Machine Learning Project Ideas
As Artificial Intelligence (AI) continues to progress rapidly in 2020, achieving mastery over Machine Learning (ML) is becoming increasingly important for all the players in this field. This is because both AI and ML complement each other.
While textbooks and study materials will give you all the knowledge you need to know about Machine Learning, you can never really master ML unless you invest your time in real-life practical experiments – projects on Machine Learning. As you start working on machine learning project ideas, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career. In this tutorial, you will find 15 interesting machine learning project ideas for beginners to get hands-on experience on machine learning.
Here are some cool Machine Learning project ideas for beginners
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This list of machine learning project ideas for students is suited for beginners, and those just starting out with Machine Learning or Data Science in general. These machine learning project ideas will get you going with all the practicalities you need to succeed in your career as a Machine Learning professional. The focal point of these machine learning projects is machine learning algorithms for beginners, i.e., algorithms that don’t require you to have a deep understanding of Machine Learning, and hence are perfect for students and beginners.
Further, if you’re looking for Machine Learning project ideas for final year, this list should get you going. So, without further ado, let’s jump straight into some Machine Learning project ideas that will strengthen your base and allow you to climb up the ladder.
1. Stock Prices Predictor
One of the best ideas to start experimenting you hands-on Machine Learning projects for students is working on Stock Prices Predictor. Business organizations and companies today are on the lookout for software that can monitor and analyze the company performance and predict future prices of various stocks. And with so much data available on the stock market, it is a hotbed of opportunities for data scientists with an inclination for finance.
However, before you start off, you must have a fair share of knowledge in the following areas:
- Predictive Analysis: Leveraging various AI techniques for different data processes such as data mining, data exploration, etc. to ‘predict’ the behaviour of possible outcomes.
- Regression Analysis: Regressive analysis is a kind of predictive technique based on the interaction between a dependent (target) and independent variable/s (predictor).
- Action Analysis: In this method, all the actions carried out by the two techniques mentioned above are analyzed after which the outcome is fed into the machine learning memory.
- Statistical Modeling: It involves building a mathematical description of a real-world process and elaborating the uncertainties, if any, within that process.
In Michael Lewis’ Moneyball, the Oakland Athletics team transformed the face of baseball by incorporating analytical player scouting technique in their gameplan. And just like them, you too can revolutionize sports in the real world!
Since there is no dearth of data in the sports world, you can utilize this data to build fun and creative machine learning projects such as using college sports stats to predict which player would have the best career in which particular sports (talent scouting). You could also opt for enhancing team management by analyzing the strengths and weaknesses of the players in a team and classifying them accordingly.
With the amount of sports stats and data available, this is an excellent arena to hone your data exploration and visualization skills. For anyone with a flair in Python, Scikit-Learn will be the ideal choice as it includes an array of useful tools for regression analysis, classifications, data ingestion, and so on. Mentioning Machine Learning projects for the final year can help your resume look much more interesting than others.
3. Develop A Sentiment Analyzer
Although most of us use social media platforms to convey our personal feelings and opinions for the world to see, one of the biggest challenges lies in understanding the ‘sentiments’ behind social media posts.
And this is the perfect idea for your next machine learning project!
Social media is thriving with tons of user-generated content. By creating an ML system that could analyze the sentiment behind texts, or a post, it would become so much easier for organizations to understand consumer behaviour. This, in turn, would allow them to improve their customer service, thereby providing the scope for optimal consumer satisfaction.
You can try to mine the data from Twitter or Reddit to get started off with your sentiment analyzing machine learning project. This might be one of those rare cases of deep learning projects which can help you in other aspects as well.
4. Enhance Healthcare
AI and ML applications have already started to penetrate the healthcare industry and are also rapidly transforming the face of global healthcare. Healthcare wearables, remote monitoring, telemedicine, robotic surgery, etc., are all possible because of machine learning algorithms powered by AI. They are not only helping HCPs (Health Care Providers) to deliver speedy and better healthcare services but are also reducing the dependency and workload of doctors to a significant extent.
So, why not use your skills to develop an impressive machine learning project based on healthcare? To handle a project with Machine Learning algorithms for beginners can be helpful to build your career with a good start.
The healthcare industry has enormous amounts of data at their disposal. By harnessing this data, you can create:
- Diagnostic care systems that can automatically scan images, X-rays, etc., and provide an accurate diagnosis of possible diseases.
- Preventative care applications that can predict the possibilities of epidemics such as flu, malaria, etc., both at the national and community level.
5. Prepare ML Algorithms – From Scratch!
This is an excellent ML project idea for beginners. Writing ML algorithms from scratch will offer two-fold benefits:
- One, writing ML algorithms is the best way to understand the nitty-gritty of their mechanics.
- Two, you will learn how to transform mathematical instructions into functional code. This skill will come in handy in your future career in Machine Learning.
You can begin by choosing an algorithm that is straightforward and not too complex. Behind the making of each algorithm – even the simplest ones – there are several carefully calculated decisions. Once you’ve achieved a certain level of mastery in building simple ML algorithms, try to tweak and extend their functionality. For instance, you could take a vanilla logistic regression algorithm and add regularization parameters to it to transform it into a lasso/ridge regression algorithm.
6. Develop A Neural Network That Can Read Handwriting
Deep learning and neural networks are the two happening buzzwords in AI. These have given us technological marvels like driverless-cars, image recognition, and so on.
So, now’s the time to explore the arena of neural networks. Begin your neural network machine learning project with the MNIST Handwritten Digit Classification Challenge. It has a very user-friendly interface that’s ideal for beginners.
7. Movie Ticket Pricing System
With the expansion of OTT platforms like Netflix, Amazon Prime, people prefer to watch content as per their convenience. Factors like Pricing, Content Quality & Marketing have influenced the success of these platforms.
The cost of making a full-length movie has shot up exponentially in the recent past. Only 10% of the movies that are made make profits. Stiff competition from Television & OTT platforms along with the high ticket cost has made it difficult for films to make money even harder. The rising cost of the theatre ticket (along with the popcorn cost) leaves the cinema hall empty.
An advanced ticket pricing system can definitely help the movie makers and viewers. Ticket price can be higher with the rise in demand for ticket and vice versa. The earlier the viewer books the ticket, the lesser the cost, for a movie with high demand. The system should smartly calculate the pricing depending on the interest of the viewers, social signals and supply-demand factors.
8. Iris Flowers Classification ML Project
One of the best ideas to start experimenting you hands-on Machine Learning projects for students is working on Iris Flowers classification ML project. Iris flowers dataset is one of the best datasets for classification tasks. Since iris flowers are of varied species, they can be distinguished based on the length of sepals and petals. This ML project aims to classify the flowers into among the three species – Virginica, Setosa, or Versicolor.
This particular ML project is usually referred to as the “Hello World” of Machine Learning. The iris flowers dataset contains numeric attributes, and it is perfect for beginners to learn about supervised ML algorithms, mainly how to load and handle data. Also, since this is a small dataset, it can easily fit in memory without requiring special transformations or scaling capabilities.
You can download the iris dataset here.
9. BigMart Sales Prediction ML Project
This is an excellent ML project idea for beginners. This ML project is best for learning how unsupervised ML algorithms function. The BigMart sales dataset comprises of precisely 2013 sales data for 1559 products across ten outlets in various cities.
The aim here is to use the BigMart sales dataset to develop a regression model that can predict the sale of each of 1559 products in the upcoming year in the ten different BigMart outlets. The BigMart sales dataset contains specific attributes for each product and outlet, thereby helping you to understand the properties of the different products and stores that influence the overall sales of BigMart as a brand.
10. Recommendation Engines with MovieLens Dataset
Recommendation engines have become hugely popular in online shopping and streaming sites. For instance, online content streaming platforms like Netflix and Hulu have recommendation engines to customize their content according to individual customer preferences and browsing history. By tailoring the content to cater to the watching needs and preferences of different customers, these sites have been able to boost the demand for their streaming services.
As a beginner, you can try your hand at building a recommendation system using one of the most popular datasets available on the web – MovieLens dataset. This dataset includes over “25 million ratings and one million tag applications applied to 62,000 movies by 162,000 users.” You can begin this project by building a world-cloud visualization of movie titles to make a movie recommendation engine for MovieLens.
You can check out the MovieLens dataset here.
11. Predicting Wine Quality using Wine Quality Dataset
It’s a well-established fact that age makes wine better – the older the wine, the better it will taste. However, age is not the only thing that determines a wine’s taste. Numerous factors determine the wine quality certification, including physiochemical tests such as alcohol quantity, fixed acidity, volatile acidity, density, and pH level, to name a few.
In this ML project, you need to develop an ML model that can explore a wine’s chemical properties to predict its quality. The wine quality dataset you’ll be using for this project consists of approximately 4898 observations, including 11 independent variables and one dependent variable. Mentioning Machine Learning projects for the final year can help your resume look much more interesting than others.
12. MNIST Handwritten Digit Classification
Deep Learning and neural networks have found use cases in many real-world applications like image recognition, automatic text generation, driverless cars, and much more. However, before you delve into these complex areas of Deep Learning, you should begin with a simple dataset like the MNIST dataset. So, why not use your skills to develop an impressive machine learning project based on MNIST?
The MNIST digit classification project is designed to train machines to recognize handwritten digits. Since beginners usually find it challenging to work with image data over flat relational data, the MNIST dataset is best for beginners. In this project, you will use the MNIST datasets to train your ML model using Convolutional Neural Networks (CNNs). Although the MNIST dataset can seamlessly fit in your PC memory (it is very small), the task of handwritten digit recognition is pretty challenging.
You can access the MNIST dataset here.
13. Human Activity Recognition using Smartphone Dataset
The smartphone dataset includes the fitness activity record and information of 30 people. This data was captured through a smartphone equipped with inertial sensors.
This ML project aims to build a classification model that can identify human fitness activities with a high degree of accuracy. By working on this ML project, you will learn the basics of classification and also how to solve multi-classification problems.
14. Object Detection with Deep Learning
When it comes to image classification, Deep Neural Networks (DNNs) should be your go-to choice. While DNNs are already used in many real-world image classification applications, this ML project aims to crank it up a notch.
In this ML project, you will solve the problem of object detection by leveraging DNNs. You will have to develop a model that can both classify objects and also accurately localize objects of different classes. Here, you will treat the task of object detection as a regression problem to object bounding box masks. Also, you will define a multi-scale inference procedure that can generate high-resolution object detections at a minimal cost.
15. Fake News Detection
This is an excellent ML project idea for beginners, especially how fake news are spreading like wildfire now. Fake news has a knack for spreading like wildfire. And with social media dominating our lives right now, it has become more critical than ever to distinguish fake news from real news events. This is where Machine Learning can help. Facebook already uses AI to filter fake and spammy stories from the feeds of users.
This ML project aims to leverage NLP (Natural Language Processing) techniques to detect fake news and misleading stories that emerge from non-reputable sources. You can also use the classic text classification approach to design a model that can differentiate between real and fake news. In the latter method, you can collect datasets for both real and fake news and create an ML model using the Naive Bayes classifier to classify a piece of news as fraudulent or real based on the words and phrases used in it.
Here is a comprehensive list of machine learning project ideas. Machine learning is still at an early stage throughout the world. There are a lot of projects to be done, and a lot to be improved. With smart minds and sharp ideas, systems with support business get better, faster and profitable. If you wish to excel in Machine Learning, you must gather hands-on experience with such machine learning projects.
Only by working with ML tools and ML algorithms can you understand how ML infrastructures work in reality. Now go ahead and put to test all the knowledge that you’ve gathered through our machine learning project ideas guide to build your very own machine learning projects!
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