Retail Sales Prediction

Project Overview

This project focuses on predicting retail sales using historical sales data and time-series regression techniques. It leverages Python, Scikit-learn, and XGBoost to build predictive models capable of forecasting sales trends. The goal is to provide actionable insights to retailers for inventory and sales strategy planning.

Dataset

• Source: Kaggle Retail Dataset
• Link: Kaggle Retail Dataset
• Description: The dataset contains daily sales data for multiple stores and items, including promotional information and sales figures over time.

Tools & Libraries

• Python 3.x
• Pandas
• NumPy
• Matplotlib & Seaborn (for visualization)
• Scikit-learn (Random Forest Regressor, model evaluation, hyperparameter tuning)
• XGBoost (advanced regression modeling)
• Joblib (model saving and loading)

Project Steps

1. Data Import & Initial Exploration

2. Data Preprocessing

3. Exploratory Data Analysis (EDA)

4. Feature Engineering

5. Model Building & Evaluation

6. Hyperparameter Tuning