Mnist_Dataset-Handwritten_Digit_Classification

MNIST digit classification with scikit-learn, Support Vector Machine (SVM), Naive Bayse, Random Forest and Decision Tree Classifier

Mnist_Handwritten_Digit_Classification

MNIST digit classification with scikit-learn, Support Vector Machine (SVM), Naive Bayse, Random Forest and Decision Tree Classifier

The goal of this project is to define a process to differentiate between a dataset of Handwritten Digits and classify them into their respective digit class. This project also demonstrates the process of classification for any dataset in a simple way especially for the beginners in the field of machine learning.

Classification Algorithm	Accuracy	precision Score	Recall Score
Naive Bayes	0.5556	0.676	0.554
DecisionTree	0.5558	0.8615	0.8615
Random Forest	0.9461	0.9416	0.9414
Linear SVM	0.856	0.854	0.846

Steps that I followed :

Firstly I Fetch the data

Then i applied different machine learning models:

• SVM
• Decision Tree
• Random Forest
• Naive Bayse

Then I trained the model using the above mentioned classifiers

I get the accuracy of 94.61% by using Random Forest Classifier.

Tool

• Pycharm