Python Assignement

Download the following archive file from Canvas:

· portfolios.zip

The archive contains 500 csv files. Each data file includes a set of car insurance policies with their respective attributes. Here, a portfolio represents a book of insurance policies. The goal is to predict the natural logarithm of loss ratio of a portfolio.

This assignment has two parts: (1) Data Generation, and (2) Modeling.

(1) [40pts] Data Generation – create a notebook (generate_dataset.ipynb) that generates a new table with 500 rows and each row contains a set of summary attributes that are extracted from the respective portfolio file.

· Create a function called read_portfolio that takes a number as an argument (portfolio number in this case)

· Load the data corresponding to the portfolio number into a Pandas data frame

· [20pts] Calculate the following summary attributes of a portfolio:

· ID – portfolio number

· VATD_NA – relative frequency of ‘Vehicle_Anti_Theft_Device’ = ‘Not Applicable’ (note this is just the count of policies that satisfy this condition divided by the total number of policies in the portfolio)

· VATD_Passive – relative frequency of ‘Vehicle_Anti_Theft_Device’ = ‘Passive Disabling-Vehicle Recovery’

· VATD_Alarm – relative frequency of ‘Vehicle_Anti_Theft_Device’ = ‘Alarm Only’

· VATD_Active – relative frequency of ‘Vehicle_Anti_Theft_Device’ = ‘Active Disabling’

· DMA_mean – average of ‘Driver_Minimum_Age’

· DMA_std – standard deviation of ‘Driver_Minimum_Age’

· VAY_mean – average of ‘Vehicle_Age_In_Years’

· VAY_std – standard deviation of ‘Vehicle_Age_In_Years’

· AP_mean – average of ‘Annual_Premium’

· AP_std – standard deviation of ‘Annual_Premium’

· ln_LR – natural logarithm of loss ratio, which is obtained using the following formula (ln_LR=log[ sum(‘Loss_Amount’) / sum(‘Annual_Premium’) ]

· The function should return a dictionaries with this values. This is how it should look like for portfolio #1:

{‘ID’: ‘1’,

‘VATD_NA’: 0.583,

‘VATD_Passive’: 0.321,

‘VATD_Alarm’: 0.071,

‘VATD_Active’: 0.024,

‘DMA_mean’: 46.163,

‘DMA_std’: 15.379823470052257,

‘VAY_mean’: 6.925,

‘VAY_std’: 2.6272185438743487,

‘AP_mean’: 335.17471999999964,

‘AP_std’: 191.69561566083144,

‘ln_LR’: -1.0224378731039194}

· Create an empty data frame to collect the summary attributes of all the portfolios.

· Write a for loop that goes from 1 to 500 and calls the read_portfolio function. Append the results of the function to the new data frame.  (You might have to ignore the index here)

· After the data frame has been populated and contains 500 rows, set the index to ‘ID’. Here is a snapshot of how your top 5 rows of your new data frame should look like.

 

· [20pts] Export this data into a file called summary_portfolios.csv

 

(2) [60pts] Modeling – create a notebook (modeling.ipynb) that will be used to build two models to predict ln_LR, a linear regression without regularization, and a Lasso regression with the regularization parameter determined by cross-validation.

· Load the data contained in summary_portfolios.csv into a pandas data frame. Make sure you set the index to ‘ID’.

· [5pts] Create numpy arrays with  the features and the target. Print the means and stds of the input features.

· [5pts] Scale the input features such that the all have zero mean and unit standard deviation. [Checkout Data Scaling in the Data Wrangling module]

· [5pts] Split the data into training and testing. Do not shuffle it! Use the first 300 rows for training and the last 200 for testing.

· [5pts] Using the training dataset build a LinearRegression. What is the intercept and the coefficients associated with the input attributes?

· [5pts] Using the training dataset build 100 L1-regularized linear models corresponding to 100 regularization coefficients evenly spaced between 0.001 and 0.1. Use the 10-fold cross validation to find the best regularization coefficient. What is the intercept and the coefficients associated with the input attributes corresponding to the best regularization coefficient?

· [5pts] Calculate the followings:

· Linear Regression – training RMSE

· Linear Regression – testing RMSE

· Lasso (best alpha) – training RMSE

· Lasso (best alpha) – average RMSE obtained by averaging the 10 RMSEs obtained during cross-validation

· Provide the 95% confidence interval in the above mean (you will need to compute the Standard Error)

· Lasso (best alpha) – testing RMSE

· [10pts] Which model do you prefer between the Linear Regression and Lasso (best alpha) and why?

· [10pts] What additional information Lasso gives about predicting the target?

· [10pts] What observation can you make by comparing the 95% confidence interval for the mean RMSE and the testing RMSE for Lasso?