Top 25 Machine Learning Projects Ideas for Beginners in 2022
Aspiring machine learning engineers are eager to work on ML projects but find it difficult...
Aspiring machine learning engineers are eager to work on ML projects but find it difficult to come up with creative ideas. Finding data science or machine learning project ideas that fascinate and motivate you is crucial if you’re a first-year or final-year student. It is up to you to choose the domain of the dataset depending on your interest, the complexity of the dataset, and the size of the dataset when picking which machine learning project to begin with. You will need some unique, entertaining, and cutting-edge machine learning project ideas to start working on if you want to start growing your machine learning portfolio. Create a list of all potential machine learning project ideas that catch your eye to get your data science or machine learning portfolio off the ground. The most intriguing project ideas can be chosen, and you can begin working on them to add machine learning projects to your CV, once you’ve accumulated a few basic machine learning project ideas for 2022. However, ProjectPro experts advise beginning with ML projects that concentrate on data cleaning before moving on to analytics, machine learning, and deep learning if you are a beginner or a student.
Machine Learning Projects for Beginners
This section has cool machine learning projects that newcomers in the domain of machine learning should try. These are basic machine learning projects that you can learn quickly.
1. Catching Illegal Fishing Project
Around the world, overfishing and illegal fishing are major issues. For instance, there are numerous reports of Asian and European boats engaging in extensive and frequently illegal fishing in West African waters, which reduces the normal harvest for the local population and raises their levels of poverty. According to Josephus Mamie, head of Sierra Leone’s Fisheries Research Unit, “being able to know which vessels are fishing where would be a big assistance in curbing illicit fishing.” In this project, you will work with Global Fishing Watch to identify fishing activity in the ocean using information gathered from various ships across the world using the satellite Automatic Identification System (AIS). The latitude, longitude, speed, and course of the vessels are all included in the AIS data.
2. Census Income Dataset Project
A census is the process of gathering, compiling, and disseminating demographic, economic, and social statistics about all people in a country or geographically defined area of a country for a certain time. Most nations include a census of dwellings as part of a population count. It is the procedure for gathering, compiling, and disseminating data on structures, dwellings, and structure-related amenities like sewage systems, restrooms, and electricity, to name a few. The “Adult Income” or simply “adult” dataset is a common imbalanced machine learning dataset that will be used in this research. We must categorize wages that fall inside a given range in order to anticipate census salaries.
3. Classification Projects on Machine Learning for Beginners
One of the most popular methods in machine learning, classification has a wide range of applications. Spam detection, risk assessment, churn prediction, and medical diagnosis classification have all been used as fairly straightforward yet effective methods for sentiment analysis. We hope to provide you with practical knowledge of several ensemble strategies as well as theoretical justifications in this project. Recognizing different Ensemble approaches and putting them to use to forecast licensing status for the given company. The used dataset was obtained under a license. It includes details on 86K different companies across a range of aspects. The license status, which has five different categories, is the target variable.
4. Classification with Neural Networks
Autoencoders are the simplest of the deep learning architectures. They are a specific type of feedforward neural network where the input is first compressed into a lower-dimensional code. Then, the output is reconstructed from the compact code representation or summary. Therefore, autoencoders have three components built inside them – encoder, code, and decoder. To begin the development process, you will need an encoding method, a decoding method, and a loss function. Binary cross-entropy and mean squared error are the two top choices for the loss function. And to train the autoencoders, you can follow the same procedure as artificial neural networks via back-propagation. Now, let us discuss the applications of these networks.
5. Count Objects in Image
Counting objects in an image is a task of computer vision. There are many computer vision libraries that you can use for this task, such as OpenCV, TensorFlow, PyTorch, Scikit-image, and cvlib. You must have not heard much about the cvlib library in Python. Well, this is a very simple, high-level, and easy-to-use computer vision library in Python. By using the features of this library, we can count the number of objects in an image using Python. To use this library, make sure you have OpenCV and TensorFlow installed in your systems. You can easily install it by using the pip command; pip installs cvlib.
6. Coupon Purchase Prediction
Recruit Ponpare is Japan’s leading joint coupon site, offering huge discounts on everything from hot yoga to gourmet sushi, to a summer concert bonanza. Ponpare’s coupons open doors for customers they’ve only dreamed of stepping through. They can learn difficult to acquire skills, go on unheard-of adventures, and dine like (and with) the stars.
Using past purchases and browsing behavior, this competition asks you to predict which coupons a customer will buy in a given period of time. The resulting models will be used to improve Ponpare’s recommendation system, so they can make sure their customers don’t miss out on their next favorite thing.
7. Covid-19 Detection
The current COVID-19 pandemic poses a threat to productivity, health, and human life. AI is crucial to the classification of COVID-19 cases since it allows us to forecast infectious cases and recovery rates using chest x-rays by utilizing machine learning models on COVID-19 case data. Patient privacy is violated when private patient data is accessed, and training a standard machine learning model necessitates accessing or uploading all of the data. As federated machine learning offers a practical solution for data privacy, centralized computation, and high compute capacity, interest in it has grown over the past few years.
8. Gender Detection
Especially with the rise in social media platform apps, gender detection is becoming more and more common on the list of machine learning projects. Gender and age are the two key components of social characteristics revealed by facial characteristics. A real-time gender detection system can be effectively built by developers using machine learning and deep learning. Following face detection using a convolutional neural network, the model can distinguish between men and women. This gender detection project uses three convolutional layers with 96, 256, and 384 nodes each.
9. Gold Price Prediction
One needs to utilize machine learning regression approaches for the gold price prediction machine learning project in order to accurately estimate the price of one of the most expensive and in-demand metals worldwide, gold. The program has a reputation for extracting pertinent data from historical Gold ETF prices and making precise gold price forecasts for the very next day. To forecast the Gold ETF close price, the data might include the daily Gold ETF price for the previous 12 years.
10. Google Play Store Sentiment Analysis
The Google Play Store sentiment analysis program makes use of Python and machine learning. The sentiment analysis challenge includes getting pertinent datasets from Kaggle and analyzing any customer reviews and comments. To better comprehend the sentiments of each customer review classified as good, negative, or neutral, three extra columns must be added to the dataset.
11. Handwriting Recognition
One of the top machine learning projects that will be very beneficial for various applications in the future is handwriting recognition. The machine learning method can distinguish characteristics from many media, including photographs and touch-screen devices, and transform them to a machine-readable format. It is well recognized for doing handwriting identification. Character recognition methods fall into three categories: image pre-processing, feature extraction, and classification.
12. Hate Speech Detection
One project utilizing Python-based NLP machine learning algorithms for hate speech identification must be built by developers. The NLP method known as Tf-Idf vectorization is used to extract pertinent keywords that are frequently used to express the significance of hate speech. Using data gathered from any library or repository, logistic regression assists in teaching computers how to categorize hate speech.
13. Heart Disease Prediction
The healthcare industry all across the world can benefit greatly from this machine learning project’s ability to anticipate cardiac disease and effectively save lives. Numerous machine learning methods, including neighbors classifier, decision tree classifier, support vector classifier, and random forest classifier, are used in the project to detect heart disease. To compare the final model efficiently and effectively, one can utilize various libraries for a better grasp of the data and various numbers of algorithms to alter their various parameters.
14. Stock Prices Predictor using TimeSeries
For data scientists and machine learning engineers who work in or intend to work in the finance domain, here is yet another intriguing machine learning project concept. A system that forecasts future stock prices by learning about a company’s performance is known as a stock prices predictor. Working with stock price data presents issues since it is quite granular and contains a variety of data kinds, including volatility indices, prices, international macroeconomic indicators, fundamental indicators, and more. The financial markets’ shorter feedback cycles make it easier for data professionals to test their predictions on new data, which is one benefit of working with stock market data. To begin working with stock market data, you can pick up a simple machine learning problem like predicting 6-month price movements based on fundamental indicators from an organization’s quarterly report. You can download Stock Market datasets from Quandl.com or Quantopian.com. There are different time series forecasting methods to forecast stock price, demand, etc.
15. Machine Learning in 5 lines of code
This machine learning model has the advantage of being exposed to a variety of inputs and providing the appropriate output. It seeks to determine the connection between the input and the result as it analyzes more and more data. One of the machine learning libraries that are facilitating machine learning is MindsDB. In less than 5 lines of code, we can build a machine learning model using the MindsDB library.
16. Market Basket Analysis
With the use of data mining, merchants may boost sales by better understanding the buying habits of their customers. Large data sets, such purchase histories, must be analyzed to identify product groups and items that are most likely to be bought together. Understanding consumer behavior by establishing connections between the products that people purchase is the aim of this project. This method searches for connections between entities and things that regularly appear together, such the assortment of items in a shopper’s cart.
17. MLOps Project for a Mask R-CNN on GCP using uWSGI Flask
MLOps promotes automation and supervision throughout the entire ML system. In this context, the term “machine learning operations” refers to various approaches, methods, and processes for automating the management and deployment of machine learning algorithms. Through the use of cloud computing, this initiative aims to give users practical MLOps experience. A cloud service provider is Google Cloud Platform. Before beginning this project, we urge you to have a basic understanding of Image Segmentation using Mask R-CNN with Tensorflow.
18. MNIST Handwritten Digit Classification
It is a typical dataset for deep learning and computer vision. The Modified National Institute of Standards and Technology dataset is recognized by the term MNIST dataset. The dataset can be used as a starting place for learning and practicing how to construct, analyse, and deploy convolutional deep learning neural networks for image classification even though it has been successfully solved. MNIST is a collection of 70,000 photographs, each annotated with the digit it corresponds to. The introduction to machine learning is MNIST.
19. Mobile Price Classification
Abhishek Sharma created the Multilayer Perceptron Topology-based Mobile Price Classification model, which was trained using data gathered from the Kaggle online community. Mobile phones are the most popular electronic items because people regularly update them anytime a new device comes out with new capabilities. introducing a Python-based machine learning project that employs a pricing classification model to categorize the price range of mobile devices.
20. Movie Recommendation System using Machine Learning
These days, recommendation systems are rather widespread. These recommendation systems are used by streaming services like Netflix, Prime Video, YouTube, and others to propose movies based on your prior viewing habits. A movie recommendation system uses machine learning to predict or filter the users’ preferences for films based on their prior actions and behavior. It is a sophisticated filtration system that anticipates a user’s potential movie tastes as well as their preferences for a domain-specific item.
21. Music Recommendation System ML Project
Using real datasets, this project will create a music recommendation engine. It will make use of the Million Songs Dataset, a dataset obtained from the outside that consists of two files: triplet file and metadata file. The triplet file contains information on the identity of the performer or band, the song’s title, and how long the song is. With a listener’s playlist as the source, it may truly comprehend their musical preferences and identify the elements that are most helpful in gauging their interests and tastes.
22. Netflix Stock Price Prediction
One of the most well-known OTT streaming services is Netflix. It provides a sizable selection of movies and television shows and produces its own work, dubbed Netflix Originals. As one of the best methods for regression analysis and time series forecasting, the LSTM neural network will be used to anticipate Netflix stock values using machine learning. Due to its popularity, firms like Netflix are frequently monitored by investors that are quite active in the stock market. The prediction of stock prices is an important application of machine learning.
23. Network Security Analysis
It is an impartial assessment of the network infrastructure’s informational security, followed by the formulation of recommendations on how to increase the network infrastructure’s security level in accordance with the best global informational security practices. The practice of securing computer networks and devices connected to the network from malicious intent, abuse, and denial is known as network security. This project replaces outdated network intrusion detection methods. Security data scientists frequently utilize this dataset to categorize network security issues.
24. Sales Prediction
Estimating future sales is done through the sales forecasting process. In order to help their clients make wise business decisions and foresee both short- and long-term success, many organizations do sales projections. The Databricks platform community edition server, which enables professionals to run their spark code on their servers for free by enrolling through email id, will be used by this project’s users to implement Apache Spark Machine Learning. Additionally, there are a number of online tools that could aid experts in effectively creating this project.
25. Sarcasm Detection
Since the dawn of language, sarcasm has been a component of it. The task of sarcasm detection involves binary classification and natural language processing. Using a dataset of sarcastic and non-sarcastic sentences, the candidates working on the project can train a machine learning model to check whether or not a sentence is sarcastic. Python is a computer language that can be used well for machine learning sarcasm detection. A dataset with labels that can be used to predict sarcasm in a sentence will be included in the sarcasm detection project.
This was all about the machine learning projects for 2022.
These are some of the ML project ideas along with the source code to help you learn and master Machine Learning.
Now, you need to make your hands dirty with the projects to boost your career, as well as, gain real-world experience.