The submission process requires two main documents to be uploaded to Moodle: 1. A report detailing your findings and methodologies. 2. A plain text document with the Python code used for

the practical application and code analysis tasks. This is to show the steps taken to generate the sections of the report. You have the option to submit your code in the form of a Google Colab or Jupyter Notebook file; however, a simple plain text file is sufficient. This ensures that the work is easily accessible and assessable. For your submission, please adhere to the following guidelines: 1. The report must be submitted in PDF format, not as a Microsoft Word document. 2. Both the report and the code file-should be named using your student ID (for example, UP1234567). 3. There is a word limit of 1500 words for the report, which does not include code or references. 4. Ensure that your student ID number is included on each page of your report; placing it in the header or footer is recommended for consistency. Additionally, be mindful of the university's stance on plagiarism. The submission of work that is not your own or heavily based on someone else's work without appropriate citation is considered plagiarism and is subject to university sanctions. It's important to ensure that your work is original and properly cites any sources used./nTasks: 1. Dataset Familiarisation Access the dataset from the given UCI repository link (click here) and peruse the dataset description on Kaggle(click here). Perform exploratory data analysis to understand the dataset's complexities. In your report, you may employ appropriate visualisations of the raw data to facilitate observation, laying the groundwork for model construction. 2. Implementation of Intelligent Systems • Select two predictive methods, such as linear regression, neural networks, or k-means clustering etc. • Construct models to forecast the stability of the power grid using the dataset. 3. Model implementation and Problem-solving Endeavour to enhance the construction of the model by measures such as hyperparameter tuning, structural design, and the selection of pre-trained models. • Tackle and resolve issues such as overfitting to improve the robustness of your models. 4. Critical Evaluation Evaluate the models' effectiveness using appropriate metrics. Critically contrast the selected methods, considering their efficiency and computational viability for Electronic Engineering applications.