code_mlstream.py

import pandas as pd
import numpy as np

df = pd.read_csv('Iris.csv')
df.head()


# Dropping the Id column
df.drop('Id', axis = 1, inplace = True)
  
# Renaming the target column into numbers to aid training of the model
df['Species']= df['Species'].map({'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2})
  
# splitting the data into the columns which need to be trained(X) and the target column(y)
X = df.iloc[:, :-1]
y = df.iloc[:, -1]
  
# splitting data into training and testing data with 30 % of data as testing data respectively
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)
  
# importing the random forest classifier model and training it on the dataset
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(X_train, y_train)
  
# predicting on the test dataset
y_pred = classifier.predict(X_test)
  
# finding out the accuracy
from sklearn.metrics import accuracy_score
score = accuracy_score(y_test, y_pred)