https://github.com/batthulavinay/divorce-status-prediction-eda-and-ml
This project focuses on analyzing data related to divorce status to uncover insights, trends, and predictive models. The analysis is conducted using Python in a Jupyter Notebook environment.
https://github.com/batthulavinay/divorce-status-prediction-eda-and-ml
datacleaning datapreprocessing exploratory-data-analysis knn-regression logistic-regression predictive-analytics random-forest xgboost-regression
Last synced: about 2 months ago
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This project focuses on analyzing data related to divorce status to uncover insights, trends, and predictive models. The analysis is conducted using Python in a Jupyter Notebook environment.
- Host: GitHub
- URL: https://github.com/batthulavinay/divorce-status-prediction-eda-and-ml
- Owner: BatthulaVinay
- Created: 2025-01-20T15:19:04.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-01T16:28:35.000Z (4 months ago)
- Last Synced: 2025-02-16T08:28:59.605Z (3 months ago)
- Topics: datacleaning, datapreprocessing, exploratory-data-analysis, knn-regression, logistic-regression, predictive-analytics, random-forest, xgboost-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 689 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Divorce Status Prediction - Exploratory Data Analysis (EDA) & Machine Learning (ML)
This project focuses on analyzing data related to divorce status to uncover insights, trends, and predictive models. The analysis is conducted using Python in a Jupyter Notebook environment.
## 📊 Project Overview
The primary goals of this project are to:
- Explore and understand the divorce status dataset
- Perform data cleaning and preprocessing
- Conduct exploratory data analysis (EDA)
- Apply machine learning models for predictive analysis
## 🚀 Features
- **Data Cleaning:** Handling missing values, correcting data inconsistencies.
- **Exploratory Data Analysis:** Summary statistics, correlation analysis, and trend identification.
- **Machine Learning Models:** Classification models to predict divorce likelihood.
- **Visualizations:** Graphs and charts for better understanding of the data.
## 📁 Project Structure
```
├── Divorce Status Prediction.ipynb
├── README.md
└── data/
└── [your dataset files]
```
## 📦 Requirements
Make sure you have the following libraries installed:
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
- jupyter
You can install the necessary packages using:
```bash
pip install pandas numpy matplotlib seaborn scikit-learn jupyter
```
## 📂 Dataset
The dataset used in this project contains detailed information about factors that may influence divorce status. Please ensure your dataset is placed in the `data/` directory.
## 💻 Usage
1. Clone the repository:
```bash
git clone [your-repo-link]
```
2. Navigate to the project directory:
```bash
cd divorce-status-prediction
```
3. Launch the Jupyter Notebook:
```bash
jupyter notebook
```
4. Open `Divorce Status Prediction.ipynb` and run the cells.
## 📊 Sample Visualizations
- Correlation heatmaps
- Divorce likelihood trends
- Predictive models for divorce status
## 🤝 Contributing
Feel free to fork this repository, make changes, and submit pull requests.
## 📜 License
This project is licensed under the [MIT License](LICENSE).
## 🔗 Useful Links
- [Jupyter Notebook Documentation](https://jupyter.org/)
- [Pandas Documentation](https://pandas.pydata.org/)
- [Matplotlib Documentation](https://matplotlib.org/)
- [Seaborn Documentation](https://seaborn.pydata.org/)
- [Scikit-learn Documentation](https://scikit-learn.org/)
---
Happy analyzing! 📈🔍