Adding new python Modules and SVM

.idea/2021-210.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.7" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

.idea/csv-plugin.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CsvFileAttributes">
+    <option name="attributeMap">
+      <map>
+        <entry key="\diet_plan\weight_data.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+      </map>
+    </option>
+  </component>
+</project>
\ No newline at end of file

.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

.idea/vcs.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file

diet_plan/logistic_regression_weight.py

 #import pandas
 import pandas as pd
-
-#creating columns for dataset preview
 col_names = ['breed', 'age', 'weight', 'label']
-dog_breeds = ["german_sheppard", "labrador", "pomeranian", "husky", "golden_retriever", "poodle", "bulldog", "shiba", "rottweiler","boxer","Dobermen"]
-
-# reading data set
+dog_breeds = ["german_sheppard", "labrador", "pomeranian", "husky", "golden_retriever", "poodle", "bulldog", "shiba", "rottweiler"]
+# load dataset
 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 to features and target variable
+
+#split dataset in 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)
@@ -32,6 +27,7 @@ logreg = LogisticRegression()
 # fit the model with data
 logreg.fit(X_train,y_train)
 
+#
 y_pred=logreg.predict(X_test)
 
 # import the metrics class
@@ -40,18 +36,16 @@ 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([[5,4,22]])
+y_prediction_test = logreg.predict([[1,1,3]])
 y_labels = ["Overweight", "Normal", "Underweight"]
 print(y_labels[y_prediction_test[0]])