Predictive Modeling for Customer Churn Prediction
Praveen Kumar Adepu
Data Visualizer
Data Analyst
Data Engineer
Introduction
The goal of this project is to develop a predictive model for customer churn prediction using Python. The project aims to analyze historical customer data and build a model that can accurately predict which customers are likely to churn in the future. By identifying potential churners, businesses can proactively take measures to retain those customers and minimize customer attrition.
Data Engineering
The scope of the project includes the following key tasks:
Data Collection: Gather relevant customer data, including demographic information, purchase history, customer interactions, and any other relevant variables that may influence churn.
Data Preprocessing: Cleanse and preprocess the collected data by handling missing values, removing outliers, and transforming variables as necessary.
Feature Engineering: Extract meaningful features from the data that can provide valuable insights into customer behavior and propensity to churn.
Model Selection: Evaluate various machine learning algorithms suitable for churn prediction and select the most appropriate one based on model performance and interpretability.
Tech Stack
Python will be the primary programming language used for this project. Several popular Python libraries, such as Pandas, NumPy, and Scikit-learn, will be employed for data preprocessing, feature engineering, and model building. These libraries provide a wide range of tools for data manipulation, statistical analysis, and machine learning.
Conclusion
Throughout the project, regular communication and collaboration with the client will be maintained to ensure alignment with their requirements and expectations. Progress updates will be shared at agreed intervals, and any deviations or challenges encountered will be promptly communicated and addressed. The final deliverable will include a well-performing predictive model for customer churn prediction, along with documentation detailing the methodology, model evaluation metrics, and recommendations for implementing the model in a production environment.