This project aims to predict the shipping time category for e-commerce orders using machine learning. By analyzing historical order data and shipping details, the model can help businesses optimize logistics and improve customer satisfaction.
The project demonstrates the full data science workflow:
- Data collection and cleaning
- Exploratory Data Analysis (EDA)
- Feature engineering
- Model selection and training
- Model evaluation
- Insights and visualizations
The dataset used in this project contains historical e-commerce order details, including:
- Order IDs
- Customer information
- Product details
- Shipping details
- Delivery times
The project is implemented in Python and uses the following libraries:
pandas– Data manipulationnumpy– Numerical operationsmatplotlib&seaborn– Data visualizationscikit-learn– Machine learning models and evaluation- Logistic Regression
- Random Forest Classifier
- Decision Tree Classifier
- Data Import & Cleaning: Handling missing values, outliers, and inconsistent data.
- Exploratory Data Analysis (EDA): Understanding patterns and relationships between features.
- Feature Engineering: Encoding categorical variables and creating meaningful features.
- Model Training & Selection: Training models
- Model Evaluation: Using accuracy, ROC curve, and other metrics to assess model performance.
- Prediction: Predicting shipping time categories for new orders.
- Visualization: Presenting insights and feature importance using graphs.
- Feature importance analysis helps identify key factors influencing shipping times.
- The model can support logistics teams in predicting delays and optimizing delivery schedules.
- Potential integration with e-commerce platforms to provide real-time shipping predictions.
- Implement more advanced models like Random Forest, LightGBM, or deep learning for better accuracy.
- Use real-time data streams to update predictions dynamically.
- Incorporate geographic and weather data to improve delivery time forecasts.