Machine learning (ML) projects are one of the best ways to gain hands-on experience and improve applied machine learning skills. Machine Learning beginners and enthusiasts can take advantage of machine learning datasets available and get started on their learning journey.
In this cheat sheet, we will look at the top 10 machine learning (ML) projects for beginners in 2020, along with the machine learning datasets required to gain experience of working on real-world problems. We have divided the projects based on tasks like classification, forecasting, prediction and mining. Here are the top machine learning projects you can explore in 2020.
The stock market data is clean, extremely granular and allows for per-day or per-minute data updation in real-time. The constantly-changing nature of the stock market also makes it suitable to test the results of any solution that is deployed. Stock markets have always had a strong background in storing data, a fact that is more true today than ever.
Project: This project can be executed on both Python and R, and Python is recommended to beginners in machine learning. Due to its easily readable language and vast selection of add-ons and libraries, Python is often the go-to choice for beginners in machine learning. By taking into account historical data and fundamental indicators of when prices have changed in the past, a machine learning project can be undertaken to find the best time to invest. In-depth tutorials for writing an ML algorithm for stock markets can be found on YouTube. A value investing algorithm can be written for the stock market.
Tools: Using the sklearn library, a machine learning project can be created to perform a variety of tasks.
Dataset: You can access the dataset here.
Social media sites like
Facebook, Twitter, and Instagram have become a rich source of
data for machine learning projects. Apart from user-generated content like images, videos, and text, social media also holds a lot of metadata that can be utilized to solve different kinds of machine learning problems.
Project: One of the most important things that this project will teach is how to scrape data from online sources. While Twitter offers data through the Twitter API, the other sites do not offer anything similar. Facebook in particular is difficult to scrape, and is even illegal in some jurisdictions. However, Instagram and other social media sites like Reddit have no such restrictions. Web extensions like TamperMonkey and Python libraries like Requests, lxml, Scrapy, and Selenium can enable scraping data from these sites. A dataset can then be constructed from this and applied in the problem.
Tools: Some machine learning projects that can be executed with this dataset include sentiment analysis and trend identification. For example, a natural language programming algorithm can be employed to detect emerging linguistic trends on Twitter, or to find the engagement of your favorite brand on the social media network.
The Iris Flowers classification problem is one of the go-to machine learning projects for beginners. Based on the Iris dataset of flowers, this project tackles one of the main problems in machine learning; classification. Classification problems are ever-present in the machine learning space owing to their versatility and ease of use. By undertaking this project, beginners can gain knowledge of classification problems.
Project: The Iris Flower dataset is comprised of data points regarding 3 species of the Iris flower, known as Iris setosa, Iris virginica, and Iris versicolor. It only has 50 samples, and does not require additional preprocessing, making it a good dataset to learn classifications from. The algorithm required to solve this measures the length and width of the petals and sepals of the flowers to determine which species it is.
Tools: Python libraries Scikit-Learn and Pandas are a great fit for this problem, and are also commonly used in the industry for similar problems. This will give the individual undertaking the project hands-on experience with the exact tools they will require. Moreover, the concepts of classification involved in this project are integral for today’s machine learning problems.
Dataset: You can access Iris dataset here.
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The entire store sales dataset for Walmart, one of America’s biggest retail chains, is in the public domain for use for machine learning problems. Featuring data for 98 products across 45 outlets, this machine learning dataset is a gold mine for machine learning enthusiasts looking to learn more about forecasting problems in machine learning. Forecasting technique in machine learning is applied on historical data to predict future patterns.
Project: This machine learning project will tackle concepts like regression, random forests, and retraining; integral to deploying an accurate forecasting solution in a real-world circumstance. Since the dataset extends from 2010 to 2012, current sales patterns can be easily accessed and checked against to test the solution. Other data points include store, the various departments in it, unemployment rates, whether a certain day is a holiday or not, and various discounts running during a specific time-period.
Tools: For this project, you can also apply basic linear regression model to predict sales. Facebook’s Prophet, an open-source library can also be used for forecasting sales data for this project.
Dataset: You can access the Walmart Stores Sales dataset here.
Moneyball movie put data science and machine learning on a global map. The ‘Moneyball’ machine learning project refers to utilizing machine learning to derive insights from sports data. The Moneyball project aims to apply machine learning concepts of prediction to a vast array of data available in the sports space. Baseball, football, and basketball all have a treasure trove of historical data which can be applied to build machine learning projects.
Machine learning datasets such as the Sports Statistics Database, Sports Reference, and Baseball Databank feature data on players, the teams they played for, statistics for their feats, and various sports-specific data.
Project: For example, a project can be created that identifies the best player to bet on depending on their past performance. By creating a prediction algorithm, it becomes possible to see if a player is likely to perform in their next game. A project can also be deployed to autonomously manage a team, saving money and capitalizing on existing partnerships. Apart from learning how to isolate specific data fields for problems, this project also allows beginners to pick up data visualization, an important tool for machine learning in the enterprise segment.
Tools: The most commonly applied technique here is Linear Regression, which falls under supervised machine learning.
Dataset: You can access the Moneyball dataset here.
Judging wine has long been reserved for connoisseurs and the likes, but the Wine Quality Dataset brings it to machine learning engineers and beginners. This dataset contains almost 5000 data points, each with 11 independent and 1 dependent variable. These are characteristics of the wine like the quantity of alcohol, volatile acidity, pH, density, and taste factors.
Project: Using the varied dataset, this machine learning project aims to determine the quality of the wine. By taking into consideration the variables and how they would influence the final product, this machine learning project can accurately gauge the quality of the wine using a confidence score.
Tools: R is recommended for this problem owing to the nature of the dataset, but this machine learning project can also be undertaken in Python. Python libraries NumPy, Pandas, and SciKit-Learn make this project a lot easier to undertake. This problem largely tackles data preprocessing to make it suitable for prediction, which is useful when considering unclean data sets in real-world circumstances.
Dataset: You can access the dataset here.
We cannot imagine a day without
recommendation systems that have become ubiquitous in the online world. From Facebook’s News Feed to Amazon’s home page, to Netflix’s movie recommendation system, they have become widely-used due to their impact on customer experience. Providing a system to customize the user’s experience based on their likes and dislikes holds a lot of value for a company.
Project: Using the MovieLens machine learning dataset, beginners can create a recommendation system of their own in Python. This project will not only introduce beginners to the basics of building a recommendation system, but also encourage better use of the dataset as a whole. Recommender systems require dimensionality reduction as a skill to increase the probability of a good recommendation. The dataset features 1 million ratings from 6000 users over a range of 4000 movies. This dataset draws from a bigger dataset known as EachMovie, which featured 2.8 million ratings from 72,000 users for 1628 movies.
Tools: Libraries like NumPy, SciPy, Pandas, Theano, and SciKit-Surprise can help create a recommendation system for beginners.
Dataset: You can access the MovieLens dataset here.
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The healthcare sector has become the biggest adopters of
machine learning, mainly due to the large amount of organized, clean data available from patient records. In healthcare, machine learning can impact areas like preventive care, easier insurance claim and predictive medicine.
Project: Healthcare datasets are a good starting point to tackle classification problems in machine learning. Apart from sorting through simple data points and making predictions or classifying symptoms, many healthcare datasets also feature images of scans for image classification.
Tools: Beginners will be able to engage in image classification tasks using Python libraries like SciKit-Image and OpenCV. These libraries not only greatly reduce the time required for certain classification use-cases, but are also commonly used in the industry for similar problems.
Dataset: You can access the datasets here.
The MNIST Handwritten Digit dataset is an open and easily-available dataset, and is one of the most basic image recognition problems in the field. Featuring handwritten numbers, the solution is to find what the input number is using machine learning algorithms.
Project: This basic image classification problem is suitable for beginners due to the small amount of possible solutions. This problem also develops on the concepts of logistic regression and the basics of neural networks, along with more complex concepts such as k-nearest neighbour and random forest.
The dataset features 60,000 images of 28×28 pixel handwritten digits. This small size makes it fit into memory easily without the need to convert the data. Moreover, as the data is in an image form, this project will give beginners the ability to convert pixel information to data that can be used by the algorithm.
Tools: Python libraries like OpenCV and SciPy will be helpful in this project.
Dataset: You can access the dataset here.
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One of the best ways to begin studying machine learning is by deploying pretrained models to existing datasets. This machine learning project allows beginners to get hands-on experience of how a machine learning model works in the real world. The way the data is harvested, processed, and delivered to machine learning solutions differs from situation to situation, requiring a knowledge of the overall workflow.
Project: This project will focus on the basic workflow involved in a machine learning solution while keeping the model and dataset out of the question, allowing beginners to identify places where they lose the most time. In this project, enthusiasts can learn how to determine the best method of deploying a pretrained model to a new dataset. For example, pre-trained models can help speed up training time
Tools: Beginners can learn how to efficiently deploy algorithms and set up a data pipeline. This project will give an overall understanding of the workflow and help beginners prioritize tasks during the project execution.
Dataset: You can use any dataset for this project.
Due to the various disciplines involved in machine learning and the quick pace of advancement in the field, machine learning has become a go-to choice for
computer programmers to specialize in. Working on cutting-edge machine learning projects can help beginners transition to this buzzing field effectively, expand skill-set and build a portfolio for recruiters.
Taking up these projects will improve a beginner’s fundamental understanding of machine learning basics, as they will each teach a different part of a typical machine learning workflow. The variety of projects featured in this article are applied in real-world business settings.
The concepts and methods described in this article are only the tip of the iceberg when it comes to learning machine learning. There are many more projects available online that an aspiring machine learning programmer can pick up. More nuanced concepts like natural language processing (NLP) and neural networks will be the next step for individuals who aim to become machine learning engineers.