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
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.
This JavaScript code utilizes the fetch() function to send a request to a specified URL (https://api.example.com/data). Upon receiving a response, it checks if the response was successful using the ok property. If the response is successful, it parses the response body as JSON using the .json() method. The parsed JSON data, which is an array of objects, is then accessible in the second .then() block. Finally, you can manipulate or utilize this array of JSON objects as needed within your application.
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.
When the JavaScript code executes successfully in the Chrome Browser, it triggers an alert dialog box to appear on the screen. This dialog box displays a message indicating the probability of getting a score above 9.0, rounded to two decimal places. This immediate pop-up notification provides clear feedback to the user about the successful completion of the operation.
In JavaScript code, show and describe code that handles failure. Describe how the code shows failure to the user in the Chrome Browser screen.
When an error occurs during the execution of the JavaScript code in the Chrome Browser, the .catch() method is triggered. This method logs the error message to the console using console.error().
Optional/Extra, Algorithm Analysis
Show algorithms and preparation of data for analysis. This includes cleaning, encoding, and one-hot encoding.
Cleaning:
Encoding:
Show algorithms and preparation for predictions.
Algorithms
Linear regression is chosen as the modeling algorithm for predicting the relationship between features and target due to its simplicity and effectiveness for regression tasks.
Preparation for Predictions
Data preprocessing, such as one-hot encoding, is applied to ensure the input data is in a suitable format for the model.
A pipeline is created to streamline the preprocessing and modeling steps, ensuring seamless integration.
The pipeline is trained on the training data, enabling both preprocessing and model learning to occur simultaneously.
Predictions are made on new data using the trained pipeline, which automatically applies the necessary preprocessing steps before making predictions.
Discuss concepts and understanding of Linear Regression algorithms.
In machine learning, linear regression serves as a foundational algorithm for modeling the relationship between independent variables and a continuous target variable. By fitting a linear equation to the observed data, it enables prediction of outcomes based on input features, making it applicable in diverse domains such as finance, healthcare, and marketing. Linear regression's simplicity, interpretability of coefficients, and ability to capture linear relationships make it a valuable tool for both predictive modeling and understanding the underlying patterns in data.
Discuss concepts and understanding of Decision Tree analysis algorithms.
Decision Tree analysis involves recursively partitioning the feature space to construct a tree-like structure where decisions are made at each node based on feature values, leading to interpretable rules for prediction. Its simplicity, interpretability, and ability to handle both numerical and categorical data make it widely applicable across domains such as healthcare, finance, and marketing. Extensions like Random Forests and Gradient Boosting enhance predictive performance by aggregating multiple trees while mitigating overfitting, further expanding its utility in machine learning tasks.
Collections
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
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. This JavaScript code utilizes the
fetch()function to send a request to a specified URL (https://api.example.com/data). Upon receiving a response, it checks if the response was successful using theokproperty. If the response is successful, it parses the response body as JSON using the.json()method. The parsed JSON data, which is an array of objects, is then accessible in the second.then()block. Finally, you can manipulate or utilize this array of JSON objects as needed within your application.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.
When the JavaScript code executes successfully in the Chrome Browser, it triggers an alert dialog box to appear on the screen. This dialog box displays a message indicating the probability of getting a score above 9.0, rounded to two decimal places. This immediate pop-up notification provides clear feedback to the user about the successful completion of the operation.
In JavaScript code, show and describe code that handles failure. Describe how the code shows failure to the user in the Chrome Browser screen.
When an error occurs during the execution of the JavaScript code in the Chrome Browser, the .catch() method is triggered. This method logs the error message to the console using console.error().
Optional/Extra, Algorithm Analysis
Show algorithms and preparation of data for analysis. This includes cleaning, encoding, and one-hot encoding.
Cleaning:
Encoding:
Show algorithms and preparation for predictions.
Algorithms
Linear regression is chosen as the modeling algorithm for predicting the relationship between features and target due to its simplicity and effectiveness for regression tasks.
Preparation for Predictions
Data preprocessing, such as one-hot encoding, is applied to ensure the input data is in a suitable format for the model.
A pipeline is created to streamline the preprocessing and modeling steps, ensuring seamless integration.
The pipeline is trained on the training data, enabling both preprocessing and model learning to occur simultaneously.
Predictions are made on new data using the trained pipeline, which automatically applies the necessary preprocessing steps before making predictions.
Discuss concepts and understanding of Linear Regression algorithms. In machine learning, linear regression serves as a foundational algorithm for modeling the relationship between independent variables and a continuous target variable. By fitting a linear equation to the observed data, it enables prediction of outcomes based on input features, making it applicable in diverse domains such as finance, healthcare, and marketing. Linear regression's simplicity, interpretability of coefficients, and ability to capture linear relationships make it a valuable tool for both predictive modeling and understanding the underlying patterns in data.
Discuss concepts and understanding of Decision Tree analysis algorithms. Decision Tree analysis involves recursively partitioning the feature space to construct a tree-like structure where decisions are made at each node based on feature values, leading to interpretable rules for prediction. Its simplicity, interpretability, and ability to handle both numerical and categorical data make it widely applicable across domains such as healthcare, finance, and marketing. Extensions like Random Forests and Gradient Boosting enhance predictive performance by aggregating multiple trees while mitigating overfitting, further expanding its utility in machine learning tasks.