Handwritten Digit Classification using Logistic Regression

This repository contains a Jupyter Notebook that demonstrates how to classify handwritten digits using Logistic Regression with the Scikit-learn library.

Dataset

The notebook utilizes the digits dataset from sklearn.datasets, which consists of 8x8 pixel grayscale images of handwritten digits (0-9).

Features

• Load the dataset and visualize sample images
• Preprocess the data and split it into training and testing sets
• Train a Logistic Regression model
• Evaluate the model's performance
• Generate a confusion matrix to analyze classification results

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Installation

To run this project, you need Python and the required dependencies installed. Use the following command to install dependencies:

pip install numpy pandas matplotlib scikit-learn

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Usage

Clone the repository and open the Jupyter Notebook to explore the code and experiment with different models.

git clone <your-repository-url>
cd <your-repository-folder>
jupyter notebook

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Code Overview

Import Necessary Libraries

import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix

Load Dataset

digits = load_digits()
print("Dataset structure:", dir(digits))

Visualizing Sample Images

plt.gray()
for i in range(5):
  plt.matshow(digits.images[i])

Splitting Data into Training and Testing Sets

X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.3, random_state=42)

Training the Model

model = LogisticRegression(max_iter=10000)
model.fit(X_train, y_train)

Evaluating Model Performance

accuracy = model.score(X_test, y_test)
print("Model Accuracy:", accuracy)

Making Predictions

sample_index = 67
plt.matshow(digits.images[sample_index])
predicted_label = model.predict([digits.data[sample_index]])
print("Predicted Label:", predicted_label)

Generating a Confusion Matrix

y_predicted = model.predict(X_test)
conf_matrix = confusion_matrix(y_test, y_predicted)
print("Confusion Matrix:\n", conf_matrix)

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Results

• The model achieves a high accuracy on the test dataset.
• The confusion matrix helps visualize misclassifications.
• The trained model can predict handwritten digits based on pixel data.

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Contributing

Feel free to fork this repository, create a feature branch, and submit a pull request! 🚀

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License

This project is open-source and available under the MIT License.