README for Machine Learning Project Repository

Project Title: Machine Learning Model Comparison and Evaluation

Overview

This project focuses on comparing various machine learning models to determine the best-performing one for a given dataset. The models evaluated include Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Classifier (SVC). The primary goal is to identify the model with the highest accuracy for predictive tasks.

Screenshots

Project Structure

1. Jupyter Notebook
   • Contains the entire workflow of the project, including data loading, preprocessing, model training, evaluation, and visualization of results.
   • The notebook is available in [21f1000531-notebook-t12024.ipynb](link to 21f1000531-notebook-t12024.ipynb).

Key Findings

• Model Accuracy: Logistic Regression achieved the highest accuracy of 77.67%, making it the preferred model for predictions in this project.
• Comparison of Models: The project compared five different models, each evaluated on their accuracy to determine the best performer.

Models Evaluated

1. Logistic Regression

   • Accuracy: 77.67%

2. Random Forest

   • Accuracy: 77.56%

3. K-Nearest Neighbors (KNN)

   • Accuracy: 73.30%

4. Naive Bayes

   • Accuracy: 76.38%

5. Support Vector Classifier (SVC)

   • Accuracy: 76.68%

Technologies Used

• Python: The primary programming language used for data manipulation and model training.
• Jupyter Notebook: For interactive coding and documenting the workflow.
• Pandas: For data loading and preprocessing.
• Scikit-learn: For model training and evaluation.
• Seaborn and Matplotlib: For data visualization and plotting model comparison graphs.

How to Use This Repository

1. Clone the Repository:

   git clone https://github.com/prem-kumar-sharma/ML-Model-Comparison.git

2. Navigate to Project Files:

   cd ML-Model-Comparison

3. Run the Jupyter Notebook:
   • Ensure you have Jupyter Notebook installed. If not, you can install it using:

     pip install notebook

   • Start the Jupyter Notebook server:

     jupyter notebook

   • Open the notebook 21f1000531-notebook-t12024.ipynb and execute the cells to reproduce the results.

Contact Information

• Name: Prem Kumar
• LinkedIn: Prem Kumar
• GitHub: Prem Kumar Sharma

Feel free to reach out for any queries or further information regarding the project.

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This README file provides an overview of the machine learning project, including key findings, model evaluation, and instructions on how to use the repository. It is intended to guide users and collaborators in understanding the project's objectives and results.