Round 1: 2 days left #classroom

# Classroom Debug

Classroom Debug

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## 🕵️ Introduction

Instructions

Introduce the challenge here.

Include :

• A picture
• A catchy one-liner introducing the problem
• A link to the getting started code
• Do not remove the contribution line.

For example, this is how we write this section for the MNIST challenge

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

Instructions

Write something about the dataset here :

• What the dataset is about?
• What are the attributes of the dataset?
• How many attributes are there in the dataset?
• What is the data type of the attributes?
• What are the classes that need to be predicted?
• Formatting Instructions

For example, this is how we write this section for the MNIST challenge

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 have been stored in the CSV file. The train.csv has 785 columns, the first column is the label and the rest 784 contain the pixel value of the associated image pixel.

## 📁 Files

Instructions

Describe the file structure for this challenge :

• Add all the relevant data files in the 'Resources' section of this challenge.
• Provide brief explationations for the files uploaded.

For example, this is how we write this section for the MNIST challenge

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

Instructions

Submission instructions :

• Replace the header and the range of predicted value below according to the dataset.

For example, this is how we write this section for the MNIST challenge

• 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.

## 🖊 Evaluation Criteria

Instructions

Description of the evaluation criteria :

• Explain the formula/method of the evaluation criteri being used to score the submissions.
• Add an image of the mathematical formula of the evaluation criteria. You can use this link to generate the image.

For example, this is how we write this section for the MNIST challenge

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

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