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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

This foundational step involves cleaning and transforming raw data to ensure accuracy and consistency, which is crucial for reliable model outcomes. Techniques include handling missing data, dealing with outliers, and creating appropriate visualisations like histograms and scatter plots.

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

R Chunks for Data Preparation.

Simple Linear Regression

A supervised Machine Learning Technique

This method models the relationship between a single independent variable and a dependent variable by fitting a linear equation. It’s useful for understanding and predicting outcomes based on one predictor, and includes assessing the strength of the relationship through correlation coefficients.

Simple Linear Regression includes

Calculating Pearson’s Correlation Coefficient
Plotting Scatter Plot
Plotting Scatter Plot with Regression Line
Identifying High-Leverage Points
Identifying Influential Observations
Checking Normality of Residuals
Checking Other Requirements on Residuals
Calculating Predictions
Plotting and Calculating Confidence and Prediction Interval
Dealing with Categories in Regression
Identifying Importance of Predictors
Plotting Importance Chart for Regression Predictors

R Chunks for Simple Linear Regression.

Multiple Linear Regression

A supervised Machine Learning Technique

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Multiple Linear Regression includes

Computing Correlation Matrix
Showing Correlation Matrix
Running Regression
Displaying 3-D Plot
Calculating Prediction with Confidence and Prediction Interval
Showing High Leverage Points and Influential Observations
Making Indicator Variables for Shelf and Running Regression
Exploring Multicollinearity
Checking Whether Residuals Are Normal
Plotting All Residual Plots
Plotting All Residual Plots against Xs
Testing Model
Preparing Data for Pie Chart to Show Relative Importance of Factors
Showing Pie Chart of Sum of Squares for Regression Model

R Chunks for Multiple Linear Regression.

Logistic Regression

A supervised Machine Learning Technique

Used when the dependent variable is categorical, logistic regression estimates the probability of an event occurring. It’s particularly valuable for classification problems, such as determining whether a customer will churn or not, based on predictor variables.

Logistic Regression includes

The following steps are performed on each dataset:

Exploring data
Plotting Data as Scatter Plot
Plotting Regression Line with 95% CI
Calculating McFadden’s Rsquared
Calculating Confidence and Prediction Interval for given X data
Calculating Odds Ratio
Calculating Deviance

R Chunks for Logistic Regression.

Classification and Decision Trees

A supervised Machine Learning Technique

Decision trees are intuitive models that split the data into branches based on feature values, leading to easily interpretable classifications or predictions. They are particularly useful for handling both numerical and categorical variables, and they can capture non-linear relationships effectively.

Classification and Regression Trees includes

Classification and Regression Tree Using CART
Classification and Regression Tree Using Conditional Inference Tree (CTREE, C4.5 Method)
Classification and Regression Tree Using Conditional Inference Tree (C5.0, C5.0 Method)
Plot Decision Tree Using Partykit
Classification Using Random Forest

R Chunks for Classification and Regression Trees.

Artificial Neural Networks

A supervised Machine Learning Technique

Artificial Neural Networks (ANN) are powerful models inspired by the human brain, capable of capturing complex, non-linear relationships in data. They are particularly effective for tasks like image recognition, natural language processing, and predictive modelling with large datasets. However, they require significant computational power and careful tuning of hyperparameters to achieve optimal performance.

Artificial Neural Networks include

Fitting Neural Network for Cereals Data
Predicting Using Neural Network for Cereals Data
Fitting ANN for Adults Data (3 hidden nodes)
Predicting Using Neural Network for Adults Data
Running the Prediction for Adults Data (3 hidden nodes)
Generating Confusion Matrix and Statistics for ANN on Adults Data

R Chunks for Artificial Neural Networks.

Model Evaluation Methods

Applied to supervised Machine Learning Techniques

Evaluating predictive models is a critical step to ensure their accuracy, reliability, and applicability to real-world scenarios.

For multiple regression models, evaluation focuses on assessing goodness-of-fit measures like R-squared and residual analysis.

Classification and Regression Trees (CART) are evaluated using metrics such as accuracy, precision, recall, and cross-validation techniques.

Comparing the performance of models, such as linear regression or logistic regression versus CART, highlights their strengths and limitations in predicting outcomes and helps select the most suitable approach for specific tasks.

Model Evaluation Methods include

Model Evaluation Techniques for Multiple Regression
Model Evaluation Techniques for Classification and Regression Trees Using Adult Income Data
Model Evaluation Techniques for Classification and Regression Trees Using Wine Data
Comparing Prediction Performance of Linear Model and CART Model

R Chunks for Model Evaluation Techniques.

Clustering

An unsupervised Machine Learning Technique

Clustering is an unsupervised machine learning technique used to group similar data points based on their characteristics.

It helps identify patterns and structures within data without predefined labels, making it useful in fields like customer segmentation, anomaly detection, and image recognition.

Common clustering algorithms include K-Means, Hierarchical Clustering, and DBSCAN, each suited for different data distributions.

The goal is to maximize intra-cluster similarity while minimizing inter-cluster similarity.

Effective clustering can reveal hidden insights, aiding decision-making and predictive modeling.

Clustering Methods include

Estimating the Optimal Number of k-Means Clusters
Visualise Clusters
Compute Cluster Indicators
Compute Cluster Details
Plot Cluster Silhouette
Check Validity with R-square and Pseudo-F

R Chunks for Clustering.

Association Rules

An unsupervised Machine Learning Technique

Association rule mining is a data mining technique used to uncover relationships between variables in large datasets.

It is commonly applied in market basket analysis to identify product purchase patterns, such as “customers who buy bread often buy butter.”

The most well-known algorithm for generating association rules is the Apriori algorithm, which uses support, confidence, and lift measures to determine rule significance.

This technique helps businesses optimise marketing strategies, improve recommendations, and enhance inventory management.

By discovering meaningful associations, organisations can gain valuable insights into consumer behaviour and data dependencies.

Association Rules include

Mining Association Rules with Certain Confidence
Defining Association Details for Specific Rule
Visualising Rules
Showing Association indicators like Support, Confidence, Lift
Evaluating Association Models

R Chunks for Association Rules.

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Data Science

15 May 2023

Predictive Modelling Using R