Top 10 Machine Learning Projects for Beginners


Introduction Machine Learning Projects for Beginners

Julian Hall once said –

“Knowledge is useless without consistent application.”

If you’ve stumbled upon this article you probably know a bit about data science and machine learning. You now want to get your hands dirty by applying your newly gained technical skills and test your prowess. We know the importance of hands-on exercises in a beginner’s learning path. That’s why we have compiled the top 10 projects for beginners to enhance their machine learning skills.

1. Iris categorization using machine learning

Difficulty level – Easy

It is one of the most famous introductory-level projects. No data scientist has learnt about clustering without having stumbled upon the Iris data set. The data includes attributes of iris flowers like the size of sepal, petal dimensions etc. The data set is small and does not require much cleaning, to begin with. This project aims to classify Iris flowers into their three variants – virginica, setosa and versicolor. For this, clustering is used.

UCI has made the data set publicly available –

Image Source – Wikimedia Commons

2. Boston Housing Price detection

Difficulty level – Easy

The Boston Housing data is another famously used dataset by beginners in machine learning. Its aim is to predict housing prices in different areas of Boston. It contains essential information like age, the property tax rate, crime rate and even proximity to employment centres that can factor into the housing rates.

The dataset is clean and small, rendering it easy to play around with for beginners. Regression algorithms are extensively used on the different attributes to find out what contributes to the housing price in Boston. It is an excellent resource to practise regression techniques and to evaluate their performance.

UCI Machine Learning repository had made this data set available but subsequently removed it. You can access it using Python through the scikit-learn package. Check it out!

from sklearn.datasets import load_boston
boston_housing_df = load_boston()
boston-housing-dataset · GitHub Topics · GitHub
Finding linear correlation between attributes using Heatmap
Image Source – Github

3. Titanic Survivors data set

Difficulty level – Easy

This is regarded as one of the best and most fun challenges to dive into the world of ML. The Titanic challenge is not only a popular machine learning project but also a way to familiarize yourself with the Kaggle data science platform. The Titanic dataset comprises actual data from the infamous incident. It consists of attributes like age, socio-economic class, gender, cabin number, departure port and most importantly, whether the person survived or not! 

The decision tree classifier and the K-Nearest Neighbor approach have found to yield the best results for this project. So if you’re up for a quick weekend challenge to build upon your Machine Learning skills, be sure to check this one out on Kaggle –

You can also check out this video released by Kaggle too.

4. Predicting Wine Quality using Machine Learning

Difficulty level – Easy

“Wine tastes better with age” is a very popular adage. With this beginner-friendly machine learning project, you can explore other factors that determine the finer quality of the wine. It is a fairly larger dataset with about 5k rows. It contains results of physiochemical tests like alcohol quantity, acidity, density, pH measure, sugar content and more. 

The UCI Machine Learning Repository has made this data accessible to all. With the use of the 11 independent variables, you’ll need to employ classification and regression techniques to determine wine quality. Find the data set here –

Box plot distribution of different physiochemical properties.

5. Stock Market Prediction

Difficulty level – Intermediate

Whether you are in the financial domain or not, this project is an interesting one. Stock market data is extensively analyzed for academics, business and even as a means of secondary income outlets. Studying and exploring time series data is also an essential skill for a data scientist to have. Stock market data is an ideal place to start. As a project, the crux behind it is to predict the future value of a stock. This is done based on current market performance and previous years’ data.

Kaggle provides data on the NIFTY-50 index accumulated since 2000 and is still updated monthly. It contains stock prices of over 50 companies since 1st January 2000. Access it here – 

Machine Learning Techniques applied to Stock Price Prediction | by Yibin Ng  | Towards Data Science
Image Source – Towards Data Science

6. Movie recommendation system

Difficult level – Intermediate

I bet you know the feeling after watching a really good movie. Ever felt the need to tickle your senses by binging on similar movies? We know that OTT platforms like Netflix have really ramped up their recommendation systems. As a machine learning student, you must learn how such systems work to target customers based on their needs and ratings.

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The IMDB data set available on Kaggle is perhaps one of the most comprehensive ones on which recommendation models based on the movie title, customer rating, genre etc. can be implied. It is also a great way to learn about Feature Engineering and Content-Based Filtering.

You can find the data set here –

How to build a content-based movie recommender system with Natural Language  Processing | by Emma Grimaldi | Towards Data Science
Mechanism behind Content-based filtering for movie recommendation
Image Source – Towards Data Science

7. Social Media Sentiment Analysis with the use of Twitter data

Difficulty level – Intermediate

Opinions and trends have become comparatively easier to extrapolate thanks to social media platforms like Twitter, Facebook and Reddit. This data is used to filter out views about events, personalities, sports etc. Machine learning projects revolving around opinion mining are being used in all walks of life from political campaigns to Amazon product reviews.

If you’ve completed some introductory level projects and are adept in Python or R, this project will be a great addition to your portfolio! One can extensively practise approaches like Support vector machines, regression and classification techniques for emotion detection and aspect-based analysis (finding facts and opinions). 

Sentiment140 provides a data set of numerous tweets for academic purposes –

Sentiment Analysis with Machine Learning: Process & Tutorial
Sentiment Analysis using Machine Learning flow
Image Source – MonkeyLearn

8. Loan Prediction using Machine learning

Difficulty level – Hard

A very popular classification-based Machine Learning Projects for Beginners, the loan data set consists of attributes like gender, marital status, employment education, income, and a loan amount of the applicant. Supervised machine learning models are deployed to understand whether a loan applicant should be given a loan or not. 

Since there are many characteristics to be accounted for, models like logistic regression, feature engineering, random forest classifiers etc are more ideal for this project. So if you’re looking to lay your hands on some complex machine learning exercises, try this one on Kaggle. 

Data source –

Machine learning for Banking: Loan approval use case | by Youssef Fenjiro |  Medium
Workflow of Loan Prediction dataset
Image Source – Medium

9. Grocery Item recommendation system

Difficulty level – Hard

Clustering, regression and classification methods aren’t the only ones vital to learn for a beginner in Machine Learning. Collaborative filtering is also a great exercise where automatic predictions are made based on the interests of a user by collecting preferences or of previous customers who have similar taste. 

The InstaCart dataset is a great way to sharpen your skills in collaborative filtering. This data set is extensive and contains data of over 3 million grocery orders stored across multiple tables – aisle, products, orders and departments. This market basket analysis data set is available on Kaggle –

Analysis Of Instacart From Kaggle Competition | by dipak tiwari | Analytics  Vidhya | Mar, 2021 | Medium
Graphical Visualization of Product reorders by Department
Image Source – Medium

10. Fake News Detection

Difficulty level – Hard

Social media platforms like WhatsApp and Twitter are being overwhelmed by unreliable sources of information. Such news does more harm than good. They are capable of inciting unnecessary fear in people. With our ever-increasing reliance on technology, it has now become important to filter out such fake news. 

Natural Language Processing (NLP) techniques and text classifiers are used for this purpose. They filter out news that may be misleading and untrustworthy. The following dataset contains features like language, headlines, source, country, news text and spam score. This can be modeled as a training dataset and used to evaluate future news articles.

You will find the data you need here –

Fake News Detection Using Machine Learning Ensemble Methods
Basic Workflow behind Fake News Detection Project
(including web scraping, you can use given dataset as well)

Image Source – Article #8885861 on Hindawi

Bonus – Covid-19 Projections using Machine Learning 

This is Bonus Machine Learning Projects for Beginners COVID-19 has taken over the world today, and not just in the pandemic sense. While medical scientists are focused on developing efficacious vaccines and inoculating the planet, data scientists are not too far behind in their involvement. Data is being made publicly available on new cases, daily active count, deaths and testing numbers. On a daily basis, forecasts are projected based on the SARS outbreak of the previous century.

In many countries, governments were able to handle the reins on the second wave because of the forecasts made by data scientists. Prediction models based on regression analysis and support vector machines have been developed for this purpose.

If you wish to keep your portfolio hand in hand with the current times, the COVID-19 data set is unquestionably worth an exploration. 

Building a COVID-19 resource hub: Tracking the virus through actionable data
Image Source – Tableau
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There you go, a comprehensive list of some important machine learning projects for beginners. It covers both supervised and unsupervised learning concepts. The article has done its job. And now it’s time for you to prove your mettle by completing these exciting projects! Make sure you upload your projects on GitHub. If possible, share them on platforms like LinkedIn. This is so that employers know that you have hands-on skills. Other students may also learn a thing or two by looking at your implementation.

Remember that ML is a growing field. Keep practicing and gaining knowledge on the various Machine Learning algorithms and tools. Only then will you be successful in your journey of becoming a great Data Scientist!


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