benhasem.blogg.se - An introduction to statistical learning python

#AN INTRODUCTION TO STATISTICAL LEARNING PYTHON FOR FREE#
#AN INTRODUCTION TO STATISTICAL LEARNING PYTHON HOW TO#
#AN INTRODUCTION TO STATISTICAL LEARNING PYTHON INSTALL#
#AN INTRODUCTION TO STATISTICAL LEARNING PYTHON MAC#

Bishop, C.M., 2006, Pattern Recognition and Machine Learning, Springer.

2006, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.

James, G., Witten, D., Hastie, T., Tibshirani, R., 2015, An Introduction to Statistical Learning with Applications in R, Springer.

Īdditional references for some of the exercises are scattered througout the solutions. We also spent some quality time on CrossValidate.įor the Python data science stack we think Wes McKinney's book is a good choice, as well as Jake VanderPlas'. We also read some other books that provide a self-contained introduction to the field of statistical learning.

#AN INTRODUCTION TO STATISTICAL LEARNING PYTHON FOR FREE#

In addition to thinking hard about them, to solve the exercises we followed several references.īesides ISLR, which is available for free and explains almost everything you need to know to solve the exercises, On GitHub, you can also fork this project and send a pull request to fix any mistakes that you have found.Īlternatively, you can also go for the classical way of collaboration and send us an e-mail.Īny effort to improve the quality of these solutions will be appreciated.

#AN INTRODUCTION TO STATISTICAL LEARNING PYTHON HOW TO#

If you want to collaborate, you can open an issue in our GitHub project and give us your suggestions on how to improve these solutions. We might change to a different scheme in the future (any suggestions), but meanwhile we used these libraries: In addition, we chose mkdocs to present these solutions in a website format, for a better presentation.

ipywidgets (so that a seaborn import warning goes away).

jupyterlab (but this should run just as well on regular ipython notebooks).

Here's the list of packages we installed:

#AN INTRODUCTION TO STATISTICAL LEARNING PYTHON INSTALL#

So after installing Anaconda, the easiest way is to create a new environment and just install the libraries we list below one by one.Īfter this is done, just make sure the desired environment is active (for example, on Linux and Mac, type 'source activate ', and you're good to go). You don't want to know what the alternative is, to not using an environment manager. You can even work on both at the same time. Imagine that you have two projects with different requirements: a recent one with, say, Python 3.5 and matplotlib 4.0, and a legacy project with Python 2.7 and matplotlib 3.5.Ī good environment manager helps you install libraries and allows you to switch between both environments easily, avoiding dependencies migraines. Anaconda allows you to easily manage several Python environments.Īn environment is a collection of installed Python packages.

#AN INTRODUCTION TO STATISTICAL LEARNING PYTHON MAC#

We recommend using Anaconda whether you are using Linux, Mac or Windows. If you're just starting out with Python, here's a more complete 'how-to'. You should be able to run this with the standard Python setup, and the additional libraries we list below. We tried to stay within the standard Python data science stack as much as possible.Īccordingly, our main Python packages were numpy, matplotlib, pandas, seaborn, statsmodels and scikit-learn. This project was developed using Python 3.5 on Jupyter notebooks (Jupyter Lab, in fact). If you're starting out in machine learning with Python (or R!), we recommend you try it! Technical requirements, and How to Install We are strong advocates of the active learning principles, and this project, once more, reinforced them in our minds. Since the book was written with R in mind, it made the use of Python a cool additional challenge. We had done other data science projects with Python, but, as we imagined, we still had a bit more to learn (and still do!). Our main goal was to use the exercises as an excuse to improve our proficiency using Python's data science stack. We chose ISLR because it is an excellent, clear introduction to statistical learning, that keeps a nice balance between theory, intuition, mathematical rigour and programming. Today there are several good books and other resources from which to learn the material we covered, and we spent some time choosing a good learning project. The main motivation of this project was learning. Note : we will release each chapter's solutions on a monthly basis (at least). The exercises were solved using Python instead of R. īoth conceptual and applied exercises were solved.Īn effort was made to detail all the answers and to provide a set of bibliographical references that we found useful. 'An Introduction to Statistical Learning with Applications in R' (ISLR) by James, Witten, Hastie and Tibshirani.This page contains the solutions to the exercises proposed in An Introduction to Statistical Learning: with Applications in R.