KNN_final.py

# -*- coding: utf-8 -*-
"""
Created on Mon Nov  9 11:25:00 2020

@author: Shreya
"""
import pandas as pd
import numpy as np

from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer

from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn import utils

######################################################################
datainput = pd.read_csv('datafile.csv')

#preprocessing
Profit = (datainput.iloc[:,5]*datainput.iloc[:,6]-(datainput.iloc[:,2]+datainput.iloc[:,3]+(datainput.iloc[:,5]*datainput.iloc[:,4]))).values
Profit = Profit.reshape(49,1)
Profitcopy = (datainput.iloc[:,5]*datainput.iloc[:,6]-(datainput.iloc[:,2]+datainput.iloc[:,3]+(datainput.iloc[:,5]*datainput.iloc[:,4]))).values

for i in range (0,49):
    if Profit[i][0]>0:
        Profit[i][0] = 1
    else:
        Profit[i][0] = 0
X = datainput[['Crop', 'State', 'Cost of Cultivation (`/Hectare) A2+FL', 
               'Cost of Cultivation (`/Hectare) C2','Cost of Production (`/Quintal) C2', 'Support price']].values


#label encoder to categorical data 
labelencoder_X = preprocessing.LabelEncoder()
X[:,0] = labelencoder_X.fit_transform(X[:, 0])
X[:,1] = labelencoder_X.fit_transform(X[:, 1])

#One hot encoder 
columnTransformer = ColumnTransformer([('encoder', OneHotEncoder(),[0])], remainder='passthrough')   
x2 = np.array(columnTransformer.fit_transform(X), dtype = np.float) 

columnTransformer = ColumnTransformer([('encoder', OneHotEncoder(),[10])], remainder='passthrough')  
x3 = np.array(columnTransformer.fit_transform(x2), dtype = np.float) 

#output col in y
y = Profit

#Scaling the features
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
x3 = sc.fit_transform(x3)

#split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(x3, y, test_size=0.25, random_state=42)

#K nearest neighbors for classification
from sklearn import neighbors
from sklearn.neighbors import KNeighborsClassifier
classifier = neighbors.KNeighborsClassifier(n_neighbors = 5)
classifier.fit(X_train, y_train)

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

#confusion matrix
from sklearn.metrics import confusion_matrix
matrix = confusion_matrix(y_test, y_pred)

#classification report
from sklearn.metrics import classification_report
result = classification_report(y_test,y_pred,labels=[1,0])
print('Classification report : \n',result)