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what is approachingalmost fr?

abhishekkrthakur/approachingalmost — explained in plain English

Analysis updated 2026-06-24

8,330Audience · dataComplexity · 1/5Setup · easy

tl;dr

Companion repository for the book 'Approaching (Almost) Any Machine Learning Problem', providing a conda environment setup file so readers can recreate the exact Python environment used in the book.

vibe map

mindmap
  root((ApproachingAlmost))
    What it does
      Book companion repo
      Environment setup
      Dataset links
    Tech Stack
      Python
      conda
    Use Cases
      Follow book examples
      ML project template
    Audience
      ML learners
      Data scientists

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what do people make with this?

VIBE 1

Set up the exact Python environment from the book to follow along with the hands-on machine learning code examples.

VIBE 2

Use the conda environment file as a starting template for your own machine learning projects.

what's the stack?

Pythonconda

how it stacks up fr

abhishekkrthakur/approachingalmostmaotoumao/musicfreedesktopbrndnmtthws/conky
Stars8,3308,3348,335
LanguageTypeScriptC++
Setup difficultyeasyeasyeasy
Complexity1/52/52/5
Audiencedatageneraldeveloper

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · easy time til it works · 30min

The book's actual code is not included, you must purchase the book separately to follow the examples.

No license information available, contact the author before reusing any files from this repository.

in plain english

This repository is the companion code for a book called "Approaching (Almost) Any Machine Learning Problem" written by Abhishek Thakur. The book is a guide for people who want to learn practical machine learning, walking through how to tackle a wide variety of problem types in a hands-on, code-along style. The repository itself contains only supporting files, primarily a conda environment configuration that lets readers set up the same Python environment the author used while writing the book. The actual code from the book is not included here because, as the README explains, sharing the full code would effectively reproduce the book itself. Datasets referenced in the book are hosted separately on Kaggle at a linked profile page. The README also notes a specific dataset for a medical imaging problem (pneumothorax detection) with a direct link. The book is available for purchase in both black-and-white and color paperback editions. The README lists purchase links for multiple countries including India, the USA, the UK, Germany, France, Spain, Italy, Japan, and Canada. It also warns Indian buyers that counterfeit copies circulate on Amazon India, and recommends buying from Flipkart or the official publisher Pothi instead. If you are not a reader of the book, there is little to use from this repository directly. It is primarily a place for readers to find the environment setup file and raise questions as GitHub issues.

prompts (copy fr)

prompt 1
Create a conda environment.yml for a machine learning project with scikit-learn, pandas, numpy, and xgboost following the style used in Abhishek Thakur's approachingalmost repo.
prompt 2
Write a cross-validation strategy in Python for a binary classification problem following the practical approach taught in 'Approaching Almost Any Machine Learning Problem'.
prompt 3
Help me set up the approachingalmost conda environment on macOS and verify all packages install correctly before working through the book's examples.

Frequently asked questions

what is approachingalmost fr?

Companion repository for the book 'Approaching (Almost) Any Machine Learning Problem', providing a conda environment setup file so readers can recreate the exact Python environment used in the book.

What license does approachingalmost use?

No license information available, contact the author before reusing any files from this repository.

How hard is approachingalmost to set up?

Setup difficulty is rated easy, with roughly 30min to a first successful run.

Who is approachingalmost for?

Mainly data.

peek the repo → explain another one

This repo across BitVibe Labs

double-check against the repo, no cap.