🏪 Walmart Weekly Sales Prediction

CDSD Certification Project — Linear & Regularized Regression

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📋 Executive Summary (click to expand)

Objective: Predict weekly sales for 45 Walmart stores to optimize inventory, marketing campaigns, and minimize overfitting.

Target KPI: R² ≥ 90% on unseen data

Dataset:

• 6,435 weekly records, 45 stores, 7 features + temporal variables
• Target: Weekly_Sales ($)
• Preprocessing: outlier removal (Z-score 3σ), temporal feature engineering, 5,912 clean rows, 80 features

Pipeline Highlights:

• ColumnTransformer + GridSearchCV
• Numerical: KNNImputer → StandardScaler
• Categorical: OneHotEncoder (handle_unknown='ignore')
• Target leakage fully prevented

Models Evaluated: Linear Regression, Ridge (α=0.01), Lasso (α=500)
Validation: Train/Test split + 5-fold CV

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🔬 Model Evaluation & Results

Model	R² Train	R² Test	Overfit	RMSE	MAE
Linear Regression	0.9714	0.9640	0.0074	130,948	103,671
Ridge (α=0.01)	0.9713	0.9630	0.0083	132,698	104,789
Lasso (α=500)	0.9708	0.9634	0.0073	131,977	102,517

Chosen model: Lasso Regression

• Excellent predictive performance
• Minimal overfitting
• Sparse coefficients (~60% zeroed)
• Improved interpretability for business stakeholders

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📊 Key Business Insights

Insight	Impact	Recommended Action
Store dominance	Top 10 stores = 45% total sales	Focus inventory on high performers
Holiday effect	+22% sales	Pre-stock 2–3 weeks before holidays
Economic sensitivity	Sales negatively correlated with unemployment	Adjust promotions during downturns
Seasonality	Nov/Dec peaks	Plan staffing & marketing campaigns

💰 Estimated annual business impact: ~$120M (forecast accuracy + inventory & holiday optimization)

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🛠️ Production-Ready Pipeline

• ColumnTransformer + GridSearchCV
• Pipeline export: preprocessor.pkl, lasso_model.pkl
• FastAPI endpoint: POST /predict_sales → store-specific weekly forecast
• Docker / AWS Lambda ready (<100ms inference)
• Drift monitoring: retrain automatically if R² < 90%

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✅ CDSD Certification Coverage

• EDA & preprocessing
• Linear regression baseline
• Regularized models (Ridge & Lasso)
• Cross-validation & overfitting control
• Feature importance & business interpretation
• Production-ready ML pipeline & deployment artifacts

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🚀 Quick Start

# Clone the repository
git clone https://github.com/Data-Science-Designer-and-Developer/Project_Walmart.git
cd Project_Walmart

# Install dependencies
pip install -r requirements.txt

# Run the notebook
jupyter notebook
<<<<<<< HEAD

1. Run the notebook sequentially
2. Use deploy_pipeline.py to generate production artifacts (.pkl)
3. Use predict.py to forecast store sales

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👨‍💻 Author

Dreipfelt — CDSD Data Science Certification Candidate GitHub: https://github.com/Dreipfelt