Have you ever wondered how Netflix or Hotstar can suggest just the right movies and TV series for you? How is it that they understand our choices and likes, sometimes, even better than a friend? Let’s find out about a few Machine Learning projects that can help you understand ML better.
Well, all this is possible because of Machine Learning (ML).
The programs used in these OTT platforms learn and improve from every choice we make. That’s how they offer such amazing suggestions.
This is just one example. Today nearly every technology solution is using Machine Learning for business processes. It is cutting across different areas, be it Healthcare, Bank or Education.
Even in Marketing, today, companies are using ML algorithms to understand customer behaviour.
“Artificial Intelligence, Deep Learning, Machine Learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within 3 years.”Mark Cuban
So what are you waiting for? Begin your Machine Learning journey with some of the coolest Machine Learning projects introduced in this blog.
- Hands-on learning is important to understand Machine Learning techniques because of diverse applications
- Beginners can start with simple classification projects such as the classification of Iris to understand loading and handling of data
- Concepts used in every project built can be handy in other projects as well because of common underlying algorithms
Innovative Machine Learning Project Ideas
1. Activity recognition using Smartphone
This is one of the popular Machine Learning project ideas.
The Smartphone dataset contains records of the fitness activity of 30 people. This data was obtained through sensors on smartphones.
In this project, you will create a classification model to identify human fitness activities with a high degree of accuracy.
By doing this project, you’ll learn the fundamentals of classification and also the way to solve multi-classification problems.
2. Healthcare prediction solution
Wearable Healthcare devices, Hemoglobin tracker, Remote-monitoring devices etc., deploying Machine Learning techniques, have transformed healthcare solutions.
Machine Learning applications have been changing the Healthcare sector on a large level with disease detections and predictions.
The Healthcare industry has enormous amounts of data at disposal.
In this project, you will be harnessing this data to create diagnostic care systems.
This system will automatically scan images, X-rays, etc., and provide an accurate diagnosis of possible heart diseases.
This project might not only help society, but could be the starting point of your career in Machine Learning.
3. Sentiment Analyzer for detecting depression
This is a mini project that has a huge scope and is the need of the hour.
In the last five years, many people have been affected by depression. According to Our World in Data, in 2017 alone, 264 million people in the world were suffering from depression. And the numbers have been increasing every year.
The numbers are getting even higher due to lockdown, reduced social interaction, fear and for other reasons.
You can easily acquire the data for this project as many people are posting their opinions, activities and feelings on social media.
From these posts, you will understand the sentiment behind each text and comment.
In this project, you will analyse linguistic markers and build a Deep Learning model to gain emotional insights. This would help to detect and beat this significant issue.
4. Recommendation system
This Recommender Systems Dataset collection contains a wide range of datasets gathered from popular websites like Goodreads, Wikipedia and Amazon product reviews.
Your goal is to create a recommendation engine like what you see on Amazon and Netflix.
You will generate personalized recommendations of products, movies, music, etc., from their online behaviour.
5. Mood-based music or movie recommendation system
People generally watch movies according to their mood and feelings. Most systems do not classify movies based on your mood.
But in this project, you will be creating an application that will detect a person’s mood by their facial expressions and recommend movies accordingly.
For this, you’ll use computer vision elements and techniques.
The aim of this project is to build a model with an Image-Based Identification system to gain a high-level understanding of videos, then build a movie recommendation system.
6. Stock price prediction
The stock prices are ever-changing with many highs and lows. Because of this, it is highly difficult to predict stock price.
But with Machine Learning, it is easily possible.
In this project, you will predict the future stock price returns by analysing data from past stock price readings such as the opening price, closing price, volume etc.
For this project, you can use the Nifty-50 Stock Market Data dataset.
7. Fake news detection
This is one of the most needed projects today, and would add huge value to your resume.
It is an amazing ML project to start with, if you are planning to pursue a career in the Machine Learning domain.
With news on social media and online platforms dominating our lives, it has become more critical than ever to differentiate fake news from the real news events.
This is where Machine Learning helps. Facebook already uses ML techniques to filter fake stories from the feeds of users.
In this project, you will use Natural Language Processing (NLP) techniques to detect fake news and misleading stories that emerge from non-reputable sources.
You can also build a Machine Learning model using the Naive Bayes classifier to classify news as fake or real based on the words used in it.
8. Iris flowers classification
If you are a beginner in Machine Learning, Iris flowers are one of the simplest datasets for hands-on Machine Learning project experience.
Iris flowers dataset is one among the simplest datasets for classification tasks.
This is the classic classification project of Machine Learning. Since, Iris flowers have various species, they will be differentiated based on the length of sepals and petals.
In this project, you will use machine learning techniques for classification of the flowers into the three species – Virginica, Setosa, or Versicolor.
9. Credit card approval system
Issuing a credit card requires careful analysis of various measures such as credit scores.
Based on this, a bank decides whether or not to issue the card after objectively quantifying trust factor and amount of risk.
In this project, you will build a model to determine if an applicant is a ‘good’ or ‘bad’ client for issuing a credit card .
The dataset for this contains data like annual income, income, education level etc.
10. Xbox Game Prediction
This is one interesting project to work on.
If you are a gamer, you would know how difficult it is to choose the games to purchase.
This project aims to predict which Xbox game an individual would likely buy based on their online search queries.
You can use the Xbox prediction dataset for this project. It contains a user ID, items that the user clicked on, the category the item belongs to, click time, and query time.
Machine learning is advancing at a rapid pace and finding new implementations in different fields.
It is deployed in almost every industry today starting from Machine Learning implementation in Finance to Education sectors.
Even for handling environmental disasters, Machine Learning is the go-to technology.
This year, In Alaska, Machine Learning was used to accurately predict the size of wildfires for deciding immediate actions.
These incidents and advancements show the future of Machine learning in technological and business advancements.
So, it is better to be prepared with this technology for staying relevant.
Frequently Asked Questions
Which project should I start with if I am just starting with my first project?
You can start with the Iris Flowers classification project as it would only require you to classify based on just 3 parameters. You can easily apply classification algorithms to this project. It would also give you confidence to work on other projects.
What are the prerequisites to working on Machine Learning projects after learning Machine Learning concepts?
You would need programming skills in languages such as Python or R. It is also better to have a basic understanding of statistical concepts. These would be necessary if you are building new solutions.
What are the popular Machine Learning libraries in Python?
SciPy, Dask, Numba, Keras, Scikit-Learn and Theano are some of the popular libraries that you might be needing for your ML projects. You will be choosing your libraries based on the type of project you are working on.