decision_tree_classifier_and_random_forest_classifier.py

# -*- coding: utf-8 -*-
"""Decision Tree Classifier and Random Forest Classifier.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1gzUvU-Prjp9ZGHiPEd0xKOn3MDGHLFdl
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier,export_graphviz
import graphviz
from sklearn.model_selection import train_test_split

df=pd.read_csv("heart.csv")

df.head(3)

"""# Normal Method"""

y=df["output"]
x=df.drop("output",axis=1)

tree=DecisionTreeClassifier()
model=tree.fit(x,y)

model.score(x,y)

"""# With Train Test Split Method"""

x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.70,random_state=16)

tree=DecisionTreeClassifier()
model=tree.fit(x_train,y_train)

model.score(x_train,y_train)

model.score(x_test,y_test)

model.predict([[63,1,3,145,233,1,0,150,0,2.3,0,0,1]])

"""# Visualization"""

dot=export_graphviz(model,feature_names=x.columns,filled=True)
gorsel=graphviz.Source(dot)
gorsel

"""# Random Forest Classifier"""

from sklearn.ensemble import RandomForestClassifier

forest=RandomForestClassifier()
model=forest.fit(x,y)
model.score(x,y)

forest=RandomForestClassifier(n_estimators=400,max_depth=4)
model=forest.fit(x_train,y_train)
model.score(x_test,y_test)

df.info()