“Machine Learning and Artificial Intelligence have reached a critical tipping point & will increasingly augment & extend virtually every technology-enabled service, thing, or application. Creating intelligent systems that adapt, learn, & potentially act autonomously rather than simply execute predefined instructions is the primary battleground for technology vendors through at least 2022.”
This couldn’t be any truer. Standing here in 2022, we are witnessing the increasing influx of AI and ML in our day-to-day lives. These intelligent technologies dictate almost every aspect of our lives now, be it healthcare and education or business and governance.
The adoption of AI and ML technologies across all sectors of the industry has increased the demand for qualified and skilled Data Science professionals. But that doesn’t mean anyone can land a promising AI/ML job role – you need the right educational qualifications, skills, and most importantly, real-world projects to showcase your experience.
Developing live projects allows you to test your theoretical knowledge, sharpen your skillset, and identify your core strengths and weaknesses. As you keep building your own projects, with time, you will gain more confidence over your professional knowledge and skills.
We’ve created this post exclusively for aspirants who wish to enter the domain of Machine Learning. In this article, we will highlight some exciting Machine Learning projects in R. Since R is the top preference when it comes to statistical computing, it is the ideal choice for building Machine Learning projects.
Before we start our discussion on Machine Learning projects in R, you should be aware of the standard steps involved in building a Machine Learning project:
- Problem definition – Before you begin designing a Machine Learning project, you must define the problem statement, that is, what problem do you aim to solve with the model and how ML fits into the picture.
- Data preparation – You must study the dataset at hand and determine whether it’s a structured or unstructured dataset, whether it’s static or streaming, and how will it complement the problem definition. This stage mainly involves cleaning and preparing the data for processing.
- Algorithm evaluation – A Machine Learning project involves different ML algorithms. It is crucial to identify which algorithms best-suit the problem definition and guarantee maximum accuracy of the outcomes.
- Data features – In this phase, you will determine which elements or features of the dataset you will use for the Machine Learning project and how the already obtained insights affect the project.
- Modeling – You must choose a particular model structure and find ways to improve it. Also, you must compare this with other models to see which one is befitting for the problem statement.
- Testing – As the name suggests, testing means studying the outcomes of the model and find ways to improve it even further. It is vital to analyze how a small change impacts the overall outcome of the model and also how it affects the following steps.
So, without further ado, let’s get started!
Table of Contents
Machine Learning Projects in R
In this project, you will build an ensemble ML model for aviation incident risk prediction. The project aims to assess the risk of uncertain and dangerous events associated with aviation. Here, the hybrid model fuses the SVM prediction on unstructured data and the ensemble of deep neural networks on structured data. The focus of this ML project is to enhance the safety level of aviation systems and to quantify the risks by accurately predicting the occurrence of abnormal events.
The project you will build will implement the static technique of classification to identify and categorize ransomware. It will begin by transforming the ransomware samples into the N-gram sequences. The model will then compute the frequency-Inverse document frequency (TF-IDF ) to facilitate the advanced segregation of the ransomware. Finally, this becomes the input for the ML model to classify the ransomware. This ML model also explores and analyzes the discrimination between opcodes across different ransomware families.
The idea here is to build an ML system that can detect harmful Android apps which are using discriminant system calls. This project leverages the Absolute Difference of Weighted System Calls (ADWSC) and Ranked System Calls using Large Population Test (RSLPT) feature selection technique for pruning a huge system call dataset.
While the feature selection is based on the correlation among the different features, these two selection techniques help uncover the most beneficial features that will further aid in classifying the malware samples with improved accuracy. The primary aim of this Machine Learning project is to find out malicious Android applications while keeping the computational complexity at a minimum.
This ML model makes use of Big Data for credit scoring. Essentially, the credit scoring model leverages social network analytics and mobile phone data to enhance financial inclusion and evaluate the credibility of a credit cardholder. By using large volumes of identical mobile data of a wide range of credits spanning across different countries, the model aims to improve the statistical performance to enhances the decision-making process for credit.
5. Life model
This Machine Learning project aims to accurately predict the anomalies in healthcare analytics using temporal data of the healthcare system and to predict the mortality rate of a patient. To do so, this project proposes the development of a Life Model (LM) based on the deep learning neural network. By exploiting the intensity of temporal sequence (ITS) tensors, the neural networks will model the lifespan of each patient based on their historical medical data. The result will be in the form of a short and concise temporal sequence.
Learn more: Deep Learning vs Neural Networks
This activity prediction system is based on the Recurrent Neural Network (RNN). It is a wearable sensor-based activity prediction system that will facilitate edge computing as a part of smart healthcare infrastructure.
The wearable will monitor the activities of patients, and further predict their actions using the information provided by the sensor. This model is designed to deal with large-scale, complex data and to promote fast computation to improve the prediction performance of smart healthcare systems.
In this Machine Learning project, you will develop a scalable support vector machine to detect faults in transportation systems. The aim here is to create a system that facilitates improved processing speed of data points. The model uses the KNN-based FSVM (KNN-FSVM) approach to mitigate fault detection constraints in the transportation system.
This method not only reduces the dimension of the data, but it also reveals how important is the training data for an imbalanced dataset. Furthermore, the KNN-FSVM method can eliminate the limitations of classification of erroneous data, thereby improving the prediction accuracy.
This Machine Learning project proposes to use a combination of ML and advanced optimization methods to handle and manage the computational complexity of water distribution systems (WDS). The model employs a regression technique along with other optimization techniques to combat the mixed-integer problem. For energy estimation, it uses curve fitting techniques. Using the semi-supervised learning approach is the best bet for this project since it helps reduce the computational time.
Also read: R Project Ideas & Topics for Beginners
In this project, you will leverage different ML techniques to create a music cognition system that can understand and cognate music and automatically generate the music score via fog computing. The project uses both the hidden Markov model and the Gaussian mixture model to recognize music and its unique features. It is recommended that you use a multiple instrument recognition scenario for designing the system. This will improve the overall performance of the cognition model.
This is an anomaly-based intrusion detection system that uses feature selection analysis. Here, you will build a hybrid model that uses different ML techniques on network transaction data to analyze the scope of the intrusion. The focus is to keep the detection time at a minimum. The model will explicitly use the Vote algorithm with Information Gain for extracting the optimal data features. Then it will use classifiers to improve the accuracy of the detection system.
This personalized basket prediction system proposes to create a recommendation list for users to best cater to their needs and preferences. You will design a model that will extract and collect the Temporal Annotated Recurring Sequences (TARS) from the purchasing history of customers. In the next step, it will use the TARS Based Predictor (TBP) to predict a personalized product basket for a customer. To analyze the features of the existing suggestion list products with the new products’ features assists in enhancing the prediction quality.
The goal of this Machine Learning project is to resolve the issues of performance forecasting in cellular networks. The model will make use of the random forest ML technique to keep the operational costs at a minimum. This technique is also excellent for resolving computational challenges and resource allocation issues. While the model will predict the performance of cellular networks, it should also be able to improve the customer experience.
This Latent Ability Model (LAM) is designed to analyze the workforce and activity-logs of the employees. The primary job of the LAM is to model a latent relation between employees and their assigned activities. So, it will compute the score between the employee and those activities that determine the employee satisfaction level.
Based on this score, the LAM will develop prediction models to predict employee performance, compare employee ability, and conduct a quality estimation of employee activities. It will further create a predictive distribution representation based on the activity log of the employees.
In this project, you will build a forecasting system for predicting the volatility of the Stock Price Index. In this hybrid model, the long short-term memory (LSTM) model is integrated with multiple GARCH (Generalized AutoRegressive Conditional Heteroscedasticity)-type models. This combination will help support and improve the volatility clustering.
This model is designed to compute the asset-level sentiment-based time series data gathered from social media. It utilizes sentiment analysis and text mining methods in combination with allocation techniques. Further, the ML model uses the long short-term memory (LSTM ) model and an assortment of the evolving clustering technique to validate the sentiment data as against the market data and statistics. Thus, the primary goal of this project is to capture the market sentiment for smart asset allocation.
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Also Check out: Data Structure Project Ideas
So, there you go – 15 interesting Machine Learning projects in R! Project building is a fun learning experience, provided you choose such topics that excite you and are closely related to your interests. Start by working on smaller and simpler projects to build your practical skills and then progress to more advanced-level projects. Lastly, always make sure that you test your models!
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Can machine learning be done in R?
Yes. R is used for many machine learning tasks. Classification, segmentation and regression are few tasks that can be done using R. The thing about R is that it comes with a wide variety of machine learning packages that can be used for different tasks. For instance, if you want to do regression then you can use randomForest package. If you are on the other hand interested in classification then you can use glmnet package.
What is supervised learning in machine learning?
Supervised learning is one of the most basic machine learning techniques. It is also a cornerstone of many other machine learning algorithms & tasks. The data used in this type of learning are labelled- these are known as supervised datasets. In this type of learning, the algorithm has to learn the mapping between the input variables and the output variables. The algorithm has to learn the rules governing the relationship between the inputs and outputs. It’s much easier for the learning algorithm to learn using this type of data as compared to learning from a dataset where the outputs are not labelled.
What is the difference between classification and regression in machine learning?
Classification is predicting the class label of data instances, whereas regression is predicting numerical values. We fit a linear model for regression and a non-linear model for classification. A simple example of linear regression is predicting the prices of used cars. To solve this problem, we need a model that takes the following features of an automobile into account: the car's length, weight, fuel efficiency, and so on. We then fit a linear equation to the data points. A good example of classification is predicting whether a patient will contract a certain disease based on their age, gender, smoking status, etc. In this case, we fit a non-linear model to the data points.