Skip to content

Latest commit

 

History

History
31 lines (22 loc) · 1.58 KB

README.md

File metadata and controls

31 lines (22 loc) · 1.58 KB
Raw

Digit Recognizer with Deep Learning Model (CNN)

This repository contains code for the "Digit Recognizer" competition on Kaggle. The goal of this competition is to accurately classify handwritten digits (0-9) from the famous MNIST dataset using a Deep Learning Model, specifically a Convolutional Neural Network (CNN).

Competition Description

The "Digit Recognizer" competition is a classic machine learning problem where participants are challenged to build a model that can identify handwritten digits from the MNIST dataset. The dataset consists of 60,000 training images and 10,000 test images, each representing a single digit (0-9). The task is to correctly classify the test images with the highest accuracy possible.

Project Structure

The repository has the following structure:

  • data/: This directory should contain the MNIST dataset. You can download the dataset from the competition page on Kaggle and place the files here.
  • Jupyter notebook contain the python code in the form of notebook (.ipynb) file
  • The Project reads the data using Pandas.
  • Maplotib.pyplot library is used to display the images.
  • CNN : Convolutional Neural Network is used to train on image data.
  • In CNN model, we make use of Convolutional, Pooling, Flatten, Hidden and Output layer to train model.
  • We use Stochastic Gradient Descent as Optimizer and Categorial Cross Entropy for Loss funtion while training.
  • Achieve Accuracy score of ~0.987 after submission on kaggle.

Dependencies

The following dependencies are required to run the code:

  • Python3
  • NumPy
  • Pandas
  • Matplotlib
  • TensorFlow
  • Keras