Learn with the Trending Machine Learning Projects to Attain Success in 2024
Are you amazed to see how Netflix can recommend your next favourite movie just in a second, or how does Coinrule fetch the best trading option for you within 5 minutes (while you take months to research)? Well, till now you knew, it’s the superpower of machine learning, and now you will learn how it works.
Indeed, the splendid rise of artificial intelligence (AI) captivates the tech world. From ChatGPT to custom-fit AI models, the tech sector welcomes vibrant shifts. As AI becomes the future of businesses, machine learning enters the frame. Our reliance on data makes a gainful space for algorithmic creations (ML). The leading MNCs are now searching for ML experts to decode changing data patterns.
Industry case: A famous social media platform, Pinterest, uses ML models for data handling and pattern recognition. Kosei is the ML service provider that helps Pinterest decode spam and modify content. Similarly, Facebook, Twitter, Google, etc., are avid users of ML algorithms, improving social media analysis.
As ML skills have become prime to future-proof a career, so does hands-on learning with projects for career success. This blog will discuss the trending ML projects to mark success in 2024.
Top Trending Machine Learning Projects for Skills Upgrades in 2024
The origin of ML takes us back to World War II times, but it has become popular in the past decade. ML algorithms are complex; experts must upskill themselves to use them optimally.
Upskilling is the right way to develop the ability to handle ML models. Yet, opting for an industry-driven course is effective as it equips experts with practical insights. Practical learning with projects in machine learning for beginners and experienced pros ascertains success.
Let’s dive deeper into the trending ML projects to learn real-world business issues and tackle them.
1. Movie Suggestion System
Description:
ML algorithms tend to support the design and development of a movie suggestion system. It filters out the user preferences and suggests the right movies. ML reads through the user’s browsing history and applies the algorithms.
ML model in streaming apps verifies the content ratings and recommends the right movies.
Dataset:
- A raw dataset with numerous movies with review ratings
- Extracted data in .csv format
Working method:
- Data pre-processing
- Apply ML algorithms to data insights
- Record facts and insights about mostly watched movies
- Suggest movies to your users
2. Stock Trend Forecasting
Description:
Stock trading is a growing market with productive financial gains. Stock investors constantly follow the trading market to track and study the changes. It helps investors understand movements and buy or sell a stock.
Users with ML skills can correctly predict stock prices and patterns. ML helps traders with bull and bear market conditions with factual data. Courses in machine learning for beginners and experienced experts help you become a skilled trader.
Dataset:
- List of ruling stocks for a specific period
- List of online trading sites
- Current stock rates
Working method:
- Research various stocks and collect insightful data
- Create a web app with Python frameworks and ML models
- Fill in the required details of the stock
- Mark the stock plots to predict behavior
- Generate stock code and related patterns
- Predict the stock prices
3. Sales Prediction for E-commerce Firms
Description:
The E-commerce sector is proliferating due to frequent changes in customer’s shopping behavior. Customers find online shopping cost and time-effective with more comprehensive, buying options available.
Experts use ML models to predict sales trends, buying preferences, and customer engagement. It helps shopping sites predict the sales rates during peak and low seasons.
AWS machine learning courses offer a learning space to leverage your prediction accuracies.
Dataset:
- Sales datasets from any company over specific years.
- Customer data with preferred products
- Product preferences in various regions
Working method:
- Get raw datasets from an e-commerce mart
- Classify and arrange data as per peak and low seasons
- Read the data patterns and extract facts
- Mark the attributes with each product and outlet
- Mark the sales trend during peak and low seasons
4. Loan Eligibility Check
Description:
There are various domain-specific ML projects, that one can select when upgrading skill sets. A loan eligibility checker is a trending project that helps experts in the BFSI sector help customers with better financing.
ML algorithms check the CIBIL score and other metrics to approve a loan. Unlike in the old days, the loan eligibility checker processes data automatically and fixes the loan.
Dataset:
- Raw data of customers with the loan application from any bank.
- Loan-related terms and conditions of the bank (to select or discard loans)
Working method:
- Statistical analysis of loan data sets
- Trace and highlight the factors to either select or discard the loans
- Cross-validation with data training models
- ML algorithms to test the accuracy of data sets
5. Tracking Mental Health
Description:
Mental health has become critical to larger population groups worldwide. ML helps medical experts track and assess mental health for timely treatment. ML helps design a tracking app to check on health and well-being regularly. It also developed custom-fit tasks that work according to the health values of the patients.
Dataset:
- Mental health data for a group of patients (you can get from online)
- Additional medical details
Working method:
- App development (You can use Flutter)
- Process and arrange medical data in the app
- Set custom-fit task metrics to track mental health regularly
- Test and add more features if needed
- Align Flutter features with ML models
- Final testing and debugging of mental health tracker app
Note: Experts upskilling with Azure machine learning programs can learn realistic uses of related models in app development.
6. News Verification and Credibility Check
Description:
ML extensively helps track fake news and deliver credible news to its readers. News consists of texts and images that are easy to fake. Spreading fake news affects readers’ sentiments, resulting in the downfall of credibility. Even social media is vital in faking news that affects many lives.
Following this, the news verification analysis project has become popular. It helps experts learn various ways to detect fake news and remove them.
Dataset:
- Language or image-based fake news
Working method:
- Apply NLP techniques to search and extract fake news
- Highlight the text or image-based fake news
- Develop authentication model with ML algorithms
- Trace and analyze misleading info
Pro tip: Experts can enroll in Azure machine learning programs to learn practical uses of deployment and management of verification models.
7. Wine Quality Forecasting
Description:
Wine is one of the largest sectors contributing to the global economy. The quality of the wine gets better with every passing time. It enriches the pH value and taste levels of wines, increasing its sales values. Quality experts in the winery sector can apply ML models to predict its quality over the years.
Dataset:
- A list of wines with all vital details (manufacturing date, pH values, acidity level, chemical composition, quantity, etc.)
- Physicochemical test reports of the listed wines
Working method:
- Extract the physicochemical test reports of the listed wines
- Derive the critical values required (pH value, acidity level, chemical properties)
- Apply ML models to verify the quality of wines
8. Social Media Feed Analysis
Description:
Social media has become an engaging platform for a wider range of users. Many businesses use social media platforms to connect with their customers and clients. Thus, experts must learn various ways to handle social media feeds for better customer reach.
Fair social media feed analysis helps content creation, moderation, and quality control experts. Training programs curated in machine learning for beginners and experienced pros include this trending project. It helps to gain skills for insightful social media handling.
Dataset:
- A list of trending social media feeds for statistical analysis
Working method:
- Enlist the trending social media feeds on various platforms
- Extract and arrange facts
- Apply statistical functions for data analysis
- Apply ML models to sort and read through the sentiments
9. Fraud Analysis
Description:
Several sectors are prone to unethical and fraudulent activities, degrading their yields. Like, BFSI sectors often face financial fraud affecting their relations with customers. Share markets are prone to fraud, creating trust issues with investors. Other service sectors deal with the fraud that limits overall progress.
Dataset:
- Data for various types of fraud across diverse sectors over the years
Working method:
- Arrange the data sets with respective variables for further processing
- Track the frequency of fraud and its effects (in quantitative terms)
- Apply ML models to analyze the patterns and extract facts
10. Hand Movement Recognition
Description:
ML algorithms can read human instincts and gestures, which helps to design user-friendly apps. We use several gadgets and apps daily for diverse uses. ML tracks the hand movements of users and delivers real-time outcomes. ML algorithms follow the hand gestures and adjust the app features accordingly.
Dataset:
- User activity records, screen reading, and evaluation reports.
Working method:
- Extract insights from raw datasets
- Build classification model
- Apply ML models to trace and decode hand gestures
- Predict the user’s activity
Similarly, you can go through this: Human Activity Recognition with Smartphone
11. Music Classification & Analysis
Description:
ML models classify and read music patterns. It automates music genres and executes them accordingly. ML helps music industry experts classify different genres, sort them properly, and use them whenever required. Experts also use ML models to detect speech emotion, analyze audio, and design music apps.
Dataset:
- Audited datasets of different music genres.
Working method:
- Data sorting, arranging, and processing
- Classifying music as per their genres with KNN
- Apply ML models to read audio files
- Apply deep learning to audio files for feature extraction
- Develop a Music classification model with ML
12. Customer Segmentation for Product Sales
Description:
Customer segmentation is crucial for a company during product sales and promotions. ML models help experts identify and segment their loyal customers into groups as per products. It helps in enhancing sales values and gaining higher profits.
ML uses Python libraries to analyze buying behavior and plan sales activities accordingly. Big e-commerce companies like Amazon use AWS machine learning tools for data-driven customer segmentation.
Dataset:
- Details of customers (name, income, relationship status, no of purchases, and types of items bought)
Working method:
- Data preprocessing
- Apply Python libraries with ML models
- Visualize data and analyze the patterns
- Extract the facts
13. Bitcoin Price Prediction
Description:
Machine learning is vital for automating the tasks and predicting the future. Today’s trading market has been the center of attraction for investors, and Bitcoin is getting popular.
Trading markets are volatile and full of uncertainties. Thus, knowing the right time to buy or sell a bitcoin is essential. ML models help financial experts predict bitcoin prices and alert investors.
Dataset:
- Bitcoin price data over a specified time.
Working method:
- Import data sets for preprocessing
- Apply Python libraries with ML
- Exploratory data analysis
- Model development and evaluation
14. Traffic Sign Recognition
Description:
Traffic signs and rules are vital for road safety. Overspeed of vehicles violating traffic rules can cause accidents. These accidents and related risks increase during winter due to fog and climatic conditions. ML models help drivers recognize traffic signs while driving automated vehicles.
Dataset:
- A .csv file of traffic signs dataset from various places and time zones.
Working method:
- Data processing
- Apply CNN and Keras in Python with ML models
- Visualize the traffic signs changing from time to time
- Prepare data for training and analysis
15. Ride Request Forecast Using ML
Description:
Riding apps have been trending lately due to the increasing number of passengers relying on time-saving travel methods. Online cabs and e-bikes have become popular these days. These companies can use ML models with Python to predict ride request frequencies at different times.
Dataset:
- A raw data set of ride requests from various city users at different times (seasons).
Working method:
- Preprocessing data sets of ride requests
- Apply Python libraries with ML models
- Exploratory analysis of data
- ML modeling with coding features
For more ML projects, read this: Top 5 Machine Learning Projects for Beginners in 2023
Conclusion
The demand for machine learning tools and trends keeps soaring owing to AI dominance. Its flexible and cost-saving methods help businesses maximize their yields. Thus, the demand for skillful ML experts keeps increasing across diverse sectors. Most working pros search for ways to gain practical skills with trending tools. Projects are vital for successful skills building and career success.
Enrolling in an Advanced AI & ML Certification Program unlocks an inspiring career path. Its GenAI-inclusive syllabus guides experts in upgrading their skills with AI/ML trends. Learners get a scope for hands-on learning via live projects in diverse domains. Plus, two IBM certificates and one Microsoft certificate unlock the path to building a global career in AI/ML.
FAQs:-
1. Is it worth learning machine learning in 2024?
The demand for ML practitioners in 2024 is relatively higher than before. Thus, upskilling with machine learning tools is worth it. It will help you future-proof your career in an AI age and crack alluring jobs across leading MNCs.
2. What is the future trend for machine learning?
The future of ML has pervasive effects on diverse sectors worldwide. Automated decision-making with a fair understanding of human instincts is the trend for machine learning. It empowers businesses to grow faster and attain resilient success over their rivals.
3. What is the easiest machine learning project?
The projects in machine learning for beginners are easy compared to others. Some easy and common ML projects are –
- Stock Price Prediction
- Cancer Analysis
- Traffic Sign Recognition
- Social Media Feed Analysis
