🤖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.

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

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

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

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

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

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

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

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

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