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https://github.com/ndleah/tsf-data-science-internship
This repository contains the tasks performed during the Data Science and Business Analytics Internship at The Sparks Foundation
https://github.com/ndleah/tsf-data-science-internship
data-science data-visualization exploratory-data-analysis machine-learning powerbi python virtual-internship
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This repository contains the tasks performed during the Data Science and Business Analytics Internship at The Sparks Foundation
- Host: GitHub
- URL: https://github.com/ndleah/tsf-data-science-internship
- Owner: ndleah
- Created: 2021-08-03T17:08:10.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-08-09T08:45:16.000Z (over 3 years ago)
- Last Synced: 2024-11-13T08:39:20.806Z (2 months ago)
- Topics: data-science, data-visualization, exploratory-data-analysis, machine-learning, powerbi, python, virtual-internship
- Language: Jupyter Notebook
- Homepage: https://www.thesparksfoundationsingapore.org
- Size: 2.27 MB
- Stars: 4
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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# The Sparks Foundation Tasks
> This repository contains the tasks that I completed while working as an intern for [The Sparks Foundation.](https://www.thesparksfoundationsingapore.org/)
## 🌟 Task 1 - Prediction using Supervised ML (Level - Beginner)
> Problem statement:
1. Predict the percentage of marks of an student based on the number of study hours.
2. This is a simple linear regression task as it involves just 2 variables.
3. What will be predicted score if a student studies for 9.25 hrs/ day?
4. Data can be found at http://bit.ly/w
**Solution:**
[](https://github.com/ndleah/TSF-data-science-internship/tree/main/Task%201%20-%20Prediction%20using%20Supervised%20ML%20-%20Beginner)
---
## 🌟 Task 2 - Prediction using Unsupervised ML (Level - Beginner)
> Problem Statement:
1. From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually.
2. Data can be found at https://bit.ly/3cGyP8j
**Solution:**
[](https://github.com/ndleah/TSF-data-science-internship/tree/main/Task%202%20-%20Prediction%20using%20Unsupervised%20ML%20-%20Beginner)
---
## 🌟 Task 3 - Exploratory Data Analysis - Retail (Level - Beginner)
> Problem statement:
1. Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’
2. As a business manager, try to find out the weak areas where you can work to make more profit.
3. What all business problems you can derive by exploring the data?
4. Data can be found at https://bit.ly/3i4rbWl
**Solution:**
[](https://github.com/ndleah/TSF-data-science-internship/tree/main/Task%203%20-%20Exploratory%20Data%20Analysis%20-%20Retail)
---
## 🌟 Task 4 - Exploratory Data Analysis - Terrorism (Level - Intermediate)
> Problem statement:
1. Perform ‘Exploratory Data Analysis’ on dataset ‘Global Terrorism’
2. As a security/defense analyst, try to find out the hot zone of terrorism.
3. What all security issues and insights you can derive by EDA?
4. Data can be found at https://bit.ly/2TK5Xn5
**Solution:**
[](https://github.com/ndleah/TSF-data-science-internship/tree/main/Task%204%20-%20Exploratory%20Data%20Analysis%20-%20Terrorism%20-%20Intermediate)
## ✨ Contribution
Contributions, issues, and feature requests are welcome!
To contribute to this project, see the GitHub documentation on **[creating a pull request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request)**.
## 👏 Support
Give a ⭐️ if you like this project!
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© 2021 Leah Nguyen