Dynamic Pricing Model for E-commerce

Project Overview

This project uses machine learning algorithms to predict optimal product selling prices based on multiple real-world factors such as demand patterns, date-based seasonality, product ratings, and product category attributes. The solution combines Random Forest Regression, GridSearchCV tuning, and label encoding strategies to deliver accurate and interpretable pricing outputs. It includes visual tools to inspect data distribution and feature importance while maintaining model robustness through preprocessing and testing across unseen data.

Objective & Business Context

In e-commerce, pricing is one of the most powerful levers to influence sales volume, customer acquisition, and profit margins. However, setting the right price is a balancing act. If the price is too high, customers churn. If it’s too low, margins shrink. In today’s competitive marketplace, especially on digital platforms where users can compare products across sellers in seconds, pricing decisions must be dynamic and data-driven.

Many businesses still rely on static price tags or manual decisions based on gut feeling, seasonality charts, or past sales averages. This approach ignores the real-time trends, changing demand, evolving customer preferences, or even competitor price shifts. As a result, businesses may lose revenue, reduce profitability, or lag behind in market response.

This project tackles this challenge by building an AI-powered dynamic pricing model. It utilizes structured product data, customer review signals, and temporal patterns to train machine learning models that predict a more optimal price point. These predictions can inform strategic pricing, automate price changes on websites, or serve as a recommendation tool for internal pricing teams.

Key Goals of the Project:

• Build a robust, flexible regression-based ML pipeline for price prediction.

• Capture key demand influencers like product category, brand, customer rating, and seasonality.

• Incorporate calendar-based features (Month, Day, Day of Year) to detect promotional or seasonal buying patterns.

• Handle unseen labels and ensure reverse label encoding for interpretability.

• Visualize important price-influencing factors to guide business decision-making.

Business Value and Real-World Scope

Business Value:

• Increase in conversion rate by aligning prices to market dynamics.

• Reduced dependency on static pricing strategies and manual revisions.

• Improved product margin control and competitor resilience.

• Integration-ready model for CRM, ERP, and inventory systems.

Real-world Industry Scenarios:

• Retail E-commerce: Automatically adjust clothing or electronics prices during festive or clearance periods.

• Travel Booking Platforms: Modify prices based on search history, ratings, and demand spikes.

• Event Ticketing: Change ticket pricing based on booking patterns, artist popularity, and customer segments.

• Subscription Services: Vary pricing tiers based on customer churn likelihood, loyalty scores, and usage trends.

Implementation Flow

• Training File: train.csv (historical product data with actual selling prices)

• Test File: test.csv (unlabeled, new product data for prediction)

• Columns of Interest:

  • Product, Product_Brand: Unique IDs for SKUs and brands

  • Item_Category, Subcategory_1, Subcategory_2: Hierarchical category labels

  • Item_Rating: Customer feedback score (numerical)

  • Date: Entry or sale date

  • Selling_Price: Target variable (present only in train.csv)

Dataset Overview

Data Preprocessing Workflow

A. Date Conversion and Feature Extraction:

• Converted Date column into datetime object.

• Extracted useful columns: Year, Month, Day, Day Of Week, and Day Of Year for seasonality.

B. Handling Missing Values:

• Imputed missing Item_Rating values using mean.

• Dropped rows post-feature alignment if still missing values.

C. Label Encoding:

• Applied label encoding to all categorical variables.

• Stored encoding dictionaries for decoding during post-processing.

D. Outlier Detection & Scaling:

• Visualized outliers in Selling_Price using boxplots.

• Scaled Selling_Price using MinMaxScaler for model compatibility.

Modeling Steps:

• Initial baseline model: Linear Regression

• Advanced modeling: RandomForestRegressor with 100 trees

• Evaluation Metrics: MAE, MSE, R² Score

• Performed hyperparameter tuning using GridSearchCV across:

  • n_estimators (number of trees)

  • max_depth (tree depth)

  • min_samples_split (min rows to split)

  • min_samples_leaf (min rows per leaf node)

Results:

• R² Score (Optimized Model): Significantly improved vs. baseline.

• MAE & MSE: Reduced after parameter optimization.

• Interpretability: Feature importance clearly highlighted product-level and date-level features

Model Building & Evaluation

Predictions & Postprocessing

Prediction Pipeline:

• Loaded trained .pkl model and label_encoders

• Preprocessed test data with same steps as training data

• Applied encoding and feature engineering

• Made predictions on test.csv

Readability Enhancements:

• Applied inverse label encoding for easier interpretation

• Saved output as test_predictions_readable.csv

Sample Output Columns:

ProductProduct_BrandItem_CategorySubcategory_1Subcategory_2Predicted_Selling_Price

✅ dynamic_pricing_model.pkl (final optimized model)

✅ test_predictions_readable.csv (decoded test predictions)

✅ Visualizations: Feature Importance, Prediction Samples

✅ Source Code: Full pipeline (preprocessing, modeling, prediction)

✅ Optional Enhancements: Streamlit-ready prediction viewer, decoding script

Key Deliverables

Tools and Libraries Used

• pandas, numpy - Data loading, processing

• matplotlib , seaborn - Data visualization

• sklearn - ML models, preprocessing, metrics

• joblib - Model saving/loading

• plotly, streamlit (optional) - Dashboard & UI visualization

• VS Code, Anaconda - Used to execute and test scripts, manage environments

• Extend model to include competitor pricing as feature input

• Add promotion flags (e.g., sale day, flash sale indicators)

• Use more advanced ensemble models (e.g., LightGBM, XGBoost)

• Expose pricing predictions as a REST API

• Create a rule-based override layer to combine human and AI decisions

Possible Next Steps & Conclusion

Conclusion

The Dynamic Pricing Model project demonstrates how intelligent, automated pricing can be implemented using historical and product-related data. By aligning real-world features such as date context, brand, and category structure with machine learning predictions, businesses gain both strategic insights and operational scalability.

The model not only predicts prices but ensures decoded outputs, bridging the gap between machine learning and decision-making teams. It shows how predictive modeling can be deployed in retail or e-commerce for real-time pricing, offer generation, and sales maximization.

With a modular structure and real-world interpretability, this project lays the foundation for advanced pricing engines adaptable across industries.

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Key Concepts

GitHub Repository

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