The archive contains 500 csv refines. Each axioms refine includes a set of car security policies delay their appertaining attributes. Here, a portfolio represents a work of security policies. The appearance is to forecast the unless logarithm of damage affinity of a portfolio. 

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

(1) [40pts] Axioms Generation - produce a musicbook (generate_dataset.ipynb) that generates a new consideration delay 500 rows and each row contains a set of epitome attributes that are extracted from the appertaining portfolio refine.

· Produce a character called read_portfolio that takes a sum as an reasoning (portfolio sum in this subject)  

· Load the axioms selfsame to the portfolio sum into a Pandas axioms bring-about

· [20pts] Calculate the forthcoming epitome attributes of a portfolio:

· ID - portfolio sum

· VATD_NA - not-absolute abundance of 'Vehicle_Anti_Theft_Device' = 'Not Applicable' (music this is fitting the sum of policies that fill this term disconnected by the aggregate sum of policies in the portfolio)

· VATD_Passive - not-absolute abundance of 'Vehicle_Anti_Theft_Device' = 'Passive Disabling-Vehicle Recovery'

· VATD_Alarm - not-absolute abundance of 'Vehicle_Anti_Theft_Device' = 'Alarm Only'

· VATD_Active - not-absolute abundance of 'Vehicle_Anti_Theft_Device' = 'Active Disabling'

· DMA_mean - medium of 'Driver_Minimum_Age'

· DMA_std - rule dissolution of 'Driver_Minimum_Age'

· VAY_mean - medium of 'Vehicle_Age_In_Years'

· VAY_std - rule dissolution of 'Vehicle_Age_In_Years'

· AP_mean - medium of 'Annual_Premium'

· AP_std - rule dissolution of 'Annual_Premium'

· ln_LR - unless logarithm of damage affinity, which is obtained using the forthcoming formula (ln_LR=log[ sum('Loss_Amount') / sum('Annual_Premium') ]

· The character should give-back a dictionaries delay this values. This is how it should appear relish 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}

· Produce an void axioms bring-encircling to convene the epitome 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 character to the new axioms bring-about.  (You jurisdiction entertain to overcontemplate the apostacy close)

· After the axioms bring-encircling has been employed and contains 500 rows, set the apostacy to 'ID'. Close is a snapshot of how your top 5 rows of your new axioms bring-encircling should appear relish.

· [20pts] Export this axioms into a refine called summary_portfolios.csv

(2) [60pts] Modeling - produce a musicwork (modeling.ipynb) that gain be used to set-up two moulds to forecast ln_LR, a rectirectirectilinear retirement delayout regularization, and a Lasso retirement delay the regularization parameter attached by ill-conditioned-validation.

· Load the axioms contained in summary_portfolios.csv into a pandas axioms bring-about. Produce stable you set the apostacy to 'ID'. 

· [5pts] Produce numpy arrays delay  the features and the target. Print the media and stds of the input features.

· [5pts] Scale the input features such that the all entertain cipher medium and individual rule dissolution. [Checkout Axioms Scaling in the Axioms Wrangling module]

· [5pts] Split the axioms into grafting and testing. Do not quibble it! Use the highest 300 rows for grafting and the definite 200 for testing.

· [5pts] Using the grafting axiomsset set-up a LinearRegression. What is the neutralize and the coefficients associated delay the input attributes?

· [5pts] Using the grafting axiomsset set-up 100 L1-regularized rectirectirectilinear moulds selfsame to 100 regularization coefficients evenly spaced between 0.001 and 0.1. Use the 10-fold ill-conditioned validation to furnish the best regularization coefficient. What is the neutralize and the coefficients associated delay the input attributes selfsame to the best regularization coefficient?

· [5pts] Calculate the forthcomings:

· Rectirectilinear Retirement - grafting RMSE

· Rectirectilinear Retirement - testing RMSE

· Lasso (best alpha) - grafting RMSE

· Lasso (best alpha) - medium RMSE obtained by averaging the 10 RMSEs obtained during ill-conditioned-validation

· Provide the 95% belief moderationtime in the aloft medium (you gain insufficiency to estimate the Rule Error)

· Lasso (best alpha) - testing RMSE

· [10pts] Which mould do you further between the Rectirectilinear Retirement and Lasso (best alpha) and why?

· [10pts] What joined notification Lasso gives encircling forecasting the target?

· [10pts] What attention can you produce by comparing the 95% belief moderationtime for the medium RMSE and the testing RMSE for Lasso? 

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