代做NBA 6215: DonorsChoose Funding Analysis代写Python语言

NBA 6215: DonorsChoose Funding Analysis

DonorsChoose is an innovative platform. that connects donors with teachers from across the United States to fund school projects. These projects range from simple supplies for a science experiment to funds for field trips, providing resources that schools otherwise might not afford. In this project, you will analyze data from the DonorsChoose platform. to identify patterns in funding, project success, and factors that influence donor contributions .

The objectives of this project are to:

1.      Analyze historical DonorsChoose data to identify patterns and trends in project funding and

determine factors that influence the success rate of projects, such as location, poverty level, type of project, and teacher characteristics .

2.      Find a business use case where the insights gained through your analysis can help improve the DonorsChoose system, making it more effective in matching donors with projects that need funding.

Methodology:

You will begin your analysis by exploring four key tables within the DonorsChoose dataset to identify potential factors that may influence project funding success and donor engagement levels. Utilize exploratory data analysis techniques to uncover patterns and trends in the data, applying the methods we discussed during the lectures .

Based on your findings, you are tasked with developing actionable business recommendations that could significantly enhance the effectiveness of the DonorsChoose platform. Below are a few example questions to guide your inquiry. These are not exhaustive; you are encouraged to explore additional dimensions and aspects revealed by the data or that spark your interest:

.       Tailored Donor Campaigns: How can analysis of historical donation patterns and donor

demographics inform. the design of targeted campaigns that resonate with potential donors based on their previous behaviors and preferences?

.       Improving Project-Donor Matchmaking: Considering the types of projects that historically receive the most support—focusing on factors like resource type, poverty level, and location — how can the platform. more effectively match these projects with potential donors who have shown interest in similar categories in the past?

.       Increasing Visibility for High-Need Projects: What are the defining characteristics of projects that struggle to meet their funding goals? How can this information guide the creation of targeted awareness campaigns or the strategic featuring of these projects on the platform. to enhance donor attention and support?

These questions are intended as examples to ignite your analytical thinking and creativity. You are encouraged to pursue additional patterns, trends, or questions that arise from your analysis of the data .

You are allowed to work in a team of up to 4 students, though working in teams is not required. All team members must contribute proportionally to the project (as required by the Johnson Honor Code) .

Abstract/Proposal Submission (due on Monday, April 21 at 11:59 pm)

Please submit a short (approximately 250 words) abstract describing the business application you plan to study in the project.

Final Report Submission (due on Saturday, May 11 at 11:59 pm)

Part 1: Please submit through Canvas a written report including a brief summary of your analysis, business use case application, results, and visualizations . The main part of the report is expected to be a succinct 3-page description of your work and findings. You can include additional analysis in an appendix .

Part 2: Please submit through Canvas Jupyter Notebook files you worked on for the project. Your code should be well-documented.

Grading

Your project will be graded on the organization and content of your final report and the correctness of your Python codes .

Below Expectations (B- grade or lower)	Meets Expectations (B or B+ grade)	Exceeds Expectations (A- or higher)
Analysis lacks depth, missing    critical data trends and insights . The business question posed is  unclear or too broad, showing a limited understanding of the dataset's potential impact. Business use-case suggestions lack specificity and actionable insights . Jupyter notebook is poorly organized and lacks sufficient documentation . Contains major errors that undermine the analysis and findings.	Analysis clearly summarizes key data trends and insights . Poses a relevant business question that demonstrates a good understanding of the potential impacts derived from the dataset. Proposes a practical business use- case based on data analysis . Jupyter notebook is well-structured with clear documentation that supports the analysis process . Contains minimal errors that do not significantly impact the integrity of the analysis .	Delivers an insightful analysis with a clear understanding of the dataset. Formulates a strong, original business question linked to a significant data-    driven opportunity or challenge. Proposes a compelling business use- case with clear potential for real impact, backed by thorough analysis . Maintains a well-organized, fully documented Jupyter notebook that   guides replication and further study. Work is precise, error-free, and demonstrates high analytical rigor.

Data Description

account.csv: This table contains one row for each user account appearing elsewhere in the database. User accounts can either be for teachers or donors (and these two groups are not mutually exclusive). (2.9M rows)

- accountid: Primary key of the table

- city, state, zip3: Location in the USA (zip3 is just the first three digits of the zip code)

- is_teacher: 1/0 for whether this is a teacher

- teacher_prefix, teacher_teach_for_america, teacher_ny_teaching_fellow: details available only

for teachers (the last two are 1/0-valued)

project.csv: This table  contains  one row  for  each  project  listed  for  funding  on  the  Donors  Choose website. (1.2M rows)

- projectid: Primary key of the table

- teacheracctid: The ID for the teacher listing the project (see accountid in the account table)

- schoolid: The ID for the school associated with this project (see schoolid in the school table)

- primary_focus_subject, primary_focus_area, secondary_focus_subject, secondary_focus_area:

subject labels for the project

- resource_type: the type of resource requested in the project (“Supplies”, “Books”,

“Technology”, “Other”, “Trips”, or “Visitors”)

- poverty_level: the poverty level of the student population (“low poverty”, “moderate poverty”,

“high poverty”, or “highest poverty”)

- vendor_shipping_charges, sales_tax, payment_processing_charges, fulfillment_labor_materials,

total_price_excluding_optional_support, total_price_including_optional_support: costs (USD)

of various components of the proposed project.

- students_reached: number of students who will be reached by the proposed project

- total_donations, num_donors: summary of donations to the project (total_donations is in USD)

- eligible_double_your_impact_match, eligible_almost_home_match: 1/0 eligibility for various

funding programs

- funding_status: The funding status of the project at data cutoff (late 2016) . “completed” means the project was fully funded; “expired” means the funding deadline passed without the project being fully funded, “reallocated” means funding was halted for the project and reallocated to a new project, and “live” means the project was live as of late 2016.

- date_posted: date and time when the project was posted to the Donors Choose site

- date_completed: date and time when the project was fully funded (missing for not fully funded projects)

- date_thank_you_packet_mailed: date and time thank you packet mailed

- date_expiration: posted expiration date for the project (time when funding needs to be

complete or else project will expire)

school.csv: This table contains one row for each school that has been listed on at least one Donors Choose project (73K rows)

- schoolid: primary key for the table

- ncesid: an identifier for the school from the National Center for Educational Statistics (NCES);      。

this id can be used to match to other information about schools that you might find on the web

- latitude, longitude, city, state, zip, district, county: location of the school

- metro: classifier for the school location (“rural”, “suburban”, or “urban”)

- charter, magnet, year_round, nlns, kipp, charter_ready_promise: 1/0 descriptors for the school;

nlns appears to indicate “National Literacy and Numerancy Strategy”, kipp is the “Knowledge is Power  Program”,  and  charter_ready_promise  indicates   schools  in  the  Los  Angeles  area participating in the College-Ready Promise coalition

donation.csv: This table contains a row for every donation made to any project (6.2M rows)

- donationid: Primary key of the table

- projectid: The project that received the donation; refers to the projectid field of the project table

- donoracctid: The account id of the donor; refers to the accountid field of the account table

- donation_timestamp: The date/time of the donation

- donation_to_project , donation_optional_support: The amount of donation (in USD) directly to the project, as well as the amount of additional donation to support the Donors Choose organization (optional support defaults to 15% of the total donation value, but donors can opt out if they choose)

- donation_included_optional_support: 1/0 if the donor provided the optional support

- payment_method: method of payment (e.g. “creditcard”)

- payment_included_acct_credit, payment_included_campaign_gift_card,

payment_included_web_purchased_gift_card, payment_was_promo_matched: 1/0 details of payment

- is_teacher_referred: 1/0 if a teacher referred the payment

- givingpageid: the giving page the facilitated the donation (if any); refers to the givingpageid

field of the givingpage table

- giving_page_type: type of the giving page (“campaign” or “teacher”)

- for_honoree: 1/0 if the donation was given in honor of somebody

- thank_you_packet_mailed: 1/0 if a thank you packet was mailed to the donor