home..

Data Structures Writeup

April 2024 (371 Words, 3 Minutes)

Collections

Python Model Code and SQLite Database
The database table has name, uid, their encrypted password, items they have, DOB, favorite food, role, points, friends, and current friend requests
There are some default users which are created in the users model. They get default items, a set uid & username, and data in other columns

Use of lists and dictionaries in the api code
Get request where python list of all users are being pulled and returned as json
Two distinct examples of dictionaries
Use of .uid and .role keys to access the user Flay’s dictionary

API code for all CRUD operations.
Series of if statements, loops, and returns to make sure that the POST request sent for login is valid. It returns specific errors based on how the request sent did not satisfy the login requirements
Postman URL and body for the POST request
Postman URL and body for the GET request
POSTman URL and body for the PUT request
Sqlite DB before PUT request
After the PUT request
Above screenshots have the 200 success already
400 error after missing password for authentication POST request
400 error after trying to edit a user who does not exist

Body of the POST request being done during authentication
JSON object being returned from the GET request in the leaderboard
Body of the PUT request being sent
After the GET request, the JSON data being returned is sorted into tables with a for loop
Redirect to main page once if there are no errors and the login is properly authenticate
Below are all the different redirects based on the error received. For example, a 404 page redirects to its own page, 401 has its own, etc

No cleaning is need because the dataset only has 2 columns; one for CPI value and the other for the date. However, the cpi rates are being converted to interest rates for later use
Since we did not implement a decision tree for predicting the inflation rate for the next month, the program uses linear regression–not logistic regression. The date is plotted on the x axis and the inflation rate is plotted on the y axis. Then, a LSRL is used to try and predict future rates
While our data science project did not use a decision tree, the titanic project did. A decison tree splits the data into categories and then at each decision it must make it predicts a value based on the data. After this process is finished it comes up with a conclusion based on the decisions the program made.