This project aims to predict customer churn using machine learning techniques. Customer churn refers to the phenomenon of customers ceasing their relationship with a company or service. By understanding the factors that contribute to churn, businesses can take proactive measures to retain customers and maximize their customer base.
The dataset used for this project contains information about customers, including various features that may influence churn. These features include contract type, tenure, payment method, monthly charges, total charges, internet service type, and more. The target variable is the churn status, indicating whether a customer has churned or not.
The project follows these steps:
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Data Cleaning: This involves handling missing values, dealing with outliers, and transforming the data to prepare it for analysis or modeling.
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Data Exploration: Perform exploratory data analysis to gain insights into the dataset, understand the distribution of features, and identify any patterns or correlations.
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Data visualization: Perform the graphical representation of data to provide insights and communicate information effectively.
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Data Preprocessing: Handle missing values, perform feature engineering if necessary, and encode categorical variables for modeling.
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Model Selection: Choose an appropriate machine learning algorithm for churn prediction. Commonly used algorithms include logistic regression, decision trees, random forests, and gradient boosting.
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Model Evaluation: Evaluate the trained model's performance using appropriate metrics such as accuracy, precision, recall, and F1 score. Additionally, analyze the confusion matrix to understand the model's predictive capabilities.
Customer churn prediction is a crucial task for businesses looking to enhance customer retention strategies. By leveraging machine learning techniques and analyzing relevant customer data, this project aims to assist businesses in making data-driven decisions to reduce churn rates and improve overall customer satisfaction.
For more details, refer to the documentation and code files in this repository.
Feel free to reach out if you have any questions or suggestions for improvement!