Introduction to Readme in markdown

4 min read

What’s Markdown?

Markdown is a markup language that is used to add formatting to a text document. In normal word documents like MS Word or Google Docs buttons are used to enhance formatting. But in Markdown language there are specific syntax to be followed to add needed formats and the way the text needs to be written. For example two asterisks are added before text to make the text bold. Markdown uses an extension of either .md or .markdown

Lets see some examples of markdown syntax-

Bold & Italics

**bold text**//text in bold
*italic*//text in italics


## This is an <H2> 
###### This is an <H6> 

To get more information on Markdown refer the following link-

What’s Readme?

Readme is a text document in markdown language that is used as a deccription in a Git site or Github repository. Readme acts as a manual for the project in the repository. Just like how a summary helps to understand a novel Readme is the first page the visitor of the Git will see. They need to know some idea of the project to do further tasks. A good Readme file helps to attract more users and thereby more efficiency.

Example of how a Readme file looks like-

Read Me

How to write a good Readme file?

To write a good yet basic Readme file we will need the following contents

1.Title in headers

2. Description

3. Images/Gifs

4.Psuedo algorithm or steps followed

5.Output photos

Lets start writing a Readme file-

This image has an empty alt attribute; its file name is image-2.png

I have taken my own github project on sentiment analysis.Use the pen/pencil icon to write a markdown code if you need to edit again-

This image has an empty alt attribute; its file name is image-1.png

Once edited the file use the following commit options to save-

Add a header and small description with code as follows-

# Sentimental-Analysis
Sentimental analysis using svm(support vector machine)
##### Description
*Sentiment analysis for a dataset of comments which are classified as positive or negative using support vector machine.*
*The steps included are data preprocessing and cleaning , label encoder, featur extraction using TF-IDF and then training and testing of model using svm.Image of the dataset used is*

The result for above code looks like-


Add an image of the dataset using following code-

The basic syntax for adding image is-

![Add text](url/image address)


Adding a pseudo code or steps-

##### Steps followed in the algorithm
1.Loading the csv file to a pandas Dataframe.
2.Performing Data cleaning such as tokenization ,removing stopwards and unique characters.
3.Label encoding the sentiemnts for better classification.
4.Feature extraction done on the dataset. Tf-idf is used to find the weightage of the importance of words.
5. Performing SVM algorithm using scikit learn library and finding accuracy.

Adding final output image-

##### Output

The resultant Readme file is

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