sales-prediction-with-ml

Sales prediction from TV, Radio, and Newspaper advertising spend using Linear, Ridge, Lasso, Polynomial, Random Forest, and XGBoost regression. Best model: Random Forest at R²=0.988. Includes feature engineering, channel ROI analysis, and business insights. CodeAlpha Data Science Internship — Task 4.

This project predicts product sales using machine learning and advertising data. Linear Regression was used to train the model, visualize trends, and evaluate prediction accuracy.

A machine learning project that predicts store sales using regression and boosting models. Includes data cleaning, feature engineering, model comparison, and performance evaluation to identify the best algorithm for accurate sales forecasting.

End-to-end machine learning project for predicting retail sales volume using EDA, SQL, feature engineering, and Random Forest Regression.

Machine Learning sales forecasting project using Linear Regression and future sales prediction with Python.

"ML-basedhttps://sales-prediction-ml-4994.streamlit.app/ app with live Streamlit demo"