# Predictive Modeling Auto
Welcome to the Predictive Modeling Auto repository. This project aims to automate predictive modeling tasks using Python and its robust data science libraries.
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Introduction
Predictive Modeling Auto is a Python project designed to streamline the process of building and evaluating predictive models. It leverages popular libraries such as Pandas, NumPy, Scikit-learn, and PyCaret to automate various steps in the modeling pipeline.
## Features
- **Data Preprocessing:** Automated handling of missing values, feature scaling, and encoding.
- **Model Training:** Automated model selection, training, and hyperparameter tuning.
- **Model Evaluation:** Comprehensive evaluation metrics and visualizations.
- **User-Friendly Interface:** Simple and intuitive user interface for ease of use.
## Installation
To clone and run this project, you'll need Python installed on your system. You can clone the repository and install the dependencies using the following commands:
```bash
# Clone the repository
git clone https://github.com/ashishs1407/predictive_modeling_auto.git
# Navigate to the project directory
cd predictive_modeling_auto
# Install the required packages
pip install -r requirements.txt
```
## Usage
Here is a simple example of how to use this project:
```Python
from predictive_modeling_auto import ModelPipeline
# Initialize the pipeline
pipeline = ModelPipeline()
# Load your dataset
data = pipeline.load_data('path/to/your/dataset.csv')
# Preprocess the data
data = pipeline.preprocess_data(data)
# Train the model
model = pipeline.train_model(data)
# Evaluate the model
pipeline.evaluate_model(model, data)
For more detailed usage instructions, refer to the documentation in the docs directory.
```
## Contributing
Contributions are welcome! Please read the CONTRIBUTING.md file for guidelines on how to contribute to this project.
## License
This project is licensed under the MIT License. See the LICENSE file for more details.
## Contact
If you have any questions or suggestions, feel free to reach out:
- **Author:** Ashish Shimpi
- **Email:** a.shimpi93@gmail.com
- **GitHub:** ashishs1407
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"description": "# Predictive Modeling Auto\n\nWelcome to the Predictive Modeling Auto repository. This project aims to automate predictive modeling tasks using Python and its robust data science libraries.\n\n## Table of Contents\n- [Introduction](#introduction)\n- [Features](#features)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Contributing](#contributing)\n- [License](#license)\n- [Contact](#contact)\n\n## Introduction\nPredictive Modeling Auto is a Python project designed to streamline the process of building and evaluating predictive models. It leverages popular libraries such as Pandas, NumPy, Scikit-learn, and PyCaret to automate various steps in the modeling pipeline.\n\n## Features\n- **Data Preprocessing:** Automated handling of missing values, feature scaling, and encoding.\n- **Model Training:** Automated model selection, training, and hyperparameter tuning.\n- **Model Evaluation:** Comprehensive evaluation metrics and visualizations.\n- **User-Friendly Interface:** Simple and intuitive user interface for ease of use.\n\n## Installation\nTo clone and run this project, you'll need Python installed on your system. You can clone the repository and install the dependencies using the following commands:\n\n```bash\n# Clone the repository\ngit clone https://github.com/ashishs1407/predictive_modeling_auto.git\n\n# Navigate to the project directory\ncd predictive_modeling_auto\n\n# Install the required packages\npip install -r requirements.txt\n```\n## Usage\nHere is a simple example of how to use this project:\n```Python\nfrom predictive_modeling_auto import ModelPipeline\n\n# Initialize the pipeline\npipeline = ModelPipeline()\n\n# Load your dataset\ndata = pipeline.load_data('path/to/your/dataset.csv')\n\n# Preprocess the data\ndata = pipeline.preprocess_data(data)\n\n# Train the model\nmodel = pipeline.train_model(data)\n\n# Evaluate the model\npipeline.evaluate_model(model, data)\nFor more detailed usage instructions, refer to the documentation in the docs directory.\n```\n\n\n## Contributing\nContributions are welcome! Please read the CONTRIBUTING.md file for guidelines on how to contribute to this project.\n\n## License\nThis project is licensed under the MIT License. See the LICENSE file for more details.\n\n## Contact\nIf you have any questions or suggestions, feel free to reach out:\n\n- **Author:** Ashish Shimpi\n- **Email:** a.shimpi93@gmail.com\n- **GitHub:** ashishs1407\n",
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