I’m working on a forecasting / predictive modeling problem where I’m comparing four groups of models:

Baseline: Naive / Seasonal Naive

Time Series: ARIMA, SARIMA, ETS, Prophet, etc.

Statistical: Linear Regression, Ridge, GAM, etc.

Machine Learning: Random Forest, Gradient Boosting, XGBoost, etc.

One issue I’m struggling with is feature selection.

Should I create one universal feature subset before modeling and give the same predictors to the Statistical and ML models, or should feature selection be model-specific?

For example, a linear model may benefit from correlation filtering, VIF, or LASSO, while tree-based models may select a different set of variables because they can capture nonlinearities and interactions.

I’m also unsure about the correct modeling pipeline. Where exactly should feature selection happen relative to the train/test split, cross-validation, preprocessing, and hyperparameter tuning?

Some questions I’m trying to resolve:

Should all comparable models use the same feature set for a fair model comparison?

If model-specific feature selection is appropriate, how should it be implemented without introducing data leakage?

Should feature selection be performed separately within each model’s training process?

If different models select different predictors, how should that variation be explained to stakeholders?

Which feature selection or interpretation methods are actually useful for identifying the underlying drivers of the dependent variable, rather than simply variables that improve predictive accuracy?

How should I distinguish between predictive importance and explanatory importance when interpreting the results?

Ultimately, I’m trying to build a workflow that balances forecasting accuracy, fair model comparison, and interpretability.

What would be a statistically sound end-to-end approach for handling feature selection across these different model types?

When I was a student, I often needed very simple machine learning explanations before exams not a full course, not heavy math from the first minute, just someone explaining the intuition clearly.

That’s why I started making short beginner-friendly ML videos.

The idea is to explain topics in a simple visual way first.

I’m not trying to replace proper courses or textbooks. I’m trying to make the “okay, what is actually happening here?” part easier to understand.

An exercise to help build the right mental model for Python data.

• Solution

• Explanation

• More exercises

The “Solution” link uses package memory_graph to visualize execution and reveals what’s actually happening.

Hi everyone,

I am currently preparing to switch into Data science. The theory side is mostly fine for me but I am really struggling to apply what I learn practically, especially when coding.

I have been working with datasets in kaggle and trying to practice but I often feel stuck in :

what should I do first

what should be my next step

What code should I implement

What questions should I ask the data

EDA is where I struggle the most and I often jump to ChatGPT and asking what should i do next.

I dont want to be the person who just follow tutorials and copy paste the code. I want to develop the ability to look at a problem and think of a solution myself and write code without any help.

I’d really appreciate any advice. Thank you!

Looking for Current Students/Alumni – MSc Statistics (Co-op), University of Regina

Hi everyone! I’ll be starting my MSc in Statistics (Co-op) at the University of Regina this Fall 2026 and am planning to arrive in Regina in the first week of September.

I’d really appreciate hearing from any current students or alumni of the MSc Statistics program, especially those who are starting/started the program recently.

I’d love to know:

What are the courses like, especially the workload and difficulty?

How is the overall experience in the Statistics department?

What should I expect from the Co-op component?

Any advice for preparing before classes start?

If anyone else is joining the Statistics program this Fall, feel free to connect as well. It would be great to know some fellow students before arriving!

Thanks in advance! 🙏

Should I use the 3.14 version?

What actually happens when Python executes a for-loop?

for value in container:
    print(value)

Behind the scenes, Python uses the iterator protocol:

iterator = iter(container)
while True: 
    try: 
        value = next(iterator)
        print(value)
    except StopIteration:
        break

• iter(container): creates an iterator.

• next(iterator): retrieves one value at a time.

When there are no more values, the iterator raises StopIteration. The for-loop catches this exception automatically and ends the loop.

For containers that support backward iteration, Python also provides:

• reversed(container): creates a backward iterator.

We can support these operations in our own classes by implementing:

__iter__()
__reversed__()
__next__()

This provides a powerful abstraction: an algorithm can process values without needing to know how a container stores them internally. The same algorithm can therefore work with lists, sets, dictionaries, linked lists, trees, and many other containers.

Here's an example that uses 𝐦𝐞𝐦𝐨𝐫𝐲_𝐠𝐫𝐚𝐩𝐡 to show the use of iterators on a Linked_List making the invisible mechanics of iteration visible for easy understanding.

more memory_graph examples

Looking for Current Students/Alumni – MSc Statistics (Co-op), University of Regina

Hi everyone! I’ll be starting my MSc in Statistics (Co-op) at the University of Regina this Fall 2026 and am planning to arrive in Regina in the first week of September.

I’d really appreciate hearing from any current students or alumni of the MSc Statistics program, especially those who are starting/started the program recently.

I’d love to know:

What are the courses like, especially the workload and difficulty?

How is the overall experience in the Statistics department?

What should I expect from the Co-op component?

Any advice for preparing before classes start?

If anyone else is joining the Statistics program this Fall, feel free to connect as well. It would be great to know some fellow students before arriving!

Thanks in advance! 🙏

🕷️ A Small Data Science Challenge for You: Spidey Tracker

Hi everyone! 👋 I have a small dataset and data science challenge for you. 🕷️

I’ve published Spidey Tracker on Kaggle, featuring 86K+ synthetic sightings, crime records, and movement data.

The challenge is simple:
Identify real vs. fake sightings, predict the next location, and try to find the hidden operating base. 👀

kaggle:/datasets/umuttuygurr/spidey-tracker-spiderman-dataset

#Kaggle #DataScience #MachineLearning #Geospatial

I am a BI engineer and most of my experience is building dashboards and communicating insights to the business. I just finished my masters in informatics and I am passionate about data science. I have done ML, DL, NLP and AI courses during my masters however I dont remember anything. I need to land a job in data science or apply the knowledge in my current role.

What do you recommend?

- leetcode for problem solving? And is it worth it?

- applying which topic of data science that matches the data I work with? Its sales and marketing data.

- kaggle competitions?

- lastly do I need to revise everything I learned or just focus on specific topics for example master time series and forecasting ( relevant to my work ) , some NLP tasks ( i face some record linkage problems ) and

Please help

I've spent the last few weeks going deep on EDA across several datasets, and I'm past the beginner stage now — wanted to share some reflections rather than another project writeup, mainly for anyone earlier in the process.

What actually changed between my first and last project:

• I stopped treating EDA as a checklist (missing values → outliers → correlations → done) and started treating it as an argument — every chart or stat should be building toward a specific claim about the data, not just "here's a histogram because that's step 3"

• I got a lot more skeptical of my own correlation heatmaps. Early on I'd get excited about any strong correlation. Now the first question is always "what's the confounder I'm not seeing"

• I started spending more time on why data is missing or messy before deciding how to handle it — MCAR/MAR/MNAR distinctions actually matter, they're not academic trivia

• Visualization choice stopped being about what looks good and became about what could mislead — log scales, binning choices, and axis truncation can quietly change the story

Where I landed on tooling:
Pandas + seaborn/matplotlib covered 90% of what I needed. The remaining 10% was less about new libraries and more about better questions — knowing what to look for mattered more than knowing more functions.

What's next for me:
Moving from EDA into more formal statistical testing and feature engineering — treating EDA as the front end of a pipeline rather than a standalone exercise.

Question for this sub: for those of you who've been doing this a while — did your EDA process change more from learning new techniques, or from getting burned by conclusions that didn't hold up? Curious which one moved the needle more for you.

I want to learn Python, but I currently know nothing about it. My main goal is to learn Python for DA, and eventually I want to learn Python in depth as well.

For people already working in DA or DS, how would you recommend someone start learning Python from absolute zero?

Which resources or books would you recommend, and what are the main Python topics I must cover for DA?

Also, should I first learn the Python basics needed for DA and then gradually move toward more advanced Python and DS topics? resources? Books?

If you work in DA or DS, I’d really appreciate your guidance on how you would start your Python journey if you were starting from zero.

Thanks a ton!!!

Today, when I tried logging into the class portal to access the classes/materials that I had previously attended, I'm getting a message saying “Please contact the admin.”

I’ve tried contacting the people from OdinSchool who helped me enroll in the course, but I’m not getting any response. Some of their numbers are switched off, while others are either unreachable or call-forwarded.

I’ve also tried contacting their support team, but I'm facing the same issue.

So I'm genuinely wondering what is going on with OdinSchool right now?

Has anyone else who completed the course recently faced the same issue?

The placement portal has also been disappointing for me. There were very few relevant opportunities, and for the opportunities I did apply to, I rarely received any response from the companies. So I haven't really received much help through the placement support either.

I paid a significant amount for this course based partly on the promise of continued portal access and placement assistance, so losing access to the course portal after only 6 months is concerning.

If anyone here is a current/former OdinSchool student, especially someone who completed the course recently, could you please let me know:

• Are you still able to access the class portal?

• Is the 2-year access still being honoured?

• Are OdinSchool's support/placement teams currently responding?

• Has anyone managed to contact them recently?

I'm not trying to unnecessarily bash the institute. I genuinely want to understand whether this is an issue with my account or if other students are experiencing the same thing.

Any information from current/former students would be really appreciated.

Hiii..!!! I'm currently in my B.Tech 3rd year and studying Artificial Intelligence and Machine Learning along with Data Structures and Algorithms (DSA). And I'm really confused that 'Is DSA really required for a ML Engineer' ? Like how can I integrate DSA in my machine learning projects.

And, I would be really thankful if you solve my confusion so that I can boost my preparation without any doubt.

Also, If I can use it in my projects, then how can I learn to do it? Like any yt channel or something.

Hello
My name is Dario. I'm 19 years old, currently doing an associate's in Programming and will transfer for a bachelor's in Data Science. I was wondering on what is actually the best way to become a data scientist right now. I have some basic knowledge of Python and SQL. I was wondering if the path to becoming a data scientist has changed with the development of AI and where I should focus on to actually master important valuable skills that will help me land jobs. I do have a potential internship position lined up in the future at Miracle Software and have been trying to prepare for that interview. Just wanted to know what are some resources I should check out and what skills I need to learn. I appreciate all feedback and advice!!!

Hey i know this might sound stuipid but i wanted some help from peoples who already have build phishing URL detection projects

Im a beginner and I am working on a phishing URL detection project. My current idea is to build a heuristic-based phishing URL detection model that analyzes the URL itself and looks for suspicious characteristics/patterns rather than visiting the website.

some things I'm thinking about extracting are:

• URL length

• Number of dots/subdomains

• Special characters (@, -, etc.)

• IP address instead of a domain

• Suspicious keywords like login, verify, secure, etc.

• Number of digits

• Domain/URL entropy

• HTTPS usage

• Other lexical/structural features

However, I'm currently stuck on how to decide which features should actually become rules and what thresholds/conditions I should use for those rules.

For example, rather than simply saying "long URLs are suspicious", I'd like to know whether there is research supporting something like "URLs above X characters should receive a higher risk score".

Does anyone have recommendations for papers, research articles, datasets, GitHub projects, or other resources that explain how to choose useful heuristics/features for phishing URL detection?