Retail Sales Forecasting

📌 Overview

This project focuses on predicting future retail sales using machine learning techniques. Accurate forecasting helps businesses make better decisions about inventory, staffing, and promotions.

🎯 Objectives

Analyze historical sales data.

Identify seasonal trends and patterns.

Build predictive models to forecast future sales.

Provide actionable insights for business planning.

⚙️ Tech Stack

Python (data analysis & modeling)

Pandas / NumPy (data preprocessing)

Matplotlib / Seaborn (visualization)

Scikit‑learn / Statsmodels (forecasting models)

Jupyter Notebook (experimentation & documentation)

📊 Methodology

Data Collection – Gather historical retail sales data.

Data Cleaning – Handle missing values, outliers, and inconsistencies.

Exploratory Data Analysis (EDA) – Visualize trends, seasonality, and correlations.

Feature Engineering – Create time‑based features (month, quarter, holidays).

Modeling – Train models such as ARIMA, Prophet, or Random Forest Regressor.

Evaluation – Measure accuracy using metrics like RMSE, MAE, and MAPE.