# MNIST

Recognise Handwritten Digits

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🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

## 🕵️ Introduction

Our writers were tired of introducing problems to you, well we decided to give them a break! Voila, the one problem which needs no introduction! life started by learning numbers, well you've come a full circle! let's get back to it !

We give you the very famous MNIST dataset of handwritten digits, can you identify them?

Understand with code! Here is getting started code for you.😄

## 💾 Dataset

The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits that is commonly used for training various image processing systems.The database is also widely used for training and testing in the field of machine learning.Each image is `28` pixels in height and `28` pixels in width, for a total of `784` pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between `0` and `255`, inclusive.

For simplification, images has been stored in csv file. The `train.csv` has `785` columns, the fist column is the label and the rest `784` contain the pixel value of the associated image pixel.

## 📁 Files

Following files are available in the `resources` section:

• `train.csv` - (`60000` samples) This csv file contains the pixel values as columns along with the digits it represent.
• `test.csv` - (`10000` samples) File that will be used for actual evaluation for the leaderboard score and it does not have the digit represented by the pixel values.

## 🚀 Submission

• Prepare a csv containing header as `label` and predicted value as digit `[0-9]` with name as `submission.csv`.
• Sample submission format available at `sample_submission.csv`.

## 🚀 Submission

• Prepare a CSV containing header as `label` and predicted value as digit `[0=9]` respectively denoting the digits
• Name of the above file should be `submission.csv`.
• Sample submission format available at `sample_submission.csv` in the resorces section.

Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$