What is Machine Learning?
Machine learning can be defined as a branch of computer science and Artificial Intelligence that uses data and algorithms to mimic how humans learn. This technique gradually enhances its accuracy. Machine learning is a crucial component of the ever-evolving field of data science. Algorithms created for machine learning can help in decision-making for applications and businesses. One such example is the algorithms for training Google Search engine and Machine learning for predicting rainfall. Machine learning algorithms are usually created by software applications like TensorFlow and PyTorch. Recently, there has been an interest in Machine learning using R.
R can be considered a programming language and software environment for statistical analysis, data manipulation, data visualisation and reporting. R is a computer language that facilitates programming using functions. Here is a step-by-step method on how to start machine learning using R.
- Download and install R software Before proceeding with machine learning, the R programming language must be downloaded first. R can be downloaded from the official website- https://www.r-project.org/. An Integrated Development Environment (IDE), known as R studio, is essential as it makes coding easier in R. R studio can be downloaded from https://www.rstudio.com/products/rstudio/download/.
- Start R
- Install the necessary packages
- Load the package
- Load the data
- Divide the data
- Select and train a Model
- Evaluate the model
- Finalise the Model
- Iteration and improvement
Open the R software and get acquainted with the layout. It can be started from any menu system. The R interface consists of a console, script editor and environment/history panes.
Packages can be considered as add-ons or libraries that can be used in R. In machine learning, a package called ‘caret’ can be beneficial. The caret package provides a consistent interface into hundreds of machine learning algorithms and valuable convenience techniques for data visualisation, resampling, model tuning, and comparison. For installing a package, the ‘install.packages()’ function can be used. For installing caret, the function is ‘install.packages(“caret”)’.
The package must be loaded before proceeding with machine learning. The ‘library ()’ function can be used for this purpose. For loading caret, the function is ‘library(caret).
The data is imported into R. Data from CSV, Excel, or other formats can be read using the ‘read’ function like ‘read.csv()’ or ‘read.excel()’. Preprocess the data by managing missing values, outliers, scaling, and normalisation as needed by your selected algorithm.
Divide the data into training and testing sets. The training set is used to train the machine learning model, and the testing set is used to evaluate its performance.
Choose the best machine learning algorithm for the task in question. The algorithm you use is determined by the type of your data (for example, classification, regression, or clustering) and the issue you are attempting to address. To train the model using the training data, use the corresponding function. For example, you can use the lm() function to train a linear regression model.
On the test dataset, evaluate the model’s performance. Depending on the nature of the problem, use relevant evaluation measures such as accuracy, mean squared error (MSE), etc. The “caret” package includes useful functions for model training and evaluation, such as train() and predict().
When you are satisfied with the performance of the model, retrain it with the whole dataset (training + testing) to maximise its learning potential.
Machine learning is a continuous process. To increase the performance, you can try other methods, preprocess data differently, and fine-tune parameters.
Students need to be updated with the latest developments in R and machine learning for their dissertations, and sometimes they may lack guidance. We at Tutors India offer assistance with programming and algorithm for Master’s students specialising in computer science. Our expert team is experienced in research and academic writing and offers guidance to students. We assist with research in dissertation coding, software development, big data analytics, quantum computation, machine learning and Artificial intelligence. We ensure the dissertation is of high quality and meets the university requirements. To know how a dissertation on coding is written,
check out our example on A study on Cloud Computing: Challenges & Security Issues.
Check out our dissertation examples to know more about how a dissertation is written in various fields.
