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TASK 6: Prediction using Decision Tree Algorithm

GRIP JUNE 2021 Data Science and Business Analytics Internship

Intern : SHUBHAM

IN THIS PROJECT WE ARE EXPLORING A DATASET NAMED AS IRIS.csv IN THIS WE HAVE USED LIBRARIES:

  1. Pandas
  2. sklearn.DATASET
  3. sklearn.TREE

Then we have imported the Dataset as X and got 4 features as columns

  1. sepal length (cm)
  2. sepal width (cm)
  3. petal length (cm)
  4. petal width (cm)

and explored by some commands like X.head() X.tail() X.info() X.describe() X.isnull().sum()

Then we have: Split dataset into train and test sets

&

from sklearn.model_selection we import train_test_split

Then

Defining the Decision Tree Algorithm and Predicting until the gini value comes to 0.00

And thats how task 6 is completed.