The Sparks Foundation

Data Science & Business Analytics Tasks

This repository consist of the tasks given during my internship at The Sparks Foundation.

Task 1 - Prediction using Supervised Machine Learning

In this task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables.

To see the implementation Notebook check this link - https://github.com/sumit0072/GRIP-Task/tree/main/Task%201-Supervised%20ML

Task 2 - Prediction using Supervised Machine Learning

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

To see the implementation Notebook check this link - https://github.com/sumit0072/GRIP-Task/blob/main/Task%202-Unsupervised%20ML/TSF-Task%20_2.ipynb

Task 3 - Exploratory Data Analysis (Retail)

From the given ‘SampleSuperstore’ dataset, we'll be performing 'exploratory data analysis' on the 'sample superstore' data'. In my analysis, I've tried to find out weak aeras where the superstore can work to make more profit.

To see the implementation check this link to README.md - https://github.com/sumit0072/GRIP-Task/blob/main/Task%203-Exploratory%20Data%20Analysis%20(Retail)/README.md
To see the implementation check this link - https://github.com/sumit0072/GRIP-Task/blob/main/Task%203-Exploratory%20Data%20Analysis%20(Retail)/TSF-Task_3.ipynb

Task 6 - Prediction using Decision Tree Algorithm

From the given ‘Iris’ dataset, Create the Decision Tree classifier and visualize it graphically.

To see the implementation Notebook check this link - https://github.com/sumit0072/GRIP-Task/blob/main/Task%206-Decision%20Tree%20Algorithm/TSF-Task_6.ipynb