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Predictive Modelling Using R

Welcome to our comprehensive guide on predictive modelling! This webpage is dedicated to providing an in-depth exploration of various modelling techniques utilised in both supervised and unsupervised machine learning. Here, you’ll find meticulously crafted code and output examples designed to showcase the power and versatility of these techniques.

Whether you’re a seasoned data scientist or just beginning your journey into the world of machine learning, our resources are tailored to enhance your understanding and practical skills. Dive in and discover how predictive modelling can transform data into actionable insights, driving innovation and informed decision-making in your field.

Data Preparation

Data preparation includes

  • Dealing with Missing Data
  • Dealing with Outliers
  • Creating Histogram and Scatter Plot
  • Performing Descriptive Statistics
  • Performing Transformations
  • Plotting side-by-side histograms
  • Understanding Skewness + Kurtosis
  • Conducting transformations to reach Normality
  • Plotting Histogram with Normal Distribution
  • Plotting Normal Q-Q Plot
  • Creating indicator Variables
  • Finding Duplicated Records

Please find R code and output here.

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