This assignment provides a comprehensive, end-to-end experience in building a dynamic pricing optimization engine.

I. Strategic Business Understanding & Problem Formulation:

II. Data Acquisition & Initial Exploration:

III. Exploratory Data Analysis (EDA):

Analysis of target variable (units_sold) distribution.
Univariate and bivariate analysis of key features (e.g., price_usd, category, promotion).
Correlation analysis.

IV. Advanced Data Preprocessing & Feature Engineering:

Missing value imputation strategies (e.g., median, zero-fill).
Outlier handling considerations for target and features.
Engineered Features:Temporal: day_of_week, month, week_of_year, day_of_year, is_weekend.
Competitive: price_ratio_to_competitor, price_diff_from_competitor.
Demand/Popularity: product_popularity_score (from ratings/reviews), stock_per_view.
Lagged Sales: lag_1_units_sold, lag_7_units_sold.
Categorical Encoding: One-Hot Encoding for low cardinality, Target Encoding with smoothing for high cardinality.
Numerical feature scaling (StandardScaler).

V. Predictive Model Development & Training:

Model selection rationale (e.g., Linear Regression, Random Forest, LightGBM).
Time-Series Data Splitting: Strict chronological train-validation split.
TimeSeriesSplit Cross-Validation for robust evaluation.
Hyperparameter tuning using GridSearchCV with MAE as the primary optimization metric.

VI. Model Evaluation & Interpretability:

Calculation and interpretation of key regression metrics (MAE, RMSE, R2).
Visual analysis of model performance (Actual vs. Predicted plots, Residuals plots).
Model Explainability with SHAP:Global feature importance analysis.
SHAP dependence plots for understanding non-linear relationships and feature interactions (e.g., price elasticity).

VII. Dynamic Pricing Optimization & Prescriptive Analytics:

VIII. Business Implications & Strategic Recommendations:

Formulating actionable pricing recommendations.
Designing an A/B testing framework for live validation.
Addressing ethical considerations and risk mitigation strategies in dynamic pricing.
Outlining future enhancements and MLOps considerations for deployment.

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