TSF_Machine_Learning_Task

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

Task 2 - Exploring 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. Data can be found at http://bit.ly/w-data.
What will be predicted score if a student study for 9.25 hrs in a day? 

To see the implementation click on this link - https://github.com/santosh8896/TSF_Machine_Learning_Task/blob/master/Task-2/Task_2_Linear_Regression.ipynb

Task 3 - Exploring Unsupervised Machine Learning

From the given 'Iris' dataset, predict the optimum number of clusters and represent it visually.

To see the implementation click on this link - https://github.com/santosh8896/TSF_Machine_Learning_Task/blob/master/Task-3/Task_3_KMeans_Clustering.ipynb

Task 4 - Exploring 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.

To see the implementation click on this link - https://github.com/santosh8896/TSF_Machine_Learning_Task/blob/master/Task-4/Task_4_DecisionTrees.ipynb

Task 5 - To explore Business Analytics

Perform ‘Exploratory Data Analysis’ on the provided dataset ‘SampleSuperstore.
You are the business owner of the retail firm and want to see how your company is performing. You are interested in finding out the weak areas where you can work to make more profit.

To see the implementation click on this link - https://github.com/santosh8896/TSF_Machine_Learning_Task/blob/master/Task-5/Task_5_Data_analysis.ipynb

Acknowledgments

Special Thanks to The Sparks Foundation for this Wonderful Internship Experience.