Lazy Predict Model Comparison

Welcome to the Lazy Predict Model Comparison repository! This project demonstrates how to utilize the Lazy Predict library for efficient model evaluation and selection in machine learning tasks.

Table of Contents

• About
• Getting Started
• Usage
• Dataset Sources
• Contributing
• License
• Contact

About

This repository showcases the capabilities of Lazy Predict, an AutoML tool designed to streamline the process of training and evaluating multiple machine learning models. 𝐓𝐡𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐢𝐬 𝐠𝐮𝐢𝐝𝐞𝐝 𝐛𝐲 𝐈𝐁𝐌 , and it provides comprehensive examples for both classification and regression tasks, aiding you in swiftly comparing model performance.

Getting Started

To get started, follow these steps:

1. Clone this repository to your local machine using git clone https://github.com/KasaVarun/Lazy_Predict_model.git
2. Install the required libraries using pip install -r requirements.txt
3. Open the respective Jupyter notebooks (prediction.ipynb) for classification and for regression) to explore the model comparison process.

Usage

Flight Delay Prediction:

In the main.ipynb Jupyter Notebook, you'll find the following process:

Data Loading and Preprocessing: The notebook loads the flight delay dataset and prepares it for analysis.

Model Evaluation: The LazyClassifier library is utilized to efficiently evaluate and compare the performance of multiple classifiers.

Admission Chance Prediction:

In the prediction.ipynb Jupyter Notebook, you'll encounter the following steps:

Data Loading and Preprocessing: The notebook loads the university admissions dataset and prepares it for analysis.

Model Evaluation: The LazyRegressor library is employed to efficiently evaluate and compare the performance of various regression models.

Dataset Sources

• Airlines : The flight delay dataset used in the classification example is available here.

• Admissions: The university admissions dataset used in the regression example is available here.

Contributing

Contributions are welcome! If you have suggestions, bug fixes, or improvements, please feel free to open an issue or submit a pull request.

License

𝐓𝐡is 𝐩𝐫𝐨𝐣𝐞𝐜𝐭 𝐢𝐬 𝐠𝐮𝐢𝐝𝐞𝐝 𝐛𝐲 𝐈𝐁𝐌 This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For questions, feedback, or collaborations, you can reach out to:

• Email: varunkasa8@gmail.com