Build a Customer Churn Prediction Model using Decision Trees

Welcome to the customer churn prediction project! Churn prediction helps businesses keep their customers happy and engaged. By using machine learning, we can predict if a customer will leave. In this project, we'll dive into the power of decision trees to build a simple yet effective churn prediction model.

Project Outcomes

✓It can effectively forecast customer churn
✓which will enable the companies to recognize the likely
✓to
✓churn customers.
✓Organizations are in a position to address churn risks if they are identified making it easy to employ suitable retention tools.
✓Offers important information about the customers and their behaviors and tendencies that lead to churn.
✓It assists companies to allocate resources and concentrate on the customers who pose more risks.
✓Combines commonly used SMOTE in order to achieve better model accuracy due to class imbalance.
✓Allows business organizations to define marketing strategies to be implemented on customers who constantly churn.
✓Builts up comprehensive model evaluation based on accuracy
✓precision
✓and ROC
✓AUC for a better understanding of the performances.
✓Find out which customers' attributes have a higher propensity to churn.
✓The model is not limited to use on small data sets and can easily accommodate large sets of data which makes it an ideal business especially those that are growing rapidly.
✓Enables businesses to find new ways of enhancing products or services in a bid to combat the churn rate.

Requirements:

  • →Understanding of basic knowledge of Python for data analysis and manipulation
  • →Knowledge of libraries such as Pandas, Numpy, and Matplotlib for data manipulation and data visualization respectively.
  • →Understanding of data preprocessing steps such as how to deal with missing values, normalization, and scaling.
  • →Familiarity with exploratory data analysis (EDA) to find out patterns and growing trends in sets.
  • →Elementary concepts about Decision Tree algorithm to learn how predictive modeling works
  • →Machine learning frameworks such as Scikit-Learn for building, training, and assessing models

Project Description

Our objective of this project is to predict customer churn using machine learning techniques. First, we will perform exploratory data analysis of the dataset containing many customer characteristics and prepare it for analysis. The primary algorithm employed is the Decision Tree Classifier, a very proficient algorithm used for classification tasks. To evaluate which of the two approaches is optimal, we employ Logistic Regression as a comparison model

We use SMOTE (Synthetic Minority Over-sampling Technique) also to address the class imbalance problem, which consists of producing samples of the minority classes. The data is then further prepared for the modeling stage by splitting it into training and testing data sets. We assess the model per important standards such as ROC-AUC, confusion matrix, accuracy, precision, recall, and F1-score to make sure that the model is useful and operational in predicting customer churn.

Build a Customer Churn Prediction Model using Decision Trees

Predict customer churn with Decision Trees! Learn data cleaning, SMOTE, and model evaluation using Python. Compare Decision Tree and Logistic Regression models to find the best approach in this hands-on, beginner-friendly project.

$15$5.0067% off