{"id":19435607,"url":"https://github.com/mchavhan1998/customer_churn","last_synced_at":"2025-10-04T16:15:26.267Z","repository":{"id":251552712,"uuid":"837743296","full_name":"Mchavhan1998/Customer_churn","owner":"Mchavhan1998","description":"customer churn data to identify key factors contributing to customer attrition and built predictive models to forecast churn","archived":false,"fork":false,"pushed_at":"2024-08-03T23:32:54.000Z","size":532,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-28T09:39:21.143Z","etag":null,"topics":["churn","churn-prediction","customer"],"latest_commit_sha":null,"homepage":"https://github.com/Mchavhan1998/Customer_churn","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📂 Customer_churn\nThis project focuses on analyzing customer churn data to identify key factors contributing to customer attrition and building predictive models to forecast churn.\n\n## 🛠 Skills\nData Analysis, Python, Numpy, Pandas, Matplotlib, Supervised learning \n\n## 📚 Project Details\n### Data Exploration and Preprocessing\n- Loaded and cleaned the customer churn dataset.\n  Extracted specific columns and filtered data based on conditions.\n### Visualizations and Insights\n- Aggregated and visualized data to understand the distribution of Internet service categories.\n  Analyzed key factors affecting customer churn.\n### Predictive Modeling\n#### Decision Tree Classifier:\n- Built a decision tree model with tenure as the independent variable.\n  Split the data into train and test sets with an 80:20 ratio.\n  Achieved a significant accuracy score and validated the model using a confusion matrix.\n#### Random Forest Classifier:\n- Built a random forest model with tenure and monthly charges as independent variables.\n  Split the data into train and test sets with a 70:30 ratio.\n  Achieved a significant accuracy score and validated the model using a confusion matrix.\n\n## 📙 How to Use\n\n1. **Clone the repository**:\n    ```bash\n    git clone https://github.com/1vig/customerchurn-data-analysis.git\n    cd customerchurn-data-analysis\n    ```\n\n2. **Install the required dependencies**:\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n3. **Run the Jupyter Notebooks** to explore the data, build models, and visualize results:\n    ```bash\n    jupyter notebook\n    ```\n\n# License\nThis project is licensed under the MIT 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