Added questions and answers for quiz on SVR

7-TimeSeries/3-SVR/README.md

@@ -2,6 +2,7 @@
 
 In the previous lesson, you learned how to use ARIMA model to make time series predictions. Now you'll be looking at Support Vector Regressor model which is a regressor model used to predict continuous data.
 
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/51/)
 
 ## Introduction
 
@@ -12,7 +13,7 @@ In this lesson, you will discover a specific way to build models with [**SVM**:
 Before understanding the importance of SVR in time series prediction, here are some of the important concepts that you need to know:
 
 - **Regression:** Supervised learning technique to predict continuous values from a given set of inputs. The idea is to fit a curve (or line) in the feature space that has the maximum number of data points. [Click here](https://en.wikipedia.org/wiki/Regression_analysis) for more information.
-- **Support Vector Machine (SVM):** A type of supervised machine learning model used for classification, regression and outliers detection. The model is a hyperplane in the feature space, which in case of classification acts as a boundary, and in case of regression acts as the best-fit line. In SVM, a Kernel function is generally used to transform the dataset, so that a non-linear decision surface is able to transform to a linear equation in a higher number of dimension spaces. [Click here](https://en.wikipedia.org/wiki/Support-vector_machine) for more information.
+- **Support Vector Machine (SVM):** A type of supervised machine learning model used for classification, regression and outliers detection. The model is a hyperplane in the feature space, which in case of classification acts as a boundary, and in case of regression acts as the best-fit line. In SVM, a Kernel function is generally used to transform the dataset to a space of higher number of dimensions, so that they can be easily separable. [Click here](https://en.wikipedia.org/wiki/Support-vector_machine) for more information on SVMs.
 - **Support Vector Regressor (SVR):** A type of SVM, to find the best fit line (which in the case of SVM is a hyperplane) that has the maximum number of data points.
 
 ### Why SVR? [^1]
@@ -361,6 +362,8 @@ MAPE:  2.0572089029888656 %
 - Try to use different kernel functions for the model and analyze their performances on the dataset. A helpful document can be found [here](https://scikit-learn.org/stable/modules/svm.html#kernel-functions).
 - Try using different values for `timesteps` for the model to look back to make prediction.
 
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/52/)
+
 ## Review & Self Study
 
 This lesson was to introduce the application of SVR for Time Series Forecasting. To read more about SVR, you can refer to [this blog](https://www.analyticsvidhya.com/blog/2020/03/support-vector-regression-tutorial-for-machine-learning/). This [documentation on scikit-learn](https://scikit-learn.org/stable/modules/svm.html) provides a more comprehensive explanation about SVMs in general, [SVRs](https://scikit-learn.org/stable/modules/svm.html#regression) and also other implementation details such as the different [kernel functions](https://scikit-learn.org/stable/modules/svm.html#kernel-functions) that can be used, and their parameters.

quiz-app/src/assets/translations/en.json

@@ -2809,6 +2809,120 @@
             ]
           }
         ]
+      },
+	  {
+        "id": 51,
+        "title": "Time Series SVR: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "SVM stands for",
+            "answerOptions": [
+              {
+                "answerText": "Statistical Vector Machine",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Support Vector Machine",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Statistical Vector Model",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these ML techniques is used to predict continuous values?",
+            "answerOptions": [
+              {
+                "answerText": "Clustering",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Classification",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Regression",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these models is popularly used for time series forecasting?",
+            "answerOptions": [
+              {
+                "answerText": "ARIMA",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "K-Means Clustering",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Logistic Regression",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 52,
+        "title": "Time Series SVR: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "By which of these methods does an SVR learn?",
+            "answerOptions": [
+              {
+                "answerText": "Finding the best fit hyperplane that has the maximum number of data points",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Learning the probability distribution of the dataset",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Finding clusters in the dataset",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is the purpose of a kernel in SVMs?",
+            "answerOptions": [
+              {
+                "answerText": "To measure the accuracy of the model predictions",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "To transform the dataset to a higher dimension space",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "To standardize the values of the dataset",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these models consider the non-linearity in the dataset?",
+            "answerOptions": [
+              {
+                "answerText": "Simple Linear Regression",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ARIMA",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "SVR using RBF kernel",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
       }
     ]
   }