-
Notifications
You must be signed in to change notification settings - Fork 0
/
untitled4.py
39 lines (36 loc) · 1.29 KB
/
untitled4.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV,RandomizedSearchCV
from sklearn.metrics import accuracy_score,f1_score,precision_score
import seaborn as sns
# Importing Dataset
Data_set= pd.read_csv("heart-disease.csv")
Data_set.info()
X= Data_set.drop(columns="target",axis=1)
Y = Data_set["target"]
x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.2,stratify=Y,random_state=(123))
models = [RandomForestClassifier(),DecisionTreeClassifier(),GaussianNB(),SVC(),
KNeighborsClassifier()]
def comparing():
for model in models:
model.fit(x_train,y_train)
predict = model.predict(x_test)
accu = accuracy_score(y_test, predict)
print("The accuracy Score for the ",model,"=",accu)
comparing()
RDD = RandomForestClassifier()
params ={
"n_estimators":[40,60,80,100,150],
"max_depth":[3,6,9,12,15],
"min_samples_split":[2,3,4,5,6]
}
GRD = GridSearchCV(RDD, params,cv=(5))
GRD.fit(X,Y)
GRD.best_estimator_