Mobile Sales Prediction

This project focuses on predicting the sales value of mobile phones using machine learning algorithms. The dataset includes various features such as product specifications, sales volume, pricing, taxes, exchange rates, and time-related information. After extensive data preprocessing, cleaning, and feature engineering, several models including Random Forest, Gradient Boosting, XGBoost, CatBoost, LightGBM, and neural networks were trained and evaluated. The results demonstrate strong predictive performance, helping businesses make data-driven decisions for sales forecasting, inventory management, and strategic planning.