Problem 4: Predictive Analytics - Model building, Training, & Assessment [70 pts] In this predictive analytics problem, we will build a LINEAR python model based on a given dataset and a prediction objective. Once the model is developed, we will train the model on a portion of the data set to develop this predictive ability. Next, we will use this predictive capability to do actual prediction on the remain dataset. As a final ask, we will evaluate how accurate these predictions were by comparing the predicted values and the actual values from the dataset. This will give us the accuracy of the predictive capability of our model. Target Dataset File: health_insurance_cost.csv (Provided as a separate file) Data Description: This file contains various attributes of an individual and his/her insurance cost. Insurance premium depends on many different factors and this data is a simplified and small version of various client profiles along with their premiums. Prediction objective: Since we are building a single linear predictive model, we will use a SINGLE variable to predict the outcome. In this problem you have the option to use either AGE or BMI (Not both) of the individual to predict the insurance premium. We will ignore all other factors. In a more complex model, we will include all variables to predict the outcome. Tasks sequence: A. Load Data File B. Examine the data C. Clean the dataset D. Handle missing data & remove any unnecessary columns E. Build Model F. Split data and train model MIS 4390 Business Analytics FALL 2021 Page 7 of 7 G. Test model H. Report & Analyze model efficiency

health_insurance_cost.csv (Provided as a separate file) (Portion of the excel file data)

age	sex	bmi	children	smoker	region	charges
19	female	27.9	0	yes	southwest	16884.92
18	male	33.77	1	no	southeast	1725.552
28	male	33	3	no	southeast	4449.462
33	male	22.705	0	no	northwest	21984.47
32	male	28.88	0	no	northwest	3866.855
31	female	25.74	0	no	southeast	3756.622
46	female	33.44	1	no	southeast	8240.59
37	female	27.74	3	no	northwest	7281.506
37	male	29.83	2	no	northeast	6406.411
60	female	25.84	0	no	northwest	28923.14
25	male	26.22	0	no	northeast	2721.321
62	female	26.29	0	yes	southeast	27808.73
23	male	34.4	0	no	southwest	1826.843
56	female	39.82	0	no	southeast	11090.72
27	male	42.13	0	yes	southeast	39611.76
19	male	24.6	1	no	southwest	1837.237