This repository contains the implementation of a Customer Churn Prediction model for a bank. The project predicts whether a customer will close their bank account (Exited) based on various customer attributes.
Customer churn prediction is crucial for banks to retain customers and optimize marketing efforts. In this challenge, the goal was to:
- Model customer churn using the provided dataset.
- Perform data analysis, pre-processing, model development, and evaluation.
- Showcase technical knowledge and present key insights.
The dataset comprises two CSV files:
- data_train.csv: Training dataset with customer details and the target variable (
Exited). - data_test.csv: Test dataset for evaluating the model.
CreditScore: Credit score of the customer.Geography: The country from which the customer belongs.Gender: Male or Female.Age: Age of the customer.Tenure: Number of years with the bank.Balance: Bank balance of the customer.NumOfProducts: Number of bank products utilized.HasCrCard: Whether the customer has a credit card.IsActiveMember: Whether the customer is an active member.EstimatedSalary: Estimated salary of the customer.Exited: Target variable (1 = churned, 0 = retained).PostExitQuestionnaire: Binary flag if a questionnaire was distributed to the customer after exiting
- Exploratory Data Analysis (EDA):
- Visualized trends, distributions, and correlations.
- Highlighted key churn indicators.
- Data Pre-processing:
- Handled missing values, encoded categorical variables, and normalized numerical features.
- Model Development:
- Implemented multiple machine learning models (e.g., Logistic Regression, Random Forest, Gradient Boosting).
- Tuned hyperparameters for optimal performance.
- Evaluation:
- Assessed models using metrics like accuracy, precision, recall, F1-score, and AUC-ROC.
- Selected the best-performing model for deployment.
- Clone the repository:
git clone https://github.com/muaviyaijaz123/customer-churn-prediction.git cd customer-churn-prediction