--- a
+++ b/LogisticRegression.py
@@ -0,0 +1,98 @@
+import pandas as pd
+import matplotlib as plt
+import numpy as np
+from sklearn import linear_model
+#from sklearn.model_selection cross_validation
+from scipy.stats import norm
+
+from sklearn.svm import SVC
+from sklearn import svm
+from sklearn.svm import LinearSVC
+from sklearn.model_selection import train_test_split
+
+from sklearn.metrics import accuracy_score
+from random import seed
+from random import randrange
+from csv import reader
+import csv
+import numpy as np
+import pandas as pd
+from pandas import read_csv
+import matplotlib.pyplot as plt
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error
+from sklearn.metrics import r2_score
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn import preprocessing
+
+
+def process(path):
+	data=pd.read_csv(path)
+	print("data.columns=",data.columns)
+	label_encoder = preprocessing.LabelEncoder()
+	data['Diagnosis']= label_encoder.fit_transform(data['Diagnosis'])
+	data['Gen']= label_encoder.fit_transform(data['Genero'])
+	X=data[['Age', 'Weight (Kg)', 'Height (cms)', 'Gen','Heart Rate', 'oxygen saturation', 'Respiratory Rate','Systolic Blood Pressure', 'Diastolic Blood Pressure','Mean Blood Pressure']]
+	y=data['Diagnosis']
+	
+	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+	
+	model2=LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,
+          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
+          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
+          verbose=0, warm_start=False)
+	model2.fit(X_train, y_train)
+	y_pred = model2.predict(X_test)
+	print("predicted")
+	print(y_pred)
+	print(y_test)
+	result2=open("results/resultLR.csv","w")
+	result2.write("ID,Predicted Value" + "\n")
+	for j in range(len(y_pred)):
+	    result2.write(str(j+1) + "," + str(y_pred[j]) + "\n")
+	result2.close()
+	
+	mse=mean_squared_error(y_test, y_pred)
+	mae=mean_absolute_error(y_test, y_pred)
+	r2=r2_score(y_test, y_pred)
+	
+	
+	print("---------------------------------------------------------")
+	print("MSE VALUE FOR Logistic Regression IS %f "  % mse)
+	print("MAE VALUE FOR Logistic Regression IS %f "  % mae)
+	print("R-SQUARED VALUE FOR Logistic Regression IS %f "  % r2)
+	rms = np.sqrt(mean_squared_error(y_test, y_pred))
+	print("RMSE VALUE FOR Logistic Regression IS %f "  % rms)
+	ac=accuracy_score(y_test,y_pred)
+	print ("ACCURACY VALUE Logistic Regression IS %f" % (ac*100))
+	print("---------------------------------------------------------")
+	
+
+	result2=open('results/LRMetrics.csv', 'w')
+	result2.write("Parameter,Value" + "\n")
+	result2.write("MSE" + "," +str(mse) + "\n")
+	result2.write("MAE" + "," +str(mae) + "\n")
+	result2.write("R-SQUARED" + "," +str(r2) + "\n")
+	result2.write("RMSE" + "," +str(rms) + "\n")
+	result2.write("ACCURACY" + "," +str(ac*100) + "\n")
+	result2.close()
+	
+	
+	df =  pd.read_csv('results/LRMetrics.csv')
+	acc = df["Value"]
+	alc = df["Parameter"]
+	colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#8c564b"]
+	explode = (0.1, 0, 0, 0, 0)  
+	
+	fig = plt.figure()
+	plt.bar(alc, acc,color=colors)
+	plt.xlabel('Parameter')
+	plt.ylabel('Value')
+	plt.title('Logistic Regression Value')
+	fig.savefig('results/LRMetricsValue.png') 
+	plt.pause(5)
+	plt.show(block=False)
+	plt.close()
+
+#process("Child_Heart_Stage_dataset.csv")