Implementation Requirements: a. Read the car.csv dataset, which designed to evaluate a car. The dataset includes a list

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Implementation Requirements A Read The Car Csv Dataset Which Designed To Evaluate A Car The Dataset Includes A List 1 (360.18 KiB) Viewed 41 times

Implementation Requirements: a. Read the car.csv dataset, which designed to evaluate a car. The dataset includes a list of 1728 cars with 6 features: buying price, cost of maintenance, number of doors, capacity in terms of persons to carry, the relative size of luggage boot and the estimated safety value of each car. The last column includes the car evaluation, classifying the cars into 4 classes: unacceptable, acceptable, good or very good. b. Use the data from (a) to build a decision tree for evaluating a car using the above six attributes. Your implementation should include: 1. split_data function which works in a similar way to Task 1. 2. Build a decision tree from the training set according to the algorithm learned in the lectures. c. Test your data then calculate and print: Total accuracy. Confusion matrix. precision, recall and F1-score values for each class, together with the macro-average and weighted -average. Plot the Learning curve (accuracy as a factor of percentage of learning example) i.e. show how the accuracy changes while learning. For example, if the training set includes 1000 samples, you may stop building the tree after 100 samples and test with the current tree, then continue building the tree and stop again and test after 200 samples, and so on until you have the final decision tree which is built using the full training set. Report Requirements: Your report should include: a. Software design: information about each function and data structure. b. The results and graphs from (c.) above. c. Conclusions.