Python is one of the most popular coding languages used in data science, with an infinite number of ways in which it can be used to perform analysis, scrape data, and build applications.
Data science projects in Python are one of the best ways to build your expertise and learn new Python skills. An end-to-end project allows for the opportunity to quickly gain hands-on experience, learn by trial and error, and get better at data science…
Another added bonus: Python projects will help you build your portfolio, and a strong portfolio is one of the best marketing tools for landing a data science job.
So where should you begin? There’s an endless array of ways in which Python can be used in data science, from building chatbots to detecting fraud. To provide inspiration, we’ve highlighted 10 great Python projects, ranging from beginning data science projects to more advanced projects and datasets for your use.
Before we get started, take a look at our list of 30+ free online datasets for inspiration. There’s a wide range of datasets in this list that can be used in Python projects like those listed below.
Here, we’ve included everything you need to get started on your next data science project, including links to datasets, tutorials, and ideas on how to ultimately make them your own.
The Million Song Dataset is a massive database of contemporary music with audio features and metadata for a million songs. With Python, you can leverage this dataset to build a recommendation engine. Get started with this helpful tutorial from Ajinkya Khobragade, which shows you how to build a collaborative-filtering recommendation engine.
How You Can Do It: Using the Million Song Database, there are a lot of different recommendation system projects you can pursue. One possible option involves using the LightFM Python implementation to quickly build a recommendation engine.
This is a great beginner Python data science project, with tons of email datasets out there for beginner spam filtering projects. One of the best is the Enron-Spam Corpus, which features 35,000+ spam and ham messages. To get started, this tutorial goes in-depth on how to build a spam filter using Python and Scikit-learn.
This Python project offers a good introduction to using classifiers (in this case, multinomial Naive-Bayes and support vector machines).
There’s a wealth of housing data online, and you can do a lot of cool things with Python using this data. Here’s a helpful tutorial from Aman Kharwal, the Clever Programmer, that utilizes a California Census dataset to predict home prices.
Here’s a cool visualization from Aman of home prices in California by location:
How You Can Do It: This is a great beginner Python data science project. You can use the California dataset, or switch it up to predict prices for things like used cars and airfare. We covered a similar project in our list of data analytics projects, which looked at Airbnb data. One option would be to use that dataset and predict prices for Airbnb listings (by city or country).
We featured this project in our list of 12 data analytics projects and datasets, coming personally from Interview Query’s co-founder, Jay. This project analyzes data scraped from Basketball-Reference to determine if 2-for-1 play in basketball actually provides an advantage. If you’re interested in sports or NBA data science projects, definitely be sure to take a second look at this project.
There are a lot of different ways to visualize sports data. Here’s an example from this project:
How You Can Do It: Take a look at the source code on GitHub. There’s really an endless variety of sports stats you can scrape and analyze.
If you’re interested in NLP, there are numerous sentiment analysis and text analytics projects to try. A solid beginner-to-intermediate sentiment analysis project could involve classifying or predicting sentiment based on existing movie reviews.
One helpful example to follow uses this dataset of 50,000+ IMDB movie reviews (you might also find some helpful hints in this Kaggle notebook). Here’s a cool word cloud visualization from the dataset from Lakshmipathi:
How You Can Do It: One option to customize this project would be to scrape your own data, using Python, from a variety of platforms. Here’s a tutorial for scraping reviews on Amazon, which you could also apply to a site like Pitchfork for music reviews.
If you’ve ever wondered how Instagram makes face-swapping so easy, check out this computer vision project. Over on Pysource, Sergio Canu created a really helpful tutorial on how to build a face swapping app with Python and OpenCV.
This is a solid intermediate-to-advanced CV project and great practice for using the OpenCV library. The tutorial walks you through all steps (and includes source code), like location mapping:
How You Can Do It: The CelebFaces dataset is great for a project like this. If you’re interested in pursuing similar projects, check out our list of free data sources for the best computer vision datasets.
The rise of fake news has skyrocketed online over the past decade – but machine learning offers a solution for combatting it. In fact, Twitter and Facebook are leveraging machine learning today to fight fake news on the crisis in Ukraine.
Interested in how you can use Python to detect fake news? Check out this tutorial on Medium from Manthan Bhikadiya, which will walk you through the entire process:
How You Can Do It: Check out this source, which features a fake news and a true news dataset on Kaggle. Because news changes so quickly, you might also consider web scraping more recent news articles with Python.
Python is a useful tool for creating chatbots. If you want to try it yourself, check out this DataFlair chatbot tutorial, which walks through how to use Natural Language Toolkit, Keras, and Python. This is a great tutorial to help you work on all three of those tools, and it includes all the source code.
Specifically, you’ll build a retrieval-based chatbot that can answer simple queries:
Interested in what conditions impact the severity of a forest fire? Take a look at this dataset on Kaggle, which you can use to predict the burned area of a fire. You might start with some exploratory analysis like this from Kaggle user Alex Beg:
Then, you move into regression or classification analysis to make predictions. See some examples of regression data science projects.
How You Can Do It: Another option would be to look at data for natural disasters like floods or tornados to make predictions.
Craigslist is one of the best places for finding data - from used car prices to apartments for rent. This project also comes from Jay, and models San Francisco housing data scraped from Craigslist.
This is an especially helpful project for working with Scrapy, the Python framework. Take a look at the source code here for an in-depth look at how to customize the Scrapy implementation for your project.
How You Can Do It: Take a look at the source code. You can scrape your own data from Craigslist to model housing costs from your own city.
These projects are all helpful for practicing skills like Python, data analytics, and Python libraries like OpenCV and Scikit-learn. You can also continue your learning with these resources from Interview Query: