This Repository is dedicated to the completion of my Task with video from The Sparks Foundation (Graduate Rotational Internship Program).
My domain is Data Science and Business Analytics for the July 2024 Batch.
Tools/IDE : Python/Jupyter Notebook
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/ruchi020897/The_Spark_Foundation-Internship2024
Requirementes
- pandas - numpy - matplotlib - seaborn - sklearn
Predicted Score for 9.25 hours: 93.69173248737539 percent
- Task Description: In this task, we will Create the Decision Tree classifier and visualize it graphically on the dataset "Iris" and try to find out if we feed any new data to this classifier, it would be able to predict the right class accordingly.
- Dataset: Iris Dataset
- Dataset Link: https://bit.ly/3kXTdox
- Tech Stack: scikit-learn, seaborn
- Task Video: