Fahad Akbar – Medium

Published in Towards Data Science

·Apr 4

Efficient Data Manipulation With Pandas

Learn to use piping for data manipulation in Pandas — We all have been using Pandas. It is the go-to tool for almost every analyst who uses python. Many of us have mastered the art of data manipulation with it. I was very happy with what I knew about the pandas and the way I was applying my knowledge until…

Data Science

5 min read

Published in Towards Data Science

·Oct 27, 2021

A Data Scientist's Dream: Python, Big Data, Multi-Processing, and PyCaret

Train multiple models using all your cores with Python’s multiprocessing module and PyCaret — Let me put it simply: the ability to tackle large data has become an absolute need if you are in data analytics or the data science domain. In this article, we will learn to design a solution that you can simply create using your laptop/desktop. …

Python

13 min read

Oct 26, 2021

Get Linux In Windows The Easy Way | Three Simple Steps | by Fahad Akbar

A quick guide on running a Linux system inside windows without going through too much trouble, through Windows Subsystem. — Linux is a great system. If you are or an aspiring data scientist, you will most likely end up working with Linux. You should start learning or at least get your hands dirty while practicing it. …

Linux

3 min read

Published in Towards Data Science

·Jul 2, 2021

Custom Estimator With PyCaret | Part 2 | by Fahad Akbar

A hands-on guide to building and deploying a Sklearn compatible estimator from scratch in Scipy through PyCaret — In part 1 we learnt about estimators, python class object, exponential function,curve_fit function, positional arguments packing /unpacking, enumerate function & finally built a more customized & flexible regression estimator. You can visit the Part1 of this hands-on tutorial below: Custom Estimator With PyCaret | Part 1 | by Fahad Akbar A hands-on guide to building & deploying a Sklearn compatible estimator from scratch in Scipy through PyCarettowardsdatascience.com

Machine Learning

7 min read

Custom Estimator With PyCaret | Part 2 | by Fahad Akbar

Published in Towards Data Science

·Jun 13, 2021

Custom Estimator With PyCaret | Part 1 | by Fahad Akbar

A hands-on guide to building & deploying a Sklearn compatible estimator from scratch in Scipy through PyCaret — Let’s agree, PyCaret is great. It does a lot for you in such a short time. But sometimes things are just not enough, and you want to bring your homegrown solution that is more suitable to your problem, or a solution that is available elsewhere and you want PyCaret to…

Pycaret

6 min read

Apr 8, 2021

The Definitive Guide to Conda Environments

1.4K

Matthew Sarmiento

The only thing that is missing is to be able to use these new conda envs in the notebook

The only thing that is missing is to be able to use these new conda envs in the notebook you can do that by using : ``` 1 ) conda install -c anaconda ipykernel 2) python -m ipykernel install --user --name=env_name ``` Afte that. select the env/kernal from within the notebook

1 min read

The only thing that is missing is to be able to use these new conda envs in the notebook

you can do that by using :

```

1 ) conda install -c anaconda ipykernel

2) python -m ipykernel install --user --name=env_name

```

Afte that. select the env/kernal from within the notebook

Published in Towards Data Science

·Jul 12, 2020

Make Your Data Science Life Easy With Docker

A mini-guide that introduces the use of Docker’s containers for your data science needs & projects — One of the preliminary steps that you take when you embark on your data science journey is dealing with the installation of different software such as Python, Jyupter Notebook, some IDEs and countless libraries. Once you successfully pass through this, you often encounter situations where your code seems to work…

Docker

12 min read

Mar 15, 2020

Data PreProcessing for Machine Learning Made Easy. Part 4

Dealing with High Cardinal Data & Extracting Features from Time This is the fourth tutorial of python package PreProcess1.The third tutorial can be found here: Data PreProcessing for Machine Learning Made Easy. Part 3 Clubbing Infrequent Levels & Dealing With Untrained Levelsmedium.com Dealing with High Cardinal Data This is one of my favourites, and I enjoyed quite a bit while solving this. High cardinal simply means that you have too many levels in a categorical feature. Imagine you have data of a…

Machine Learning

5 min read

Mar 7, 2020

Data PreProcessing for Machine Learning Made Easy. Part 3

Clubbing Infrequent Levels & Dealing With Untrained Levels This is the third tutorial of python package PreProcess1.The second tutorial can be found here: Data PreProcessing for Machine Learning Made Easy. Part 2 Label Encoding & Dealing with Zero / Near Zero Variancemedium.com Clubbing Infrequent Levels Occasionally you will come across categorical variables that have sufficient variance, however, there are certain levels in a feature that don't really appear frequently. Since there aren't many examples, it is likely that the model…

Data Science

5 min read

Data PreProcessing for Machine Learning Made Easy. Part 3

Mar 4, 2020

Data PreProcessing for Machine Learning Made Easy. Part 2

Label Encoding & Dealing with Zero / Near Zero Variance This is the second tutorial of PreProcess1. The first part can be found here: https://medium.com/@fahadakbar_50702/data-preprocessing-for-machine-learning-made-easy-part-1-b10ffc481747 Today we will talk about label encoding and about features where there isn’t enough information available for model to learn from it. Label Encoding: We often find text features in our data set. Some times they are…

Data Science

4 min read

Data PreProcessing for Machine Learning Made Easy. Part 2