The final project is designed to integrate and apply the knowledge and skills you have acquired throughout the MATLAB Programming Course. This project will involve a comprehensive task that requires you to use various MATLAB functionalities, including data analysis, visualization, and possibly advanced topics like optimization or machine learning.

You will develop a MATLAB application that analyzes a dataset, performs necessary preprocessing, applies statistical analysis, and visualizes the results. You can choose a dataset from a domain of your interest, such as finance, healthcare, engineering, or social sciences.

1. Select a Dataset

   • Choose a dataset relevant to your field of interest. You can find datasets from sources like Kaggle, UCI Machine Learning Repository, or government databases.
   • Ensure the dataset is complex enough to demonstrate your skills but manageable within the project scope.

2. Import and Explore the Data

   • Use MATLAB functions to import the dataset.
   • Perform an initial exploration to understand the structure and contents of the data.
   • Identify any missing values or anomalies.

3. Data Preprocessing

   • Handle missing values, outliers, and any inconsistencies in the data.
   • Normalize or standardize the data if necessary.
   • Split the data into training and testing sets if you plan to use machine learning techniques.

4. Data Analysis

   • Apply descriptive statistics to summarize the data.
   • Perform any necessary statistical tests to understand relationships within the data.
   • If applicable, apply regression analysis or other statistical models.

5. Data Visualization

   • Create various plots to visualize the data and the results of your analysis.
   • Use 2D and 3D plots, histograms, scatter plots, etc., to present your findings clearly.
   • Customize the plots for better readability and presentation.

6. Advanced Techniques (Optional)

   • If your project involves machine learning, implement and train a model using MATLAB's machine learning toolbox.
   • Apply optimization techniques if your project requires solving an optimization problem.
   • Use parallel computing if your dataset is large and requires efficient processing.

7. Documentation and Presentation

   • Document your code with comments and explanations.
   • Prepare a report summarizing your project, including the methodology, analysis, results, and conclusions.
   • Create a presentation to showcase your project, highlighting key findings and visualizations.

1. Select a Dataset

   • Use the "House Prices: Advanced Regression Techniques" dataset from Kaggle.

2. Import and Explore the Data

   data = readtable('house_prices.csv');
   summary(data);
   

3. Data Preprocessing

   % Handle missing values
   data = fillmissing(data, 'constant', 0);
   
   % Normalize numerical features
   data = normalize(data, 'range');
   
   % Split data into training and testing sets
   [trainData, testData] = splitData(data, 0.8);
   

4. Data Analysis

   % Descriptive statistics
   stats = summary(trainData);
   
   % Regression analysis
   mdl = fitlm(trainData, 'SalePrice ~ .');
   

5. Data Visualization

   % Scatter plot of actual vs predicted prices
   predictedPrices = predict(mdl, testData);
   scatter(testData.SalePrice, predictedPrices);
   xlabel('Actual Prices');
   ylabel('Predicted Prices');
   title('Actual vs Predicted House Prices');
   

6. Advanced Techniques (Optional)

   % Train a machine learning model
   model = fitrensemble(trainData, 'SalePrice');
   

7. Documentation and Presentation

   • Document the code with comments.
   • Prepare a report and presentation summarizing the project.

• Submit your MATLAB code files (.m files).
• Include the dataset used in your project.
• Provide a report in PDF format summarizing your project.
• Prepare a presentation (PowerPoint or PDF) to showcase your findings.

• Completeness: The project should cover all the required steps.
• Correctness: The code should be correct and produce the expected results.
• Clarity: The code should be well-documented, and the report should be clear and concise.
• Creativity: The project should demonstrate creativity in the choice of dataset and the analysis performed.
• Presentation: The final presentation should effectively communicate the project's findings.

The final project is an opportunity to showcase your MATLAB skills and apply them to a real-world problem. By completing this project, you will gain hands-on experience in data analysis, visualization, and possibly advanced techniques like machine learning. Good luck!