A data science solution implementing a predictive model to establish an optimal, dynamic pricing strategy for ride-sharing services, maximizing revenue and optimizing fleet deployment.
Title: Retail Price Elasticity Modeling and Optimization
Description: This project develops a predictive regression model to optimize retail product pricing (unit price) by analyzing the influence of historical sales, product attributes, temporal factors, and competitive pricing dynamics on total revenue.
The primary goal is to build a robust model capable of predicting the optimal retail price (Total Price / Unit Price) for various products. By integrating features such as competitor pricing, holiday seasonality, and product specifications, the system aims to provide data-driven price recommendations that maximize total revenue and sales volume.
The project utilizes core Python libraries for data science, statistics, modeling, and interactive visualization:
| Category | Tool / Library | Purpose |
|---|---|---|
| Data Handling | Pandas NumPy | Data loading cleaning and numerical transformations |
| Visualization | Plotly | Interactive data visualization for EDA (e.g., Box Plots Scatter Plots) |
| Modeling | Scikit-learn (Implied) | Used for model building (Regression) and data splitting |
| Features | Lagging Features | Creating time-series features like lag_price for historical context |
- Data Loading and Inspection: Loaded the
retail_price.csvdataset, inspected the column types, and confirmed there were no missing values. - Exploratory Data Analysis (EDA): Conducted detailed analysis to understand feature relationships, including:
- Quantity vs. Total Price scatter plots.
- Total Price by Holiday box plots to quantify seasonal or event-based pricing impacts.
- Feature Integration: Utilized a rich set of features including product metadata, customer behavior (
customers), temporal data (holiday,month), competitor prices (comp_1,comp_2,comp_3), and a lagged price (lag_price) to capture time-series dependency. - Model Training: A regression model (implied) was trained using the prepared features (
X_test) to predict the retail price (y_test). - Evaluation: Model performance was visually assessed by plotting Predicted vs. Actual Retail Price against an ideal diagonal line, showing strong alignment and prediction reliability.
- Pricing Drivers Identified: EDA confirmed clear relationships between sales quantity, total price, and key temporal variables like
holiday. - High Predictive Accuracy: The final scatter plot of predicted vs. actual prices shows that predicted values cluster tightly around the "Ideal Prediction" line, indicating the model is highly accurate and suitable for real-world price recommendations.
- Competitor Influence: The inclusion of multiple competitor price points (
comp_1,comp_2,comp_3) provides a foundation for sophisticated competitive pricing strategies.
RetailOptimization PriceElasticity MachineLearning Regression PredictiveAnalytics DataScience Python Plotly CompetitorAnalysis
Install all necessary packages using the provided requirements.txt file:
pip install -r requirements.txt