ENG335 ECA (End-of-Course Assessment) SUSS : July Semester 2024 – Machine Learning

Question 1

Download the mobile phone price dataset from
https://www.kaggle.com/datasets/ganjerlawrence/mobile-phone-price-predictioncleaned-dataset. Understand and prepare the dataset by selecting FOUR (4) relevant features to predict the mobile price. Design a linear regression model using the function available in sklearn package. Estimate the performance of the algorithm using TWO

(2) suitable parameters. Implement a neural network-based regression with a hidden layer and a output layer. Tune the neural network parameters such that it performs better than the linear regression model implemented using the sklearn package.

Question 2

Load the corporate credit rating with financial ratios dataset from https://www.kaggle.com/datasets/kirtandelwadia/corporate-credit-rating-withfinancial-ratios. The objective is to design a decision tree classifier to classify the companies as either invest grade or junk. Analyze the dataset, drop the appropriate columns and perform encoding wherever required. Set-up an optimized decision tree classifier and assess whether its performance is better than the Naïve Bayes classifier.

Question 3

Download the Comprehensive Diabetes Clinical from https://www.kaggle.com/datasets/priyamchoksi/100000-diabetes-clinical dataset. Prepare the dataset by dropping appropriate features and perform feature encoding wherever necessary. Perform exploratory data analysis. Construct a random forest algorithm for diabetes. Propose optimal values for the depth and number of trees in the random forest.

Question 4

Use the potato disease dataset available in  https://www.kaggle.com/datasets/hafiznouman786/potato-plant-diseases-data/data. Load the data into the programming environment. Perform exploratory data analysis and show a random sample of SIX (6) images of each class of potato disease. Design a CNN with TWO (2) convolutional layers and THREE (3) dense layers (including the final output layer). Employ ‘relu’ activation and MaxPooling. Keep 18% of the training dataset for validation and use at least 10 epochs.

Question 5

Select any stock listed on the New York stock exchange. Using Yahoo finance, download the daily stock data (Open, High, Low, Close, Adj Close, Volume). Download the data such that 5 years of data is available and use the 2024 data as test data. Prepare the training dataset by randomly selecting a date and extracting the previous 14 days of data (closing price and volume) to predict the stock price of the selected date. Design an LSTM network to do the predictions. You are required to use LSTM with a cell state of at least 100 dimensions and do at least 50 epochs of training. Rate the performance of the LSTM classifier and provide necessary plots.

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