ENG335 Machine Studying Pause-of-Course Evaluate Questions 2026 | SUSS, Singapore
ENG335 Pause-of-Course Evaluate
Instructions to Students
- This Pause-of-Course Evaluate paper comprises FIVE (05) pages (including the quilt internet page).
- You are to embody the following particulars in your submission: Course Code, Title of the ECA, SUSS PI No., Your Title, and Submission Date.
- Gradual submission will likely be subjected to the marks deduction scheme. Please refer to the Student Instruction manual for foremost parts.
Answer all Questions
(Total 100 marks)
Request 1
Download the cell mobile telephone tag dataset from
https://www.kaggle.com/datasets/ganjerlawrence/mobile-mobile telephone-tag-predictioncleaned-dataset. Understand and put collectively the dataset by deciding on FOUR (4) relevant aspects to foretell the mobile tag. Like a linear regression model the exercise of the characteristic on hand in sklearn package. Estimate the performance of the algorithm the exercise of TWO (2) true parameters. Put into effect a neural community-based mostly regression with a hidden layer and a output layer. Tune the neural community parameters such that it performs better than the linear regression model implemented the exercise of the sklearn package.
(20 marks)
Request 2
Load the company credit standing with financial ratios dataset from. The target is to create a choice tree classifier to classify the companies as either make investments grade or junk. Analyze the dataset, descend the correct columns and create encoding wherever required. Device-up an optimized choice tree classifier and assess whether or no longer its performance is better than the Naïve Bayes classifier.
(20 marks)
Request 3
Download the Complete Diabetes Clinical dataset. Prepare the dataset by losing relevant aspects and create feature encoding wherever foremost. Assassinate exploratory info evaluation. Originate a random woodland algorithm for diabetes. Propose optimum values for the depth and quantity of trees in the random woodland.
(20 marks)
Request 4
Utilize the potato disease dataset on hand in https://www.kaggle.com/datasets/hafiznouman786/potato-plant-illnesses-info/info. Load the guidelines into the programming ambiance. Assassinate exploratory info evaluation and uncover a random pattern of SIX (6) photography of every class of potato disease. Like a CNN with TWO (2) convolutional layers and THREE (3) dense layers (including the closing output layer). Expend ‘relu’ activation and MaxPooling. Preserve 18% of the practicing dataset for validation and exercise a minimum of 10 epochs.
(20 marks)
Request 5
Opt out any inventory listed on the Contemporary York inventory exchange. Utilizing Yahoo finance, rep the on a fashioned foundation inventory info (Delivery, Excessive, Low, Discontinuance, Adj Discontinuance, Volume). Download the guidelines such that 5 years of information is on hand and exercise the 2024 info as take a look at info. Prepare the practicing dataset by randomly deciding on a date and extracting the old 14 days of information (closing tag and volume) to foretell the inventory tag of the chosen date. Like an LSTM community to provide the predictions. You are required to exercise LSTM with a cell state of a minimum of 100 dimensions and produce a minimum of 50 epochs of practicing. Rate the performance of the LSTM classifier and provide foremost plots.
(20 marks)
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