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
• Features
• Installation
• Usage
• Contributing
• License
• 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:

# 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:

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