SVM for Beginners: Support Vector Machines in R Studio

Install R and R Studio on your system from r-project.org, selecting the Windows installer. Launch R Studio and write and run scripts in its four-window interface (script top-left, console bottom-left).

Learn r studio basics: use script and console windows, run commands with control+enter, assign x <- 2, and manage workspace with ls and rm, plus comments with #.

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Install and manage R packages, load them with library or require, and detach or remove as needed; explore inbuilt and external packages like gg plot and lib linear.

Learn to input data in R by using the built-in data sets package, manually entering data, or importing from csv, then inspect with data() and str() and load iris.

Learn how to input data manually in R Studio: assign values to variables, use concatenation and sequence to create vectors, and enter numbers with scan for SVM analyses.

Import tab-delimited text and csv data into R Studio with read.table and read.csv, handling headers and separators, then inspect with str and view.

Learn to create bar plots in R to visualize regional frequency distributions, order and orient bars, customize colors and borders, add titles and axis labels, and export images.

Create histograms in R to explore the age distribution of customers, using hist, defining breaks or widths, toggling frequency, color, and main labels, and exporting the chart.

Explore machine learning by showing how programs learn from past data to classify, predict, and optimize decisions, with real-world examples like fraud detection and price estimation.

Formulate the business problem as a statistical task, identify dependent and independent variables, tidy and preprocess data, and split into train and test sets for model training and evaluation.

Explore the progression from the maximal marginal classifier to the support vector classifier and to support vector machines, highlighting linear separability limits, nonlinear boundaries, and multiclass extensions.

Define a hyperplane as a p minus one dimensional separator that splits a space into two parts, yielding a line or plane that classifies blue and purple observations.

Describe the maximal margin classifier that selects the hyperplane with the largest distance to training points in a two-dimensional predictor space, relying on the support vectors.

The maximal margin classifier struggles when data are not perfectly separable and is sensitive to new observations, risking overfitting as we move toward the support vector classifier.

Explore soft margin support vector classifiers in R Studio, balancing maximum margin with a budget of misclassifications, and tuning the cost parameter c to improve test set performance.

Explain the limitation that support vector classifiers require linear separability, show a nonlinear example like a circle boundary, and introduce the kernel method to achieve nonlinear boundaries with SVMs.

Learn how support vector machines use kernels to create non linear decision boundaries. Tighten boundaries by tuning C and gamma with cross validation on linear, polynomial, and radial kernels.

Build a classification model to predict whether a movie wins the Start_Tech_Oscar using a dataset of 506 movies, with a train/test split to evaluate performance.

Learn to import a CSV dataset into R with read.csv, handle file paths and headers, preview data, and perform missing value imputation as part of data preprocessing for SVM modeling.

Split the data into training and test sets using an 80/20 ratio to train the svm model and evaluate its performance on unseen data.

Train a classification svm model with a linear kernel in R using the e1071 package, convert the dependent variable to a factor, scale features, and assess with a confusion matrix.

Tune the cost parameter of a linear kernel SVM in R with the e1071 tune function, using 10-fold cross-validation to identify the best model for test set performance.

Explore SVM with a polynomial kernel, tuning cost and degree via grid search and cross-validation, to select the best model and assess performance with a confusion matrix.

Train a radial SVM in R with gamma and cost tuned by cross-validation, selecting a model. Compare test predictions and note how the kernel type affects performance, with regression next.

Analyze a dataset of 506 movies with 17 predictors to predict box office collection using regression. Distinguish qualitative (yes/no, genre) and quantitative variables, and plan data import for modeling.

Train a regression svm in R on a movie dataset, import data and impute missing values, split data, train with e1071 svm, and compare kernels via MSE.

Identify the business context and key variables by conducting primary and secondary research, gathering data, and understanding the customer journey to tackle online cart abandonment.

Identify data needs, source internal and external data, and perform quality checks to prepare a tidy dataset for cart abandonment analysis, including channels, buying steps, and variable definitions.

Assemble a 506-observation, 19-column housing dataset, explain header conventions and underscore-separated variable names, and define a comprehensive data dictionary with variable definitions and the primary key for analysis in R.

Import the house pricing dataset into the R workspace using read.csv to create df, view df, and inspect its structure with str to reveal 506 observations and 19 variables.

Explore univariate analysis of a variable with descriptive statistics including mean, median, mode, dispersion and percentiles, plus categorical counts, using extended data dictionary (EDD) to identify outliers and missing values.

Perform univariate analysis in R using histograms, bar plots, and summaries to detect outliers, skewness, and missing values, and identify useless variables; inspect relationships with scatterplots.

Identify outliers using box plots, scatter plots, and histograms, then impute or cap values with percentile-based limits or sigma rules to stabilize the mean, median, and prediction accuracy.

Apply outlier treatment in R by capping and flooring: cap n_hot_rooms at three times the 99th percentile and floor rainfall at 0.3 times the 1st percentile, based on quantile calculations.

Learn how to handle missing values in data for SVM in R Studio by comparing deletion and imputation methods, including zero, mean, median, mode, and segment-based approaches.

Learn how to impute missing values in R by replacing NA with the mean using na.rm = TRUE, identify NA positions with which(is.na()), and update the variable accordingly.

Discover seasonality in time-based data with recurring patterns. Learn to normalize by applying a correction factor derived from year and month means to improve model fit.

Master bivariate analysis using scatterplots and correlation matrices to explore relationships. Learn how variable transformations—log, exponential, polynomial forms, averaging, and ratios—improve linearity and help manage multicollinearity in regression models.

Transform crime rate with log(1+crime rate) to linearize its relationship with price; create an average distance from dist1–dist4 and remove the four distance variables and bus terminal.

Identify non usable variables by applying univariate and bivariate analysis, remove constants and variables with low fill rates, and iteratively refine with imputation, regulatory, and business considerations.

Learn how to handle categorical variables in regression by creating dummy variables. Assign n minus one 0/1 dummies for each category and interpret regression results for nominal data.

Learn to create dummy variables in R using the dummies package, converting categorical data like airport and water body to numerical columns with one fewer dummy per category for regression.

Explore how correlation matrices reveal positive, negative, and near-zero relationships via correlation coefficients; distinguish correlation from causation and address multicollinearity by selecting key variables.

Learn to compute and read a correlation matrix in R with cor and round to two decimals, identify variables related to price, and avoid multicollinearity by removing highly correlated predictors.

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