The goal of this project is to develop a customer churn prediction model that can accurately identify customers at risk of churn. By proactively identifying potential churners, businesses can implement targeted retention strategies to reduce customer attrition and improve overall customer satisfaction and profitability.

Business Understanding --> Data Understanding --> Data Preprocessing --> Modelling --> Evaluation

The dataset contained the following features:

• Gender -- Whether the customer is a male or a female
• SeniorCitizen -- Whether a customer is a senior citizen or not
• Partner -- Whether the customer has a partner or not (Yes, No)
• Dependents -- Customer has dependents or not (Yes, No)
• Tenure -- Number of months the customer has stayed with the company
• Phone Service -- Whether the customer has a phone service or not (Yes, No)
• MultipleLines -- Whether the customer has multiple lines or not
• InternetService -- Customer's internet service provider (DSL, Fiber Optic, No)
• OnlineSecurity -- Whether the customer has online security or not (Yes, No, No Internet)
• OnlineBackup -- Whether the customer has online backup or not (Yes, No, No Internet)
• DeviceProtection -- Whether the customer has device protection or not (Yes, No, No internet service)
• TechSupport -- Whether the customer has tech support or not (Yes, No, No internet)
• StreamingTV -- Whether the customer has streaming TV or not (Yes, No, No internet service)
• StreamingMovies -- Whether the customer has streaming movies or not (Yes, No, No Internet service)
• Contract -- The contract term of the customer (Month-to-Month, One year, Two year)
• PaperlessBilling -- Whether the customer has paperless billing or not (Yes, No)
• Payment Method -- The customer's payment method (Electronic check, mailed check, Bank transfer(automatic), Credit card(automatic))
• MonthlyCharges -- The amount charged to the customer monthly
• TotalCharges -- The total amount charged to the customer
• Churn -- Whether the customer churned or not (Yes or No)

• Used Simple Imputer using the mean to Impute the missing values
• Used label encoder for the target column(Churn column) to encode the two classes 'Yes' and 'No'
• Used One Hot Encoder to encode the other remaining categorical columns
• Used SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance in target variable(Churn column).
• Used Standard scaler to normalize the dataset

The following Models were trained:

• Logistic Regression
• Random Forest
• Gradient Boosting
• Support Vector Machine
• Gaussian Naive Bayes
• K-Nearest Neighbors
• Decision Tree
• XGBoost
• LightGBM

Random Forest with a F1 score of 0.991486574983628

Newton Kimathi

1. Data Exploration
2. Missing value computations
3. Feature engineering
4. Data Standardization and Balancing
5. Model Development using Machine Learning Algorithms
6. Model Evaluation and Interpretation
7. Model Optimization and hyperparameter tuning