Problem 1: Clustering

The dataset given is about the Health and economic conditions in different States of a country. The Group States based on how similar their situation is, so as to provide these groups to the government so that appropriate measures can be taken to escalate their Health and Economic conditions.

1.1. Read the data and do exploratory data analysis. Describe the data briefly. (Check the null values, Data types, shape, EDA, etc, etc)

1.2. Do you think scaling is necessary for clustering in this case? Justify

1.3. Apply hierarchical clustering to scaled data. Identify the number of optimum clusters using Dendrogram and briefly describe them.

1.4. Apply K-Means clustering on scaled data and determine optimum clusters. Apply elbow curve and find the silhouette score.

1.5. Describe cluster profiles for the clusters defined. Recommend different priority based actions that need to be taken for different clusters on the bases of their vulnerability situations according to their Economic and Health Conditions.

Data Dictionaryfor State_wise_Health_income:

1.States:names of States

2.Health_indeces1:A composite index rolls several related measures (indicators) into

a single score that provides a summary of how the health system is performing in the

State.

3.Health_indeces2:A composite index rolls several related measures (indicators) into a

single score that provides a summary of how the health system is performing in

certain areas of the States.

4.Per_capita_income-Per capita income (PCI)measures the average income earned per

person in a given area (city, region, country, etc.) in a specified year. It is calculated by

dividing the area's total income by its total population.

5.GDP:GDP provides an economic snapshot of a country/state, used to estimate the

size of an economy and growth rate.

Dataset for Problem 1: State_wise_Health_income.csv

Problem 2: CART-RF-ANN

Mortality Outcomes for Females Suffering Myocardial Infarction

The mifem data frame has 1295 rows and 10 columns. This is a Dataset of females having

coronary heart disease (CHD). you have to predict with the given information whether the female is dead or alive so as to discover important factors that should be considered crucial in the treatment of the disease. Use CART, RF & ANN, and compare the models' performances in train and test sets.

2.1. Data Ingestion: Read the dataset. Do the descriptive statistics and do null value condition

check, write inference on it.

2.2. Encode the data (having string values) for Modelling. Data Split: Split the data into test

and train, build classification model CART, Random Forest, Artificial Neural Network.

2.3 Performance Metrics: Check the performance of Predictions on Train and Test sets using

Accuracy, Confusion Matrix, Plot ROC curve, and get ROC_AUC score for each model.

2.4 Final Model: Compare all the models and write inference which model is

best/optimized.

2.5 Inference: Basis on these predictions, what are the insights and recommendations?

Dataset for Problem 2: mifem.csv

Data Dictionaryfor mifem.csv :

1. Outcome:mortality outcome: a factor with levels live, dead

2. Age:age at onset

3. Yronset:year of onset (The year of onset is the year on which an individual acquires,

develops, or first experiences a condition or symptoms of a disease or disorder)

4. Premi:previous myocardial infarction event, a factor with levels y, n, nk not known

5. Smstat:smoking status, a factor with levels c current, x ex-smoker, n non-smoker, nk not

known

6. Diabetes:a factor with levels y, n, nk not known

7. Highbp:high blood pressure, a factor with levels y, n, nk not known

8. Hichol:high cholesterol, a factor with levels y, n for yes and no

9. Angina:a factor with levels y, n, nk not known

10. Stroke:a factor with levels y, n, nk not known