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.
Category: Code
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’
I have a large amount of text data with patient demographics stored in a very poorly designed format. I
would like it parsed into a more useful XML format of my own devising.
I will also upload the instructions. and I can email the other doucments needed for assingnment.
Run the data provided and come up with a chart to show the graph of Reese’s comments.
Use R to run the chart and analyze
Project Area 8: Reese’s new product launch of Caramel big Cups during Super Bowl
Reese’s brand returned to the Super Bowl 2024 to launch a new product the Caramel big Cup. Reese also collaborated with DoorDash to deliver the new product to fans in selected cities between Feb. 5 and Feb. 11.
It is interesting to explore how consumers shared their experience/comments on Reese’s new product and as a brand and whether the super bowl campaign.
We will use Reddit to collect the data.
INTRODUCTION
AIMS OF THE ASSIGNMENT
1. Demonstrate principles of systems engineering through modelling and
analysis.
a. Extreme Value Analysis (EVA)
b. Monte Carlo Modelling
2. Introduce component tolerance concepts.
3. Practise working with and analysing awkward datasets.
4. Develop your technical report writing.
ASSESSMENT METHOD
A written report describing your calculations, simulations and the
conclusions
AIMS OF THE ASSIGNMENT
1. Demonstrate principles of systems engineering through modelling and
analysis.
a. Extreme Value Analysis (EVA)
b. Monte Carlo Modelling
2. Introduce component tolerance concepts.
3. Practise working with and analysing awkward datasets.
4. Develop your technical report writing.
ASSESSMENT METHOD
A written report describing your calculations, simulations and the
conclusions
i don’t want to have AI plagirism or any plagrisim this will affect my grades
check lab3 and lab4 essay how to make the code + check the link which is in project word file because the dataset is there maybe i will give you the dataset excel file here in this program
The
following mark distribution will be followed, hence, make sure to include these
in your report and code:
1.
Introduction (3 points)
2.
Database Loading, Pre-processing, and Train-test
split (3 points)
3.
Training process Screenshot and Training Results (3
points)
4.
Testing, Confusion Matrix, accuracy, and saved model
(3 points)
5.
Explanations for each step (6 points)
6.
Conclusion (2 points)
1.1 Developing
the Application or Website (10 points)
Using
the model that you saved in Task 1, use Matlab App Designer (or any preferred
interface software) to create a user interface application that satisfies the
following requirements:
a)
Allows the user to upload test features. (2 points)
c)
Implements the model saved in Task 1 to determine the class of the features
uploaded. (2 points)
d)
Provides an option to display the confusion matrix in Task 1 within a separate
Axes. (2 points)
e)
Ensure creativity and use of unique widgets (2 points)
f)
Explanation of the app design and code (2 points)
· ntroduction paragraph – introduce what your dataset
about, the number of classes, and number of features, and what the features are
and why you chose them. (points included in Model development)
·
Model Development and results – include screenshots of the code
in your report to explain them, but ensure to attach the original codes in the
submission (20 points – spread between codes and report)
o
The points to be discussed is detailed in Section
1.1
·
Application Design – include screenshots in your
report to explain them, but ensure to attach the original codes in the
submission (10 points – spread between codes and report)
o
The points to be discussed is detailed in Section
1.2
·
Conclusion –
conclude and summarize the assignment (points included in Model development)
please draw a multi graphed histogram with the data that will be provided. Be detailed of the information that was found and release a copy of the code outline that you input.