Webinar Registration

Overview: Are you curious to learn Python and how it can be utilized to improve data analysis and modeling? This course will build a foundational understanding of how to best use Python & Jupyter notebook for data analysis and model fitting. You’ll learn from top lecturers in the industry and at the end of the course, you will be able to fit GLM and GBM models. Registered individuals will have on-demand access to the lesson library and code. This course may count toward CE credit.

Why you should attend:

You will build personal auto pricing models
You’ll create visualizations of data including maps
Hear from industry experts
Learn using actual depersonalized data

Who should attend: This course is open to all. However, it is tailored to insurance modeling actuaries and will use samples of actual depersonalized insurance data.

Cost: FREE

Duration: We anticipate each lesson to occur monthly and be around 1 hour in length starting at 2:00 PM EST. Please see the syllabus for specific dates.

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Lesson 1: Python vs other languages - a gentle introduction

You can view the recorded lesson here.

Topics Covered:

List comprehension including enumeration
Create dictionary and convert to dataframe
Adjust dtype in the dataframe + basic function
Basic dataframe statistics using .agg

Lesson 2: Working with basic data and visualization

You can view the recorded lesson here.

Topics Covered:

Import data from .csv + cover other options (sql, pickle, feather)
Rename columns + downcast to minimum size date type
Display data using matplotlib + function
Merge other data sources to dataset

Questions?

Please feel free to send any questions by email to [email protected].

Lesson 3: Exploratory data analysis and more visualization

You can view the recorded lesson here.

Topics Covered:

Build EDA tool using ipywidgets
Perform data cleanup & feature engineering using iterative function application and EDA process

Questions?

Please feel free to send any questions by email to [email protected].

Lesson 4: Creating maps with Python

You can view the recorded lesson here.

Topics Covered:

Visualize geographic data using plotly and folium
Build function & widget for EDA with geographic focus

Questions?

Please feel free to send any questions by email to [email protected].

Lesson 5: Advanced maps with Python

You can view the recorded lesson here.

Topics Covered:

Edit geojson dictionary for better folium map experience

Questions?

Please feel free to send any questions by email to [email protected].

Lesson 6: Generalized linear models

You can view the recorded lesson here.

Topics Covered:

Fit GLM with statsmodels
Review holdout lift chart
Build and analyze residual plots
Review map of model results
Double lift chart

Questions?

Please feel free to send any questions by email to [email protected].

Lesson 7: XGBoost gradient boosting machine models

You can view the recorded lesson here.

Topics Covered:

Fit GBM with xgboost
Run SHAP values and review model impacts
Review holdout lift chart, residual plots, and map of model results

Questions?

Please feel free to send any questions by email to [email protected].

Lesson 8: Using Ray and Modin for faster processing

You can view the recorded lesson here.

Topics Covered:

Modin and Ray packages speed up processing with parallelization
Import large csv files quickly
Parallelize data frame calculations
Run multiple model grid search processes at once

Questions?

Please feel free to send any questions by email to [email protected].

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