reggression

an attempt at making neural networks from scratch.

applying Linear Regression (Simple and Multiple), Polynomial Regression, and Logistic Regression on real-world datasets.

Real Estate Investment Advisor is an end-to-end analytics to decode the Indian housing market. The app provides real-time property price predictions and classifies investment .

Machine Learning project for predicting Car prices using Linear, Ridge, and Lasso Regression with performance evaluation (MSE, RMSE, MAE, R²) .

A Regression project aiming to forecast the expenses of individuals within an insurance company where we could then decide whether to charge them a premium package or not.

Modeling on a timeseries dataset

Regression model predicting NYC taxi fares based on trip data

ML-powered Streamlit app to predict student academic performance using regression models

This project explores the prediction of energy consumption using machine learning, with a focus on comparing Linear Regression and Random Forest models. It emphasizes understanding evaluation metrics (RMSE and R²) and examines how feature engineering—such as encoding categorical variables and extracting temporal features—affects model performance.

This project predicts fish weight based on species, length (1, 2, 3), height, and width data using multiple linear regression. Data preprocessing, feature selection via Backward Elimination, model evaluation, and a Flask-based web interface have been implemented.

End-to-end ML project predicting California house prices using tuned RandomForest (CV RMSE: $48,713) with FastAPI REST endpoint and EDA notebook.

This project is dedicated to predicting real estate prices in order to increase the number of transactions and simplify the work of a real estate agency using various regression models on real data.

Uncover customer churn patterns in telecom data. Utilizing logistic regression, we predict churn and assess model performance. The README guides users through dataset overview and steps. Find detailed insights in the accompanying presentation.

Regression for Coffee dataset in the US market using R and Python

House price prediction using Decision Tree Regression with data analysis and machine learning techniques.

Machine learning project for predicting cognitive performance scores using lifestyle and demographic data.