Create modeling.py

modeling.py

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+import pandas
+import sklearn
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import confusion_matrix
+
+#open and read file
+df = pandas.read_csv('RSL_copy.csv')
+print(df.dtypes)
+data= df.values
+
+#split into features and target
+X_array = data[:,0:2]
+Y_array = data[:,2]
+
+#split data into training set and test set
+
+X_train, X_test, y_train, y_test = train_test_split(X_array,Y_array,test_size=0.2)
+
+#create an insance of the model
+lrmodel=LogisticRegression(solver='newton-cg')
+# Train the MOdel to get line of best FIT
+lrmodel.fit(X_train,y_train)
+# make your prediction with x_train and compare it with y_train
+train_prediction = lrmodel.predict(X_train)
+#find the accuracy of the model by  comparing  it with y_train
+
+accuracy = accuracy_score(train_prediction,y_train)
+print('train prediction is',accuracy*100,'%')
+
+#after training the model, test the model
+prediction =lrmodel.predict(X_test)
+#find the accuracy of your prediction
+accuracy = accuracy_score(prediction,y_test)
+print('test predcition:', accuracy*100,'%')
+
+#confusion_matrix  #cannot handle multiclass probelms
+confusion_matrix(y_test,prediction)
+print(confusion_matrix(y_test,prediction))
+
+
+
+
+
+#sve your model
+import pickle
+filename = 'MWmodel.sav'
+pickle.dump(lrmodel,open(filename, 'wb'))