🧠 Alphabet Image Classification Project

This project aims to classify images of alphabets (A–Z) using different machine learning algorithms and determine the most accurate model.

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Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Results
• Insights

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📝 Introduction

This project involves classifying images of alphabets using machine learning algorithms such as:

• Logistic Regression
• Decision Tree
• Support Vector Machine (SVM)
• Random Forest
• K-Nearest Neighbors (KNN)

The project evaluates the performance of these models and identifies the best-performing one based on accuracy.

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📁 Dataset

The dataset used in this project contains images of alphabets (A–Z).
Each image is a 28×28 pixel grayscale image.

📦 The dataset is extracted from a .zip file containing image folders labeled A/, B/, ..., Z/.

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🧰 Installation

To run this project, install the following Python libraries:

• numpy
• pandas
• matplotlib
• seaborn
• scikit-learn
• tqdm
• pillow (PIL)

pip install numpy pandas matplotlib seaborn scikit-learn tqdm pillow

▶️ Usage

1. Mount Google Drive (if using Colab) and extract the dataset.
2. Preprocess and visualize the data.
3. Split the data and train models using various ML algorithms.

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📊 Results

The accuracy of each model is as follows:

Model	Accuracy
Logistic Regression	81.90%
Decision Tree	71.98%
SVM	93.40% ✅
Random Forest	90.76%
KNN	89.30%

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🔍 Insights

✅ SVM (Support Vector Classifier) achieved the highest accuracy of 93.40%.
It is the best-fitted model among all tested based on accuracy.