Project 1

1. Explain the data. Check It is supervised or Unsupervised. Demonstrate?

Data are measurement or observation that are collected as a source of information, here the give data is about the insurance.

Supervised learning: Algorithms are trained using labeled data.

Unsupervised learning: Algorithms are trained using unlabeled data.

According to the given data since the data is labeled it falls under supervised learning.

2. Estimate the minimum sample size n to get the 99% accurate predictions. (precision = 0.02)

n= p*(1-p)(Z_alpha/2^2) /E^2

confidence level= 1 - alpha

alpha = 1 - confidence leve

= 1 - 0.99

= 0.01

alpha /2 = 0.005

p = 0.5

q = 0.5

= 0.5 x 0.5 (2.58^2)/ 0.02^2

= 0.25 (6.45) / 0.0004

1.6125 / 0.0004

=4,156.25

Attached the Ipynb.file for next set of questions .

Cleaned the given data by removing the null value and also outliers . Replaced the outliers with null values and then removed nun values.

Trained 80% of the data and 20% is for test and predicted the y value.