Hide Assignment Information
In the Content for Module 10, you will find a folder with case study descriptions and data files. Select one case study as the basis for your final report.
Evaluate the data file and decide how best to analyze the data based on the scenario provided. Conduct the analysis by running three data mining strategies that produce defendable and significant results. The strategies you choose will depend on the type of information provided in the chosen data set. Choose from the strategies you have learned throughout the year, which may include:
Descriptive data exploration and aggregation (including pivot tables, histograms, and scatter plots)
Sampling
Hypothesis testing
ANOVA
Regression analysis
Time series analysis
Optimization
Clustering
Classification
Using the results of each of the three analyses, select and produce at least three MS Excel visualizations that show the significance of the results. Make sure that:
At least one visual is from Excel
At least one visual is from Tableau Public
The analyses, the results, and the related visualizations are to be used to produce a persuasive argument in the form of an 8-10 page report that will describe any recommended actions to fully address the problems described in the initial scenario. Use APA formatting.
Hide Assignment Information
In the Content for Module 10, you will find a folder with case study descriptions and data files. Select one case study as the basis for your final report.
Evaluate the data file and decide how best to analyze the data based on the scenario provided. Conduct the analysis by running three data mining strategies that produce defendable and significant results. The strategies you choose will depend on the type of information provided in the chosen data set. Choose from the strategies you have learned throughout the year, which may include:
Descriptive data exploration and aggregation (including pivot tables, histograms, and scatter plots)
Sampling
Hypothesis testing
ANOVA
Regression analysis
Time series analysis
Optimization
Clustering
Classification
Using the results of each of the three analyses, select and produce at least three MS Excel visualizations that show the significance of the results. Make sure that:
At least one visual is from Excel
At least one visual is from Tableau Public
The analyses, the results, and the related visualizations are to be used to produce a persuasive argument in the form of an 8-10 page report that will describe any recommended actions to fully address the problems described in the initial scenario. Use APA formatting.
Case Study 1: Stroke Patient Prediction
Context
According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths.
This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.
Attribute Information
id: unique identifier
gender: “Male”, “Female” or “Other”
age: age of the patient
hypertension: 0 if the patient doesn’t have hypertension, 1 if the patient has hypertension
heart_disease: 0 if the patient doesn’t have any heart diseases, 1 if the patient has a heart disease
ever_married: “No” or “Yes”
work_type: “children”, “Govt_jov”, “Never_worked”, “Private” or “Self-employed”
Residence_type: “Rural” or “Urban”
avg_glucose_level: average glucose level in blood
bmi: body mass index
smoking_status: “formerly smoked”, “never smoked”, “smokes” or “Unknown”*
stroke: 1 if the patient had a stroke or 0 if not
*Note: “Unknown” in smoking_status means that the information is unavailable for this patient
Acknowledgements
(Confidential Source) – Use only for educational purposes
Click here to access healthcare-dataset-stroke-data.xlsCase Study 1: Stroke Patient Prediction
Context
According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths.
This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.
Attribute Information
id: unique identifier
gender: “Male”, “Female” or “Other”
age: age of the patient
hypertension: 0 if the patient doesn’t have hypertension, 1 if the patient has hypertension
heart_disease: 0 if the patient doesn’t have any heart diseases, 1 if the patient has a heart disease
ever_married: “No” or “Yes”
work_type: “children”, “Govt_jov”, “Never_worked”, “Private” or “Self-employed”
Residence_type: “Rural” or “Urban”
avg_glucose_level: average glucose level in blood
bmi: body mass index
smoking_status: “formerly smoked”, “never smoked”, “smokes” or “Unknown”*
stroke: 1 if the patient had a stroke or 0 if not
*Note: “Unknown” in smoking_status means that the information is unavailable for this patient
Acknowledgements
(Confidential Source) – Use only for educational purposes
Click here to access healthcare-dataset-stroke-data.xls
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