Project 1

Attach the python file Project 1

Check the file.

Q1.This is a Supervised learning problem :

The data is medical insurance charges with each row representing a single patient and various features about that patient.The features includes in this dataset are :

1. Age(in Years)
2. Sex(Male or Female)
3. Body Mass Index(BMI)
4. Number of children 
5. SMoking Status(Yes or No )
6. Region(Northeast,Northwest, Southeast,Southwest)
7. Charges(Amount charged for medical insurance)

• In the supervised learning, If the goal is to predict or estimate the insurance charges based on the given features (age, sex, BMI, children, smoker, and region), then it can be considered a supervised learning problem. In this case, the "charges" column would serve as the target variable or the dependent variable, while the other columns would be the independent variables or features.
•  In unsupervised learning, In this case we would not have a specific target variable to predict or estimate.
•  So the above data asumes the goal is supervised learning, but if the objective is different, such as identifying customer segments based on the features, an unsupervised learning approach like clustering algorithms (e.g., k-means, hierarchical clustering) could be used instead.

 Q2

 The minimum sample size n required for 99curate predictions can be calculated using

the formula:

n = (Z^2*p* (1-p)) / e^2

where Z is the Z-score corresponding to the desired confidence level (for 99% confidence, Z 2.576), p is the estimated proportion of the population with a particular characteristic (e.g. income, age, etc.), and e is the desired precision or margin of error (e.g. 0.02).

Since the proportion p of the population is unknown, it can be estimated as 0.5. With a precision of 0.02 and Z = 2.576, we get:

n = (2.576^2* 0.5 * (1-0.5)) / 0.02^2 = 165.67

Therefore, the minimum sample size required for 99curate predictions with a precision of 0.02 is approximately 326 individuals.