The-Spark-Foundation-Internship-DS-BA

This repository contains all the tasks that I completed while working as an intern for The Sparks Foundation under the domain Data Science and Business Analytics

Task-1 : Prediction using Supervised ML

• Predict the percentage of marks of an student based on the number of study hours.
• This is a simple linear regression task as it involves just 2 variables.
• Data can be found at http://bit.ly/w-data
• You can use R, Python, SAS Enterprise Miner or any other tool.
• What will be predicted score if a student studies for 9.25 hrs/ day?

Task-2 : Prediction using Unsupervised ML

• From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually.
• Use R or Python or perform this task.
• Data can be found at https://docs.google.com/spreadsheets/d/e/2PACX-1vQPinANAUnj2ztuT6vS8fLW0gEnuTw4Acsuao3hyT9XBMdoFjezBv2LttwcorP9bvREg-VcwhIZY_hS/pub?gid=180061103&single=true&output=csv (the same data is present as a file in the repository named iris.csv)

Tool/IDE used - Google Colaboratory Language : Python