{"id":6346,"date":"2021-12-02T11:00:00","date_gmt":"2021-12-02T19:00:00","guid":{"rendered":"https:\/\/www.springboard.com\/?p=6346"},"modified":"2023-07-13T02:04:35","modified_gmt":"2023-07-13T09:04:35","slug":"machine-learning-projects","status":"publish","type":"post","link":"https:\/\/www.springboard.com\/blog\/data-science\/machine-learning-projects\/","title":{"rendered":"21 Machine Learning Projects [Beginner to Advanced Guide]"},"content":{"rendered":"\n

While theoretical machine learning knowledge is important, hiring managers value production engineering skills above all when looking to fill a machine learning role. To become job-ready, aspiring machine learning engineers must build applied skills through project-based learning. Machine learning projects can help reinforce different technical concepts and can be used to showcase a dynamic skill set as part of your professional portfolio. <\/p>\n\n\n\n

No matter your skill level, you\u2019ll be able to find machine learning project ideas that excite and challenge you. For inspiration, we\u2019ve gathered examples of real-world ML projects geared towards beginner, intermediate, and advanced skill levels. Using these projects as templates, we\u2019ll explore what a completed project should look like and discuss actionable tips for building your own impressive machine learning project. <\/p>\n\n\n\n

Machine Learning Projects<\/h2>\n\n\n\n

First, we\u2019ll examine basic machine learning projects geared toward learners who are proficient with R or Python (the most renowned language in the field of data science<\/a> and machine learning) programming language and want to experiment with machine learning fundamentals. Next, we\u2019ll review ML project ideas suited for those with intermediate and advanced machine learning skills<\/a>. <\/p>\n\n\n\n

Machine Learning Project Ideas for Beginners<\/h3>\n\n\n\n

Titanic Survival Project<\/h4>\n\n\n\n

After striking an iceberg, the so-called \u201cunsinkable\u201d RMS Titanic disappeared into the icy waters of the North Atlantic on April 15th, 1912. Over half of the ship\u2019s 2224 passengers and crew members perished, and demographic data shows that some people aboard were more likely to survive than others. <\/p>\n\n\n\n

This Kaggle project<\/a> asks participants to build a model that predicts passenger survival based on passenger information like ticket class, gender, age, port of embarkation, and more. Kaggle offers a training data set that participants can use to build their own machine learning models, which can be constructed locally or on Kaggle Kernels (a no-setup, customizable Jupyter Notebooks environment with free GPUs). <\/p>\n\n\n\n

Identifying Twits on Twitter Using Natural Language Processing<\/h4>\n\n\n\n
\"Identifying<\/figure>\n\n\n\n

Try your hand at determining the probability that a given tweet originated from a particular user with sentiment analysis. This natural language processing technique can scan thousands of text documents for specific filters in a matter of seconds. This technique is how Twitter, for example, can scan and separate out tweets that contain racist or misogynistic content. <\/p>\n\n\n\n

For inspiration, check out Eugene Aiken\u2019s application of natural language processing to determine the probability that certain tweets were published by either Donald Trump or Hillary Clinton. To conduct a similar project, you\u2019ll need to pick two users, scrape their tweets, run your twitter data through a natural language processor, classify it with a machine learning algorithm, and use the predict-proba method to determine probabilities. Learn more about the original project here<\/a> and download the data set here<\/a>. <\/p>\n\n\n\n

Housing Prices Prediction<\/h4>\n\n\n\n

This Kaggle competition<\/a> will help you practice creative feature engineering as well as regression techniques like random forest and gradient boosting. The goal of the project is to predict the final sales price of a home based on the Ames Housing Dataset. <\/p>\n\n\n\n

The data includes 79 explanatory variables that describe vital attributes of homes in the city of Ames, Iowa. These data points range from zoning classification to lot size, remodel date, proximity to a railroad, and even masonry veneer type. The effect of each characteristic on house prices might surprise you!<\/p>\n\n\n\n

Google Search Analysis With Python<\/h4>\n\n\n\n

Google users perform roughly 3.5 billion search engine queries per day. If you\u2019re wondering what people are Googling, try using Google Trends to analyze a keyword of your choice. Google Trends offers an API called pytrends, which Aman Kharwal used to analyze<\/a> the performance of the keyword, \u201cmachine learning.\u201d<\/p>\n\n\n\n

Aman used this tool to pinpoint 10 countries with the highest number of searches for \u201cmachine learning,\u201d and also determined how the number of \u201cmachine learning\u201d search queries changed over time. After conducting his analysis, Aman then used data visualizations to communicate his findings. Try building your own data visualization and consider what story your results might tell, and how that information could be important in a business context. <\/p>\n\n\n\n

Chatbot Using Python<\/h4>\n\n\n\n

Chatbots are AI-powered applications that simulate human conversation, and are often implemented to field simple customer queries online. If you\u2019re interested in natural language processing, try creating a Chatbot with Python\u2019s NTLK library. <\/p>\n\n\n\n

First, you\u2019ll need to compile a list of queries and their correlating responses for the chatbot. Next, you\u2019ll run the program and try out your queries with the chatbot. Once you\u2019re satisfied with your baseline chatbot, you can use additional Python NLP packages or add more queries. To get started, take a look at Aman Khalwar\u2019s guide to creating a chatbot with Python<\/a>. <\/p>\n\n\n\n

Image Recognition<\/h4>\n\n\n\n

If you\u2019re curious about computer vision, check out this Kaggle competition<\/a>, which invites participants to build a digit recognizer using the classic MNIST dataset of handwritten numbers. The MNIST dataset\u2014commonly referred to as the “Hello World” of machine learnings\u2014comes equipped with pre-extracted features, which will streamline your data processing. Overall, this competition is an excellent introduction to simple neural networks<\/a>, computer vision fundamentals, and classification methods like SVMs (Support Vector Machines) and K-nearest neighbors. <\/p>\n\n\n\n

The competition also includes links to Python tutorials, as well as information about the details of the dataset (including previously applied algorithms and their levels of success). <\/p>\n\n\n\n

Python Recommendation Engine<\/h4>\n\n\n\n

Building a recommendation engine sounds like a difficult task for a beginner, but your code can be as simple or as complex as you\u2019d like. To create a basic content-based recommendation system, you\u2019ll just need to maintain a log of items a user has seen and liked and calculate the top-N most similar products that user has not yet seen. A simple collaborative filtering recommendation engine can be powered by a user-user similarity matrix that recommends items that similar users like. <\/p>\n\n\n\n

To learn more about building a Python recommendation engine, check out this Kaggle notebook<\/a>, which explains how to implement collaborative filtering and content-based filtering in Python to generate personalized recommendations. The notebook explores these concepts using a rich, rare dataset<\/a> about article sharing.<\/p>\n\n\n\n

Intermediate Machine Learning Projects<\/h3>\n\n\n\n
\"machine<\/figure>\n\n\n\n

Finding Frauds While Tracking Imbalanced Data<\/h4>\n\n\n\n

From banking via smartphones to AI-fueled stock price prediction, the financial sector embraces a cloud-based future. Thanks to a rising financial crime rate<\/a>, the importance of AI-powered fraud detection is greater than ever. But because fraudulent financial interactions comprise only a small portion of the total number of financial transactions that occur daily, analysts must figure out how to reliably detect fraud with imbalanced data. <\/p>\n\n\n\n

Fraud detection is a classification problem that deals with imbalanced data, meaning the issue to be predicted (fraud) is in the minority. As such, predictive models often struggle to generate real business value from imbalanced data, and sometimes results may be incorrect. <\/p>\n\n\n\n

To address the issue, you can implement three different strategies: <\/p>\n\n\n\n