Construct a Convolutional Neural Network (CNN) architecture from scratch to extract features from the images.

Write a python program to perform the following tasks:
1. Load the dataset for all three classes and resize each image to (32 x 32). Apply the required preprocessing steps to employ the data into Machine Learning / Deep Learning algorithms.
2. Construct a Convolutional Neural Network (CNN) architecture from scratch to extract features
from the images. (HINT: Extract features for train and test set separately. Extract the features
constructed by the convolutional layers from an intermediate dense layer. Please refrain from
using any pre-trained model for implementing this step)
3. Apply the K-Nearest Neighbor (KNN) algorithm to the extracted features from CNN and find the
optimal value of K. The value of K can be considered as [3, 5, 7, 9]. Determine the performance of
the model using an appropriate performance metric. Draw a graph of K values and their
corresponding performance in order to represent your results.
4. Apply Random Forest (RF) algorithm to the extracted features from CNN. Tune at least two
hyperparameters using random search. Determine the model’s optimal performance, the
confusion matrix, and the value of hyperparameters producing the optimal performance.
5. Report the performance of each model and explain your results. (eg. overfitting, underfitting, etc.)

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Description of the how the software selection criteria (and which ones) were chosen for your company

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