Case Study #1 - Good Fitness Project
Objective - Preliminary Data Analysis. Explore the dataset and practice extracting basic observations about the data, using Python libraries.
Tasks
1. Come up with a customer profile (characteristics of a customer) of the different products
2. Perform univariate and multi-variate analyses
3. Generate a set of insights and recommendations to help company in targeting new customers
Context – The data is for customers of the treadmill product(s) of a retail store called Cardio Good Fitness. The file “CardioGoodFitness.csv” contains the following variables
1. Product – the model no. of the treadmill
2. Age – in no of years, of the customer
3. Gender – of the customer
4. Education – in no. of years, of the customer
5. Marital Status – of the customer
6. Usage – Avg. # times the customer wants to use the treadmill every week
7. Fitness – Self rated fitness score of the customer (5 – very fit, 1 – very unfit)
8. Income – of the customer
9. Miles- expected to run
Explore the dataset to identify differences between customers of each product. You can also explore relationships between the different attributes of customers. You can approach it from any other line of questioning that you feel could be relevant for the business.
Minimum Steps for exploration:
1. Importing the dataset into Python & understanding the structure of the dataset
2. Basic summary of data and graphical exploration
3. Observations from the dataset
Best Practices for Notebook :
• The notebook should be well-documented, with markdown cells containing comments on the observations and insights.
• The notebook should be run from start to finish in a sequential manner before submission.
• The notebook should be submitted as an HTML file (.html) and NOT as a notebook file (.ipynb)
Best Practices for Presentation :
• The presentation should be made keeping in mind that the audience will be a business leader like CMO, COO, CFO, or CEO.
• The key points in the presentation should be the following
o business overview of the problem and solution approach
o key findings and insights which can drive business decisions
o business recommendations
• Copying and pasting from the notebook is not a good idea, and it is better to avoid showing codes unless they are the focal point of your presentation.
• The presentation should be submitted as a PDF file (.pdf) and NOT as a .pptx file.
Submission Guidelines :
1. There are two deliverables for this assignment
1. A well commented Jupyter notebook [format - .html]
2. A presentation as you would present to the top management/business leaders [format - .pdf]
GRADING:
1. Quality of analyses and clarity of visualizations: 10 points
2. Well organized and commented Jupyter notebook 5 points
3. Quality of the presentation 5 points
20 Points
Objective - Preliminary Data Analysis. Explore the dataset and practice extracting basic observations about the data, using Python libraries.
Tasks
1. Come up with a customer profile (characteristics of a customer) of the different products
2. Perform univariate and multi-variate analyses
3. Generate a set of insights and recommendations to help company in targeting new customers
Context – The data is for customers of the treadmill product(s) of a retail store called Cardio Good Fitness. The file “CardioGoodFitness.csv” contains the following variables
1. Product – the model no. of the treadmill
2. Age – in no of years, of the customer
3. Gender – of the customer
4. Education – in no. of years, of the customer
5. Marital Status – of the customer
6. Usage – Avg. # times the customer wants to use the treadmill every week
7. Fitness – Self rated fitness score of the customer (5 – very fit, 1 – very unfit)
8. Income – of the customer
9. Miles- expected to run
Explore the dataset to identify differences between customers of each product. You can also explore relationships between the different attributes of customers. You can approach it from any other line of questioning that you feel could be relevant for the business.
Minimum Steps for exploration:
1. Importing the dataset into Python & understanding the structure of the dataset
2. Basic summary of data and graphical exploration
3. Observations from the dataset
Best Practices for Notebook :
• The notebook should be well-documented, with markdown cells containing comments on the observations and insights.
• The notebook should be run from start to finish in a sequential manner before submission.
• The notebook should be submitted as an HTML file (.html) and NOT as a notebook file (.ipynb)
Best Practices for Presentation :
• The presentation should be made keeping in mind that the audience will be a business leader like CMO, COO, CFO, or CEO.
• The key points in the presentation should be the following
o business overview of the problem and solution approach
o key findings and insights which can drive business decisions
o business recommendations
• Copying and pasting from the notebook is not a good idea, and it is better to avoid showing codes unless they are the focal point of your presentation.
• The presentation should be submitted as a PDF file (.pdf) and NOT as a .pptx file.
Submission Guidelines :
1. There are two deliverables for this assignment
1. A well commented Jupyter notebook [format - .html]
2. A presentation as you would present to the top management/business leaders [format - .pdf]
GRADING:
1. Quality of analyses and clarity of visualizations: 10 points
2. Well organized and commented Jupyter notebook 5 points
3. Quality of the presentation 5 points