Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge from structured and unstructured data. Data scientists perform data analysis and preparation, and their findings inform high-level decisions in many organizations.
This documentation is like a quick snapshot of my project in the data field, showing off my skills and know-how in this area.
A Heart Attack Risk Prediction Project
Random forest analysis of match statistics and team performances in five seasons of the English Premier League (EPL)
The welcome content for the Halerium platform
A pipeline to scrape, extract, and analyze book data from web pages to insights.
Data analysis project using Python on NCRB Crime in India 2020 dataset. Includes data cleaning, visualization, and insights with Pandas, Seaborn, and Matplotlib.
We solve a regression problem in which it consists of calculating the health insurance charge in the United States Where we will break down the project into 5 phases: Exploratory Analysis. Feature Engineering. Selection of the ideal model. Development of the final model. Creation of a web application in streamlit.
Master's Final Degree Project on Artificial Intelligence and Big Data
The project is to recognize fraudulent credit transactions. You only need to put the dataset and model will detect the fraudulent credit transactions.
A machine-learning model predicting crypto price from the time-series data
This is a Movie Recommendation System that suggests movies to users based on their preferences. The system uses machine learning techniques to recommend similar movies.
Data science project consisting of 4 parts: 1-Web Scraping 2-Data Preprocessing 3-Data Visualization 4-Machine Learning
This is a Loan Approval Prediction Web App built with FastAPI (Backend) and HTML, CSS, JavaScript (Frontend). It predicts whether a loan application will be approved or not approved based on user input.
This project is a Streamlit-based interactive web application that allows users to explore 128 years of Olympic history (1896–2024) using data analysis and visualizations.
Using exploratory data analysis and k-means clustering to analyze competitive balance in soccer/football
Scraping Data Science jobs, performing EDA, building a regression model and productionizing it.
This project focuses on detecting fraudulent payment transactions using machine learning techniques.
A simple and intuitive web application that predicts house prices based on user input (location, total square feet, number of bathrooms, and number of bedrooms). This app is built using Flask on the backend and a trained Linear Regression model pipeline using scikit-learn.
This is a machine learning web application built with Flask that predicts house prices in Lahore. It uses an XGBoost model trained on custom data and a clean frontend with HTML, CSS, and JavaScript.
Time Series Analysis of Stock Prices. A data science practice real world project taught by Shan Singh