https://github.com/preethi2805/customer-churn
https://github.com/preethi2805/customer-churn
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/preethi2805/customer-churn
- Owner: Preethi2805
- Created: 2025-01-28T04:48:42.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-20T04:53:03.000Z (over 1 year ago)
- Last Synced: 2025-02-20T05:30:50.467Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 1.56 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Customer Churn Prediction
## Project Overview
The goal of this project is to predict customer churn using various machine learning models. The project involves:
- Data cleaning and preprocessing.
- Exploratory Data Analysis (EDA) to understand the dataset.
- Handling class imbalance using **SMOTE** (Synthetic Minority Oversampling Technique).
- Training and evaluating multiple machine learning models, including Decision Tree, Random Forest, KNN, Support Vector Machine, and Logistic Regression.
---
## Dataset
The dataset used in this project is the **Telco Customer Churn Dataset** from Kaggle, which contains the following features:
- **customerID**: Unique identifier for each customer.
- **gender**: Gender of the customer (Male/Female).
- **SeniorCitizen**: Whether the customer is a senior citizen (0 or 1).
- **Partner**: Whether the customer has a partner (Yes/No).
- **Dependents**: Whether the customer has dependents (Yes/No).
- **tenure**: Number of months the customer has stayed with the company.
- **PhoneService**: Whether the customer has a phone service (Yes/No).
- **MultipleLines**: Whether the customer has multiple lines (Yes/No/No phone service).
- **InternetService**: Type of internet service (DSL, Fiber optic, No).
- **OnlineSecurity**: Whether the customer has online security (Yes/No/No internet service).
- **OnlineBackup**: Whether the customer has online backup (Yes/No/No internet service).
- **DeviceProtection**: Whether the customer has device protection (Yes/No/No internet service).
- **TechSupport**: Whether the customer has tech support (Yes/No/No internet service).
- **StreamingTV**: Whether the customer has streaming TV (Yes/No/No internet service).
- **StreamingMovies**: Whether the customer has streaming movies (Yes/No/No internet service).
- **Contract**: Type of contract (Month-to-month, One year, Two year).
- **PaperlessBilling**: Whether the customer has paperless billing (Yes/No).
- **PaymentMethod**: Payment method (Electronic check, Mailed check, Bank transfer, Credit card).
- **MonthlyCharges**: Monthly charges for the customer.
- **TotalCharges**: Total charges for the customer.
- **Churn**: Target variable indicating whether the customer churned (Yes/No).
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## Data Preprocessing
- **Handling Missing Values**: Missing values in the `TotalCharges` feature were replaced with `0.0`.
- **Encoding Categorical Variables**: Categorical features were label encoded using `LabelEncoder`.
- **Handling Class Imbalance**: The dataset was imbalanced, with only 27% of customers churning. **SMOTE** was used to balance the dataset by oversampling the minority class.
---
## Exploratory Data Analysis (EDA)
- **Distribution of Features**: Histograms and box plots were used to understand the distribution of numerical features like `tenure`, `MonthlyCharges`, and `TotalCharges`.
- **Geographical Visualization**: Scatter plots were used to visualize the relationship between `MonthlyCharges` and `Churn`.
- **Correlation Analysis**: A correlation matrix was used to identify the relationship between numerical features.
- **Count Plots**: Count plots were used to visualize the distribution of categorical features.
---
## Model Training
Five models were trained and evaluated:
1. **Decision Tree**: Achieved an accuracy of **78%**.
2. **Random Forest**: Achieved an accuracy of **84%**.
3. **K-Nearest Neighbors (KNN)**: Achieved an accuracy of **77%**.
4. **Support Vector Machine (SVM)**: Achieved an accuracy of **64%**.
5. **Logistic Regression**: Achieved an accuracy of **79%**.
The **Random Forest** model performed the best and was selected for further evaluation.
---
## Results
- **Random Forest Model**:
- **Accuracy**: 77.86%
- **Confusion Matrix**:
- True Positives (TP): 219
- True Negatives (TN): 878
- False Positives (FP): 158
- False Negatives (FN): 154
- **Classification Report**:
- Precision (Class 0): 0.85
- Recall (Class 0): 0.85
- F1-Score (Class 0): 0.85
- Precision (Class 1): 0.58
- Recall (Class 1): 0.59
- F1-Score (Class 1): 0.58
---
## Technologies Used
- **Python**: Primary programming language.
- **Pandas**: Data manipulation and analysis.
- **NumPy**: Numerical computations.
- **Matplotlib & Seaborn**: Data visualization.
- **Scikit-learn**: Machine learning models and evaluation.
- **SMOTE**: Handling class imbalance.
- **Pickle**: Saving and loading models and encoders.
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---
## Future Work
- Experiment with more advanced models like Gradient Boosting or Deep Learning.
- Perform hyperparameter tuning to improve model performance.