnitude, depth, and location to estimate the potential damage caused by earthquakes.