In this technology-driven world, machine learning has become a key skill to make intelligent machines or electronic devices. One can ace the machine learning skills only with tremendous practice and research. Having theoretical knowledge surely helps but it’s the hands-on practise that matters the most. There are numerous machine learning projects that one can work on. However, we have narrowed down the top 10 machine learning applications that you can start working on.
In this blog post, you will learn about different real-world machine learning case studies that will help you make great progress in your career. Let’s get started!
Machine Learning Applications
- Stock Price Predictions
If finance excites you, then predicting stock prices using machine learning might be interesting for you. In these kinds of projects, you have different types of data to choose from such as prices, global macroeconomic indicators, fundamentals, volatility indices, and much more. These data can be very granular. Typically, financial markets have short feedback cycles, so you can validate your predictions on new data.
Points to Note
- You can download stock market machine learning datasets from Quandl.com or Quantopian.com.
- If you are a beginner, you can limit the project to predict six-month price movements based on your quarterly organisation report.
2. Disease Prediction
With machine learning on the rise, the medical science industry is undergoing drastic changes. Based in Seattle, Washington, KenSci is a healthtech company that employs machine learning to predict illness to help physicians intervene earlier. They predict health risk by identifying patterns and surfacing high-risk markers and model disease progression and more. The company is harnessing AI and machine learning for improving health records and workflow.
What do they do?
- KenSci collects the health system’s data from sources like wearables, EMR records, medical devices, Claims, and ERP systems.
- Using machine learning, they carry out data integration, format conversion, data scrubbing, feature engineering, censoring and more.
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3. Fake News Detection
In this digitally connected world, fake news is spreading like wildfire and affecting millions of people on a daily basis. Countless articles are published every day on the internet – how do you distinguish between real and fake? How to deal with such a sensitive issue? This is where fake news detection project comes up.
Points to Note
- Using the machine learning applications of natural language processing (NLP), fake news can be identified.
- For instance, Facebook is employing AI algorithms to filter fake news out of users’ feeds.
4. Road Accidents Analysis
The number of road accidents is becoming unavoidable and growing day by day. With the help of machine learning, you can analyze the patterns in different situations by building appropriate prediction models that are capable of automatically differentiating various accidental scenarios. These clusters help in preventing accidents and developing safety measures.
5. Sentiment Analysis
As social media platforms like Facebook, Instagram, and Twitter are generating tremendous big data, analyzing data is critical to understand users’ sentiments. Sentiment analysis is one of the most interesting machine learning applications and it proves beneficial for digital marketers.
This project allows digital marketing and branding companies to understand the customers’ responses toward a product or a service. Beginners in the machine learning domain can also undertake this project.
Points to Note
- If you are a beginner, you can start this project in Python.
- You can use Twitter to collect data as the platform consists of countless tweets.
- Tweets are simple to pre-process as they comprise text, URLs, and hashtags.
- You can also integrate Twitter API libraries into your project.
6. Sales Forecasting
Sales forecasting is one of the easiest and interesting machine learning applications. One such project is the Walmart sales forecasting project. The aim of this project was to predict sales for every department in every outlet. This project helped Walmart in creating better knowledge-driven choices for inventory designing and channel improvement.
What you can learn
- Throughout this project, you can learn data manipulation in R.
- By developing this project, you can understand data visualization in sales.
- Also, you can learn how to apply machine learning techniques in sales prediction in Python.
7. Human Activities Recognition
The goal of the human activities recognition project is to develop a classification model that can accurately identify human fitness activities using machine learning. Working on this project will help you solve multi-classification problems.
Points to Note
- To develop this project, you can use a smartphone dataset, which consists of fitness activities of 30 people recorded through smartphones.
- 70% of the dataset has been segregated for the training phase and the remaining 30% is for testing.
- If you are a beginner, then this project will help you polish your machine learning skills.
8. Product Bundles Identification
Identifying product bundles from sales data is one of the most interesting machine learning projects in R. To develop this project, you need to employ a clustering technique, which is a subjective segmentation to identify the product bundles from sales data.
Points to Note
- To develop this project, you must have knowledge of data science and R programming language (read about what data scientists do here).
- Also, you need to be familiar with machine learning approaches such as an unsupervised technique for clustering.
- To identify bundles, you need to use market basket analysis.
9. Recommender Systems
Google, Amazon, Netflix, and many others have been using recommender systems to curate content and products for its consumers. Today, millennials prefer watching a movie online rather than on TV. With growing online platforms such as Netflix, the need for an efficient recommender system has gained popularity among today’s generation. Netflix recommends movies and TV shows due to its highly efficient recommender system. Building a well-organized recommender system has become an innovative and exciting project idea. If you want to learn to build recommender systems, then you can use the Movielens dataset, which is one of the popular machine learning datasets available on the web.
What you can Learn
- Movielens dataset contains 1,000,209 movie ratings of about 3,900 movies made by 6,040 Movielens users.
- These recommender systems can be developed using R and Python.
TensorFlow is one of the most popular open-source machine learning projects which help professionals to improve their machine learning skills. It offers a flexible ecosystem of tools, libraries, and community resources that allows developers to easily build and deploy machine learning-powered applications.
What you can learn
- With TensorFlow, project developers can create data flow graphs, arrays of applications, and projects using Java.
- Working on this project, you can easily build and train machine learning models using high-level APIs like Keras, which supports immediate model iteration and debugging.
- You can deploy machine learning models in the cloud, on-premise, in the browser, or on-device no matter what language you use.
Getting your hands on real-world machine learning case studies can be exciting. If you are a beginner, then working on the above-mentioned projects will help you shape your career in the machine learning field. Also, mentioning these projects in your portfolio will land you in a good machine learning job with rewarding perks. But before you get started with these kinds of projects, it’s important for you to understand the concepts of machine learning. Don’t worry, we have got you covered.