Create Python model

diet_plan/logistic_regression_weight.py

+#import pandas
+import pandas as pd
+col_names = ['breed', 'age', 'weight', 'label']
+# load dataset
+pima = pd.read_csv("weight_data.csv", header=None, names=col_names)
+print("===================")
+print("Dataset head")
+print("===================")
+print(pima.head())
+
+
+#split dataset in features and target variable
+feature_cols = ['breed', 'age', 'weight']
+X = pima[feature_cols] # Features
+y = pima.label # Target variable
+
+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
+from sklearn.linear_model import LogisticRegression
+
+# instantiate the model (using the default parameters)
+logreg = LogisticRegression()
+
+# fit the model with data
+logreg.fit(X_train,y_train)
+
+#
+y_pred=logreg.predict(X_test)
+
+# import the metrics class
+from sklearn import metrics
+cnf_matrix = metrics.confusion_matrix(y_test, y_pred)
+print("===================")
+print("Confusion matrix")
+print("===================")
+print(cnf_matrix)
+
+print("===================")
+print("Accuracy")
+print("===================")
+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_labels = ["Overweight", "Normal", "Underweight"]
+print(y_labels[y_prediction_test[0]])