Top 10 Real-Time ML Projects for Students & Professionals: Where technology is concerned, having the mere academic information from textbooks will only get you so far. Only when you take a real-world tactic can you master the technology or skill concerned. And what healthier way to do that than get your hands on some real-time projects?
A similar goes for the ground of machine learning (ML) and artificial intelligence (AI). Machine learning projects benefit you study all the practicalities you need to gain real-time work environment knowledge and brand you employable in the industry. Additionally, the current and forecast global artificial intelligence market size only make it rational for players in the arena to achieve command over machine learning. So, without further ado, we current to you the top 10 deep learning projects and machine learning development thoughts for learners and specialists who want to make their resumes stand out(.
Machine Learning Project Ideas for Students and Professionals (Top 10 Real-Time ML Projects)
Below is a list of attractive machine learning project ideas for students and professionals to get first-hand contact with machine learning(Top 10 Real-Time ML Projects).
1. MNIST digit classification
The MNIST digit classification is one of the most stimulating deep learning plans for beginners. Deep learning and neural networks positively have advanced real-world applications such as involuntary text generation, image recognition, self-driving cars, etc. But earlier you deal with those complex applications, working on the MNIST dataset is a countless ice-breaker. This project aims to train your machine learning perfect to identify handwritten digits using the MNIST datasets and convolutional neural networks (CNNs). Overall, it is the perfect project for those who find it less stimulating to work with personal data over image data.
2. Iris flowers classification
Frequently observed as the “Hello World” of machine learning projects, the iris flowers classification project is the best place for learners to start their machine learning journey. The project is founded on the iris flowers dataset and goals to classify the attractive purple flowers into its three species – Versicolor, Virginia. One can differentiate the species founded on their petals and sepals. The dataset has numeric attributes and wants learners to learn about supervised machine learning procedures and how to load and grip data. What’s more, the dataset is small and simply fits into memory without needful any additional transformation or scaling.
3. Music recommendation system
In online spending sites like Amazon, the scheme makes product references during checkout – those that the customer is probable to buy based on their preceding purchases. Similarly, movie/music streaming sites like Netflix and Spotify are pretty good at signifying movies and songs that a specific user may like. Using the music flowing service dataset, you can create a similar modified recommendation scheme in your machine learning project. The goal is to control which new song or artist a user might like based on their preceding choices and predict the chances of a user tuning in to a song repeatedly in a given time.
4. Stock prices predictor
If you are motivated towards finance, the stock prices forecaster is one of the best machine learning projects you can discover. Most data-driven business organizations and companies today are in continuous essential of software that can precisely screen and analyze the company’s presentation and forecast the future price of numerous stocks. With the huge amount of stock market data obtainable out there, working on a stock prices forecaster is a stimulating opportunity for data scientists and machine learning enthusiasts alike. However, working on this project will need a sound knowledge of predictive analysis, action analysis, regression analysis, and statistical modeling.
5. Handwritten equation solver
Making your machine learning model know handwritten digits is only the beginning. Those who have overcome the beginner-level MNIST digit classification project can go a step ahead and build a project that can resolve handwritten equations using CNNs. Identifying handwritten mathematical equations is one of the most baffling subjects in the field of computer dream research. Though, with a combination of CNN and some image processing techniques, it is conceivable to train a handwritten equivalent solver through mathematical digits and handwritten symbols. The project is a step toward digitizing the steps of resolution of a mathematical equation written using pen and paper.
6. Sentiment analysis based on social media posts
A social media stage like Facebook or Instagram may just be a place to fast personal feelings and opinions to the average user. Still, for businesses, it is a street to study consumer behavior. Social media is packed with user-generated content. Sympathetic the sentiments behind every text or image are serious for business organizations to recover customer service based on a real-time study of customer behaviors. Moreover, analysis of language markers in social media posts can help make a deep learning model capable of giving personalized visions into the user’s mental health previous to conservative methods. You can mine data from Reddit or Twitter to get started with this project.
7. Loan eligibility prediction
Banks typically follow a very severe process before positive a loan. But thanks to the advancements in machine learning, it is likely to foresee the suitability of loans faster and with much more correctness. The machine learning model for loan suitability prediction will be skilled using a dataset containing data connected to the applicant, such as their loan amount, gender, income, marital status, number of dependents, qualifications, credit card history, and the like. The project will include training and testing the model using cross-validation, and you will learn how to build statistical models such as Boost, Gradient Boosting, and metrics like MCC scorer, ROC curve, etc.
8. Wine quality prediction
The wine quality prediction dataset is fairly general among students starting in the data science field. It includes using volatile sharpness, fixed acidity, density, and alcohol to expect the quality of red wine. You can take either the classification or reversion approach for this project. The wine excellence variable you have to forecast in the dataset ranges between 0-10, and you can do so by constructing a regression model. Another approach would be to create three groups (low, medium, and high), break down the 0-10 into distinct intervals, and transform them into definite values. Hence, you can build any classification model for the forecast.
9. House price prediction
If you are a machine learning beginner, you can use the house pricing dataset of Kaggle to build a house price forecast project. The price of a specific house is the target adjustable in this dataset. Your ML model has to forecast the price using information like locality, the number of rooms, and values. Since it is a regression problem, beginners can take the linear reversion approach to shape the model. Persons who wish to take a more advanced method can use incline increasing or random forest regressor to forecast house prices. The dataset also has many categorical variables, which would need techniques like label-encoding and one-hot training.
10. Customer segmentation in Python
For those who want to get started with unconfirmed machine learning, the client segmentation dataset on Kaggle is your best call. The dataset contains customer details such as gender, age, annual income, and spending score. You need to use these variables to group customers who are alike into alike collections. The project’s primary goals are to attain customer segmentation, classify target customers for numerous marketing strategies, and understand the real-world devices of marketing strategies. You can use graded clustering or k-means cluster to achieve these tasks.