GRIP -The Spark Foundation

Task 1 - LinkedIn Profile

Task 2 - simple linear regression task

Predict the percentage of marks that a student is expected to score based upon the number of hours they studied

dataset- http://bit.ly/w-data

https://github.com/sachinkatageri/GRIP-TSF/blob/master/Task2_Linear_Regression_TSF.ipynb

Reruirementes

• pandas - numpy - matplotlib - seaborn - sklearn

Predicted Score for 9.25 hours: 93.69173248737539 percent

Task 3 - To Explore Unsupervised Machine Learning

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

dataset- https://drive.google.com/file/d/11Iq7YvbWZbt8VXjfm06brx66b10YiwK-/view?usp=sharing

https://github.com/sachinkatageri/GRIP-TSF/blob/master/Task_3_Explore_Unsupervised_ML(iris_data).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.

dataset- https://drive.google.com/file/d/11Iq7YvbWZbt8VXjfm06brx66b10YiwK-/view?usp=sharing

https://github.com/sachinkatageri/GRIP-TSF/blob/master/Task4_Decision_Tree_Algo.ipynb

Task 5 - 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. What all business problems you can derive by looking into the data? You can choose any of the tool of your choice (Python/R/Tableau/PowerBI/Excel).

dataset-https://drive.google.com/file/d/1lV7is1B566UQPYzzY8R2ZmOritTW299S/view

https://github.com/sachinkatageri/GRIP-TSF/blob/master/task_5_EDA.ipynb