Assignment: Build a Simple Linear Regression Model in Julia

Objective: Implement a basic linear regression model to predict house prices based on a single feature $-$ the house size $($in

square feet$).$

Requirements:

Install Julia and Relevant Packages: Ensure Julia is installed on your system. Install the following Julia packages: CSV$,$

DataFrames, and GLM$.$

Dataset Creation:

The file should contain two columns: Size $($square feet$)$ and Price.

Populate the file with sample data $($at least $30$ rows$).$ You can generate this data or use real$-$world data.

Create a CSV file named house$\_$prices.csv$.$

Data Loading and Processing:

Split the data into features $($Size$)$ and labels $($Price$).$

Normalize the feature data for better model performance.

Write a Julia script to load data from the CSV file.

Model Building and Training:

Train the model using the features and labels.

Use the GLM package to create a linear regression model.

Model Evaluation:

Evaluate the model's performance using appropriate metrics $($e$.$g$.,$ mean squared error$).$

Predict the prices for a given set of house sizes.

Report:

Include both your code and the output in the report.

Write a brief report summarizing your methodology, findings, and any challenges you faced.

Deliverables:

$`$house$\_$prices.csv$`-$ The dataset file.

$3.$ A report in PDF or Markdown format detailing your approach and findings.

A Julia script $(`.$jl$`$ file$)$ containing the code for data processing, model building, training, and evaluation.

Tips:

Start by exploring the data to understand its distribution.

$-$ Experiment with different sets of data and observe how your model performs.

Make sure to comment your code for clarity.

This assignment will help you understand the basics of data handling, model creation, and evaluation in machine learning using Julia. It's a practical way to apply the concepts of features, labels, and linear regression in a real$-$world context. Deliverables:

$1.`$house$\_$prices.csv$`-$ The dataset file.

$3.$ A report in PDF or Markdown format detailing your approach and findings.

$2.$ A Julia script $(`.$jl$`$ file$)$ containing the code for data processing, model building, training, and evaluation.

Tips:

$-$ Start by exploring the data to understand its distribution.

$-$ Experiment with different sets of data and observe how your model performs.

$-$ Make sure to comment your code for clarity.

This assignment will help you understand the basics of data handling, model creation, and evaluation in machine learning using Julia. It's a practical way to apply the concepts of features, labels, and linear regression in a real$-$world context.