From VSCode using SQLite3 Editor, show your unique collection/table in database, display rows and columns in the table of the SQLite database.
From VSCode model, show your unique code that was created to initialize table and create test data.
Lists and Dictionaries
Blog Python API code and use of List 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.
The argument "hashmap" when creating a user is an example of a dictionary because it has the keys "job" and "Company" and also has its values such as "football player" and "Seattle Seahawks".
The return command in the function "read" outputs a dictionary with keys: id, userID, note, image and base64 and its corresponding values.
APIs and JSON
Blog Python API code and use of Postman to request and respond with 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 the update function, we check that the number of characters in each name, uid, and password are greater than one character in order to update the database.
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
Blog JavaScript API fetch code and formatting code to display JSON.
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.
"fetch(url, authOptions)" gets the url from the backend server (http://127.0.0.1:8088) followed by the suffix "/api/users/authenticate". This is first done to authenticate the user, before they gain access to the array of JSON objects that are formatted into the browser's screen. When the login is successful, the page is redirected to the Database of users that was formatted in the browser. Next, the url is passed as "http://127.0.0.1:8088/api/users/" since we want to obtain the actual JSON data of the users in the database. Another fetch is done with the url and when the data is fetched, HTML formats all of it into a table, to make it resemble the SQLite database.
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.
If the user's username and password match correctly, the user will be redirected to the formatted HTML database table.
In JavaScript code, show and describe code that handles failure. Describe how the code shows failure to the user in the Chrome Browser screen.
If the user's username or password do not match correctly, or if an error is caught, then the user will not be able to access the database table, which redirects them to the 401 page, saying that they are "Unauthorized". The console logs that there was an error.
Optional/Extra, Algorithm Analysis
In the ML projects, there is a great deal of algorithm analysis. Think about preparing data and predictions.
Show algorithms and preparation of data for analysis. This includes cleaning, encoding, and one-hot encoding.
Show algorithms and preparation for predictions.
Discuss concepts and understanding of Linear Regression algorithms.
Linear regression involves the relationship between two variables (x and y). Given a large dataset (pandas dataframe) of x and y values, the linear regression algorithm will try to find a correlation in the scatterplot and plot the line of best fit. Usually, 80% of the data is the training data, and the rest 20% is the test data. This is done so that the maximum possible accuracy is achieved. After the model is trained, it is tested with the test data. Given test x values, we get our y prediction values based on the line of best fit that was calculated during training. Linear regression is an example of supervised learning because the machine trains off data that is labeled.
Discuss concepts and understanding of Decision Tree analysis algorithms.
Decision Trees are used mainly for classification and regression (usually logistic regression) purposes. We start at the root node (or the first node), which contains the entire dataset. Then, the algorithm splits the data based on certain criteria and new nodes are created accordingly. Then, the data is partitioned into more subsets based on the possible values of that feature. This is done repeatedly until we reach a stopping condition, and the leaf nodes (last nodes) are formed. The leaf nodes show our output as a result of the decision tree.
The picture above is an example of a decision tree being used for logistic regression, on deciding whether a person should go outside or not, based on the weather conditions.
We start with the root node "Outlook" which means that we are looking at the weather and starting the decision tree. Next, we are provided with three categories: "Sunny", "Overcast" and "Rain".
If the weather is sunny, a new node "Humidity" is created.
If the Humidity is high, then we shouldn't go outside. If the Humidity is low, then we can go outside. The decision tree stops as either "Yes" or "No" and they become the leaf nodes or our output.
If the weather is overcast, then the leaf node "Yes" is immediately created and we can go outside.
If the weather is rainy, then a new node "Wind" is created.
A strong wind means that we shouldn't go outside. Similarly, a weak wind means that we can go outside. Again, the decision tree ends at the leaf nodes "Yes" or "No"
Collections
Blog Python Model code and SQLite Database.
From VSCode using SQLite3 Editor, show your unique collection/table in database, display rows and columns in the table of the SQLite database.
From VSCode model, show your unique code that was created to initialize table and create test data.
Lists and Dictionaries
Blog Python API code and use of List and Dictionaries.
The argument "hashmap" when creating a user is an example of a dictionary because it has the keys "job" and "Company" and also has its values such as "football player" and "Seattle Seahawks".
The return command in the function "read" outputs a dictionary with keys: id, userID, note, image and base64 and its corresponding values.
APIs and JSON
Blog Python API code and use of Postman to request and respond with JSON.
In the update function, we check that the number of characters in each name, uid, and password are greater than one character in order to update the database.
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
Blog JavaScript API fetch code and formatting code to display JSON.
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.
"fetch(url, authOptions)" gets the url from the backend server (http://127.0.0.1:8088) followed by the suffix "/api/users/authenticate". This is first done to authenticate the user, before they gain access to the array of JSON objects that are formatted into the browser's screen. When the login is successful, the page is redirected to the Database of users that was formatted in the browser. Next, the url is passed as "http://127.0.0.1:8088/api/users/" since we want to obtain the actual JSON data of the users in the database. Another fetch is done with the url and when the data is fetched, HTML formats all of it into a table, to make it resemble the SQLite database.
If the user's username and password match correctly, the user will be redirected to the formatted HTML database table.
If the user's username or password do not match correctly, or if an error is caught, then the user will not be able to access the database table, which redirects them to the 401 page, saying that they are "Unauthorized". The console logs that there was an error.
Optional/Extra, Algorithm Analysis
In the ML projects, there is a great deal of algorithm analysis. Think about preparing data and predictions.
Show algorithms and preparation of data for analysis. This includes cleaning, encoding, and one-hot encoding.
Show algorithms and preparation for predictions.
Discuss concepts and understanding of Linear Regression algorithms.
Linear regression involves the relationship between two variables (x and y). Given a large dataset (pandas dataframe) of x and y values, the linear regression algorithm will try to find a correlation in the scatterplot and plot the line of best fit. Usually, 80% of the data is the training data, and the rest 20% is the test data. This is done so that the maximum possible accuracy is achieved. After the model is trained, it is tested with the test data. Given test x values, we get our y prediction values based on the line of best fit that was calculated during training. Linear regression is an example of supervised learning because the machine trains off data that is labeled.
Decision Trees are used mainly for classification and regression (usually logistic regression) purposes. We start at the root node (or the first node), which contains the entire dataset. Then, the algorithm splits the data based on certain criteria and new nodes are created accordingly. Then, the data is partitioned into more subsets based on the possible values of that feature. This is done repeatedly until we reach a stopping condition, and the leaf nodes (last nodes) are formed. The leaf nodes show our output as a result of the decision tree.
The picture above is an example of a decision tree being used for logistic regression, on deciding whether a person should go outside or not, based on the weather conditions.