This project was initialised to learn python from scratch. Currently I am visting the Techlabs and Edyoucated curse. With this Project I try to apply my new skills in Python.

The Dateset and Taskes are inspired by Kaggle: https://www.kaggle.com/jackdaoud/marketing-data/tasks?taskId=2986

Task Details

You're a marketing analyst and you've been told by the Chief Marketing Officer that recent marketing campaigns have not been as effective as they were expected to be. You need to analyze the data set to understand this problem and propose data-driven solutions.

Expected Submission

Submit a well documented notebook with these four sections:

Section 01: Exploratory Data Analysis

Are there any null values or outliers? How will you wrangle/handle them? Are there any variables that warrant transformations? Are there any useful variables that you can engineer with the given data? Do you notice any patterns or anomalies in the data? Can you plot them? Section 02: Statistical Analysis

Please run statistical tests in the form of regressions to answer these questions & propose data-driven action recommendations to your CMO. Make sure to interpret your results with non-statistical jargon so your CMO can understand your findings.

What factors are significantly related to the number of store purchases? Does US fare significantly better than the Rest of the World in terms of total purchases? Your supervisor insists that people who buy gold are more conservative. Therefore, people who spent an above average amount on gold in the last 2 years would have more in store purchases. Justify or refute this statement using an appropriate statistical test Fish has Omega 3 fatty acids which are good for the brain. Accordingly, do "Married PhD candidates" have a significant relation with amount spent on fish? What other factors are significantly related to amount spent on fish? (Hint: use your knowledge of interaction variables/effects) Is there a significant relationship between geographical regional and success of a campaign? Section 03: Data Visualization

Please plot and visualize the answers to the below questions.

Which marketing campaign is most successful? What does the average customer look like for this company? Which products are performing best? Which channels are underperforming? Section 04: CMO Recommendations

Bring together everything from Sections 01 to 03 and provide data-driven recommendations/suggestions to your CMO.

Evaluation

This is not a formal competition, so results won't be measured using a strict metric. Rather, what one would like to see is a well-defined process of exploratory and statistical analysis with insightful conclusions.

Data Exploration - Was the data wrangled properly? How well was the data analyzed? Are there any useful visualizations? Does the reader learn any new techniques through this submission? A great entry will be informative and thought provoking. Statistical Analysis - Were the right statistical tests used? How well was the statistical output interpreted? A great entry will interpret results without the use of any statistical jargon. Business Recommendation - Were the recommendations tied to your analysis in Sections 1-3? Are they data-driven and focused on marketing concepts such as targets, channels, or products? Documentation - Are your code, and notebook well documented so a reader can understand what you did? Are your sources clearly cited? A high quality analysis should be concise and clear at each step so the rationale is easy to follow and the process is reproducible.