I've been with a mid-sized P&C company for three years. Most of my work was data support + building/maintaining SAS raters for indications / on-leveling / backing into factors / competitive analysis. I'm one exam away from ACAS, but I feel I haven't done much traditional reserving or ratemaking. The chief wants to split apart with me and perform predictive modeling (MAS-I and MAS-II material), and I can keep my exam support. I like the material from those exams and find modeling interesting. I worry that not having more traditional ratemaking / reserving will hurt future job opportunities. Any thoughts /r/actuary?

Hi everyone!

In a recent post, I detailed some work I had been doing on predicting whether reviewers would add upcoming games to their collection. I’ve since updated that project so that I can take anyone with a collection on BGG (I’ve gotten solid results for folks with as few as 30 games) on BGG and spit out an analysis like so for your collection..

This post is about a different but related analysis: predicting the geek (and average) rating for upcoming games. I’m still tinkering with the models, but I’d like to share some of the early results with the community to gather feedback and to also facilitate some discussion about additions to the model.

Here are the model's full predictions for games released in 2021, as a preview of what this project is about.

Motivation:

Can we predict which upcoming games will be the most highly rated on BGG?

Let’s wind the clock back to the end of 2018. Could we have predicted which 2019 and 2020 (and so on) games would go on to become the top rated games on the geek list?

Upcoming games enter the BGG database well before they are published, so we already have a lot of information to work with in estimating newly released games - we know categories, designers, publishers, mechanics, artists, etc. Every game starts out at 5.5 on the geek rating, and starts to slowly shift if a game accumulates enough ratings - games that achieve high geek ratings need to have 1) lots of user ratings and 2) a high average rating. We won’t know a game’s geek rating for a while, but we can train a model to estimate it for us. We have plenty of historical data on games and their geek rating, we can use a model to learn the relationship between a game’s features and its geek rating, which we can then use to predict new games.

Examples:

To illustrate how this works, let’s look at a past example. On Mars first showed up in the historical BGG data on January 24, 2019. At that point, we knew its publisher, its mechanics, its designer, its artist - we all probably had a decent guess that it was going to be a popular game just from the latter two alone. And indeed [On Mars has steadily climbed the geek list s

My company is trying to better estimate and time the actual cash collected from our commercial customer on a DAILY basis for the coming quarter. We have about 6 months of data to work with. But it seems there can be some serious variability and its quite hard to predict.

I was thinking of implementing an ARIMA model in python but was curious if there are any other predictive modeling techniques anyone has used for similar problems?

I have tried naive and simple moving average but it has proven inaccurate.

https://preview.redd.it/3e1o45f9dyq71.jpg?width=2000&format=pjpg&auto=webp&s=f587b3a5a8cf40e269197d0a802f01a0b42c6305

TomTom Maps and API services produce massive volumes of data. Data scientists can access this data to gain insight and make predictions supporting business decisions for better performance and customer satisfaction.

We data scientists and developers can find various historical and real-time data to help with our projects, such as traffic stats, real-time traffic, location history, notifications, maps and routing, and road analytics. TomTom’s extensive documentation and developer community support us as we play with this rich, easily accessible data. For example, we can use TomTom’s data for predictive modeling.

In this article, we’ll do a hands-on exploration of how to use TomTom API data for predictive modeling. We’ll use accident data to learn when and where accidents are most likely to take place. You’ll see just how easy it is to use TomTom’s mapping data for your data science projects. We’ll pull traffic data from a map application, which connects to TomTom APIs, then extract a dataset to build a model for predictions. For our supervised learning task, we’ll be using RandomForestRegressor.

After training the model, we’ll evaluate and refine it until it is accurate, then deploy it to work with the TomTom Maps API in real-time. Finally, we’ll use Python and some favorite data science tools (Jupyter Notebook, NumPy, Pandas, and scikit-learn) to explore our data and make predictions.

https://preview.redd.it/d0p0eu0edyq71.jpg?width=468&format=pjpg&auto=webp&s=ded1ff8601917e3d9e74d463eed6f35e703d94b1

CREATING A PREDICTIVE MODEL BASED ON TOMTOM DATA

To create our model, we first pull data from TomTom APIs connected to a map application. Then, we follow the framework in the image below to prepare the dataset, build, train, and evaluate the model. If necessary, we refine our model. Finally, we deploy our model to deliver t

I have offers in both EL pricing and EL predictive modeling work. I would greatly appreciate any advice/input in deciding which to pursue. Some of my (possibly dumb/obvious) questions/concerns:

• Which provides quicker and/or better exposure to multiple lines of business?
• Which provides a better work-life balance with regards to the ability to learn/do the job & study for exams?
  • If applicable, how much automation of tasks can I expect to be able to do in either role?
• Which do you think is easier to learn in a remote environment?
• How should the compensation compare between the two?
• Which offers more interesting work?
  • Which has fewer mundane tasks? Was thinking filing/regulation stuff here versus model maintenance along with data cleansing/prep.
• Which do you think offers more long-term growth potential and/or opportunities? The recent data science shift had me thinking PM, but who knows.
• How much do you think employers value iCAS credentials?
  • Would it make sense to go for one of those credentials sooner rather than later (i.e. post-ACAS/FCAS)?
• possible burnout from coding & the associated frustration
• being knowledgeable enough to start in predictive modeling

Thanks in advance!

As the title says, I am trying to gauge interest for a project that I have had floating around in my head for a while. The reason I am interested is that I want to provide enterprise-level tools for small ops and everyday growers at a minimal cost level.

I want the information to be open-sourced and made available. I, however, would need an enormous amount of data from small-scale growers to generate the training models. I have been looking at using growdaries as a possible place to gather some of this data but need to make sure it is within terms of service.

That's why I am posting this here. If there is enough interest, I can set up a place to drop and centralize data. I know data is going to be the backbone of future growth and I understand how valuable that can be, so asking to make it open-source is a huge leap. But the more accessible the tool, the more people that can utilize it, keeping competition rich and the art of growing away from large corps.

Post below with questions, blasts, or opinions. Hope every is staying safe and healthy

Looking for someone who has some experience and wouldn't mind having a private conversation about what they do. Trying to decide if I'd prefer that flavor of work over traditional actuarial work.

My Perspective:

• I am here to get a degree, that is/was my perspective in giving this advice.
• I started with the rubric and worked forward. It may have slowed me down at points, but how you learn is up to you.
• I had spent the last 6 months trying to master SAS, and then they tell me that I can drop the advanced SAS cert (C748) and take D208. So...
• I did my work in SAS, so if you need advice on Python/R, you will not find it here
• You can do your work in any analytics tool you know (still ask the course mentor before you start).
• D208 leads you DIRECTLY to your capstone work. I would have had a harder time trying to complete the capstone without D208.

D208 Task 1 Multiple Linear Regression - MLR & Task 2 Logistic regression

• Pick any continuous variable in the dataset. Try to find one that is available for all rows and has some length/depth to it (make sure it spreads well).
• I used two sets of code to complete the work. 1) for the clean and extract, 2) for the analysis and visuals. Depending on system, you will need to save the code as txt or csv before turning in. I find that this style allows you to process the staging part once, then you can do your analysis iterations without running the full code each time.
• Annotate you code... always. Lots of notes will only help you and the evaluator. Comment Out code as you build your model (do not delete code - you may need to reference later). Delete the marked out code before submission.
• Think of your research question as the pre-cursor to the hypothesis statements. Example: In an ANOVA your research question is "Are my variable means significantly different across [dimension]"
• SPELL IT OUT! The evaluator will be going line by line through the rubric. Write your paper with the exact Headings from the rubric:
  • C1: Data Goals
    • 1. Data Collection - blah, blah, blah.
    • 2. Data Discovery and Profiling - blah, blah, blah.
• Actual Advice: C2. Summary Statistics
  • I got this wrong on both Task 1 and Task 2 - Include a text section where you identify the response variable and all of the predictor variables and types of data (continuous, binary, categorical). AND INCLUDE a table that lists each of the continuous variables and the categorical variables. Take the rubric as literal as possible.
  • Please provide summary statistics, such as mean, median, and quartiles, for all variables.
• Should you use the same dataset in Task 1 as in Task 2? *

Hi all,

I'm an aspiring sports AI/ML data analyst and am writing a blog to showcase and practice analytical modeling work. My most recent post delves into NBA Betting Models for spread, total points, and FanDuel DFS lineups. Check it out:

data-dunk.blogspot.com

I have been practicing with Kaggle competitions and am now trying to answer a real problem for a friend and am immediately stuck. The data is in two tables:

- One table has 1 row per user per day. It has the advertisement the user watched to install the game , the revenue from the user per day, and the age of the user on the day

- The other table is a list of each advertisement and how much it cost to run that ad

So as an easy example, let's say "Ad 1" cost him $3 to run and "Ad 2" cost him $5 to run. And the user table is:

If I was modelling this by hand on Thursday, I would see that "Ad 1" cost $3 and brought in 2 users who seem to pay $1 per day consistently: their projected revenue would be $2 for Thursday. And "Ad 2" cost $5 and brought in one user and if the trend continues I expect him to bring in $1.25 on Thursday. So Thursday's expected revenue is $3.25 (of course this is a massive exaggeration but I'm just trying to get across what I'm trying to do).

Basically, I want to train a model to (1) predict the expected daily revenue per day for the next few weeks and (2) predict the revenue for each Ad type. Ideally by blending with the other Ad's data (for example, I haven't ran Ad 2 for many months but the other Ads have been performing better due to game improvements. I would like the model to follow the trend of Ad 2 in the past but lifted by the amount the other Ads have recently lifted). I am confused because my actual model should predict a simple "per day, per Ad" expected revenue, but that looks nothing like my tables and I'm not sure how to create a new table to feed it without losing a lot of data in the process.

I am having trouble visualizing how to train the model or what I am really doing. I could really use a nudge in the right direction. Thanks!

I want to get a book that details the entire modeling process to patch up some gaps I may have. I keep coming back to Applied Predictive Modeling but it was published in 2013. (From what I can tell, the 2018 edition isn't updated.) R has changed a lot since then and I'd like to read something that assumes a tidyverse-oriented workflow.

Anyone have any ideas, or is that book just the gold standard? R4DS is great but is far more about R than about the DS and I'm really wanting something that focuses on the latter. Thanks y'all!

Hello everyone!

This year I found a very interesting piece of software that will enable a designer to predict a user’s time on task at design-time.

Basically, you mockup a design (just as you would in Balsamiq or Sketch), then demonstrate a task (tell CogTool what the user would do). Then the software predicts how long it would take a human to perform that task. (using GOMS / ACT-R) The best part is the tool is completely visual.

Download: www.cogtool.org

This seems like very powerful tool!

I’ve taken this open source software project under my wing. If you find it useful, please share your experience. (If you find bugs, please let me know in the comments or via our GitHub)

I know how to use R, but need to learn predictive modeling/analysis relatively quickly. Thanks!