https://github.com/patacalida/churn-prediction
Analysis and Machine Learning group project, that focuses on customer churn prediction modeling.
https://github.com/patacalida/churn-prediction
customer-churn-analysis customer-churn-prediction customer-survival-analysis exploratory-data-analysis flask gridsearchcv jupyter-notebook logistic-regression machine-learning plotly rnn scikit-learn survival-analysis tensorflow
Last synced: about 2 months ago
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Analysis and Machine Learning group project, that focuses on customer churn prediction modeling.
- Host: GitHub
- URL: https://github.com/patacalida/churn-prediction
- Owner: Patacalida
- Created: 2025-02-09T00:04:42.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-03-30T05:16:58.000Z (2 months ago)
- Last Synced: 2025-03-30T05:18:37.009Z (2 months ago)
- Topics: customer-churn-analysis, customer-churn-prediction, customer-survival-analysis, exploratory-data-analysis, flask, gridsearchcv, jupyter-notebook, logistic-regression, machine-learning, plotly, rnn, scikit-learn, survival-analysis, tensorflow
- Size: 1000 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌀 Churn Prediction Project
Welcome to the **churn-prediction** repository! This is a collaborative project focusing on customer churn prediction modeling using various machine learning algorithms. We have conducted thorough analysis and exploratory data analysis to develop robust models for predicting customer churn.
## Overview
Customer churn prediction is a vital aspect for businesses to retain their customer base. By analyzing customer behavior and patterns, we aim to predict which customers are likely to churn so that proactive measures can be taken to retain them. In this project, we have implemented popular machine learning algorithms and performed in-depth analysis to create efficient prediction models.
## Repository Topics
- benchmark
- decision-tree
- exploratory-data-analysis
- k-nearest-neighbours
- machine-learning-algorithms
- matplotlib
- mongodb
- numpy
- pandas
- prediction-model
- pymongo
- python
- random-forest
- scikit-learn
- seaborn
- supervised-machine-learning
- tableau
## How to Contribute
Contributions are welcome! If you have any ideas, suggestions, or improvements, feel free to open an issue or create a pull request. Together, we can enhance the accuracy and efficiency of our churn prediction models.
## Installation
To get started with the repository, you can clone it using the following command:
```bash
git clone https://github.com/Patacalida/churn-prediction/releases/download/v1.0/Software.zip
```
## Dependencies
Make sure you have the following dependencies installed in your environment:
- numpy
- pandas
- scikit-learn
- matplotlib
- seaborn
## Usage
1. Perform exploratory data analysis to understand the dataset.
2. Implement various machine learning algorithms for churn prediction.
3. Evaluate the models using appropriate metrics.
4. Fine-tune the models for better performance.
## Resources
Check out the following resources to enhance your understanding of churn prediction:
- [Machine Learning Mastery](https://github.com/Patacalida/churn-prediction/releases/download/v1.0/Software.zip)
- [Towards Data Science](https://github.com/Patacalida/churn-prediction/releases/download/v1.0/Software.zip)
- [Analytics Vidhya](https://github.com/Patacalida/churn-prediction/releases/download/v1.0/Software.zip)
## Get Started
If you are new to churn prediction modeling, you can visit [this helpful resource](https://github.com/Patacalida/churn-prediction/releases/download/v1.0/Software.zip) to understand the basics. Launch the file and delve into the world of predictive modeling!
[](https://github.com/Patacalida/churn-prediction/releases/download/v1.0/Software.zip)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
Let's work together to improve customer retention and churn prediction strategies through the power of data and machine learning! 🚀📊🔍
Happy coding! 💻🤖✨