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better than logistic regression
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saketGuptaTiger authored Nov 15, 2017
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32 changes: 32 additions & 0 deletions KNN.py
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# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
os.chdir('C:\\Users\\saket\\Desktop\\cricket machine learning')
# Importing the dataset
dataset = pd.read_csv('worldCup.csv')
X = dataset.iloc[:, [1,2,3,4,5,6,7,8]].values
y = dataset.iloc[:, 9].values

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Fitting K-NN to the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)

# Predicting the Test set results
y_pred = classifier.predict(X_test)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

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