From VSCode using SQLite3 Editor, show your unique collection/table in database, display rows and columns in the table of the SQLite database.
Collections
From VSCode model, show your unique code that was created to initialize table and create test data.
Lists and Dictionaries
In VSCode using Debugger, show a list as extracted from database as Python objects.
In VSCode use Debugger and list, show two distinct example examples of dictionaries, show Keys/Values using debugger.
APIs and JSON
In VSCode, show Python API code definition for request and response using GET, POST, UPDATE methods. Discuss algorithmic condition used to direct request to appropriate Python method based on request method.
In VSCode, show algorithmic conditions used to validate data on a POST condition.
In Postman, show URL request and Body requirements for GET, POST, and UPDATE methods.
In Postman, show the JSON response data for 200 success conditions on GET, POST, and UPDATE methods.
In Postman, show the JSON response for error for 400 when missing body on a POST request.
In Postman, show the JSON response for error for 404 when providing an unknown user ID to a UPDATE request.
Frontend
In Chrome inspect, show response of JSON objects from fetch of GET, POST, and UPDATE methods.
In the Chrome browser, show a demo (GET) of obtaining an Array of JSON objects that are formatted into the browsers screen.
In JavaScript code, describe fetch and method that obtained the Array of JSON objects.
In JavaScript code, show code that performs iteration and formatting of data into HTML.
In the Chrome browser, show a demo (POST or UPDATE) gathering and sending input and receiving a response that show update. Repeat this demo showing both success and failure.
In JavaScript code, show and describe code that handles success. Describe how code shows success to the user in the Chrome Browser screen.
In JavaScript code, show and describe code that handles failure. Describe how the code shows failure to the user in the Chrome Browser screen. (same code)
Decision Tree Writeup:
Linear regression is like looking at how two things, let’s call them x and y, are connected. Imagine we have a big set of data in a table, organized with x and y values. The goal of linear regression is to find a line that fits this data best, showing any relationship between x and y. We usually take 80% of this data to train the line, and the remaining 20% to test how well it works. This is to make sure our line is as accurate as possible. After training, we use the test data to predict y values based on our line. This whole process is called supervised learning because the machine learns from labeled data, like how students learn with the help of teachers.
Decision Trees are like flowcharts used mainly for sorting things into categories or making predictions. It’s often used for tasks like figuring out if something is a “Yes” or “No” (classification) or predicting a number (regression). We start at the beginning of the tree, called the root node, which has all the data. Then, we split the data based on certain rules, creating new nodes as we go. This splitting continues until we reach the end of the tree, where we have the final decisions, called leaf nodes.
Let’s say we’re using a Decision Tree to decide if we should go outside based on the weather. We start with the “Outlook” at the root node, which means we’re looking at the weather first. If it’s “Sunny,” we might look at “Humidity.” If the humidity is high, the decision is not to go outside, but if it’s low, it’s a yes. If it’s “Overcast,” we don’t even need to think about it - it’s a “Yes.” For “Rain,” we might consider “Wind.” If the wind is strong, it’s a “No,” but if it’s weak, it’s a “Yes.” So, the Decision Tree gives us these clear paths to decide whether we should go outside or not, based on different weather conditions.