Predicting store sales is one of the most practical and in-demand data science skills today. Businesses rely heavily on accurate sales forecasts to manage inventory, plan marketing campaigns, set budgets, and reduce losses.
The Store Sales project from Kaggle’s “Getting Started” competition is a perfect opportunity for beginners to learn time series forecasting, feature engineering, and model ensembling. In this project, you work with real store sales data, clean it, analyze time-based patterns, scale features, and train powerful multivariate time series models.
If you’re starting your Data Science journey, check out hands-on courses at Forsk Coding School.
What Is the Store Sales Prediction Project?
The goal is simple:
👉 Predict future store sales using historical data with time series machine learning models.
Participants train different models and try to improve their Kaggle leaderboard score. You will learn essential data science skills while working on a real, industry-relevant problem.
What Data Do You Work With?
The dataset includes daily sales for multiple stores along with additional features.
| Column | Description |
|---|---|
| Date | When the sale occurred |
| Store ID | Unique store identifier |
| Item ID | Product identifier |
| Sales | Number of items sold (target) |
| Promotions | Promo or discount information |
| Holidays | Special days affecting sales |
Skills You Learn in This Project
| Skill | Why It Matters |
|---|---|
| Data Cleaning | Fix missing dates, incorrect values |
| Time Series Analysis | Identify trends & seasonality |
| Feature Scaling | Useful for ML models |
| Multivariate Forecasting | Consider multiple variables |
| Model Ensembling | Improve accuracy using multiple models |
| Evaluation Metrics | MAE, RMSLE, MSE, R² |
Time Series Concepts You Will Explore
✔ Trend
Long-term increase or decrease in sales.
✔ Seasonality
Repeating patterns—weekends, holidays, festivals.
✔ Lag Features
Previous day, week, or month sales.
✔ Rolling Statistics
Moving averages to smooth the data.
🛠 Models Commonly Used in Store Sales Prediction
| Model | Type | Strength |
|---|---|---|
| Linear Regression | ML | Simple baseline |
| Random Forest | ML | Handles noise well |
| XGBoost | ML | Excellent on Kaggle |
| LSTM / RNN | Deep Learning | Great for sequence data |
| Prophet | Facebook TS Model | Beginner-friendly forecasting |
However, the project focuses on multivariate ML models.
Improve Your Score with Ensembling
To rank higher on Kaggle, you can apply ensemble techniques, such as:
✔ Bagging Regressor
Reduces variance by training multiple models.
✔ Voting Regressor
Combines predictions of several models for better overall performance.
Ensembling is one of the simplest ways to boost accuracy without complex tuning.
Sample Table: Model Comparison
| Model | MAE Score | Strength |
|---|---|---|
| Linear Regression | High error | Good for baseline |
| Random Forest | Medium | Works with non-linear data |
| XGBoost | Low | Best overall performance |
| Voting Regressor | Lowest | Combines strengths of multiple models |
Why This Project Matters in Real Life
- Retail companies rely on accurate sales forecasting
- Helps reduce out-of-stock issues
- Prevents overstocking and storage expenses
- Supports marketing, staffing, and supply chain planning
- Makes businesses more efficient and profitable
This project is a perfect addition to your ML portfolio.
Learn Time Series & Machine Learning at Forsk Coding School
If you want to master Data Science with real-world projects, explore programs at:
👉 Forsk Coding School – Data Science & Machine Learning Courses
Suitable for students, job seekers, and working professionals.
Frequently Asked Questions (FAQs)
1. What is the main goal of the Store Sales project?
To predict future sales using historical time series data.
2. Is this a beginner-friendly Kaggle competition?
Yes, it is designed for beginners learning time series forecasting.
3. Which model performs best?
XGBoost or ensemble models often achieve the best results.
4. What is multivariate time series?
A forecasting method that uses multiple features, not just dates and sales.
5. Do I need deep learning for this?
No, machine learning models can perform very well.
6. Why do we scale features?
Scaling helps models learn patterns more effectively.
7. What are lag features?
Previous days’ sales used as inputs for forecasting.
8. What is ensembling?
Combining multiple models to improve accuracy.
9. Is store sales prediction used in real companies?
Absolutely—retail and e-commerce depend heavily on sales forecasting.
10. How can I learn time series forecasting from scratch?
Join the ML course at Forsk Coding School for hands-on training.
