The company, specializing in electronic products, seeks insights from its customer database to optimize delivery performance and enhance customer satisfaction.
The dataset comprises 10999 observations across 12 variables, detailing customer interactions, product characteristics, and delivery outcomes. Key variables include:
- Warehouse block
- Mode of shipment
- Customer care calls
- Product cost
- Prior purchases
- Product importance
- Delivery performance (target variable)
Data Preprocessing: Cleaned and prepared data, handling missing values, duplicates, and irrelevant columns.
Exploratory Data Analysis (EDA): Investigated distribution of variables, customer behavior, and logistics factors using visualizations.
Feature Engineering: Transformed categorical variables using label encoding.
Model Building: Deployed machine learning models like Random Forest, Decision Tree, Logistic Regression, and KNN to predict delivery outcomes.
Model Evaluation: Assessed models based on accuracy, confusion matrix, and classification reports.
Product weight and cost significantly impact delivery timeliness. Warehouse F, likely near a seaport, handles most shipments, predominantly via shipping. Customer engagement (calls, prior purchases) and promotional discounts correlate with delivery performance.
Decision Tree Classifier demonstrated the highest accuracy at 69%. Random Forest and Logistic Regression showed comparable performance, with accuracies around 68% and 67%. KNN had the lowest accuracy at 65%.
The project highlights critical factors affecting product delivery timelines and offers a robust predictive model to aid the e-commerce company in streamlining its logistics operations.