Please define the problem statement and perform the descriptive analysis and later perform the predictive analysis based on the defined problem stat

Requirement:

Please define the problem statement and perform the descriptive analysis and later perform the predictive analysis based on the defined problem statement.

Predictive Analysis:

Hypothesis 1: Impact of Donation Asks on Engagement and Giving

Hypothesis 2: Different campaign types (email, direct mail, online) have varying impacts on donation amounts and engagement levels. Certain campaign types may be more effective in driving higher donations

Hypothesis 3: Predicting Conversion Rate: to predict whether a supporter will make a donation after receiving communication

Hypothesis 4: Influence of Demographic Factors on Engagement and Donation Behavior

Hypothesis 5: Churn Prediction: Predict Donor Churn: Determine which factors indicate a donor is likely to stop donating, so WVA can proactively re-engage them.

Hypothesis 6: Letter Writing as a Predictor of Long-Term Engagement: - Sponsors who write at least one letter to their sponsored child are more likely to remain donors for a longer period and contribute higher amounts over time compared to sponsors who do not write letters.

Provided:

Dataset 6 files: Audience, Contacted, response, portal, supporter demographic, ChildElEtterData Dictionary

Submission:

Separate Python Code file for each hypothesis.

Report 2000 Word:

Executive Summary

High-level summary of the report, focusing on the business context and problem, key hypotheses,

results and recommendations. Write this for an executive audience in plain language, and placing

emphasis on the main findings and recommendations.

Introduction and Approach

{Overview of business context and business strategy. This should contextualise the business

problem, and focus all subsequent analyses}

Assumptions

{State any assumptions explicitly. Assumptions should be reasonable and justifiable}

Data Analysis

Descriptive analysis

{the descriptive analysis describes key patterns/trends/observations within the dataset. Describe

only those aspects that pertain to the overall business problem. The emphasis in this section on the

visualisation and/description, not prediction. Include potentially useful external data/trends that are

pertinent to the business background and problem}

Predictive analysis

{informed by the descriptive analysis, develop a predictive data model comprising of a set of working

hypotheses. These hypotheses focus of the predictive

analysis. As such, each hypothesis should explore a particular aspect of the overall business problem.

Develop hypotheses in such a way that they could inform recommendations. Hypotheses should

therefore stretch current understanding, i.e. if they turn out to be correct after being tested against

the data, then fresh/new insights should emerge for the business.

Suggestion: set the scene for each hypothesis, why this matters for the business, how it relates to

the overall problem. Then state the hypothesis. Discuss the testing of each of the hypotheses.

Describe the specific datasets and methods used, and justify why these methods are appropriate.

Include the detailed workings in the appendices. Repeat this structure for all hypotheses}

Interpretation

Interpret the results of both the descriptive and predictive analysis. What do these findings mean for

the business problem? What are the important observations/patterns? Were there any surprises?

How could these be understood, given the methods followed in the analysis and testing?}

Recommendations

{list of recommendations following from the data analysis (i.e. descriptive and predictive analysis

and testing). Recommendations should emerge from the data analysis and address the overall

business problem. Recommendations should be actionable by the business}