14 Data Science Projects From Beginner to Advanced Level
From natural language processing to sentiment analysis, data science is now in demand across many industries. One of the best ways to develop the mastery of this skill is through an assortment of learning data science projects.
What is a typical data science project?
A typical data science project will take a data set and analyze it for a specific purpose, such as taking a list of transactions and identifying their cost-basis and ROI. When you develop a data science project, you need to consider both the data you have and the desired results.
How do I start a data science project?
A data science project always begins by collecting and validating data. It can be impossible for a data science project to yield the right results with incorrect data, regardless of how rigorous the methodology might be otherwise.
Consider trying out some of these data science projects to expand your portfolio, test your data science skills, and get practice in a swiftly-growing field.
Last Updated April 2022
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Beginner data science projects
1. Customer segmentation
One of the first things a business learns to do is segment its customers. Customer segmentation requires splitting customers along major demographics so customers can be targeted better and delivered superior service.
Customer segmentation is performed on a company-by-company basis because every company has different demographics. So, it’s not enough to simply create a known template for customer segmentation; you have to analyze your customers and split them into reasonably accurate groups.
Customer segmentation involves mining your data for your customer demographics and those customer behaviors.
- There are different types of clustering: K-means, support vector classification, hierarchical, Fuzzy, density-based, and more. Which is better for your system?
- As customers are added, how would your segmentation shift to remain balanced? What if your customers started moving in a different direction?
- What would you do if you wanted to integrate your customer segmentation processes into an existing system, such as Salesforce?
View code: customer-segmentation-python / Hari365
2. Credit card fraud detection
Credit card fraud detection requires basic pattern recognition, making it an excellent beginner data science project. It’s also a perfect practical addition to your data science portfolio because it’s something that most websites and apps need today.
To detect fraud, you can analyze previous transactions, determine whether a client is in the location that you expect, and otherwise identify whether the transaction appears abnormal (such as occurring at an unusual time for the customer’s timezone). Given that, all factors will need to be weighted and adjusted so that your system can reliably detect credit card fraud without a detrimental number of false positives.
This project is a great way to get started in data analysis.
- What would indicate that a credit card was being used fraudulently?
- How much data would you need to feed into the system until it had an acceptable amount of accuracy?
- What is an acceptable number of false positives? Why?
3. Natural disaster prediction systems
Wouldn’t it be great if you could detect fire, flood, or earthquakes before they ever occurred? There’s a lot of data out there now for exactly that purpose.
While you can’t always detect a natural disaster, you can predict the chances of things such as massive wildfires. A natural disaster prediction system would be fed in historical attributes of days during which that disaster occurred — then the system would identify when those disasters are most likely to occur in the future.
But, as with any data-based system, it requires accurate data. That would mean that you would need to mine through many historical disaster-related data to gather your information.
This is a challenging problem. For beginners, you need to know enough to utilize existing systems. More advanced users will dig into technologies such as convolutional neural networks and recurrent neural networks for making predictions from time-series data.
- What factors generally lead to a given disaster? Is there a factor you think might be missing?
- What level of error are you comfortable with? Every system has some level of error. When would a false positive be acceptable?
- Where will you get your data from? What sources would have the most accurate data available?
4. Recommendation systems
When Netflix starts playing another movie after the one you just watched, what’s happening? Netflix uses a complicated algorithm to determine what you’d most like to see. And it’s basing this on all the information it has about what you’ve watched before.
A recommendation system can be used for television shows, movies, books, music, etc. You simply need to gather information about the things you have liked in the past, and then the system can compare this with other things that are available.
Of course, you will need an archive of information and data relative to what the user has liked. This presents the opportunity to develop a user interface that other users can use and a front-end interface for a back-end database.
This project arms you with a tool chest of recommender system algorithms to try in the R programming language. If Python code is your preference, you might want to check out the SurpriseLib project.
- What media would you like to be able to recommend — books, movies, TV?
- What aspects of that media would most likely impact someone’s decision to watch it?
- How would you like to collect information about what people like?
View code: recommenderlab.github / cran
5. Sentiment analysis
Everyone from politicians to brands loves sentiment analysis. Sentiment analysis looks for mentions of a given person, product, or place and then identifies the general tone of the comment. Sentiment analysis is frequently used to look through hundreds or even thousands of product reviews to see whether those reviews are generally positive.
For eCommerce stores, sentiment analysis can determine which products are more popular. For politicians, sentiment analysis can determine general popularity rates and identify those who may be less than pleased. And for brands, sentiment analysis shows what customers think of them.
But sentiment analysis isn’t easy. You need to develop a rubric intended to determine whether someone is sad, happy, angry, or pleased with a given concept.
- What type of thing would you like to perform sentiment analysis on? How will you collect the related data?
- How would you distinguish between something like “happiness” and “sarcasm”? What are the hallmarks of sarcasm?
- How would you display that information if your sentiment analysis is uncertain or along a spectrum? Would you be able to visualize it?
This particular project uses a dictionary-based approach using R. You might also explore the use of recurrent neural networks and other machine learning techniques for sentiment analysis. Data sets that include review text and explicit positive or negative ratings can train a neural network and perform sentiment analysis without a dictionary.
View code: SentimentAnalysis.github / cran
6. Design a chatbot
Just a decade ago, designing a chatbot was quite complex. It’s still complicated, but there are better tools for it now. In the last decade, we’ve learned that machine learning with analysis is one of the best methods of developing a chatbot. Chatbots can figure out what it means to sound human through machine learning.
You can begin with publicly accessible source code and start “training” your chatbot. You can control what your chatbot knows and even how it sounds in terms of tone. From there, you can start to understand how machine learning works and how the input is analyzed.
Through a chatbot, you can learn more about artificial intelligence and narrow AI and its applications.
- Do you want to develop a chatbot for recreational purposes or something like a business-to-consumer application?
- What should your chatbot do when it doesn’t understand something — should it pretend or ask for clarification?
- What data sets do you think you should feed your chatbot to sound natural?
View code: Chatterbot.github / gunthercox
Intermediate data science projects
7. Fake news detection
Fake news is everywhere today. And most people have no idea when they see it. But there’s a reason why Twitter, Facebook, and other social media accounts have been able to flag fake news — or, at least, news that you should be skeptical about.
These systems use certain signals, such as the apparent authenticity of the account that information is being shared to and the “trust” they have with certain domains. Fake news is usually distributed by the same individuals and usually on untrustworthy websites.
Using machine learning, you may need to build an archive of what you should or shouldn’t trust. And you may need to have some component of user feedback to continue to refine and fine-tune the system.
- What happens when your system falsely flags real news as fake news? How is it corrected?
- At what threshold is something considered “fake” or just “suspicious”?
- What are the leading factors that make something “fake news” instead of simply obscure?
This project lets you experiment with several classification algorithms, including naive Bayes, random forest, SVM, SGD, and logistic regression. You’ll gain experience with a wide variety of machine learning and data processing techniques.
View code: fake_news_detection.github / nishitpatel01
8. Gender detection, age prediction, and other demographics
Did you know that you can use a sample of someone’s writing to determine gender, age, and other demographics?
Whether they write circuitously or more directly can sometimes indicate gender socialization. Whether they use certain words or slang can determine age and location. Do you use “pop” or “soda”? Would you say that you “were mad” or “were a little mad”?
These systems are a great introduction to “training” an AI. You don’t tell the AI that, for instance, women tend to write indirectly (“I think I…” instead of “I”). Instead, you feed the AI multiple samples and let it make its own conclusions. Once it has enough samples, it should be able to determine who is talking to it with a certain degree of accuracy.
(Of course, there aren’t many innate differences in language; these are mostly learned. So the AI won’t always be correct!)
- How many samples would the AI need to provide a reasonable amount of accuracy? Would your AI be able to pinpoint how many samples it needed?
- What would you be testing for, and how would you mark your samples?
- Imagine you wanted a system that could tell what city someone likely lived in. How would you begin your data collection?
View code: age-gender-estimation.github / yu4u
9. Speech emotion analyzer
There’s a reason this is an intermediate project: It’s hard even for people to identify emotions. But the speech emotion analyzer is another machine learning system that’s very clever. You don’t need to be able to identify the hallmarks of emotion because machine learning systems can compare large sets of data and draw conclusions about patterns.
You would feed audio into the machine and tell the machine whether it was displaying happiness, sadness, anger, etc. And then the system itself would start to learn what that sounded like, as a human might, without you having to say that “loud means angry” or “sadness is quieter.”
Over time, you’d develop a pretty accurate method of identifying emotion.
- What would the applications of a speech emotion analyzer be? Be creative about what this could be implemented in.
- What emotions would you want to include in your speech emotion analyzer? The more emotions, the more sample data you would need.
- How would you attempt to identify multiple emotions at once? Could you rate them by percentile?
This project lets you practice with convolutional neural networks and LSTM (Long Short Term Memory) neural networks, as well as the particulars of dealing with audio data.
View code: Speech-Emotion-Analyzer / MITESHPUTHRANNEU
10. Handwriting recognition
Data science projects can be complex in two ways. One, they may require highly complex algorithms. Two, they require extensive data sets. Many data science projects aren’t very difficult algorithmically, but they are held back by the sheer number of datasets they may require.
Handwriting recognition is similar. You need to feed a lot of data into a system, and you also need to be able to show the system what makes a handwriting sample “belong” to another person — that’s the most challenging aspect.
You can’t just feed any handwriting samples into this system; you need handwriting samples of the people you hope to match. So, if you had, for instance, a “suspect,” you would need quite a few samples of their handwriting to even start to compare. The easiest way to start this project is by using signatures specifically.
- How many samples would you need from a single person before it became accurate?
- What if someone was switching between cursive and print? How much would that throw your system off — and what could you do to compensate?
- Could you take a signature and identify other text that had been written? Why or why not?
This project uses Tensorflow to construct artificial neural networks that decode handwriting images into text. Still, it goes well beyond the standard “MNIST” example that’s limited to recognizing handwritten numerical digits. It can recognize words and lines of text, which places this activity firmly within the “intermediate” category.
View code: SimpleHTR / githubharald
Advanced data science projects
11. Cancer cell identification
A lot of projects are just for fun. But cancer cell detection is an example of how machine learning is truly advancing the human race. Most cancer cells look very similar — and very different from other cells. So, artificial intelligence can automatically detect these cells, potentially leading to better health results.
Interestingly, one of the first cancer detection systems was actually meant to identify types of bread. It sounds like an amusing anecdote, but it highlights something important about machine learning. Whenever a machine learning system is comparing images to identify them, it’s essentially doing the same thing. That’s true, whether it’s cancer or bread!
So, this project can teach you a lot about image recognition.
- What made the bread machine so likely to detect cancer cells as well? What other items might be similar?
- How would you be able to detect different types of cancer?
- What could you do to reduce the chances of false positives or false negatives?
12. Driver drowsiness detection
They often say that driving while drowsy is actually more dangerous than driving while drunk.
But people may not always know how tired they are. A drowsiness detection app is very clever. It looks at a person and where their eyes are and notes if they are having trouble keeping their eyes on the road.
Today, these types of apps actually appear in many cars; they warn a driver if they’ve looked away from the street. These apps are great practice at mining video data for information, such as where someone’s looking. They’re also great for eye-tracking.
But it’s also challenging because it does require inputting and analyzing video — something that can come in handy in many applications.
- Are there ways other than eye-tracking that you could potentially use to detect a driver being drowsy?
- How can you differentiate between someone being drowsy and someone looking at their mirrors (away)?
- How long would a reaction (such as closing your eyes) need to be to constitute “drowsiness”?
13. Automated image captions
Did you know that many leading news sites have images captioned by AI?
It’s not always feasible to have a human comb through this information. Sometimes it’s just better for it to be done automatically. But it’s also more challenging than it seems.
An image caption generator has to do multiple things to be successful. First, it has to identify all the items that are in the image. Second, it has to create a sentence that is grammatically correct (“child on bike”). Third, it has to superimpose that sentence onto the image.
And if anything goes wrong, you’ll have a nonsensical passage (“human by wheels”). If everything isn’t perfectly correct, the data revealed could be insensible.
- What data sets would you need to feed into the system? Would it be a good idea to use news sites? Why or why not?
- How detailed do you think the captions should be? What changes if you add two, three, or four elements to each caption?
- How would your caption system be able to be trained by users to be more accurate?
View code: Image-Caption-Generator.github / dabasajay
14. Traffic sign recognition
Why is traffic sign recognition harder than identifying cancer cells?
When identifying cancer cells (or bread), the image is usually isolated. Traffic signs are generally inundated with other information: other signs, business signs, people, trees, equipment, and more.
Further, traffic signs change worldwide; you need to be able to identify a wide breadth of them. And many things look like traffic signs, such as business signs. Finally, you usually need to do so quite fast when you need to recognize traffic signs.
With a cancer cell, you’re only looking at a few options, you have time, and you’re looking at a single image — so, in some ways, it’s easier to implement.
Nevertheless, traffic sign recognition has become extremely popular as autonomous vehicles have become more common. It’s just one example of the wider field of computer vision autonomous vehicles rely on.
- What do traffic signs look like in the rest of the world? Will you include all of them or just those that are local?
- What is the inherent difference between a traffic sign and a regular sign on the side of the road?
- How quickly can you process a traffic sign? Is that fast enough when blowing past it at 60 MPH?
View code: traffic-sign-recognition.github / wolfapple
Learning data science
Those are just some exciting data science project ideas. As you start working as a data scientist in the real world, you’ll undoubtedly find new, interesting applications for data science. Today, a data scientist can work in fraud detection, deep learning, or exploratory data analysis. Whatever your field of interest, a data scientist can find a career in it.
But because data science is such a broad and versatile field, prospective data scientists will want to develop their expertise and keep up-to-date with data science news and applications. As you build your data science portfolio, consider learning another programming language, such as R. The R programming language is constructed specifically for data science and data analysis, so you’ll get extra practice with data visualization.
After you’ve finished a few data science projects, you should consider looking at some data science interview questions. Together, these projects and questions can help you prepare for a career in data science.
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