Retail Sales Forecasting Machine Learning Project : Python + Time Series

Retail Store Sales Forecasting using Python

Want to learn how companies predict future sales using data science and machine learning?

This project walks you through a complete real-world retail sales forecasting pipeline using Python, time-series analysis, and deep learning models.

You will learn how to analyze historical sales data, detect trends, build forecasting models, and evaluate predictions using modern machine learning techniques.

Perfect for students, beginners in data science, and anyone building a machine learning portfolio.

Key features:

Data cleaning and preprocessing

✔ Exploratory Data Analysis (EDA)

✔ Time Series Forecasting techniques

✔ ARIMA forecasting model

✔ SARIMA seasonal forecasting

✔ Facebook Prophet model

✔ Deep Learning forecasting using LSTM

✔ Model evaluation using RMSE and MSE

✔ Sales classification and feature engineering

✔ Business analytics interpretation

Models Implemented in the Project

The project implements multiple forecasting techniques to compare performance:

ARIMA (AutoRegressive Integrated Moving Average)

SARIMA (Seasonal ARIMA)

Facebook Prophet Forecasting

LSTM Neural Network (Deep Learning)

This allows learners to understand traditional vs modern forecasting

Technologies Used

Programming Language

• Python

Libraries Used

• Pandas
• NumPy
• Matplotlib
• Seaborn

Stats Models

• Pmdarima
• Prophet
• TensorFlow / Keras
• Scikit-Learn

Perfect for (Who This Project Is For)

This project is perfect for:

1. BCA / BSc / MSc Data Science students
2. Machine Learning beginners
3. Data Science learners
4. Python developers
5. Students building data science portfolios
6. Anyone preparing ML academic projects

Files Included in the Download

You will get:

• Complete Jupyter Notebook (.ipynb)
• Retail sales dataset
• Fully implemented forecasting models
• Visualization graphs
• Model evaluation results
• Clean and well-commented code
• Documentation
• Powerpoint Presentation