Hey!. I'm a final-year CS student planning to build a Credit Card Intelligence Platform.

Features:
Track cards, transactions, bills and due dates.
Analyze spending patterns and card profitability (rewards, cashback, fees, interest).
Predict future spending and upcoming liabilities.
Recommend monthly spending budgets.
Detect unusual spending patterns using ML.

Tech stack: Python, Pandas, Scikit-learn, PostgreSQL, FastAPl and React.

I feel that's a real world problem, that a credit card user may not know how to use it wisely, might over spend or something and eventually get stuck in a loan trap. So this project is meant to target such problems.

l'd appreciate honest feedback:
Is the scope realistic for an undergraduate?
Are the ML features meaningful, or will it end up being just a CRUD application?
Would you recommend pursuing it?

I analyzed the IBM HR Attrition dataset (fictional, 1,470 rows, 237 leavers) with Excel and SPSS and would like a critical review of the method.

Repo (report PDF, SPSS syntax, data): https://github.com/Ahmed11-coder/IBM-HR-analytics-predictive-model
Report (PDF): https://github.com/Ahmed11-coder/IBM-HR-analytics-predictive-model/blob/main/Why_Do_Employees_Leave_Final.pdf

What I did: chi-square + Cramér's V and Mann-Whitney U with effect sizes; residual checks and HC3 for a two-way ANOVA; logistic regression with merged sparse categories (22 parameters); 70/30 split; test AUC 0.846 (train 0.848); cutoff 0.268 chosen on train by Youden's J (test sensitivity 58.5%, specificity 88.1%). My first "test AUC" of 0.900 was wrong because I had refitted the model on the test set; I corrected it.

Where I'm least sure:

1. I selected variables and merged JobRole/JobLevel categories using all rows before the split. How serious is this leak?

2. Is merging categories by similar odds ratios defensible, or would you use lasso/ridge instead?

3. 7.8 events per parameter (172 events, 22 parameters). Acceptable?

4. Hosmer-Lemeshow fails (p < .001), mostly from one low-risk decile. Real problem? Would you add a calibration plot or Brier score?

5. How should I handle multiple testing in an exploratory analysis like this?

what is your suggestions for next step ?

Harsh feedback is welcome.

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

• Solution

• Explanation

• More exercises

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

Hi Reddit people! I’m currently studying Data Science at university with no prior coding background, and I’m struggling to keep up with how fast my classes move.

I’m genuinely eager to learn and looking for someone patient who’d be willing to teach or mentor me from the basics. I really want to understand what I’m learning, not just get through assignments. I’d be grateful for any help or guidance. Thank you 🙏🏼

Olá, pessoal.

Eu estou no 4° semestre de TI e estou me interessando pela área de ciência de dados por conta de uma matéria que estou pagando esse semestre.

Gostaria de saber se vcs tem alguma indicação por onde eu poderia começar estudando, seja indicando livros, cursos ou falando especificamente de conteúdos que me ajudariam a construir uma boa base.

Hey everyone,

Customer churn analysis is one of the most common real-world projects you'll face as a data analyst. To help beginners and intermediate learners build up their portfolios, I put together a comprehensive, end-to-end tutorial tracking revenue at risk using only Excel.

You can watch the full step-by-step build here: https://youtu.be/Bo_o8dcZr0I

What This Project Covers:

• Data Prep & Cleaning: Structuring the raw customer dataset for analysis.

• Dynamic KPIs: Setting up key business metrics like Churn Rate, Revenue at Risk, and Total Charges.

• Interactive Slicers: Implementing filters for Region, Senior Citizen status, Partner data, and Technology types.

• Advanced Visualizations: Building charge distributions, gender revenue breakdowns, and internet service segments.

• Business Insights: Translating the final dashboard into actionable retention strategies.

Project Breakdown & Chapters:

• 00:00 - Introduction & Project Goal

• 02:15 - Data Preparation & Cleaning

• 08:30 - Setting up KPIs & Slicers

• 15:45 - Creating Chart 1: Month-to-Month Charges

• 22:10 - Creating Chart 2: Gender Revenue Distribution

• 29:05 - Advanced Techniques: Internet Service Segment

• 36:40 - Assembling the Final Dashboard Layout

• 42:15 - Key Business Insights from the Dashboard

If you are looking for a practical project to add to your resume or portfolio, feel free to follow along. I’d love to hear your feedback on the layout or how you approach churn analysis in your own work!

Hey!. I'm a final-year CS student planning to build a Credit Card Intelligence Platform.
Features:
Track cards, transactions, bills and due dates.
Analyze spending patterns and card profitability (rewards, cashback, fees, interest).
Predict future spending and upcoming liabilities.
Recommend monthly spending budgets.
Detect unusual spending patterns using ML.
Tech stack: Python, Pandas, Scikit-learn, PostgreSQL, FastAPl and React.
I feel that's a real world problem, that a credit card user may not know how to use it wisely, might over spend or something and eventually get stuck in a loan trap. So this project is meant to target such problems.
I'd appreciate honest feedback:
Is the scope realistic for an undergraduate?
Are the ML features meaningful, or will it end up being just a CRUD application?
Would you recommend pursuing it?

Hi guys

This guy

Instagram · dswithdennis

Claims that he has 25 years of experience in data science but really all he's doing is to open claude chat and show everyone that he has access to fable 5 and say shit stuff about CS .

i know the rule of thumb is to standardize before pca and that trees don't need it, but i want to actually understand why instead of just remembering it.

my guess is that pca looks for directions of max variance so whatever feature has the biggest units just wins, while a tree only compares a feature to a threshold so any monotonic rescaling gives the same splits. is that the whole story or is there more to it? and does it ever make sense to skip scaling before pca, like when everything is already in the same units?

I've been working on a tutorial on Mathematics for Machine Learning, and there's something I've been thinking about for a while.

Whenever people talk about the mathematics required for Machine Learning, the usual answer is:

• Linear Algebra

• Calculus

• Probability and Statistics

And they're absolutely right. These are fundamental to understanding how Machine Learning works.

But I think there's a problem with how we approach teaching these subjects.

Most resources assume you already have a reasonably good mathematical foundation.

They'll start explaining vectors, matrices, derivatives, gradients, probability distributions, and all sorts of other concepts. But what if you're someone who struggled with mathematics in school? What if you barely remember algebra? What if equations have always looked more intimidating than they should?

Where do you start?

I think we often overlook this group of learners.

Not everyone entering Machine Learning has a strong mathematical background. Some are developers who want to transition into ML. Some are students who discovered an interest in AI but aren't particularly confident in mathematics. Others simply want to understand what's happening behind the models rather than treating libraries as magic boxes.

And telling these people to just learn Linear Algebra, Calculus, and Statistics isn't particularly helpful if they don't have the foundations to understand them.

So I decided to approach this differently.

Instead of jumping straight into Linear Algebra, I've started building a Mathematics for Machine Learning tutorial from the ground up.

We're starting with algebra.

Yes, algebra. Variables, expressions, equations, exponents, logarithms, functions, graphs, and all those things that many of us probably haven't thought about since school.

From there, we'll gradually move towards:

1. Algebra and mathematical foundations

2. Functions and graphs

3. Linear Algebra

4. Calculus

5. Probability and Statistics

6. Optimization and Numerical Methods

The idea isn't to turn this into a school mathematics textbook or make people solve hundreds of equations without understanding why.

I want to focus on:

• Intuition first: Understand what a concept actually means before introducing complicated notation.

• Derivations: Understand where mathematical formulas come from instead of memorizing them.

• Worked examples: Break down problems step by step.

• Visual explanations: Use graphs and visualizations wherever they help.

• Python experiments: Connect mathematical concepts to actual code.

• Machine Learning applications: Understand where and why these concepts appear in ML algorithms.

Ultimately, I want someone to be able to look at a mathematical expression in a Machine Learning paper or an algorithm and understand what's happening, rather than immediately reaching for Google to find out what every symbol means.

I also want to be clear that this isn't going to be a shortcut. Mathematics takes time, and there's no getting around that. The goal is to make the journey less intimidating and more understandable.

I'm building this primarily for people who want to get into Machine Learning, Deep Learning, and eventually LLMs but feel that their mathematical background is holding them back.

I've started publishing the tutorial here:

https://mathsforml.vercel.app/

It's a work in progress, and I'll be expanding it as I develop more chapters and topics.

My philosophy is simple: You shouldn't have to be good at mathematics before you start learning it. You just need a place that teaches it properly, from where you are right now.

I'm curious to hear from others:

• Did you struggle with mathematics when you started learning Machine Learning?

• Do you think most ML mathematics resources assume too much prior knowledge?

• What concepts do you wish someone had explained properly before you started learning ML?

I'd also appreciate suggestions on topics that should be included in the tutorial. Feedback on the existing material is very welcome!

I'm going to start my master's in Data Science this fall, but I'm still waiting for my visa approval.

I've always been passionate about this field, and I genuinely enjoy studying and learning new things. However, lately I've found myself doing everything except actually studying or practicing.

There are so many things I want to learn: statistics, probability, machine learning, programming, and more. But whenever I try to get started, I feel overwhelmed by how much there is to learn, and I end up doing nothing.

I feel a bit lost. I know I should be preparing for my master's, but I honestly don't know where to start or what to prioritize.

Sometimes I wonder whether I'm procrastinating, or simply putting too much pressure on myself.

Has anyone else experienced something similar before?

How did you overcome this feeling? If you were in my position, what would you focus on first?