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Published Date: May 27, 2018

Available In

ZIP

$0.00

In the ZIP file, you will get a) the self instructed recipe (code) - Python script (DSR-026.py), b) the dataset used in the recipe - Pima Indian Diabetes dataset (pima.indian.diabetes.data.csv). and c) the predicted outcome of the model (finalResult.csv). 

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Description

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In this Data Science Recipe, the reader will learn:

  1. How to organise a Predictive Modelling Machine Learning project step by step.
  2. What are the different steps in Predictive Modelling and Applied Machine Learning.
  3. How to summarise and present feature variables in Predictive Modelling (Descriptive statistics).
  4. How to visualise features through histogram, density plot, box plot and scatter matrix.
  5. How to find correlations among features variables.
  6. How to visualise target variables.
  7. How to do data analysis for feature and target variables.
  8. How to utilise scikit-learn and pandas packages in Python.
  9. How to implement Boosting Algorithms for Multiclass Classification Algorithm in Python.
  10. How to implement GradientBoosting for Multiclass Classification Algorithm in Python.
  11. How to setup hyper parameters: manual and automatic tuning in Python.
  12. How to setup RandomSearchCV and GridSearchCV for parameter tuning in Python.
  13. How to compare Algorithms with Accuracy and Kappa in Python.
  14. How to implement an end-to-end Data Science Project using MySQL and Python.

 

 

Disclaimer

The information and recipe presented within this eArticle/code is only for educational and coaching purposes for beginners and learners. Anyone can practice and apply the recipe presented here, but the reader is taking full responsibility for his/her actions. 
The author of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. Some of the information presented here could also be found in public knowledge domains.



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