https://github.com/robcyberlab/machine-learning-classifier
🤖Machine Learning Classifier⚙️
https://github.com/robcyberlab/machine-learning-classifier
ai artificial-intelligence classifiers data-analysis data-science deep-learning digit-recognition machine-learning pca-algorithm python svm-classifier
Last synced: 3 months ago
JSON representation
🤖Machine Learning Classifier⚙️
- Host: GitHub
- URL: https://github.com/robcyberlab/machine-learning-classifier
- Owner: RobCyberLab
- Created: 2024-11-16T20:34:34.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-16T21:26:33.000Z (7 months ago)
- Last Synced: 2025-02-06T15:53:31.300Z (5 months ago)
- Topics: ai, artificial-intelligence, classifiers, data-analysis, data-science, deep-learning, digit-recognition, machine-learning, pca-algorithm, python, svm-classifier
- Language: Python
- Homepage:
- Size: 249 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🤖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.
---
## Table of Contents📋
- [Overview](#overview)
- [Dependencies](#dependencies)
- [Dataset](#dataset)
- [Implementation](#implementation)
- [Running the Code](#running-the-code)
- [Experimentation](#experimentation)
---
## 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
---
## Dependencies🛠️
```python
from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn import svm
```
---
## 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
---
## Implementation💻
### 1. Data Loading
```python
digits = load_digits()
x, y = digits.data, digits.target
```
### 2. Dimensionality Reduction
```python
pca = PCA(n_components=8)
pca.fit(x)
x = pca.transform(x)
```
### 3. Model Training and Prediction
```python
svc = svm.SVC(kernel='linear')
svc.fit(x_train, y_train)
y_predicted = svc.predict(x_valid)
```
---
## 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
---
## 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