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Business Problem: We all know that Health care is very important domain in the market. It is directly linked with the life of the individual;

Business Problem:
We all know that Health care is very important domain in the market. It is directly linked with the life of the individual; hence we have to be always be proactive in this particular domain. Money plays a major role in this domain, because sometime treatment becomes super costly and if any individual is not covered under the insurance then it will become a pretty tough financial situation for that individual. The companies in the medical insurance also want to reduce their risk by optimizing the insurance cost, because we all know a healthy body is in the hand of the individual only. If individual eat healthy and do proper exercise the chance of getting ill is drastically reduced.
Goal & Objective: The objective of this exercise is to build a model, using data that provide the optimum insurance cost for an individual. You have to use the health and habit related parameters for the estimated cost of insurance
Target variable: insurance_cost
Using the attached Data set please perform the below using python and provide me with the results and interpretations.
1. Problem Understanding
a) Defining problem statement b) Need of the study/project c) Understanding business/social opportunity 1. Problem Understanding
2. Data Report
a) Understanding how data was collected in terms of time, frequency and methodology b) Visual inspection of data (rows, columns, descriptive details) c) Understanding of attributes (variable info, renaming if required)
3. Exploratory Data Analysis
a) Univariate analysis (distribution and spread for every continuous attribute, distribution of data in categories for categorical ones) b) Bivariate analysis (relationship between different variables , correlations) a) Removal of unwanted variables (if applicable) b) Missing Value treatment (if applicable) d) Outlier treatment (if required) e) Variable transformation (if applicable) f) Addition of new variables (if required)
4. Business insights from EDA
a) Is the data unbalanced? If so, what can be done? Please explain in the context of the business b) Any business insights using clustering (if applicable) c) Any other business insights
5. Model building and interpretation
a) Build various models (You can choose to build models for either or all descriptive, predictive or prescriptive purposes)
b). Test your predictive model against the test set using various appropriate performance metrics
c). Interpretation of the model(s)
6. Model Tuning and business implication
a). Ensemble modelling (if necessary)
b). Any other model tuning measures (if applicable)
c). Interpretation of the most optimum model and its implication on the business

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