House price estimation from visual and textual features using both machine learning and deep learning models

Worked on AFLW2000-3D dataset which is a dataset of 2000 images. The regression model of predicting the 3 angles (pitch - yaw - roll) of head pose estimation was XGboost Regressor.

Notebooks for Kaggle competition

A regression model to predict calories burnt using values from multiple sensors.

A machine learning web app that predicts house prices across 5 major cities of Pakistan. It uses features like location, property type, area, bedrooms, and bathrooms to give an estimated price. The model achieves an impressive R² score of 99.9%, showing how accurate the predictions are.

Real-time household energy consumption forecasting system powered by XGBoost and FastAPI. Includes MLOps automation with DVC, MLflow, Prefect, and Streamlit dashboard.

This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.

Doing Analysis of the sales of video games across the globe and predicting the sales using various Machine Learning Algorithms

Quick cheatsheet about XGBoost, a Gradient Boosted regularized technique published in 2014

Prediciting the Prices of House using the Boston House Price Dataset by applying the XGBoost Regressor Model

A person’s creditworthiness is often associated (conversely) with the likelihood they may default on loans.

Machine learning for predicting relevance of scientific abstracts using TF-IDF, SMOTE, regression and classification models

🎬 Predictive Analytics: Forecasting Netflix Title Performance using a Random Forest Model

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A machine learning-based web app to predict the price of used cars in India based on various features like brand, model, location, fuel type, and more. Built with Streamlit for an interactive user interface and powered by an XGBoost (multiple-non-linear-regression) model for accurate predictions.

Build a Machine Learning model to predict the total count of cabs booked in each hour by the new data. Research on Cyclic features

In this project, a regression-based performance prediction model was developed to estimate building energy consumption based on simplified façade attribute information and weather conditions.

Machine learning model for predicting compressive strength of fiber-reinforced concrete with waste rubber.

Student Performance Predictor is an end-to-end machine learning project that implements a complete predictive modeling pipeline. It analyzes the impact of demographic, socioeconomic, and academic factors on student mathematics performance, performing data preprocessing, feature engineering, regression modeling (Linear, Ridge, Lasso, Random Forest,