temp.py

#importing the required libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

#Reading the csv file
data=pd.read_csv("./cpdata.csv")
print(data.head(1))

#Creating dummy variable for target i.e label
label= pd.get_dummies(data.label).iloc[: , 1:]
data= pd.concat([data,label],axis=1)
data.drop('label', axis=1,inplace=True)
print('The data present in one row of the dataset is')
print(data.head(1))

# # Import label encoder 
# from sklearn import preprocessing 
  
# # label_encoder object knows how to understand word labels. 
# label_encoder = preprocessing.LabelEncoder() 

# #storing targetvariables
# target_variables=data['label'].unique()
# print(target_variables)

# # Encode labels in column 'label'. 
# data['label']= label_encoder.fit_transform(data['label']) 
  
# print(data['label'].unique()) 

train=data.iloc[:, 0:4].values
test=data.iloc[: ,4:].values

#Dividing the data into training and test set
X_train,X_test,y_train,y_test=train_test_split(train,test,test_size=0.2)

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

# # Importing Decision Tree classifier
# from sklearn.tree import DecisionTreeRegressor
# clf=DecisionTreeRegressor()

#importing KNeighboursClassifier
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier(n_neighbors=30) 
  
clf.fit(X_train, y_train) 
   

#Fitting the classifier into training set
clf.fit(X_train,y_train)
pred=clf.predict(X_test)

from sklearn.metrics import accuracy_score
# Finding the accuracy of the model
a=accuracy_score(y_test,pred)
print("The accuracy of this model is: ", a*100)

l=[[23,36,6.9,280],[36,80,6.8,150]]
print(clf.predict(l))

# #Using firebase to import data to be tested
# from firebase import firebase
# firebase =firebase.FirebaseApplication('https://cropit-eb156.firebaseio.com/')
# tp=firebase.get('/Realtime',None)

#importing pickle
import pickle
pickle.dump(clf,open('models/final_prediction.pickle','wb'))


# ah=tp['Air Humidity']
# atemp=tp['Air Temp']
# shum=tp['Soil Humidity']
# pH=tp['Soil pH']
# rain=tp['Rainfall']


# l=[]
# l.append(ah)
# l.append(atemp)
# l.append(pH)
# l.append(rain)
# predictcrop=[l]

# # Putting the names of crop in a single list
# crops=['wheat','mungbean','Tea','millet','maize','lentil','jute','coffee','cotton','ground nut','peas','rubber','sugarcane','tobacco','kidney beans','moth beans','coconut','blackgram','adzuki beans','pigeon peas','chick peas','banana','grapes','apple','mango','muskmelon','orange','papaya','watermelon','pomegranate']
# cr='rice'

# #Predicting the crop
# predictions = clf.predict(predictcrop)
# count=0
# for i in range(0,30):
#     if(predictions[0][i]==1):
#         c=crops[i]
#         count=count+1
#         break
#     i=i+1
# if(count==0):
#     print('The predicted crop is %s'%cr)
# else:
#     print('The predicted crop is %s'%c)

#Sending the predicted crop to database
#cp=firebase.put('/croppredicted','crop',c)