Body Performance Project

Environment setup

Photo by Kanhaiya Sharma on Unsplash

This article showcases a project I embarked on two months; July. Having finished it. Thought would be nice to give you all a walkthrough especially for those that would be getting into Data Science as a full time profession (beginners).

This article would be in the of series namely;

1. Environment setup
2. Data Preprocessing and Feature Engineering
3. Exploratory Data Analysis (EDA)
4. Clustering Analysis
5. Classification
6. Regression Analysis

By the end of this series you would become a lot more confident executing your own project.

Google, reading the docs and several resources on the internet are your best friends.

Let’s begin!

Python Installation

First off, you’d need to download and install Python on your system. Python installation: Kindly go to this link Python link, scroll down and click on the Windows installer (64bit or 32bit) depending on your system configuration. If stuck, use this tutorial video to aid you.

Conda Installation

The installation of Conda is fairly easy, kindly follow this link

Visual Studio Code Setup

Once again, fairly easy to install and setup too, download link. Once installed, there would be some configuration to be done. Would be leaving that to you.

Hint: Google and research!😁

Environment Setup

Once the above have been achieved, you’re good to go! Activate your Conda environment (refer to the hint above to achieve this).

Navigate to your desktop while using the Conda terminal, you should have something similar like this;

Activated Conda Environment

With the Conda terminal (refer to photo above), follow the instructions below;

1. Run mkdir folder_name1
2. Open up your vs code and create the following yaml file and save it as env.yaml;

name: ml
channels:
  - conda-forge
  - defaults
dependencies:
  # # Latest versions
  # # conda channels
  - scikit-learn=1.3.0
  - seaborn=0.12.2
  - python=3.11
  - plotly=5.15.0
  - pandas=2.0.3
  - matplotlib
  # - numpy=1.25.0
  # - scipy=1.11.1
# All are on the latest versions
# pandas would install the following by default;
# - scipy
# - numpy
# - statsmodels

The above file creates a folder named ml with the installed packages to get your conda isolated virtual environment up an running for usage without affecting the main conda environment which you earlier installed.

3. Create another folder within folder_name1; mkdir ml, the name should correspond with the name variable in the yaml file

4. Run conda env create -f env.yml -p ../folder_name1/ml

5. Run conda env list to list all environments created using Anaconda

6. Run conda activate ./ml to activate the environment

7. Run conda list to check if all dependencies have been installed

With all instructions followed above, you are set for the next set in the series! You did it ! 😎

Helpful links

Anaconda environment bash prefix too long

https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html