TSF-Internship-Business-Intelligence-Analytics

##Task 2 - To Explore Supervised Machine Learning In this regression 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. What will be predicted score if a student study for 9.25 hrs in a day?

##File - TSF_Supervised Machine Learning.ipynb

##Task 3 - To Explore Unsupervised Machine Learning From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually.

##File - TSF_Unsupervised Machine Learning Task.ipynb

##Task 4 - 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.

##File - TSF_Decision_Tree_Algorithm.ipynb

##Task 5-To explore Business Analytics Perform ‘Exploratory Data Analysis’ on the provided dataset ‘SampleSuperstore’

##File - TSF_Intenship.ipynb