The-Sparks-Foundation-Internship-Tasks

📒 Here I will be documenting and storing the projects that I execute for my internship at The Sparks Foundation

• Internship Category - Data Science and Business Analytics
• Internship Duration - 1 Month ( July-2020 )
• Internship Type - Work from Home

📈 Task-01: Prediction using Supervised Machine Learning Algorithm (Level - Beginner)

Please click on the images on right side to view my solution.

● Predict the percentage of an student based on the no. of study hours.

● This is a simple linear regression task as it involves just 2 variables.

● You can use R, Python, SAS Enterprise Miner or any other tool

● Data can be found at http://bit.ly/w-data

● What will be predicted score if a student studies for 9.25 hrs/ day?

● Sample Solution : https://bit.ly/2HxiGGJ

● Task submission:

1. Host the code on GitHub Repository (public). Record the code and output in a video. Post the video on YouTube
2. Share links of code (GitHub) and video (YouTube) as a post on YOUR LinkedIn profile, not TSF Network.
3. Submit the LinkedIn link in Task Submission Form when shared.

📊 Task-03 : Exploratory Data Analysis - Retail (Level - Beginner)

Please click on the images on right side to view my solution.

Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’

● As a business manager, try to find out the weak areas where you can work to make more profit.

● What all business problems you can derive by exploring the data?

● You can choose any of the tool of your choice (Python/R/Tableau/PowerBI/Excel/SAP/SAS)

● Dataset: https://bit.ly/3i4rbWl

● Beginner Level - Create dashboards. Screen-record along with your audio explaining the charts and interpretations.

● Task submission:

1. Create the dashboards and/or storyboard and record it
2. Upload the recording either on YouTube or LinkedIn
3. Create a LinkedIn post as suggested in FAQs

🌳 Task-06: Prediction using the Decision Tree Algorithm (Level - Intermediate)

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://bit.ly/3kXTdox

● Sample Solution : https://bit.ly/2G6sYx9

● Task submission:

1. Host the code on GitHub Repository (public). Record the code and output in a video. Post the video on YouTube
2. Share links of code (GitHub) and video (YouTube) as a post on YOUR LinkedIn profile
3. Submit the LinkedIn link in Task Submission Form when shared.
4. Please read FAQs on how to submit the tasks.

👨‍👨‍👧‍👦 Computer Vision and IOT function Task 03: Social Distancing Detection

This project is in regards with the Letter Of Recommendation eligibility criteria.

Implement a real time Social Distancing detector which can identify the distance between two individuals in a crowd.

🌈 Computer Vision and IOT function Task 02: Colour Identification from images

● Implement an image color detector which identifies all the colors in an image or video. ● We implement an application that describes the color name and other details in box at location of mouse pointer when user double clicks on image