Merge branch 'IT18523256_GunarathnaK.A.G.I.P.T' into 'master'

Adding Comments and dog breeds

See merge request !14

diet_plan/logistic_regression_weight.py

 #import pandas
 import pandas as pd
+
+#creating columns for dataset preview
 col_names = ['breed', 'age', 'weight', 'label']
-# load dataset
+dog_breeds = ["german_sheppard", "labrador", "pomeranian", "husky", "golden_retriever", "poodle", "bulldog", "shiba", "rottweiler","boxer","Dobermen"]
+
+# reading data set
 pima = pd.read_csv("weight_data.csv", header=None, names=col_names)
 print("===================")
 print("Dataset head")
 print("===================")
+#previewing the head of data set
 print(pima.head())
 
-
-#split dataset in features and target variable
+#split dataset in to features and target variable
 feature_cols = ['breed', 'age', 'weight']
 X = pima[feature_cols] # Features
 y = pima.label # Target variable
 
+#Use sklearn to train dataset
 from sklearn.model_selection import train_test_split
 X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=0)
 
 # import the class
+#use logistic Regression
 from sklearn.linear_model import LogisticRegression
 
 # instantiate the model (using the default parameters)
@@ -26,7 +32,6 @@ logreg = LogisticRegression()
 # fit the model with data
 logreg.fit(X_train,y_train)
 
-#
 y_pred=logreg.predict(X_test)
 
 # import the metrics class
@@ -35,16 +40,18 @@ cnf_matrix = metrics.confusion_matrix(y_test, y_pred)
 print("===================")
 print("Confusion matrix")
 print("===================")
+#printing a confusion matrix
 print(cnf_matrix)
 
 print("===================")
 print("Accuracy")
 print("===================")
+#printing the accuracy of model
 print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
 # print("Precision:",metrics.precision_score(y_test, y_pred))
 # print("Recall:",metrics.recall_score(y_test, y_pred))
 
 # Prediction test
-y_prediction_test = logreg.predict([[12,3,48]])
+y_prediction_test = logreg.predict([[5,4,22]])
 y_labels = ["Overweight", "Normal", "Underweight"]
 print(y_labels[y_prediction_test[0]])