In this project, we need to build a labeled dataset that distinguishes between healthy and pneumonia x-ray images.This dataset would eventually be used to build a product that helps doctors quickly identify cases of pneumonia in children.we are going to take the next step and build the classification model that would serve as the backbone of this product.For this task,we will use Google AutoML,an automated machine learning tool that will allow us to build the model and host it in the cloud. In order to appreciate how training data impact models,we will build models with 4 variants of the dataset.The goals of this product are

1. Help flag serious cases,
2. Quickly identify healthy cases,
3. And, generally, act as a diagnostic aid for doctors.

We will train four different models using four variants of the pneumonia dataset.The dataset contains childrens' chest x-ray images, and that they are classified into two classes, "normal" and "pneumonia". The following cases describe the steps we must take to create each model.

We'll start by training a model simply using 100 images from the “normal” class and 100 images from the “pneumonia” class.We are asked to evaluate the following:

• Train/test split: How much data is used for training and how much is used for testing?
• Confusion matrix: What do each of the cells in the confusion matrix describe?
• Precision & recall: What do these measure and how are they calculated?

Next, use 100 images from the “normal” class, and add 200 more "pneumonia" class images. At this moment, the total count of images must be:

“normal” class = 100 "pneumonia" class = 300 The model will be trained on very unbalanced classes; this will show what happens when the number of images in different classes is very different. We are asked to evaluate:

• Confusion matrix: What does the confusion matrix tell you about unbalanced data?
• Precision & recall: How are precision and recall affected?
• Unbalanced classes: How do unbalanced classes impact a machine learning model?

In this iteration, start with the original dataset of 100 "normal" and 100 "pneumonia" images. Then switch the labels of 30 images in each class. After we've done this, 30% of the data are mislabeled. We are asked to evaluate:

• Confusion matrix: How is the confusion matrix affected?
• Precision & recall: How are precision and recall affected?
• Dirty data: How do dirty data impact the model?

For the final model, note that the "pneumonia" images actually have two different classes: "bacterial" pneumonia and "viral" pneumonia. These labels are indicated in the image filenames. For this model, add 100 "normal" images, 100 "bacterial pneumonia" images, and 100 "viral pneumonia" images (for a total of 3 classes). We are asked to evaluate:

• Confusion matrix: What does the 3-class confusion matrix look like?
• Precision & recall: What are the model's precision and recall? How are these measures calculated?
• More data: Can we continue to add data to each class (while keeping the data balanced) and get the model to 85% precision and recall? How many data did you end up using?