45+ Best Machine Learning Project Ideas For Beginners [2024]

Summary:

In this Article, you will learn

1. Stock Prices Predictor
2. Sports Predictor
3. Develop A Sentiment Analyzer
4. Enhance Healthcare
5. Prepare ML Algorithms – From Scratch!
6. Develop A Neural Network That Can Read Handwriting
7. Movie Ticket Pricing System
8. Iris Flowers Classification ML Project
9. BigMart Sales Prediction ML Project
10. Recommendation Engines with MovieLens Dataset
11. Predicting Wine Quality using Wine Quality Dataset
12. MNIST Handwritten Digit Classification
13. Human Activity Recognition using Smartphone Dataset
14. Object Detection with Deep Learning
15. Fake News Detection…. and so on..

Read the full blog to know all the 45+ ML Projects in detail.

Machine Learning Project Ideas

As Artificial Intelligence (AI) continues to progress rapidly in 2022, 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. So, if you are a beginner, the best thing you can do is work on some Machine Learning projects.

We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting Machine Learning projects which beginners can work on to put their Machine Learning knowledge to test. In this article, you will find 15 top machine learning project ideas for beginners to get hands-on experience.

But first, let’s address the more pertinent question that must be lurking in your mind: why to build Machine Learning projects?

When it comes to careers in software development, it is a must for aspiring developers to work on their own projects. Developing real-world projects is the best way to hone your skills and materialize your theoretical knowledge into practical experience. The more you experiment with different Machine Learning projects, the more knowledge you gain.

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. 

These courses will guide you to create the best ML projects.

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What are the uses of machine learning?

Machine learning has various uses across various industries and domains due to its ability to analyze and learn from data to make predictions, identify patterns, and automate tasks. Here are some common uses of machine learning:

• Predictive Analytics

Predictive analytics is a cornerstone of machine learning applications. Machine learning models can predict future trends and outcomes by analyzing historical data. This is invaluable for industries such as finance, where predicting stock prices, currency exchange rates, and market trends can provide a competitive edge. Retailers also use predictive analytics to forecast demand, optimize inventory, and enhance supply chain management.

• Image and Video Recognition

Machine learning algorithms can be trained to recognize objects, people, and patterns in images and videos. Applications include facial recognition, object detection, medical image analysis, and autonomous vehicles.

• Natural Language Processing (NLP)

NLP is a subset of machine learning that deals with human language. It’s the foundation of voice assistants like Siri and language translation services like Google Translate. Sentiment analysis, another NLP application, helps businesses understand the public sentiment around their products or services through social media and reviews.

• Recommendation Systems

These systems use machine learning to suggest products, services, or content to users based on their past behavior and preferences. Examples include Netflix’s movie recommendations and Amazon’s product recommendations.

• Fraud Detection

Machine learning can detect fraudulent activities by identifying unusual patterns in data. This is used in financial institutions to detect credit card fraud, insurance fraud, and other types of scams.

• Healthcare Applications

Machine learning has revolutionized healthcare by assisting in early disease detection, personalized treatment, and drug discovery. Models trained on medical data can identify patterns that may not be apparent to human physicians. Medical imaging analysis using machine learning aids in diagnosing conditions from X-rays, MRIs, and CT scans. Additionally, predictive models can anticipate disease outbreaks, enhancing public health responses.

• Autonomous Vehicles

Machine learning algorithms enable self-driving cars to perceive their environment, make decisions, and navigate safely. They process data from sensors like cameras, lidar, and radar to drive autonomously.

• Customer Segmentation

Businesses use machine learning to segment customers into groups based on their behavior, preferences, and demographics. This helps in targeted marketing and improving customer experiences.

• Financial Analysis

Machine learning can be used to analyze large financial datasets, detect patterns, and make investment decisions. High-frequency trading, credit scoring, and risk assessment are some examples.

• Industrial Automation

Machine learning helps optimize manufacturing processes, predict equipment failures, and manage supply chains more efficiently. It can also enhance quality control and reduce downtime.

• Energy Management

Machine learning is used to optimize energy consumption in buildings, predict demand, and improve energy efficiency in various industries.

• Agriculture

Machine learning aids precision agriculture by analyzing data from drones, sensors, and satellites. This helps farmers make informed decisions about irrigation, fertilization, and pest control, leading to higher crop yields and reduced resource waste.

• Gaming and Entertainment

Machine learning is employed for character animation, game strategy optimization, and generating realistic graphics.

• Social Media Analysis

Machine learning algorithms can analyze social media data to extract insights, sentiment analysis, and trends for businesses and researchers.

• Environmental Monitoring

Machine learning models can process data from sensors and satellites to monitor environmental changes, weather patterns, and natural disasters.

• Enhanced Customer Experience

Businesses leverage machine learning to understand customer preferences and behaviors, leading to better-targeted marketing and improved customer experiences. Recommendation systems, commonly seen on platforms like Netflix and Amazon, suggest products and content based on user history. Chatbots powered by machine learning offer instant customer support, enhancing engagement and satisfaction.

So, here are a few Machine Learning Projects which beginners can work on:

Here are some cool Machine Learning project ideas for beginners

Watch our video on machine learning project ideas and topics…

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. 

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.

This is one of the best and easiest Machine Learning project ideas for not only beginners but also for final year students. 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.  

What is Machine Learning and Why it matters

Building a Stock Prices Predictor is one of the best hands-on Machine Learning project for students, involving the collection and preprocessing of historical stock price data. Students engage in feature engineering, exploring data trends, and selecting regression algorithms for training. 

The project emphasizes model evaluation using metrics like MAE and MSE, fostering a practical understanding of machine learning concepts. Through iterative refinement, students optimize the model’s performance and gain valuable insights into data manipulation, feature engineering, and project management. The project’s real-world relevance lies in its application to financial data, making it a comprehensive learning opportunity that integrates finance and machine learning.

Source Code: Stock Prices Predictor

2. Sports Predictor

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! This is an excellent machine learning projects for beginners.

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.

6 Times Artificial Intelligence Startled The World

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.

Source Code: SportsPredictor

3. Develop A Sentiment Analyzer

This is one of the interesting machine learning project ideas. 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.

Must Read: Free deep learning course!

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.

This sentiment analyzer project not only provides hands-on experience with NLP and machine learning but also aligns with practical applications for businesses. It is one of the most commonly pursued Machine Learning projects as it is highly relevant in the world of social media, helping to understand and respond to public sentiments on these platforms. The insights gained from this project can contribute to enhancing customer service strategies, brand management, and decision-making processes based on public sentiment analysis.

Source Code: Develop A Sentiment Analyzer

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.

These 6 Machine Learning Techniques are Improving Healthcare

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.

Source Code: Enhance Healthcare

5. Prepare ML Algorithms – From Scratch!

This is one of the excellent machine learning project ideas 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. Mentioning machine learning projects can help your resume look much more interesting than others.

Source Code: Prepare ML Algorithms

6. Develop A Neural Network That Can Read Handwriting

One of the best ideas to start experimenting you hands-on Java projects for students is working on neural network. 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.

Machine Learning Engineers: Myths vs. Realities

It is additionally considered one of the easiest projects on machine learning, particularly for students exploring neural networks and deep learning with Java. It is an excellent entry point for students due to its simplicity, well-defined nature, and the availability of resources. It can help students foster a solid understanding of fundamental concepts that can be applied to more intricate Machine Learning projects for final year students in the future.

Source Code: Develop A Neural Network That Can Read Handwriting

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.

Therefore, it is one of the best ML projects to choose from due to its industry relevance, the complexity of factors involved, the potential impact on business outcomes, and the opportunity for students to explore various aspects of predictive analytics and optimization in a real-world context.

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.

Source Code: Movie Ticket Pricing System

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.

It is one of the most widely pursued ML project ideas because it is simple, easy to pursue and has high educational value. It’s a beginner-friendly introduction to classification problems, featuring a well-structured dataset of iris flower measurements. Learners can easily experiment with different classification algorithms, gaining hands-on experience in key ML concepts such as data preprocessing and model evaluation. The project’s straightforward nature makes it an ideal starting point, building a solid foundation for tackling more complex machine learning challenges.

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. And this is the perfect idea for your next machine learning project!

You can download the iris dataset here.

Source Code: Iris Flowers Classification ML Project

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.

This project also stands out as one of the most common AI ML projects for final year students as well due to its real-world applicability and comprehensive learning opportunities. It involves predicting sales for different products in multiple stores, reflecting the complexities of retail scenarios. Both beginner-level and final year students can gain practical experience in handling large datasets, addressing missing values, and selecting relevant features for prediction. 

Moreover, the project allows for the exploration of regression algorithms, enabling students to understand how to predict continuous numerical values. The real-world relevance of sales prediction in retail makes it an engaging project, providing valuable insights into business analytics and decision-making. 

Source Code: BigMart Sales Prediction ML Project 

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.

Creating a recommendation engine with the MovieLens Dataset is also considered one of the best Machine Learning project topics due to its practical relevance and broad learning scope. It allows students to explore collaborative filtering and content-based filtering, essential techniques in recommendation systems.

This project is not only instructive but also mirrors real-world applications on platforms like Netflix, offering students a valuable opportunity to understand and implement machine learning in personalized content recommendations.

You can check out the MovieLens dataset here.

Source Code: Recommendation Engines with MovieLens Dataset

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.

Predicting wine quality using the Wine Quality Dataset is also an excellent machine learning project for beginners. The dataset is well-organized, allowing beginners to focus on key machine learning concepts like data exploration, preprocessing, and model evaluation.

This project offers hands-on experience in essential tasks like feature selection and model training while showcasing the practical use of machine learning in predicting real-world outcomes, making it an ideal choice for beginners seeking a straightforward introduction to machine learning.

Source Code: Predicting Wine Quality using Wine Quality Dataset

12. MNIST Handwritten Digit Classification 

This is one of the interesting machine learning projects. 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.

Regarded as one of the best projects on Machine Learning, it serves as an effective starting point for learners, providing a structured introduction to computer vision and neural networks.

You can access the MNIST dataset here.

Source Code: MNIST Handwritten Digit Classification

13. Human Activity Recognition using Smartphone Dataset

This is one of the trending machine learning project ideas. 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.

This is one of those projects on Machine Learning that holistically offers a comprehensive and practical learning experience, making it an excellent choice for those seeking a project that combines real-world impact with a broad range of machine learning skills.

It mirrors real-world challenges seen in wearable tech and health monitoring with the help of signal processing, and feature engineering, and offers learners a well-rounded grasp of the entire machine learning process.

This practical application not only makes the project interesting but also prepares learners for the kind of complexities they might encounter in various industries, making it an excellent choice for those wanting hands-on experience with real-world data.

Source Code: Human Activity Recognition using Smartphone Dataset

14. Object Detection with Deep Learning

This is one of the interesting machine learning projects to create. 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. 

This is one of the best ML projects due to its broad applications in crucial industries like autonomous vehicles, surveillance, and image recognition. To implement this project successfully, learners delve into deep learning architectures such as Convolutional Neural Networks (CNNs) and use frameworks like TensorFlow or PyTorch.

It offers hands-on experience to learners includes preprocessing image data, fine-tuning pre-trained models, and optimizing for accuracy and efficiency. This project will provide the practical skills you need to apply while working in diverse domains of Machine Learning.

Source Code: Object Detection with Deep Learning

15. Fake News Detection

This is one of the excellent machine learning project ideas 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.

Source Code: Fake News Detection

16. Enrol Email Project

The Enron email dataset contains almost 500k emails of over 150 users. It is an extremely valuable dataset for natural language processing. This project involves building an ML model that uses the k-means clustering algorithm to detect fraudulent actions. The model will separate the observations into ‘k’ number of clusters according to similar patterns in the dataset.

This dataset holds immense value for natural language processing applications. This project not only delves into the complexities of natural language processing but also addresses the crucial task of detecting fraudulent activities, making it a noteworthy and multifaceted undertaking in the realm of machine learning.

As one of the highest pursued ML project ideas, it offers a practical and comprehensive learning experience, making it an excellent choice for both beginners and final year students looking to engage with impactful real-world machine learning applications.

Source Code: Enrol Email Project

17. Parkinson’s project

The Parkinson dataset includes 195 biomedical records of people with 23 varied characteristics. The idea behind this project is to design an ML model that can differentiate between healthy people and those suffering from Parkinson’s disease. The model uses the XGboost (extreme gradient boosting) algorithm based on decision trees to make the separation.

XGBoost excels at handling complex relationships within the data, making it particularly suitable for medical diagnosis tasks. The model is trained on the dataset’s features, which likely include various biomedical measurements, and learns to distinguish patterns indicative of Parkinson’s disease.

Through Machine Learning project topics like these, learners can gain hands-on experience in feature engineering, model training, and the interpretability of decision tree-based algorithms. The ultimate goal of is to create a reliable predictive tool that aids in the early detection of Parkinson’s disease, showcasing the impactful intersection of machine learning and healthcare.

Source Code: Parkinson’s project

18. Flickr 30K project

The Flickr 30K dataset consists of more than 30,000 images, each having a unique caption. You will use this dataset to build an image caption generator. The idea is to build a CNN model that can effectively analyze and extract features from an image and create a befitting caption describing the image in English.

This project offers hands-on experience to learners in merging computer vision and natural language processing. By working on it, practitioners learn to prepare image data, create and train a CNN model, and seamlessly connect it with a natural language processing component for generating captions.

The primary goal is to develop an intelligent system capable of interpreting images and conveying their content through human-like captions. It highlights the intriguing blend of image analysis and language generation within the field of artificial intelligence.

Source Code: Flickr 30K project

19. Mall customers project

As the name suggests, the mall customers dataset includes the records of people who visited the mall, such as gender, age, customer ID, annual income, spending score, etc. You will build a model that will use this data to segment the customers into different groups based on their behavior patterns. Such customer segmentation is a highly useful marketing tactic used by brands and marketers to boost sales and revenue while also increasing customer satisfaction.

Machine learning models, particularly clustering algorithms like K-means, can be applied to achieve this segmentation. This project provides hands-on experience in data analysis and model building, offering insights into consumer behavior and helping businesses make informed marketing decisions. Ultimately, it demonstrates how machine learning can optimize marketing strategies and enhance the overall customer experience.

Source Code: Mall customers project

20. Kinetics project 

For this project, you will use an extensive dataset that includes three separate datasets – Kinetics 400, Kinetics 600, and Kinetics 700 – containing URL links of over 6.5 million high-quality videos. Your goal is to create a model that can detect and identify the actions of a human by studying a series of different observations.

This project with the Kinetics datasets is valuable for its real-world applications, like enhancing surveillance, improving human-computer interaction, and optimizing content recommendation systems. For instance, an action recognition model developed in this project could be used in video surveillance to automatically identify and categorize human activities.

Engaging in this project offers practical experience in managing extensive video datasets, preparing video data for analysis, and building sophisticated deep learning models. It also provides insights into the complexities of analyzing temporal data and the nuances of identifying human actions in videos.

Source Code: Kinetics project 

21. Recommendation system project 

This a rich dataset collection containing a diverse range of datasets gathered from popular websites like Goodreads book reviews, Amazon product reviews, social media, etc. Your goal is to build a recommendation engine (like the ones used by Amazon and Netflix) that can generate personalized recommendations for products, movies, music, etc., based on customer preferences, needs, and online behavior.

ML projects ideas like these are highly significant because they help develop skills like processing diverse data, implementing recommendation algorithms, and evaluating system performance. These skills are highly applicable across industries, highlighting the universal use of recommendation systems in the digital landscape.

Practically, the project provides hands-on experience and delves into ethical considerations like privacy and algorithmic bias. All in all, it’s a practical exploration of how machine learning enhances user interactions and decision-making in various domains.

Source Code: Recommendation system project

22. The Boston housing project

The Boston housing dataset consists of the details of different houses in Boston based on factors like tax rate, crime rate, number of rooms in a house, etc. It is an excellent dataset for predicting the prices of different houses in Boston. In this project, you will build a model that can predict the price of a new house using linear regression. Linear regression is best suited for this project since it is used where the data has a linear relationship between the input and output values and when the input is unknown.

Source Code: The Boston housing project

23. Cityscapes project

This open-source dataset includes high-quality pixel-level annotations of video sequences collected from the streets across 50 different cities. It is immensely useful for semantic analysis. You can use this dataset to train deep neural nets to analyze and understand the urban cityscape. The project involves designing a model that can perform image segmentation and identify various objects (cars, buses, trucks, trees, roads, people, etc.) from a street video sequence. 

Source Code: Cityscapes project

24. YouTube 8M project 

The Youtube 8M is a huge dataset that has 6.1 million YouTube video IDs, 350,000 hours of video, 2.6 billion audio/visual features, 3862 classes, and an average of 3 labels for each video. It is widely used for video classification projects. In this project, you will build a video classification system that can accurately describe a video. It will consider a series of different inputs and classify the videos into separate categories.

Source Code: YouTube 8M project 

25. Urban sound 8K

The urban sound 8K dataset is used for sound classification. It includes a diverse collection of 8732 urban sounds belonging to different classes such as sirens, street music, dog barking, birds chirping, people talking, etc. You will design a sound classification model that can automatically detect which urban sound is playing.

Source Code: Urban sound 8K

26. IMDB-Wiki project 

This labeled dataset is probably one of the most extensive collections of face images gathered from across IMDB and Wikipedia. It has over 5 million face images labeled with age and gender. with labeled gender and age. You will create a model that can detect faces and predict their age and gender with accuracy. You can make different age segments/ranges like 0-10, 10-20, 30-40, and so on. 

Source Code: IMDB-Wiki project 

27. Librispeech project

The librispeech dataset is a massive collection of English speeches derived from the  LibriVox project. It contains English-read speeches in various accents that span over 1000 hours and is the perfect tool for speech recognition. The focus of this project is to create a model that can automatically translate audio into text. You will build a speech recognition system that can detect English speech and translate it into text format. 

Source Code: Librispeech project

28. German traffic sign recognition benchmark (GTSRB) project

This dataset contains more than 50,000 images of traffic signs segmented into 43 classes and containing information on the bounding box of each traffic sign. It is ideal for multiclass classification which is exactly what you will focus on here. You will build a model using a deep learning framework that can recognize the bounding box of signs and classify traffic signs. The project can be extremely useful for autonomous vehicles as it detects signs and helps drivers take the necessary actions.

Source Code: German traffic sign recognition benchmark (GTSRB) project

29. Sports match video text summarization

This project is exactly as it sounds – obtaining an accurate and concise summary of a sports video. It is a useful tool for sports websites that inform readers about the match highlights. Since neural networks are best for text summarization, you will build this model using deep learning networks such as 3D-CNNs, RNNs, and LSTMs. You will first fragment a sports video into multiple sections by using the appropriate ML algorithms and then use a combination of SVM(Support vector machines), neural networks, and k-means algorithm.

Source Code: Sports match video text summarization

30. Business meeting summary generator

Summarization involves extracting the most meaningful and valuable bits of information from conversations, audio/video files, etc., briefly and concisely. It is generally done by feature capturing the statistical, linguistic, and sentimental traits with the dialogue structure of the conversation in question. In this project, you will use deep learning and natural language processing techniques to create precise summaries of business meetings while upholding the context of the entire conversation.

Source Code: Business meeting summary generation

31. Sentiment analysis for depression

Depression is a major health concern globally. Each year, millions of people commit suicide due to depression and poor mental health. Usually, the stigma attached to mental health problems and delayed treatment are the two main causes behind this. In this project, you will leverage the data gathered from different social media platforms and analyze linguistic markers in social media posts to understand the mental health of individuals. The idea is to create a deep learning model that can offer valuable and accurate insights into one’s mental health much earlier than conventional methods.

Source Code: Sentiment analysis for depression

32. Handwritten equation solver 

Handwritten mathematical expression recognition is a crucial field of study in computer vision research. You will build a model and train it to solve handwritten mathematical equations using Convolutional Neural Networks. The model will also make use of image processing techniques. This project involves training the model with the right data to make it adept at reading handwritten digits, symbols, etc., to deliver correct results for mathematical equations of different complexity levels.

Source Code: Handwritten equation solver 

33. Facial recognition to detect mood and recommend songs

It is a known fact that people listen to music based on their current mood and feelings. So, why not create an application that can detect a person’s mood by their facial expressions and recommend songs accordingly? For this, you will use computer vision elements and techniques. The goal is to create a model that can effectively leverage computer vision to help computers gain a high-level understanding of images and videos.

A good deep learning project is developing a facial recognition system, as it involves various complex tasks like image processing, feature extraction, and the application of convolutional neural networks (CNNs), providing a comprehensive learning experience.

Source Code: Facial recognition to detect mood and recommend songs

34. Music generator

A music composition is nothing but a melodious combination of different frequency levels. In this project, you will design an automatic music generator that can compose short pieces of music with minimal human intervention. You will use deep learning algorithms and LTSM networks for building this music generator.

Source Code: Music generator

35. Disease prediction system

This ML project is designed to predict diseases. You will create this model using R and R Studio and the Breast Cancer Wisconsin (Diagnostic) Dataset. This dataset includes two predictor classes – benign and malignant breast mass. It is essential to have a basic knowledge of random forests and XGBoost for working on this project.

Source Code: Disease prediction system

36. Finding a habitable exo-planet 

In the past decade, we’ve been successful in identifying many transiting and exo-planets. Since the manual interpretation of potential exoplanets is pretty challenging and time-consuming (not to forget, it is also subject to human error), it is best to use deep learning to identify exoplanets. This project aims to find out if there are any habitable exoplanets around us using CNNs and noisy time-series data. This method can identify habitable exoplanets with more precision than the least-squares method.

Source Code: Habitability of Exo-planets

37. Image regeneration for old & damaged reels

Restoring old or damaged picture reels is a challenging task. It is almost always impossible to restore old photos to their original state. However, deep learning can solve this problem. You will build a deep learning model that can identify the defects in an image (scuffs, holes, folds, decoloration, etc.) and using Inpainting algorithms to restore it. You can even colorize old B&W images.

Source Code: Restore old and damaged photos

38. Loan Eligibility Prediction

Loans are currently the core business especially for banks because their key profit derives from the interest levied on loans. Generally, economic growth is guaranteed when individuals put some part of their money into some business with the hope that it could multiply in the future. Although it comes with risk, sometimes it becomes inevitable to take a loan. Because loans contribute to one of the most important components of our lives, loan eligibility prediction can be greatly beneficial. Therefore, it is one of the important ML mini projects. Moreover, it is among those ML projects with great influence on various sectors.

The model for evaluating the loan eligibility prediction needs to be trained through a dataset that comprises data including data. Examples of data can be marital status, gender, income, credit card history, loan amount, etc. Moreover, this machine learning idea guarantees better planning in addition to the loan being accepted or rejected. If you are looking for some AI ML projects for final year, this could be a great opportunity.

Source Code: Loan Eligibility Prediction

39. Inventory Demand Forecasting

Zomato is a famous mobile app in India that connects customers to neighboring food chains by offering them their delivery persons. Preparing enough inventories is a responsibility that Zomato and the registered restaurants have to complete. The majority of the companies that provide need to ascertain that they have sufficient stock to meet their customers’ expectations. Therefore, it becomes vital to get a rough approximation of how much preparation is required. You can achieve this preparation using one of the valuable ML projects for beginners i.e. Inventory Demand Forecasting.

The corresponding predictions in demand forecasting could be accomplished using the application of corresponding ML algorithms. Moreover, these ML projects for beginners can be executed by using ML algorithms like Boosting, Bagging, Gradient Boosting Machine (GBM), XGBoost, Support Vector Machines, and more.

Source Code: Inventory Demand Forecasting

40. Customer Churn Prediction Analysis Using Ensemble Techniques in Machine Learning

This is one of the best Machine Learning projects. Customers are the greatest asset of any company. Retaining customers is vital to enhance revenue and develop a lasting relationship with them. Furthermore, acquiring new customers is approximately five times more expensive than retaining a prevailing customer.

One of the prevalent ML mini projects when it comes to predicting customers’ churn is the “Customer Churn Prediction Analysis Using Ensemble Techniques in Machine Learning”. For this project idea, the question is how to begin solving the customer churn rate prediction ML problem. Like other ML problems, machine learning engineers or data scientists must gather and prepare the relevant data for processing. Moreover, it must use data engineering in the proper format to ensure effectiveness.

It is important to note that for these ML mini projects, Feature Engineering is the greatest creative aspect of the churn prediction ML model. It implies that data specialists apply their domain knowledge of the data, business context, experience, and creativity to design features. Also, these aspects help to personalize the ML model to comprehend why customer churn takes place in a business.

Source Code: Customer Churn Prediction Analysis

 41. Predict Credit Default -Credit Risk Prediction Project

For MBA or management course students, this one is one of the important machine learning projects for final year. It aims to predict customers who would default on a loan. When implementing this project idea, the banks may encounter losses on credit card products from different sources. One probable reason for this loss is whenever the customers default on the loan, their debt prevents banks from collecting the payments for the offered services.

In these types of machine learning projects for final year, you will scrutinize a group of the customer database to determine the number of customers seriously aberrant in paying in the subsequent 2 years. Various ML models are available to predict which customers default on a loan. Based on this information, the banks can cancel the credit lines for precarious customers or reduce the credit limit issued on the card to reduce losses.

Source Code: Predict Credit Default -Credit Risk Prediction Project

 42. Predicting Interest Levels of Rental Listings

We all want to comfortably lie in our homes after working for long hours at the workplace. The pandemic has revamped the work culture and facilitated work from home culture. So, the significance of finding a comfortable house has increased.

This project idea performs a sentimental investigation on the viewers for different rental listings. It becomes easy to evaluate their reactions to specific houses. Accordingly, it becomes easy to determine the popularity of those houses available for rent. Furthermore, it can predict the interest rates of new locations yet to be listed.

Source Code: Predicting Interest Levels of Rental Listings

 43. Driver Demand Prediction

Food delivery services and ride-sharing worldwide depend on the drivers’ availability. This is an easy-to-use ML project for beginners that predicts the driver demand by transforming a time series problem into a controlled machine learning problem. Moreover, exploratory analysis needs to be carried out on the time series to recognize patterns. Partial Auto-Correlation Function (PACF) and Auto-Correlation Function (ACF) will be employed to evaluate the time series. Furthermore, this project idea implies building the regression model to solve the time-series problem.

Source Code: Driver Demand Prediction

 44. Market Basket Analysis

In terms of customer purchase patterns, Market Basket Analysis is one of the valuable machine learning based projects.  It understands the combinations in which the customers usually purchase different commodities. Moreover, it is somewhat similar to the AI ML projects because it uses a data mining technique that observes purchasing patterns of consumers to understand them and eventually boost sales effectively.

This project idea is such that if a customer buys an item(s), it raises the chances of buying another item(s). The interest in other items (s) is based on the purchasing behaviors of former customers. Furthermore, this project idea is used for targeted promotions and to provide customers with tailored recommendations.

Source Code: Market Basket Analysis

45. Production Line Performance Checker

Leading engineering and technology companies, for example, Bosch deals with various business sectors like consumer goods, industrial technology, etc. One of the greatest challenges such companies face is to keep track of the manufacturing of the companies’ mechanical modules.

One of the most practical machine learning based projects is the Production Line Performance Checker. Like AI ML projects, this one also uses the latest technologies to predict the failures in the components’ production over the assembly line. It faces a challenge while implementing the analytical techniques because the production lines are usually complex, and the data may not be analyst-friendly. This challenge makes this machine learning project idea interesting.

Source Code: Production Line Performance

Real-world industry projects 

Magenta

This research project focuses on exploring the applications of machine learning in the creation process of art and music. You will develop unique reinforcement learning and deep learning algorithms that can generate images, songs, music, and much more. It is the perfect project for creative minds passionate about art and music. 

BluEx

BluEx is among the leading logistics company in India that has developed quite a fanbase, thanks to its timely and efficient deliveries. However, as is true of all logistics providers, BluEx faces one particular challenge that costs both time and money – its drivers do not frequent the optimal delivery paths which causes delays and leads to higher fuel costs. You will create an ML model using reinforcement learning that can find the most efficient path for a particular delivery location. This can save up to 15% of the fuel cost for BluEx. 

Motion Studios

Motion Studios boasts of being Europe’s largest Radio production house with revenue exceeding a billion dollars. Ever since the media company launched their reality show, RJ Star, they’ve received a phenomenal response and are flooded with voice clips. Being a reality show, there’s a limited time window for choosing candidates. You will build a model that can differentiate between male and female voices and classify voice clips to facilitate quicker filtration. This will help is faster selection, easing the task of the show executives. 

LithionPower

Lithionpower builds batteries for electric vehicles. Usually, drivers rent the company’s batteries for a day and replace them with a charged battery. The battery life depends on factors like distance driven/day, overspeeding, etc. LithionPower employs a variable pricing model based on a driver’s driving history. The goal of this project is to build a cluster model that will group drivers according to their driving history and incentivize drivers based on those clusters. While this will increase profits by 15-20%, it will also charge more from drivers having a poor driving history. 

Steps to Keep in Mind to Complete a Machine Learning Project for Beginners – 

You must adhere to a set of established procedures when working on AI and ML projects. For each initiative, we must first gather the information in accordance with our operational requirements. The following stage is to clean the data, which includes deleting values, addressing outliers, handling unbalanced datasets, and converting them to a numeric value, among other things. There are different algorithms that you can follow to create the best machine learning projects. 

• Gathering Data 

When collecting data for AI ML projects, it is necessary to ask certain questions yourself. For example, what is the problem you are trying to solve? Are there previously existing data sources? Is the data publicly available? 

When talking about structured data, they can be of different types, like, as categorical, numerical, and ordinal. 

1. Categorical data – Categorical data in AI ML projects refers to the data that is collected based on the name, age, sex, or even hair colour. For example, when selling a car, there are several categories, like colour, type of wheel, etc. 
2. Numerical – Any data that is collected in the form of numbers is called numerical data. It is also known as quantitative data. For example, if you are selling a house, the numerical data would be the price or the surface area. 
3. Ordinal – Ordinal data in AI ML projects  refers to a set order or scale is used with ordinal data, which is a type of categorical data. For example, using a scale of 1-10, a person’s response indicates their level of financial happiness. 

• Preparing the Data 

The act of data preparation for AI and ML projects involves gathering the information you need, converting it to a computer-readable format, and testing its accuracy and bias by asking hard questions about it. 

Instead of concentrating exclusively on the data of the AI ML projects for beginners, take into account the problem you’re attempting to solve. That could make decisions regarding the sort of data to collect, how to make sure it serves the main objective, and how to structure it appropriately for a particular sort of algorithm easier to make.

In addition to allowing them to adjust to model performance drifts and changes in direction to data analytical challenges, good information preprocessing may result in more precise and effective methods and ultimately spare data analysts and entrepreneurs a great deal of time and effort. This could help you prepare AI ML projects for beginners. 

• Evaluation of Data 

Plans for evaluation of best ML projects should include where, how, and from what sources data is gathered. The structure used to gather both quantitative (numerical) and qualitative data must keep up with performance objectives, project schedules, and programme goals. 

• Model Production

This is one of the most important steps in preparing for AI ML projects for beginners. This step helps you determine how the model is performing. To make sure that the testing is fine, you may use machine learning tools like PyTorch Serving, Sagemaker, Google AI Platform, and more. You can also use MLOps (a mixture of machine learning and software engineering), which includes all the technologies that are required to make sure that the machine learning model works just fine. This is also an important step when making AI ML projects for final year. 

Conclusion

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.

You can also check our Executive PG Programme in Machine Learning & AI from IIT Delhi. IIT Delhi is one the most prestigious institutions in India. With more the 500+ In-house faculty members which are the best in the subject matters.

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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