Category: Code

Write a program using python asking a user for their name and weight in lbs. In the program convert the lbs. to kg, calculate their RDA for protein. Print out to the user their converted weight, their name and RDA for protein. RDA allowance 0.8 grams. 1 lb. is equal to 0.45359237 kg

Problem Description Develop a simple and intuitive mobile application that enables people to buy and sell items online. Deploy the app to a test environment for initial user testing.
Basic Requirements (1) User Registration and Login: (2) Product Listings: (3) Search and Browse Functionality: (4) User Profile:

https://amazrouei.github.io/almazrouei-github.io/
I want you to create a website similar to this one using visual studio, coding in html java and css. However use any sample pictures and ill upload my own later on. When u open the link, the background color is black and the buttons are blue. But I want the color of the background to be like a dark midnight blue with tiny little stars, and the buttons to be like white, text is white as well. The project tab I want it to be squares instead of circles. That’s all. Thank you.  If you cant access the link, i’ll be sure to screenrecord how it looks like. Thanks again.

Answer these questions in a narrative format.
12 pt font, Times New Roman, double spaced, 1 inch margins
Canvas will not process .pages or txt files. You will need to upload a Word doc or PDF. All students have access to Microsoft 365 at https://o365.fullcoll.edu/Links to an external site.
No need to repeat the questions in your answers
500 words, minimum.
Make an appointment with the FC Writing CenterLinks to an external site. to get help on your draft before submitting it.
1. Consider Arirang. What were Dosan’s and Dr. Rhee’s signature contributions to the movement for Korean liberation? Do you connect their work with other Asian Americans we have learned about in this class? How? What are 5 new things you learned from it?
2. Arirang Part 2 has several elders in it who make pointed references/critiques of media portrayals of the riots. What were they? And, what was one glaring omission in Part 2? (Also, did you recognize Ralph Ahn (1926-2022), son of Dosan, in it? He played Mr. Tran in New Girl.)
3. Consider Sa-I-Gu: from Korean Women’s Perspectives. What were 5 things that stood out to you about the testimonies featured?
4. What is your knowledge of the L.A. uprisings? How did you react to KTown92Links to an external site.? What are 5 things you learned from Grace Lee’s interactive film?
5. If you are part of the Korean American community, has your family talked about surviving the war and/or living through Sa-I-Gu? If you are not part of the Korean American community, ask your elders & family members what their memories are of the 1992 events surrounding Rodney King and the Los Angeles K-Town riots and report on your findings. NB: This made global and national news for over a year. Southern Californians from all backgrounds were impacted by it.

Using the data provided in the Excel file.
Build a model to predict transaction charges best(use Google colab).
Use only variables that are predictive of transaction charges.
Examine the statistical validity of the model, the variables chosen, and the coefficient of determination. Present detailed information on the validity of the model, including visualizations.
Explain what the company should do to maximize transaction values using the model output.
Use only the data provided. 

Below is my proposal of what  I want to do along with the source at the very bottom I have to submit a physical copy and present in class. Add more sources.
Through its investigation of fire occurrences, enforcement of fire safety laws, and promotion of fire prevention measures, the Bureau of Fire and Investigations (BFI) plays a crucial role in maintaining public safety. Through the use of data analytic tools, my research seeks to give a breakdown of the BFI’s data of fire events, its ability to see trends, and its ability to allocate resources for both fire response and prevention as efficiently as possible between the years 2016 and 2020.
Objectives:
1.      Examine past fire incidence data between 2016-2020 to find trends and patterns.
2.      Create predictive algorithms to identify risk variables and probable fire hotspots.
3.      Utilize data insights to optimize the distribution of resources for emergency response and fire prevention.
4.      Improve the BFI’s decision-making procedures by using data-driven suggestions.
Dataset:
Source: The dataset will be sourced from the BFI’s internal records of fire incidents.
Format: CSV (Comma-Separated Values) for easy compatibility with Python libraries.
Attributes: Key attributes will include year of incident, location (borough), cause of fire, precinct, category, and community district.
Tools:
Python Libraries: Pandas for data manipulation, Matplotlib and Seaborn for data visualization, Scikit-learn for predictive modeling, and GeoPandas for geographical analysis.
Jupyter Notebook: We will use Jupyter Notebook for code development, documentation, and presentation of results.
Additional Tools: Google Maps API for geospatial visualization and analysis, and Flask for developing interactive dashboards if required.
References:
NYC Open data
Bureau of Fire Investigations
Conclusion:
By gathering and displaying the necessary information, I aim to improve my knowledge about data analysis techniques as I highlight operations at the Bureau of Fire and Investigations between 2016-2020, and how they ensure public safety.
‘Bureau_of_Fire_Investigations_-_Fire_Causes_20240306.csv’