📊 Multiclass Logistic Regression Classification

A machine learning project that demonstrates multiclass classification using Logistic Regression, along with detailed performance evaluation using a classification report.

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📌 Project Overview

This project implements a Logistic Regression classifier to predict multiple classes from a given dataset.
Model performance is evaluated using precision, recall, F1-score, and accuracy, showing perfect classification on the test data.

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📁 Project Structure

Logistic_Classification
│
├── Logistic_classification.ipynb
├── classification_report.png
└── README.md

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⚙️ Technologies Used

• Python
• NumPy
• Pandas
• scikit-learn
• Jupyter Notebook

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🧠 Machine Learning Model

• Algorithm: Logistic Regression
• Problem Type: Multiclass Classification
• Evaluation Metric: Classification Report

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📈 Model Evaluation

The model was evaluated using classification_report from scikit-learn.

🔍 Classification Report Summary

Class	Precision	Recall	F1-Score	Support
0	1.00	1.00	1.00	16
1	1.00	1.00	1.00	14
2	1.00	1.00	1.00	15

Overall Accuracy: 1.00
Total Samples: 45

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🖼️ Output Screenshot

Classification report generated after model evaluation:

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📦 Requirements

• numpy
• pandas
• scikit-learn
• matplotlib

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▶️ How to Run

1. Clone the repository

git clone https://github.com/btboilerplate/Logistic_Classification.git

2. Install required libraries

pip install numpy pandas matplotlib scikit-learn

3. Open Logistic_classification.ipynb and run all cells sequentially

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🧪 Key Observations

• Perfect precision, recall, and F1-score for all classes
• Excellent class separability in the dataset
• Logistic Regression performed extremely well for this multiclass problem
• No misclassifications observed on the test data

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🚀 Future Improvements

• Test on larger and noisier datasets
• Apply cross-validation
• Compare with other classifiers such as SVM and Random Forest
• Add confusion matrix visualization

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