-
Notifications
You must be signed in to change notification settings - Fork 0
/
winequalitydesicion.py
42 lines (29 loc) · 1.34 KB
/
winequalitydesicion.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
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 8 14:52:58 2021
@author: HaticeOzdemir
"""
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
col_names = ['fixed acidity','volatile acidity','citric acid', 'residual sugar','chlorides', 'free sulfur dioxide', 'total sulfur dioxide' ,'density', 'pH', 'sulphates', 'alcohol', 'quality']
veriler=pd.read_csv('C:/Users/HaticeOzdemir/Desktop/opencvdersleri/winequality.csv')
feature_cols = ['fixed acidity','volatile acidity','citric acid', 'residual sugar','chlorides', 'free sulfur dioxide', 'total sulfur dioxide' ,'density', 'pH', 'sulphates', 'alcohol']
print(veriler.head())
X = veriler[feature_cols] # Features
y = veriler.quality # Target variable
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=1)
from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
scaler.fit(X_train)
X_train=scaler.transform(X_train)
X_test=scaler.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier()
classifier=classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))