<jupyter_start><jupyter_text>**IRIS FLOWERS CLASSIFICATION ML PROJECT**  **IMPORTING REQUIRED LIBRARIES**<jupyter_code>import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt<jupyter_output><empty_output><jupyter_text>**LOADING DATASET**<jupyter_code>iris = sns.load_dataset('iris')
coloumns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
iris
iris.describe
iris.head
iris.tail
iris.info()
iris.groupby('species').mean()<jupyter_output><empty_output><jupyter_text>**VISUALISNG THE DATA**<jupyter_code>#plot
plt.plot(iris['sepal_length'])
#heat_map
corrmat = iris.corr()
top_corr_features = corrmat.index
plt.figure(figsize=(10,10 ))
g=sns.heatmap(iris[top_corr_features].corr(),annot=True,cmap="RdYlGn")
sns.pairplot(iris, hue='species')
plt.show()
plt.hist(iris['species'])<jupyter_output><empty_output><jupyter_text>**LABELS AND FEATURES**<jupyter_code>x = iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']].values
x
y = iris[['species']].values
y<jupyter_output><empty_output><jupyter_text>**IMPORTING DECISIONTRESS CLASSIFIER(ML ALGORITM)**<jupyter_code>from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier<jupyter_output><empty_output><jupyter_text>**TESTING AND TRAINING THE DATA**<jupyter_code>x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2)
d = DecisionTreeClassifier()
print(d.fit(x_train , y_train))
p = d.predict(x_test)<jupyter_output><empty_output><jupyter_text>**ACCURACY**<jupyter_code>d_accuracy = d.score(x_test , y_test)
d_accuracy*100<jupyter_output><empty_output>
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