SmogJ/Supermarket-Sales-Forecaster

Predictive modeling for supermarket sales forecasting using Machine Learning. Developed as part of the DeepTech-Ready program (Google x 3MTT Nigeria) to optimize retail inventory and business strategy. # Supermarket-Sales-Forecaster Predictive modeling for supermarket sales forecasting using Machine Learning. Developed as part of the DeepTech-Ready program (Google x 3MTT Nigeria) to optimize retail inventory and business strategy. ## 📊 Project Overview This project focuses on building a predictive model to forecast Total Sales per Transaction for a supermarket. By leveraging the Kaggle Supermarket Sales Dataset, the goal is to provide data-driven insights that improve inventory management and operational efficiency. ## 🎯 Key Objectives Data Analysis: Identify key patterns and features influencing transaction totals. Modeling: Develop and train machine learning models to predict sales accurately. Evaluation: Compare model performance using industry-standard metrics. Business Intelligence: Translate technical findings into actionable strategy for supermarket management. ## 🛠️ Tech Stack __Language__: Python __Libraries__: Pandas, NumPy, Matplotlib/Seaborn (Visualization), Scikit-Learn (Modeling) __Platform__: Google Colab / Jupyter Notebook