Could you answer this question

Question

Objective: The objective of this assignment is to compare the performance of three popular machine learning algorithms for regression tasks: Support Vector Regression (SVR), Decision Tree, and Random Forest to determine the medium value (MEDV) of a Boston home based on one or more of the other independent variables.

Dataset: The dataset for this assignment is the Boston Housing dataset, which is a popular dataset used in machine learning for regression tasks. The dataset contains information about different properties of houses in Boston, such as the number of rooms, crime rate, and distance from employment centers, and the target variable is the median value of owner-occupied homes in thousands of dollars.

Tasks:

1. Load the Boston Housing dataset into R Studio.

2. Split the dataset into a training set and a testing set using a 70/30 split. The training set will be used to train the models, and the testing set will be used to evaluate their performance.

3. Implement three different machine learning algorithms for regression tasks: SVR, Decision Tree, and Random Forest.

4. Evaluate the performance (We are learning this coming week, so you can wait until after we have completed the lesson to do this part.

Deliverables:

All students should submit a report that includes the following:

1. A brief introduction to the Boston Housing dataset and the problem statement (What are you trying to accomplish? Look at the objective)

2. A description of the machine learning algorithms used in the assignment (SVR, Decision Tree, and Random Forest) and their implementation in R Studio.

3. A discussion of the performance of each algorithm on the testing set, including the MSE and R2 metrics.

4. A comparison of the performance of the three algorithms and insights into which algorithm performed the best and why.

5. A discussion of the strengths and weaknesses of each algorithm and their potential use cases.

6. Conclusion.