Those are the videos I love the most: insights into the actual work!! :) thank you!

Incredible video! Great to see a practical case on how you used machine learning to solve a real issue! Just showing some code snipets makes all the difference for someone like me, who is interested in these topics but hasnt really dived deep into them but seeing everything doesnt make it so "overwhelming"! Hope to see some more videos like these!

Great vid and realistic! A lot of tech vids are like “then I go to yoga”, “then I go to the spa” etc and I’m like, no that’s not what we do at all in the real world 😂😂 you make me miss SA so much. Thanks for the video ❤

It's the first time I see someone actually working with this mouse...

Just diiscovered your channel based on a YT recommendation, i m amazed at the production and the content quality. I m a cloud architect with formal stats background, considering to delve more into ML. Your practical context and insights are very inspiring :)

A day in the life of a tech employee doing an actual job.

Thanks for sharing your day in life!!🙏

As a long-time code slinger (began with 6502 chipset) let me recommend you dramatically improve your ergonomics. In addition to the comments by others on this topic, raise your chair and use a split ergo keyboard (I use Cloud Nine ErgoFS, love it) and ergo mouse that is, preferably, a thumball (love my Logitech MX ERGO). Both may feel odd at first but in less than a week you'll master them and you won't get carpal tunnel, cold hands/fingers, shoulder/neck aches, etc.

Why wouldn’t you just use the Mac terminal to merge your excel files instead of using python you can merge them all pretty fast without having to write any code. There’s nothing wrong with doing python. I was just curious? personally, I would import all the data into SQL, then clean it up. That’s probably because I’m not a fan of Excel.

Loved the quip on feature engineering 😮 Havent touched ML and AI in a few years, and the explanation really hit.. like ohhhh, lol 😅

Awesome, home office working schedule, love your natural break views. Thanks for sharing, see you on your next video. 👌💪🥰👩‍💻✨💎💃

It would be interesting to enrich your dataset with, as an example, information about their client's credit score and, if they (your client) have access, something like gross salary or something related to how much they've made in the last year. That could help the model to make better predictions about their actual ability to repay their (your clients' clients) debt. :) Very informative video!

Except for you not showing the actual making of your coffee, great vid. Seriously though, it’s good perspective to see how you structure and do the work. Not too far off from some of my days (remote since 2005)

Your monitor is not in middle of the desk thats interesting, how is your neck after a day?

i would love a 2nd more technical video on everything you discussed today. would your course on Python for Data Analysis: Projects to Power Your Resume teach me to do what you did in this video? please assist I would genuinely like to know skills tools etc needed to do what you did in this video

@1:25 As a freelancer, you must be your own project manager. How did I never really get that before? Thanks Dee!

This kind of thing bugs me. The most important feature is age, which probably really is the single biggest factor in predicting whether someone will pay. However, what bugs me is the question what a company wants with that information. Are they going to treat customers differently based on the outcome of feeding their info to this model? We recently had a big scandal here in the Netherlands where a model like this was used for fraud risk assessment and it turned out the single biggest factor was nationality. The problem with this technology is that it's going to lead to decisions based on a person's traits that are only a statistical correlation; not relevant on an individual level, but it will affect individuals.

Hii im glad i found ur channel, I've always wondered if i could learn Data analytics as software developer, i guess i can seeing That's what you do

Machine learning part is very good. I didn't know anything about it.

you're on fire with the content! :)

Interesting! May I note one thing: Your sitting position is not ideal. Your desk and monitor is too high, therefore you always need to bend your arms upwards, also you need to look upwards, which in long term can result in neck strain oder maybe also arm and back pain. I think if you could just use a lower table this could be mostly fixed.

When you work with ML why do you call yourself a data analyst instead of a data scientist?

wrist pain, back pain, neck pain - hard to watch someone sit and type in such un-ergonomic position.

Would you say it’s worth being a Data Analyst. What would you say to the beginning Sally salary should be good for

That bump @0:31

Is feature importance similar to parameter tuning?

That keyboard makes my wrists yell for help. Edit. That mouse also. Ergonomy out of the window.

What IDE were you using for the Python development? It wasn’t Pycharm. You were not using Jupyter either which is popular with Data Scientists.

thanks for sharing this. pretty interesting to see

Thanks, they was interesting!

Did you do any assessment of potential legal or PR risk? I see gender in the feature importance graph. Isn't that a protected category?

Is there a specific reason not using Pandas? Or is it just me cannot imagine looking at data and organise it without it? 😅

Your desk seems a little to high. I think your arms should rest in a neutral position and your wrist should be somewhat straight. I've learned that recently. 🤷

data analyst doing ml?

So I guess this machine learning program works by making correlations? What country are you from anyway? Place looks nice.

I think you're the perfect woman! 😊I've never met a woman programmer before.😐

The toughest part to me feels like what to do with the predictions made by the model. Are they going to deny services to customers which the model predicts might not pay within a certain timeframe?

I have zero clue as to what you do; I can, however, watch you open a can of tuna for two hours.

Hello, since 15 years i code bots for scraping automation (php or node js etc..) im not using python but i know it is common langage for big data ,my question is " do i am data analyst? i mean i do exactly same as you people except that the data was millions products scraping daily or dozen millions of data wanted by some company but anyway this is just data scraping right ?

Auch! Your setup is so un-ergonomic that it hurts.

As a software engineer I've been thinking about moving into data analytics. However a former colleague who works in data science has said that it's mainly people with PhDs who are competing for jobs. Do you agree with her, or is it more open than that? (I just have a Bsc).

Love your videos, but please, I hope that is not your real workarea? As others noted, you need to fix that to avoid future problems with your sholders, wrists, elbows and back. It may seem ok now but it will come with a vengeance later on. We want more tech-videos in the future. 😀

I would love to do some machine learning with you.

data analyst doing a job of data scientist...😑😑. I hope that your salary is bigger than a "simple Data Analyst"

Tell me you are not going to get addicted by coffee like NetworkChuck is. :D He is hillarious with the coffee.

Wait was that instant coffee ? That code will never work

Are you actually writing python? I get chatGPT to do about 90% of my python code now.

ASMR accent 😊

So in the "real version" you do all that while not having your yourself made camera-ready looking like Lebowski?

I don't know where you acquired your technical skill sets to build your ML model but with all due respect, that model is garbage and that is being kind. Your information is completely misguided and is grossly inaccurate. I guess that's what happens when you have a Data Analyst exploring the creation of a ML model instead of the appropriate position for which this work is better suited - a Data Scientist. As a Data Scientist, I can't begin to identify how many errors were presented in this video. The presenter did not provide the results which is really what I was hoping to review. Perhaps that was by design that this output was withheld from the video. If I were grading this video on content accuracy I would give it a flat F. Let me briefly explain, correctly, what was egregious about this video. 1) Feature Engineering was incorrectly defined. Your definition is flat wrong. Feature Engineering analyzes the existing variables in a dataset to determine what variables are most impactful to the response variable. What you described was the creation of what are called CALCULATED VALUES, which are new variables that are added to the dataset based on data from other variables therein defined. 2) You did not resolve the issue of overfitting which your model will actually be because you missed a crucial sub-step to prevent it. What you've done is what 99% of new practitioners in the field do before understanding the statistical side of ML modeling. You need to identify the standard error of every numerically defined variable in the samples of your dataset to determine whether the variable sample statistically approximates its underlying population. Beyond that, you actually missed an entire step in your process model when you combined all the data from your spreadsheets. You didn't need to do that and you didn't need all that data to create a model. So what you've done is create a high degree of goodness of fit which was your 85% predictive results for THAT dataset (only) but your overall predictive capability will most likely be less than 50% when your model is tested against data it has never before seen because you have not tested your data for the standard error. 3) Finally, you did not validate the data you used before establishing your training and testing dataset. How do you know the data you are using is correctly defined? How do you know whether or not some of that data was incorrectly defined? You don't and because you haven't provided a test for it you've essentially included garbage data into your model. Consequently, how did you handle missing data within your dataset? You never addressed that issue and it's one that is paramount to a discussion of ML modeling. If you had no missing data that should have been presented to your audience. I could go on for about another hour but I will stop there. This was one of the most misleading videos on ML that I've seen on KZfaq in a while. Stop putting misleading, half-baked information about ML out there. This is egregious at best. The final lesson here ladies and gentlemen is NEVER hire a Data Analyst to conduct the work that is unique to, and consistent with a DATA SCIENTIST, who knows how to correctly build a ML model.

cutie ❤

You are so beautiful :)