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A Comprehensive Machine Learning Workflow with Python

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in Kaggle, I want to solve a simple machine learning problem as a comprehensive workflow with python packages.Then

After reading, you can use this workflow to solve other real problems and use it as a template to deal with machine learning problems.

you can follow me on:

GitHub
LinkedIn
Kaggle

Notebook Content

  • 1- Introduction
  • 2- Machine learning workflow
  • 3- Problem Definition
  •   3-1 Problem feature

  •   3-2 Aim

  •   3-3 Variables
  • 4- Inputs & Outputs
  • 4-1 Inputs
  • 4-2 Outputs
  • 5- Installation
  •   5-1 jupyter notebook

  •   5-2 kaggle kernel

  •   5-3 Colab notebook

  •   5-4 install python & packages

  •   5-5 Loading Packages
  • 6- Exploratory data analysis
  •   6-1 Data Collection

  •   6-2 Visualization

  •       6-2-1 Scatter plot

  •       6-2-2 Box

  •       6-2-3 Histogram

  •       6-2-4 Multivariate Plots

  •       6-2-5 Violinplots

  •       6-2-6 Pair plot

  •       6-2-7 Kde plot

  •       6-2-8 Joint plot

  •       6-2-9 Andrews curves

  •       6-2-10 Heatmap

  •       6-2-11 Radviz

  •   6-3 Data Preprocessing

  •   6-4 Data Cleaning
  • 7- Model Deployment
  •   7-1 KNN

  •   7-2 Radius Neighbors Classifier

  •   7-3 Logistic Regression

  •   7-4 Passive Aggressive Classifier

  •   7-5 Naive Bayes

  •   7-6 MultinomialNB

  •   7-7 BernoulliNB

  •   7-8 SVM

  •   7-9 Nu-Support Vector Classification

  •   7-10Linear Support Vector Classification

  •   7-11 Decision Tree

  •   7-12 ExtraTreeClassifier

  •   7-13 Neural network

  •   7-14 RandomForest

  •   7-15 Bagging classifier 

  •   7-16 AdaBoost classifier

  •   7-17 Gradient Boosting Classifier

  •   7-18 Linear Discriminant Analysis

  •   7-19 Quadratic Discriminant Analysis

  •   7-20 Kmeans
  • 8- Conclusion
  • 9- References

Introduction

This is a comprehensive ML techniques for IRIS data set, that I have spent for more than two months to complete it.

it is clear that everyone in this community is familiar with IRIS dataset but if you need to review your information about the dataset please visit this link.

I have tried to help beginners in Kaggle how to face machine learning problems. and I think it is a great opportunity for who want to learn machine learning workflow with python completely. I have covered most of the methods that are implemented for iris until 2018, you can start to learn and review your knowledge about ML with a simple dataset and try to learn and memorize the workflow for your journey in Data science world.

I am open to getting your feedback for improving this kernel