Exploratory Data Analysis for ML Practitioners

Dynamic Pricing Mastery: Assignment Content Overview

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

I. Strategic Business Understanding & Problem Formulation:

• Defining the business objective: Profit Maximization in E-commerce.
• Understanding the competitive landscape and challenges of static pricing.

II. Data Acquisition & Initial Exploration:

• Loading and inspecting raw sales data.
• Initial data quality assessment (missing values, data types).

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:

• Defining the profit maximization objective.
• Simulating demand and profit across a range of prices.
• Identifying optimal price points for individual products.
• Quantifying potential profit uplift.

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.