Preprocessing Images and Creating Additional Datasets - Machine Learning Algorithms - IT Assignment Help

Assignment Task:

PART 1: Loading/preprocessing images and creating additional datasets 

1. Code a Python script that is able to go through the image dataset and load the pixels into a Python data structure (NumPy array or Pandas data frame) along with its corresponding label/target/class. Consider if the symbols should be resized/binarised. or processed in any other way, before creating the selected data structure. Provide a justification/description of the technique used (word limit: 100 words). 

2. Then, Implement AT LEAST one feature extraction, one Image/data augmentation and one class decomposition technique to create a minimum of 3 additional data structures. Provide a justification/description of the techniques used (word limit: 500 words). 

PART 2 UPDATED: Classifying and comparing the performance of different machine learning algorithms based on the data structures produced 

1. Write Python code that Is capable to load all the symbol datasets that you created in Part 1 (and/or the ones that I provided you) to perform a classification evaluation/validation. You should include AT LEAST three classifiers: a) ANN, b) a CNN (which can only work with your 'pixel* datasets) and c) any other supervised learning classifier (e.g. SVM, Random Forests, decision trees, etc.). 

A. Provide a description of the classifiers used and a justification for the selected architectures/parameters (word limit: 500 words). 

2. Explain how you ran your tests (word limit: 200 words) (i.e. how you will split the data in test/validation/train sets, if cross-validation is needed, etc.) and which performance metrics you will use In order to determine which dataset/classifier combination is the best for this scenario. You can present your results in tables or plots as best required for your purposes. 

3. Provide a reflection of your results (word limit: 1000 words) answering questions such as 

A. Which is the best combination and why do you think so? 
B. Which metric is more significant in this scenario? 
C. Is there any trade-off in using any combination In favour of others? 
D. How likely is to improve the obtained results provided that you have more resources/time? 
 

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