60+ Python Projects for All Levels of Expertise

Python is one of the most important programming languages to learn when becoming a data scientist. However, to truly master Python, learning by doing is essential. This is where Python projects come in. 

Building Python projects will help you build confidence in the skills you’re learning, develop a portfolio that helps you stand out in the job hunt, and have fun along the way. In this article, we’ll outline 60+ Python project ideas to accelerate your learning journey across skill levels and domains. 

Before You Start on Python Projects

If you’re already familiar with Python, you can get started with these projects right away. However, if you would like to build the necessary foundational skills to get started on Python projects, check out DataCamp’s list of 140+ Python courses. All our courses are interactive and designed to help you break the coding barrier and develop your Python skills. 

Once you’re ready to start working on projects, check out DataLab, and start working on and publishing your projects in DataCamp's fully configured data notebook, right in the browser. 

Beginner Python Projects

As a beginner, you should leverage Python projects to retain what you learned and acquire new skills. These set of projects mostly revolve around exploratory data analysis tasks, alongside simple modeling and forecasting tasks on relevant real-world datasets.

1. Diamond Prices Data Analysis

Diamonds are divided into five impurity types based on the structure of their carbon atoms. The Diamonds dataset from Kaggle gives you even more info — cut, clarity, color, and price. Develop your data visualization skills on it with some exploratory data analysis. 

2. Age of Abalone Shells Data Analysis

This is a unique dataset from zoology. Abalone shells are miracles of nature, and you can determine their age by counting the circles inside their shells. Can you determine the age of Abalone shells with Python data analysis skills?

3. Premier League Data Analysis

A football (or soccer) dataset where you can explore, analyze, and visualize events from the 2018-2019 season of the English Premier League. The Soccer Data dataset offers an excellent beginner Python project for data analysis. With a rich set of features ranging from basic game details to intricate statistics, the dataset provides ample opportunities for data exploration, visualization, and statistical analysis. The project comes with a clear data dictionary and guided challenges, making it accessible for newcomers. Additionally, it includes real-world scenarios that not only make the project more engaging but also serve as a comprehensive exercise that can be included in a portfolio. It's a well-rounded project that balances guided learning with open-ended exploration, making it ideal for skill development.

Data from a beginner Python project analyzing soccer trends

4. Telecom Churn Prediction

Customer churn is one of the most foundational machine learning problems. In this customer dataset, you’ll be able to predict churn for a telecom provider based on usage data from their customers. The dataset includes a variety of features, such as call failures, subscription length, and customer value, making it a rich resource for in-depth analysis. The beginner project comes with guided challenges focusing on data exploration, visualization, and statistical analysis, providing a structured learning path. The real-world scenario adds an extra layer of complexity and relevance, asking you to predict customer churn in the face of a new market competitor.

5. Stock Prices Analysis and Prediction

Do you want to find out the reason behind the 100% spike in Tesla's stocks two years ago? If yes, the 2010–2021 tech stocks dataset will be the first place to start.

6. NBA Shooting Data

At which range do basketball players are most likely to score a shot? In this NBA shooting dataset captured from the 2021 NBA playoffs, you’ll be able to answer just that question. 

7. Forecast E-commerce Sales

Using this e-commerce dataset from an online retailer, leverage data visualization and forecasting techniques to predict future sales. The dataset is rich, covering orders from multiple countries over a year and includes a variety of variables like invoice numbers, stock codes, quantities, and unit prices. This allows for a multi-faceted analysis that can include time-series trends, customer segmentation, and product categorization. The challenges provided encourage you to explore data anomalies like negative order quantities, visualize profits in different time frames, and compare order sizes between countries. These tasks not only help you practice essential data science skills but also mimic real-world business questions. The scenario adds another layer of complexity, asking you to categorize over 4000 unique products based on various characteristics, a task that is highly relevant in the e-commerce industry. 

Another beginner Python project looking at eCommerce data

8. Analyze Airbnb Listings

This is an excellent dataset for understanding the dynamics behind Airbnb rental listings. With exploratory data analysis and visualization, you’ll be able to understand which neighborhoods have the most popular listings, understand the relationship between price and room type, and more. 

9. Analyze GDP Data 

Gross domestic product is one of the strongest indicators of a region or nation’s economic health. In this dataset, analyze how GDP has evolved for countries over the past 50 years. 

10. Olympics Data Analysis 

Who is the most successful country in Judo? How does athlete height impact success in a sport? With exploratory analysis of the Olympics dataset, you’ll be able to answer these questions and more.   

An example of the findings of the Olympics project

Intermediate Python Projects

Going beyond beginner tasks and datasets, this set of Python projects will challenge you by working with non-tabular data sets (e.g., images, audio) and test your machine learning chops on various problems.

1. Classify Song Genres from Audio Data

Are you a genuine music lover? Then, you will enjoy predicting music genres with machine learning on a music dataset in this audio recognition project. This intermediate Python project covers the entire data science pipeline, from data exploration and feature engineering to implementing and evaluating multiple machine learning algorithms. The project also tackles advanced topics like dealing with imbalanced data and model evaluation techniques like cross-validation. This multi-step, library-intensive project serves as an excellent learning experience and portfolio piece for those looking to advance their skills.

2. Analyze and Visualize Uber Pickups in New York

Datasets with geolocations are always fun to analyze and visualize on a map. This uber pick-up dataset of more than 20 million ride-hails in New York City is no exception. 

3. Handwritten Character Recognition

MNIST digits recognition is a great starting point for practicing deep learning. However, this dataset adds another layer of challenge because you are predicting English handwritten letters.

4. Credit Card Fraud Detection

Credit card fraud is always a challenge — mainly because there will be a severe class imbalance in the data. See if you can get around that in this credit card fraud dataset. project is well-suited for intermediate to advanced Python users interested in data science and machine learning applications in the finance and security sectors. The project involves a comprehensive analysis of credit card transactions to identify fraudulent activities. It covers a broad range of skills, from exploratory data analysis, including geospatial plotting, to predictive modeling. The project also poses real-world challenges such as dealing with imbalanced classes and the ethical considerations of false positives in fraud detection. This makes it a multifaceted learning experience that not only enhances technical skills but also encourages critical thinking about the implications of machine learning models in sensitive areas like financial security.

An example of geospatial plotting from this intermediate Python project

5. Gender Prediction Using Sound

In this audio data project, you will use the fuzzy package to categorize the gender of names based on phonemes and how they sound.

6. Hotel Booking Cancellation Rates

If you are into real estate, this is an excellent dataset to play around with to understand hotel booking cancellation rates. With simple machine learning techniques, you can try to predict the likelihood of hotel cancellations based on historical data.

7. Face Detection in Images

Ever wonder how your iPhone puts little boxes around your face? That's because it performs face detection under the hood. You can create similar functionality using this small dataset of annotated images with faces.

8. Predict the Species of Bees from Images

Can a machine learning algorithm detect the species of bees based on an image? In this image recognition project, you’ll do just that. 

9. Analyze and Predict Bike Sharing Demand

This bike-sharing dataset contains a wealth of information on bike rides for a bike-sharing startup. This intermediate project involves using Python to analyze a dataset that includes various factors like weather conditions, time of day, and public holidays to predict the demand for bike rentals in Seoul. It offers a comprehensive learning experience, covering skills from exploratory data analysis to predictive modeling. The project challenges include comparing rental patterns across different times of day and seasons, visualizing the impact of temperature on bike rentals, and identifying the most influential variables for bike demand. This makes it an excellent project for those looking to hone their skills in data manipulation, visualization, and machine learning, while also gaining insights into the operational challenges faced by a scaling startup.

An example from the Python project on bike rentals

10. Build a Tweet Classifier

Different personalities have distinct tweeting styles. In this social media analysis project, you’ll use machine learning and natural language processing to classify whether tweets are authored by Donald Trump or Justin Trudeau.

Advanced Python Projects

These advanced projects go beyond complex datasets and challenge you to apply creative solutions to interesting problems. Whether it is creating movie recommender systems, network analysis between characters in books, or interpreting sign language with machine learning, these projects will provide you with enough complexity to learn new skills on the go.

1. Build a Movie Recommender System

Streaming platforms provide granular recommendations based on how you and others like you interact with content. In this recommendation system project, you’ll learn how to build a movie recommender system.

2. American Signal Language Recognition

American Sign Language is the primary language used by many deaf individuals in North America. In this image recognition project, you’ll use Deep learning to recognize ASL letters.

3. Real-time license plate recognition

An awesome project on recognizing license plate numbers in real-time using deep learning on video datasets. Check out the GitHub project containing the dataset and the code. 

An advanced Python project on license plate detection - source

4. Sentiment Analysis in Stock News Headlines

Investor sentiment is an incredibly important indicator when looking for clues on the future performance of a stock. With natural language processing and machine learning, you can extract sentiment from news headlines automatically in this natural language processing project. 

5. SMS Spam Detection

Spam detection is a cornerstone of data science and requires a combination of natural language processing and machine learning techniques. Create a spam detection tool with this SMS dataset.

6. Network Analysis of Game of Thrones

While a bit dated at this point, Game of Thrones captured the world’s imagination, unlike any other show. With such a vast set of characters and lore, was the most important one of them of all? In this Network Analysis project, you’ll answer just this question.

7. Reducing Traffic Mortality with Machine Learning

In this traffic mortality project, you’ll dig through historical data on traffic mortality in the USA by state and apply machine learning to find similarities and differences between states and provide granular policy recommendations. You can check out our other machine learning projects in a separate article. 

8. Movie Similarity in Plot Summaries 

With so many movies available, it’s easy to think of movies that are similar to each other. What if you can find natural language processing and machine learning to categorize movies based on their plot summaries? With this movie similarity dataset, you’ll do exactly that. This advanced Python project offers challenges in exploratory data analysis, text mining, and trend analysis. The most advanced task involves constructing a network graph to analyze professional relationships among cast members and directors, requiring skills in complex data manipulation and graph theory. This project provides a robust platform for applying advanced data science techniques to real-world data.

An advanced Python project on movie data

9. Movie Genre Classification with Multi-Label Output

A movie can combine genres. With this Netflix movie dataset, you can apply multi-label classification to predict the many genres a movie may have based on its description, rating, and more.

10. Build and Deploy a Machine Learning Pipeline 

While this is not a specific project, deploying and maintaining the other projects on this list is an incredibly useful skill to showcase to employers. In this tutorial, you’ll learn exactly how to do that. 

Fun Python Projects to Build Your Python Skills

While not the most complex, these projects provide interesting and engaging datasets to explore and get started with to accelerate your Python learning journey. 

1. Spooky Author Identification

Classify the works of mystery writers. Find out if an excerpt belongs to either Edgar Allen Poe, HP Lovecraft, or Mary Shelley.

2. Video game sales prediction

Are you waiting for an upcoming game from Activision or EA? Try predicting how well it would sell using the data from 16k+ past video games. 

3. Myers-Briggs (MBTI) personality type prediction

There are 16 personality types according to the MBTI indicator. Instead of Googling it, try predicting your personality using this personality type dataset.

4.Explore Bitcoin Price Data

Cryptocurrency prices have enamored the world with their extreme volatility. In this project, you’ll apply time series analysis and data visualization techniques to Bitcoin prices. 

5. Song Popularity Prediction

In this great dataset of songs from the 50s, you can predict a song's popularity based on several attributes.

6. Analyze Fitness Tracker Data

With the rise of fitness trackers comes an abundance of data that you can analyze. In this data analysis project, you’ll analyze and visualize Runkeeper fitness tracker data.  

7. Bust Myths with Data

A 1991 study found that left-handed people die nine years earlier than right-handed people on average. Is this actually true? Find out in this statistical analysis project. 

8. Analyze Breathalizer Data

Using data collected from breathalizers in the state of Iowa, you’ll be able to visualize and analyze drunkenness in Iowa and find patterns that can lead to better policy decisions. 

9. Get on Top of the Music Billboards

With this Spotify dataset of ~600 songs from 2010 to 2019, you’ll be able to explore and analyze how popular genres have evolved over the past decade, predict a song’s genre based on key attributes, and more. 

10. Analyze a Lego Database

While this project also requires some SQL skills, this Lego database allows you to dig through thousands of Lego sales throughout the year and understand which Lego sets drive the most sales. 

Additional Guided & Unguided Python Projects For Practice

Throughout this article, we’ve linked to many DataCamp projects and datasets. DataCamp provides a host of guided and unguided projects depending on the level of difficulty you’re aiming for. Here is a list of additional projects for practice

Guided Python Project for Practice

1. Predicting Credit Card Approvals

Automated credit card approvals are a huge machine learning use case in banking. In this card approvals project, you will learn how to predict whether a credit card application gets accepted or rejected by banks.

2. Uncover Trending Topics in Machine Learning Research

Using this trending topics dataset, you will apply machine learning to discover the future of machine learning research trends by analyzing the past decade's Neural Information Processing Systems papers.

3. Blood Donor Classification

Blood donations are life saviors. In this project on blood donors, analyze the patterns in blood donations and predict if a person will donate again in the future.

4. Comparing Cosmetics by Ingredients

Choosing a cosmetic product that won't jeopardize your skin health is hard. In this guided project, you learn to process the ingredients of cosmetics to make a more informed decision about whether a new cosmetic is good for you.

5. A Visual History of Nobel Prize Winners

Almost everyone in research dreams of getting a Nobel once in their lives. But does your age, race, and gender affect your chances? Find out by analyzing the data on the winners since 1901.

6. The GitHub History of the Scala Language

Scala ranks as the 34th most popular programming language according to the TIOBE index. Learn how it came to be so by analyzing the history of its GitHub repository in this guided project.

7. Exploring the Evolution of Linux

Version control systems like Git store rich information about a software project’s evolution. In this Linux evolution project, you will analyze and transform the real Git repository of the Linux Kernel and understand how 700K+ commits created one of the most widely used operating systems on earth. 

8. Recreating John Snow’s Ghost Map

Doctor John Snow (not the Game of Thrones character) mapped Cholera cases by hand and deduced the origins of outbreaks in his area, giving birth to modern epidemiology. In this historical project, you’ll recreate his work and his famous map. 

9. A New Era of Data Analysis in Baseball

Moneyball ushered in the era of sports analytics. In this project, you’ll analyze MLB Statcast data to compare different baseball players and understand what drives home runs.  

10. Generating Keywords for Google Ads

Generating keywords for search ads is an incredibly meticulous and cumbersome process. What if you can automate this task with Python? In this Google Ads keyword project, you’ll learn how to do exactly that. 

11. Mobile Games A/B Testing 

A/B testing fuels the success of so many digital products and services, and mobile games are a great testament to that. In this project, you’ll understand the impact of an experiment run in the popular Cookie Cats game on user retention. 

12. Prioritize Debt Collection with Machine Learning

Debt delinquency is a big problem for banks and financial institutions. In this project, you’ll use machine learning and regressions to understand how to prioritize debt collection for a bank. 

13. Book Recommender System from Charles Darwin

Charles Darwin was an avid reader and had an extensive bibliography. In this project, you’ll use Charles Darwin’s favorite books to create a recommender system that provides book recommendations based on his tastes. 

Unguided Python Projects for Practice

1. Investigating Netflix Movies and Guest stars in the Office

In this project on The Office, you’ll manipulate and visualize the performance of Netflix movies and the guest stars in the cultural phenomenon series “the Office.”

2. Exploring the History of Lego

About 1140 pieces of Lego are produced every second. Find out how the most popular toy brand in the world became so dominant by analyzing its historical sales data. 

3. The Discovery of Handwashing

Washing hands is second nature to all of us, but it has not always been so in the past. In fact, Hungarian physician Ignaz Semmelweis discovered the benefits of hand washing by analyzing the mortality data of patients in hospitals. Recreate his data analysis using this dataset.

4. The Android App Market in Google Play

The Android app market is vast and competitive. Analyze and visualize this dataset scraped from the Google Play Store to find out what makes a great app.

5. Word Frequency in Classic Novels

In this project, you’ll scrape a novel from the website project Gutenberg and then analyze the distribution of words in a large corpus of books. 

6. Bad Passwords and the NIST Guidelines

Almost every site requires a password, so how do you know if you’re using the best one? In this project, you will create a system that automatically checks if your password conforms to the National Institute of Standards and Technology.

7. Comparing Search Interest with Google Trends

Google exposes its Trends API in Python so that users can find out the search interest of any keyword. It is an excellent source of time series data with records dating back to 2004. In this project, you’ll explore worldwide search interest in five major internet browsers.

8. Exploring the NYC Airbnb market

Leverage data cleaning and manipulation to uncover insights into the Airbnb market of New York City.

How to Choose Which Python Projects to Add to Your Resumé

With this long list of Python projects, how do you choose one to add to your resumé? According to Nick Singh, author of the best-selling book "Ace the Data Science Interview," here are four key principles to think of when you’re pursuing Python projects.

1. Projects Should Come Out of Genuine Interest

Doing a project on a topic you care about will make the whole process more engaging to you and increase your chances of completion. Moreover, this enthusiasm will carry over when speaking to a hiring manager about your project. 

2. Simplicity Trumps Complexity

Today, it is easy to get distracted by fancy tools and cutting-edge techniques. However, data science in the real world requires a simplistic, pragmatic approach to solution building. One of the goals of a project is to showcase your ability to develop useful data science solutions with relatively simple techniques. 

3. Always Complete Your Project

It’s easy to fall into scope creep when doing a project. As a rule of thumb, always scope out a project that you know you can complete from A to Z — even if it means just a simple data analysis exercise. 

4. The Project Should Have a Quantifiable Impact

Once a project is complete, make sure to share your work and gain feedback from the community in a quantifiable manner. Whether it is GitHub stars, LinkedIn shares, or Reddit mentions—sharing your work is the best way to showcase the quantifiable impact of your project to potential hiring managers.

Take Your Python Learning to the Next Level

We hope you enjoyed this list of Python projects and that they can accelerate your Python learning journey. If you would like to get started and could use a Python refresher first, make sure to check out DataCamp’s Python curriculum and the additional resources below.