🤖Machine Learning Classifier⚙️

A Python implementation of handwritten digit recognition using Support Vector Machine (SVM) and Principal Component Analysis (PCA).

Note: Due to privacy policies, I am not allowed to post the dataset publicly.

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Table of Contents📋

• Overview
• Dependencies
• Dataset
• Implementation
• Running the Code
• Experimentation

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Overview📝

This project implements a machine learning pipeline for recognizing handwritten digits using the following techniques:

• Dimensionality reduction with PCA
• Classification using linear SVM
• Performance evaluation on validation set

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Dependencies🛠️

from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn import svm

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Dataset📂

The project uses the Digits Dataset from scikit-learn, which contains:

• Handwritten digit images (0-9)
• Features extracted from the images
• Target labels indicating the digit

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Implementation💻

1. Data Loading

digits = load_digits()
x, y = digits.data, digits.target

2. Dimensionality Reduction

pca = PCA(n_components=8)
pca.fit(x)
x = pca.transform(x)

3. Model Training and Prediction

svc = svm.SVC(kernel='linear')
svc.fit(x_train, y_train)
y_predicted = svc.predict(x_valid)

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Running the Code▶️

1. Load the digits dataset
2. Apply PCA transformation
3. Split data into training and validation sets
4. Train SVM classifier
5. Make predictions and evaluate performance

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Experimentation⚗️

The following parameters can be modified to optimize performance:

• Number of PCA components
• Training set size
• SVM kernel and parameters

Learning curves can be plotted to visualize the impact of:

• Number of PCA components vs. accuracy
• Training set size vs. accuracy