prog - Copy.py

import pandas as pd
import numpy as np
from sklearn.metrics import classification_report
from sklearn import metrics
import seaborn as sns
from sklearn import tree
import warnings
import matplotlib.pyplot as plt
import pickle
from sklearn.svm import SVC
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score

d = pd.read_csv('Data/crop_recommendation.csv')

features = d[['N', 'P','K','temperature', 'humidity', 'ph', 'rainfall']]
target = d['label']
labels = d['label']

acc = []
model = []


Xtrain, Xtest, Ytrain, Ytest = train_test_split(features,target,test_size = 0.2,random_state =2)


RF = RandomForestClassifier(n_estimators=20, random_state=0)
RF.fit(Xtrain,Ytrain)

predicted_values = RF.predict(Xtest)

x = metrics.accuracy_score(Ytest, predicted_values)
acc.append(x)
model.append('RF')
# print("RF's Accuracy is: ", x)

# print(classification_report(Ytest,predicted_values))
score = cross_val_score(RF,features,target,cv=5)


RF_pkl_filename = 'model/RandomForest.pkl'
RF_Model_pkl = open(RF_pkl_filename, 'wb')
pickle.dump(RF, RF_Model_pkl)
RF_Model_pkl.close()

data = np.array([[30,5, 80, 70.603016, 60.3, 3.7, 140.91]])  
prediction = RF.predict(data)
print(prediction)