19 Best Machine Learning Projects in 2021
There are several machine learning projects ideas out there, but getting started can seem daunting.
Here are some of the best machine learning projects in 2021:
- Iris flower classification.
- Mall customer analysis.
- Loan prediction.
- Black Friday dataset.
- Stock price prediction.
- MNIST dataset.
- Wine quality dataset.
- Titanic dataset.
- IMDB reviews.
- Boston housing problem.
- Customer segmentation.
- Credit card fraud detection.
- Mood detection.
- Image caption generation.
- Traffic sign recognition.
- Twitter sentiment analysis.
- Brain tumor detection.
- Color detection.
- Recommendation system project.
Let’s discuss each machine learning projects idea in more detail.
Iris flower classification.
Iris flower data set is the first dataset that people getting into machine learning start with.
The dataset focuses on three related species of the Iris flower. The species included in the dataset were the Iris setosa, Iris virginica, and the Iris versicolor.
Four features of each species are included, namely the lengths of the petals and sepals, along with the width of the petals and sepals. These features are in centimeters.
There are a total of 150 entries in the data set. Fifty samples of each species are included.
You can learn quite a lot by practicing on this data set. Polish your data analytics skills and learn more about classification problems and how you can solve them.
You can find the Iris dataset here: https://www.kaggle.com/arshid/iris-flower-dataset
Mall customer analysis.
The mall customer analysis is another easy-to-understand problem that can be a solid learning experience for beginners.
Malls around the world want people to spend more money. But you can’t please everyone. To get the most return on your investment, you will need to spend your money to attract the right customer segment.
There are only 200 entries in the dataset. All values are valid, and there are no missing values, so you can directly start analyzing the data at hand.
Five columns are present. They include customer id, gender, age, annual income, and spending score.
You can download the dataset mall customer dataset from here: https://www.kaggle.com/shwetabh123/mall-customers
Now we are slowly starting to get to more exciting projects that give you a taste for real-world problems.
Getting a loan from a bank or another financial institution depends on several factors.
The loan prediction dataset helps you explore this problem by using a minimal dataset considering the size of the problem. You will have two files available, one for training and one for testing.
The training file has 614 unique values, while the testing file has 367 unique values. There are a total of 13 columns if you are counting the loan status.
Other column names include things like loan id, gender, marital status, education, etc.
You can find the dataset here: https://www.kaggle.com/altruistdelhite04/loan-prediction-problem-dataset
Black Friday dataset.
Black Friday is a top-rated holiday across the globe. People spend a lot of money thanks to the sales, discounts, and deals available for a limited time only.
The black Friday dataset consists of samples of transactions that happened in retail stores.
Most people focus on finding the amount of purchase based on other variables, but there is a lot of room to explore other problems.
Note that this is a kind of a regression problem where you’re aiming to find the dependent variable based on the data from other variables.
Have a look at the dataset here: https://www.kaggle.com/sdolezel/black-friday
Stock price prediction.
Interested in the stock exchange?
Well then, this project will put a smile on your face.
With this dataset, you can do a lot of things. Which company do you think will be most profitable? Where will you get ROI the fastest?
One thing to note with this dataset is that the data is up till 2016.
If you would like to make a bot, it would be best to get the latest data.
You can find NY exchange data here: https://www.kaggle.com/dgawlik/nyse
If exploring computer vision is on your to-do list, then practicing on the MNIST dataset is going to make a lot of sense.
The MNIST (Modified National Institute of Standards and Technology) dataset consists of handwritten digits has a training set of sixty thousand entries, while the testing set has ten thousand entries.
Your goal will be to train your computer to read handwritten digits and give the correct output.
Generally, people use deep learning algorithms, neural networks, support vector machines, etc.
Find the MNIST dataset here: https://www.kaggle.com/c/digit-recognizer
Wine quality dataset.
If you enjoy wine and, in particular white wine, then this project will
There are 6497 entries in the dataset with 13 columns in all. The columns consist of chemical information for the wine, including density, pH, sulfates, etc.
Your job will be to predict the quality of a given wine.
Luckily, there are no missing values that you need to fix. You can get straight to analyzing and predicting the quality of the wine.
Here’s a helpful video:
You can find the dataset here: https://www.kaggle.com/rajyellow46/wine-quality
The sinking of the Titanic was indeed a harrowing event, but even in a disaster, we can find means to learn and improve.
The challenge with the Titanic dataset is to identify groups of people who would have been more likely to survive.
In this dataset, you will also have to deal with missing values and ascertain the most relevant features.
There are a total of 891 entries in the training set. The features include things like the pclass (passenger class), age, gender, etc.
Get the data for this project here: https://www.kaggle.com/c/titanic
If you are looking for machine learning projects ideas that are a bit more complex, you should give sentiment analysis a try.
Understanding how people feel about an event or an object can be applied to real-world scenarios.
But before you get into that, you might want to try something that’s a bit more fun. Most people like to watch movies and have opinions about them.
The IMDB reviews dataset has only two columns, the review, and the sentiment.
The dataset is pretty substantial, given that it has 25000 entries for training and 25000 for testing.
You can get the IMDB reviews data set from here: http://ai.stanford.edu/~amaas/data/sentiment/
Boston housing problem.
The Boston housing problem is used as a benchmark to evaluate machine learning algorithms.
Your goal with this project will be to find the price of a house in the Boston area.
The dataset is relatively small, with only 506 entries. There are a total of 14 attributes that you can use to train your model on.
Find the data set here: http://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html
Segmentation is when you divide a group based on specific attributes.
In machine learning, segmentation is a popular subject as it is applied to real-world problems. One everyday use case is to find customers that will be more valuable to a business.
A popular dataset that you can use to practice customer segmentation is the online retail data set.
It has eight columns and a total of 541909 entries. The columns include country, unit price, description, etc.
This data set shares similarities with the mall customer analysis, but it is much bigger, so it should be a bit more challenging and give you room to experiment.
Find the dataset here: https://archive.ics.uci.edu/ml/datasets/Online+Retail#
Credit card fraud detection.
Credit card fraud does not happen thanks to improving security, but it does still occur.
It’s an anomaly, and anomaly detection is one of those machine learning projects that will challenge you to think differently.
The biggest challenge that you have to deal with is the data itself. Think about it. Several credit card transactions are happening every moment, but almost all of them are legitimate.
You will need to take into account that only 0.172% of the transactions are fraudulent.
There are around 285k entries with 31 columns, so needless to say that you have your work cut out for you.
The credit card fraud detection data set can be found here: https://www.kaggle.com/mlg-ulb/creditcardfraud
People generally convey their feelings and emotions through different channels. Verbally expressing feelings is well known, but you can tell a lot about their mood through their facial expressions.
The mood detection dataset can be found here: https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data
The training set has 28709 entries, while the test set has 3589 entries.
The data is comprised of 48X48 grayscale images of people’s faces. There are only two columns; emotions and pixels.
There are seven facial expressions that you will need to train your machine on.
Image caption generation.
Generating captions for images is an exciting project idea.
To make this project a success, you will need to ensure that you have wrangled and cleaned the data correctly. Additionally, you will need to choose a suitable machine learning algorithm, some sort of deep learning algorithm, if you want to get acceptable results.
You can find the image caption dataset here: https://www.kaggle.com/ming666/flicker8k-dataset
Traffic sign recognition.
Autonomous vehicles are complex machines that need to rely on several sensors and machine learning algorithms to function correctly.
Recognizing traffic signals is essential because it will play a crucial role in the safety of the autonomous vehicle’s passengers and everyone else on the road.
The German Traffic Sign Benchmark dataset can be used to train a model using deep learning algorithms.
It is relatively large and should be a considerable challenge for people looking to establish themselves as machine learning practitioners.
The dataset for this machine learning project can be found here: https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign
Twitter sentiment analysis.
Twitter is one of the most popular social media platforms available today.
People use Twitter to convey their feelings and voice their opinions about burning issues and topics.
As a machine learning enthusiast, this is an excellent opportunity for you to learn how to extract data and use it for meaningful purposes.
We’ve previously discussed sentiment analysis, but the data sources in those cases might not have been accessible to outsiders in a real-world scenario.
However, Twitter allows you to scrape data on your own. This will put you right at the source of the data and in control of data quality.
There are several datasets that you can experiment with and learn from. Here are two of them:
Brain tumor detection.
Medical sciences can benefit a lot from machine learning. There have been recent instances where machine learning has aided medical practitioners and supported them through a complex problems.
Image processing is another sub-field of machine learning that you might want to look at if you would like to get into the healthcare industry.
The data set below comprises images that have tested positive for a brain tumor and the ones that have tested negative.
Create and train a model that can determine the presence of a brain tumor within an image and then move on from there.
The brain tumor detection dataset can be found here: https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection
Colors make the world brighter.
There can be several applications of color detection. Think about how it would help the fashion and ecommerce industry. If someone wanted clothing that was of a specific color, machine learning algorithms could tell the vendor exactly what color is needed.
You can find the data set here: https://www.kaggle.com/adityabhndari/color-detection-data-set
Recommendation system project.
Recommendation systems are a common occurrence, but we hardly think about the math or science behind them.
Amazon is a behemoth in the ecommerce industry.
It makes money when people make a purchase. To increase purchases, it enlists the help of a recommendation engine that recommends products to the user based on several factors and considerations.
Find the data set here: https://www.kaggle.com/skillsmuggler/amazon-ratings
The machine learning projects and ideas mentioned here are generally listed from easy to difficult.
As a beginner enthusiast, someone who is just learning about machine learning, you should stick with the easier projects first. Learn how to handle data, train models, and then move on to more nuanced topics.