Show The Variation Of Goals, Sum, And Average With Respect

Show The Variation Of Goals Sum And Average With Resp

1. Show the variation of goals (sum and average) with respect to funding status as shown in the lecture video.

2. Show the variation of number of donors (sum and average) with respect to funding status as shown in lecture video. Also include school state as filter.

3. Create a dashboard including above charts. Submit one document with screenshots of each of the above visualizations. Also, submit your Tableau file.

4. Create a pandas data-frame with columns: (1) Project_ID; (2) School_city; (3) Goal; (4) num_donors.

5. Show mean values of Goal and num_donors for each school_city using groupby() function.

6. Present descriptive statistics for the data-frame using describe() function.

7. Create another data-frame from Crowdfunding_data_1000_projects.xlsx with columns: (1) Project_ID ; (2) school_state, and merge with data-frame from step (1).

8. Using the merged data-frame from step (7), select rows where school_state is MO and num_donors>1. Upload one Jupyter Notebook file in submission.

Paper For Above instruction

The analysis of crowdfunding projects often involves examining various factors that influence their success and understanding the distributions of key variables such as goals and donor participation. This paper discusses the visualization of goals and donor data with respect to funding status and school location, as well as the creation of a comprehensive dashboard to combine these insights. Additionally, it covers the construction of pandas data-frames to perform descriptive statistics and data merging operations using datasets related to crowdfunding projects.

Firstly, visualizations illustrating the variation in project goals—both sum and average— based on funding status are crucial for understanding the impact of funding progress. Projects can be classified into different funding statuses such as 'funded' or 'not funded,' and analyzing how goals vary across these categories reveals funding patterns. For example, projects that achieve funding may have higher goal amounts, or the average goals of funded projects may differ significantly from unmet ones. Visualization techniques like bar charts or box plots are typically used to depict these variations, providing intuitive insights. Similar analyses apply to the number of donors, where summing and averaging donor counts across funding statuses help identify donor engagement trends relative to project funding success.

Including school state as a filter in these visualizations enables geographic segmentation, allowing stakeholders to compare funding behaviors and goals across different regions. This regional analysis can identify areas with higher donor activity or larger funding goals, assisting organizations in targeted fundraising strategies. Combining these visualizations into an interactive dashboard enhances decision-making by providing a consolidated view, enabling users to explore correlations between funding status, goals, donors, and location interactively.

Secondly, a practical component involves data manipulation using pandas. Creating a data-frame with project-specific information—such as Project_ID, School_city, Goal, and num_donors—serves as the foundational dataset for analysis. Calculating mean values of Goals and num_donors for each school city using the groupby() function offers insights into regional project characteristics and donor participation levels. Descriptive statistics, obtained through the describe() function, further summarize the dataset, indicating data distribution, variability, skewness, and potential outliers.

In addition to the main dataset, it is essential to merge additional data from an external Excel file containing Project_ID and school_state. Merging this dataset with the primary data-frame enriches the analysis by enabling regional segmentation based on school state. The final step involves filtering the merged data to identify projects from Missouri (MO) with more than one donor, highlighting active or successful projects in that specific region. Successfully performing this filtering demonstrates proficiency in data manipulation, merging, and conditional selection in pandas.

Overall, these analytical tasks, visualization, and data processing techniques collectively contribute to a comprehensive understanding of project goals, donor behavior, and regional fundraising patterns in the context of crowdfunding campaigns. Presenting findings through visualizations and a well-documented Jupyter Notebook ensures clarity and reproducibility of the analysis, supporting impact-driven decision-making for stakeholders involved in crowdfunding initiatives.

References

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