This project focuses on predicting customer churn within an e-commerce platform using advanced data mining techniques. Understanding customer behavior is pivotal in today's digital marketplace to enhance retention strategies and address churn. The primary goal is to explore an e-commerce dataset, predict churn, and derive actionable insights for better customer retention.
- Source: Kaggle link
- Size: 5630 observations, 20 attributes
- Nature: Mix of categorical, numerical, and textual attributes capturing various facets of customer behavior and transactional information.
CustomerID
: Unique identifierChurn
: Target variable (1 for churned, 0 for active)- Other attributes like
Tenure
,PreferredLoginDevice
,PreferredPaymentMode
,SatisfactionScore
, etc., crucial for understanding customer behavior and predicting churn.
- Talend Data Preparation: Exploratory analysis of dataset attributes to understand their impact on churn prediction.
- Talend Data Integration: Preprocessing, handling missing values, dropping irrelevant attributes, and transforming categorical variables for modeling.
- SAS Enterprise Miner: Building decision trees, Random Forest, and Gradient Boost models for churn prediction and comparison.
- Model Comparison: Decision Tree emerged as the most accurate for predicting churn, surpassing Random Forest and Gradient Boost.
- Key Predictors: Attributes like
Tenure
,Complain
,PreferredPaymentMode
, among others, significantly influence churn behavior. - Implications: Insights aid in enhancing customer retention strategies, targeted marketing, and improving service quality.
- Clone the repository and follow instructions in each tool's respective folder for replicating data preparation, preprocessing, and modeling steps.
For more examples, please refer to the Documentation.
.
├── WQD7005_AA1_Analysis # Contains SAS e-Miner analysis & modeling scripts.
├── WQD7005_AA1_Data_Integration # Includes Talend Data Integration preprocessing scripts.
├── WQD7005_Report
├───── Assessment.pdf # The assessment requirement file of this project.
├───── Report.pdf # The documentation (report) file of this project.
├── dataset
├───── E Commerce Dataset.xlsx # Raw dataset in Excel format.
├───── E Commerce Dataset.csv # Raw dataset in CSV format.
├───── E Commerce Dataset (processed).csv # Preprocessed dataset in CSV format.
- Name: LOO JIA HAO
- Student No: 17218692/1
- Student Email: 17218692 (at) siswa (dot) um (dot) edu (dot) my
Feel free to explore each folder for detailed scripts, reports, and documentation related to specific phases of the project.