Sales Forecasting Using Machine Learning

Computer Science Engineering

This paper presents a comprehensive approach for customer churn prediction and segmentation in the context of data- driven marketing. The proposed methodology involves multiple modules, including data processing, churn prediction using machine learning algorithms, customer segmentation through K-means clustering, and result analysis. The data processing stage ensures the quality and consistency of the dataset by performing data transformation, cleaning, and normalization. In cases of data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. The churn prediction module utilizes bagging tree, extra trees, and random forest algorithms for accurate identification of potential churners, followed by k-fold cross-validation and feature selection for model evaluation and variable importance determination. The customer segmentation module employs EDA techniques to gain insights from the data, and K-means clustering is used to group customers based on their similarities and behaviors. The results are then analyzed to assess the effectiveness of the churn prediction and segmentation models. This paper offers valuable insights for businesses seeking to proactively manage customer churn and implement targeted marketing strategies for different customer segments, ultimately leading to improved business performance and customer satisfaction.

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