https://github.com/mchavhan1998/customer_churn
customer churn data to identify key factors contributing to customer attrition and built predictive models to forecast churn
https://github.com/mchavhan1998/customer_churn
churn churn-prediction customer
Last synced: 8 days ago
JSON representation
customer churn data to identify key factors contributing to customer attrition and built predictive models to forecast churn
- Host: GitHub
- URL: https://github.com/mchavhan1998/customer_churn
- Owner: Mchavhan1998
- License: mit
- Created: 2024-08-03T22:24:35.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-03T23:32:54.000Z (about 1 year ago)
- Last Synced: 2025-06-28T09:39:21.143Z (4 months ago)
- Topics: churn, churn-prediction, customer
- Language: Jupyter Notebook
- Homepage: https://github.com/Mchavhan1998/Customer_churn
- Size: 520 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 📂 Customer_churn
This project focuses on analyzing customer churn data to identify key factors contributing to customer attrition and building predictive models to forecast churn.
## 🛠Skills
Data Analysis, Python, Numpy, Pandas, Matplotlib, Supervised learning
## 📚 Project Details
### Data Exploration and Preprocessing
- Loaded and cleaned the customer churn dataset.
Extracted specific columns and filtered data based on conditions.
### Visualizations and Insights
- Aggregated and visualized data to understand the distribution of Internet service categories.
Analyzed key factors affecting customer churn.
### Predictive Modeling
#### Decision Tree Classifier:
- Built a decision tree model with tenure as the independent variable.
Split the data into train and test sets with an 80:20 ratio.
Achieved a significant accuracy score and validated the model using a confusion matrix.
#### Random Forest Classifier:
- Built a random forest model with tenure and monthly charges as independent variables.
Split the data into train and test sets with a 70:30 ratio.
Achieved a significant accuracy score and validated the model using a confusion matrix.
## 📙 How to Use
1. **Clone the repository**:
```bash
git clone https://github.com/1vig/customerchurn-data-analysis.git
cd customerchurn-data-analysis
```
2. **Install the required dependencies**:
```bash
pip install -r requirements.txt
```
3. **Run the Jupyter Notebooks** to explore the data, build models, and visualize results:
```bash
jupyter notebook
```
# License
This project is licensed under the MIT License.