https://github.com/anuraganalog/grip-tasks

Notebooks of the tasks which I have done during the internship
https://github.com/anuraganalog/grip-tasks

april21 data-science grip internship tsf

Last synced: 8 months ago
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Notebooks of the tasks which I have done during the internship

• Host: GitHub
• URL: https://github.com/anuraganalog/grip-tasks
• Owner: AnuragAnalog
• License: gpl-3.0
• Created: 2021-04-11T05:18:12.000Z (about 5 years ago)
• Default Branch: main
• Last Pushed: 2021-04-12T01:58:13.000Z (about 5 years ago)
• Last Synced: 2025-03-12T17:17:54.377Z (over 1 year ago)
• Topics: april21, data-science, grip, internship, tsf
• Language: Jupyter Notebook
• Homepage:
• Size: 13.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
GRIP -The Spark Foundation 



                                                          

# Task 0 - LinkedIn Profile 

[![Linkedin: Peddi Anurag](https://img.shields.io/badge/LinkedIn-Anurag%20Peddi-black?style=flat&logo=linkedin&labelColor=blue&link=https://www.linkedin.com/in/peddi-anurag-01767a166/)](https://www.linkedin.com/in/peddi-anurag-01767a166/)


# Task 1 - Simple linear regression task

Predict the percentage of marks that a student is expected to score based upon the number of hours they studied

**Dataset:** http://bit.ly/w-data

**Notebook:** https://github.com/AnuragAnalog/GRIP-Tasks/blob/main/task1/GRIP%20TASK1.ipynb

> Predicted marks for 9.25 hours is 94.75272937202871

# Task 2 - To Explore Unsupervised Machine Learning

From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually.

**Dataset:** https://drive.google.com/file/d/11Iq7YvbWZbt8VXjfm06brx66b10YiwK-/view?usp=sharing

**Notebook:** https://github.com/AnuragAnalog/GRIP-Tasks/blob/main/task2/GRIP%20Task2.ipynb

 

# Task 6 - To Explore Decision Tree Algorithm.For the given ‘Iris’ dataset

create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.

**Dataset:** https://drive.google.com/file/d/11Iq7YvbWZbt8VXjfm06brx66b10YiwK-/view?usp=sharing

**Notebook:** https://github.com/AnuragAnalog/GRIP-Tasks/blob/main/task6/GRIP%20Task6.ipynb