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Published Date: May 19, 2018

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In the ZIP file, you will get a) the self instructed recipe (code) - R script (DSR-025.r), b) the dataset used in the recipe - IRIS dataset (iris.data.csv). and c) the predicted outcome of the model (finalResult.csv). d) the pre-trained model.

Sample Codes


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In this Data Science Recipe, you will learn:

  1. How to organise a Predictive Modelling Machine Learning project.
  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 CARET packages in R.
  9. How to implement Gradient Boosting Tree for Multiclass Classification Algorithm in R.
  10. How to implement Neural Networks for Multiclass Classification Algorithm in R.
  11. How to tune parameters: manual tuning and automatic tuning in R.
  12. How to compare Algorithms with Accuracy and Kappa using caret package in R.
  13. How save a trained model in R.
  14. How to connect to MySQL database to query prediction dataset.
  15. How to prepare prediction dataset and load a pre-trained model in R.
  16. How to make prediction using the trained model and report the result.

What is Machine Learning?

Machine learning is the science of getting computers to act without being explicitly program. It is a subset of AI: Artificial Intelligence. Predictive modelling is a branch of Machine Learning that particularly deals with tabular data to explicitly find patterns and/or insights from the data available.

Types of Machine Learning Problems

There are common classes of problems in Machine Learning. The problems discussed below are standards for most of the ML based predictive modelling problems.

  • Classification (or Supervised Learning): Data are labelled meaning that they are assigned to classes, for example spam/non-spam or fraud/non-fraud. The decision being modelled is to assign labels to new unlabelled pieces of data. Classification should be Binary classification and Multi-class classification.
  • Regression (or Supervised Learning): Data are labelled with a real value (think of a real number) rather than a label/class. Examples that are easy to understand are time series data like the price of a stock over time, monthly sales volume of a store etc. The decision being modelled is what value to predict for new unpredicted data.
  • Clustering (or Unsupervised Learning): Data are not labelled, but can be divided into groups based on similarity and other measures of natural structure in the data.




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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