As artificial intelligence continues to grow rapidly in the year 2020, achieving mastery over machine learning (ML) has become crucial for all players in the field. The reason for that is both AI and ML complement each other.
If you are still studying and are interested in innovative machine learning projects, I would suggest looking at some of these exciting machine learning project ideas mentioned below in this write-up.
I hope that these machine learning project ideas will prove to be a catalyst in the careers of the youths by creating unique machine learning projects for final year students.
What is Machine Learning?
Machine learning is the art that involves the utilization of artificial intelligence to permit machines to learn and enhance a task from experience automatically. In short, it helps in creating artificial intelligence mini projects. The good thing is, all this is done without prior programming them specifically for the task.
The process commences with feeding them with good quality data known as training data and then training the machines by building different machine learning models with data and other algorithms. The algorithms you choose are contingent on the type of data you have and the kind of task you perform to make predictions or decisions.
What is a Machine Learning project?
A project that deals with machine learning is known as a machine learning project. Everyone knows that no amount of theoretical knowledge can replace hands-on practice. Theories and lessons online can soothe you into a false belief of mastery. The reason behind this is the code and solution that are right in front of you. However, once you try to apply it, you might find it extremely difficult to execute.
Now, as promised, let’s look at the eleven machine learning project ideas for beginners.
11 Handpicked Machine Learning Project Ideas for Beginners
Table of Contents
- Stock Prices Predictor
- Developing Sentiment Analyser
- Personality Prediction Project
- Loan Prediction Project
- Sales Prediction Project
- Movie Ticket Pricing System
- Wine Quality Predictions
- Music Recommendation System Project
- Building Neural Network for Handwriting Recognition
- Housing Prices Prediction Project
- Air Ticket Pricing Prediction Project
1. Stock Prices Predictor
Stock prices predictor is the best way to start experimenting with hands-on machine learning projects for students. Today, businesses are on the lookout for software that can monitor and scrutinize the company’s performance and predict future stock prices. This is an excellent opportunity for data scientists interested in financial activities as there is a lot of data available on the stock market. This can be achieved with the help of an ingenious stock market database.
Let’s now look at some of the tried-and-tested ways to utilize predicting stock prices.
Tried-and-Tested Techniques for Predicting Stock Prices
Since we are conducting prediction of continuous values, you can utilize any of these regression techniques mentioned below:
- Linear regression which can effectively be used to predict continuous values
- Time series models are models employed for time-related data
- ARIMA is a well-renowned model employed for envisaging futuristic time-related predictions
- LSTM is another such technique that is being employed for stock price predictions. The full-form of LSTM is Long Short Term Memory. It utilizes neural networks to predict continuous values. The good thing about LSTMs is that they are quite robust and known for their long-lasting memory
What is the Best Way to Predict Stock Market Prices by Employing Reinforcement Learning?
Today, it has become possible to employ the concept of reinforcement learning to predict stock price of a specific stock. The reason being, it employs the same fundamentals of needing lesser historical data, working in an agent-based system to ascertain higher returns on the basis of the current environment.
Here are some of the steps for designing a reinforcement learning model to predict stock market prices:
- The first step is to import libraries
- Then create the agent who is going to be the key decision maker
- Next step is to determine the basic functions that will be used for formatting the values, sigmoid function, reading the data file, etc.
- Once you are done with this step train your agents
- The last step is to ascertain the agent performance
However, before commencing the project, you need to have a fair idea of these areas:
Predictive Analysis
It is all about leveraging different AI techniques for various data processes like data mining, data exploration, etc., to envisage the behavior of all possible outcomes.
Regression Analysis
Regression analysis is a predictive technique based on the interaction between a dependent (target) and an independent variable (predictor).
Action Analysis
Action analysis involves actions carried out by the two techniques mentioned above. Both of them are scrutinized, after which the outcome is put into the machine learning memory.
Statistical Modelling
Under statistical modeling, a mathematical description of the real-world process is built by elaborating the uncertainties, if present, within that process.
2. Developing Sentiment Analyser
This is one of the exciting machine learning projects for beginners. Most of us use social media platforms to showcase our personal feelings and opinions to the world. However, the biggest challenge lies in deciphering the “sentiments” behind social media posts. You can consider it to be one of the perfect ideas for your next machine-learning project as a final-year student.
Today, social media is thriving on user-generated content. Creating an ML system that could scrutinize the sentiments behind texts or posts would make organizations easier to comprehend consumer behavior. This will enable them to enhance their customer, ultimately aiding in optimal customer satisfaction.
You can start by mining the data from Twitter or Reddit to commence your sentiment-analyzing machine-learning project. This might be one of the rare cases of in-depth learning projects which can significantly impact other aspects.
With the use of social media sentiment analysis, insightful and meaningful analyses can be done to understand consumer behavior and allow organizations to provide the best customer service experience.
Several Companies Conducting Sentiment Analysis
Several companies are conducting sentiment analysis. Let’s look at some of them below:
1. MonkeyLearn
MonkeyLearn is a SaaS-based company providing sentiment analysis regarding its collection of formidable machine learning tools.
2. Repustate
Repustate provides sentiment analysis API supporting text in 24 different languages. It then delivers reliable insights from social media data. Its software combines sentiment and semantic analysis to determine emotions expressed in terms of emojis, abbreviations, slang, and hashtags.
3. Lexalytics
Lexalytics has been providing semantic analysis solutions since the year 2003. It offers an on-premise solution known as Salience and could API known as Semantria, which can be integrated in your workflows. You can even build customized models and adapt them according to your business needs.
4. Rapidminer
Rapidminer is a data science platform that assists businesses in creating predictive models from data. With the help of Auto Model and Rapidminer Go, you can employ pre-built models for sentiment analysis and customize them without code.
5. Lionbridge
Lionbridge offers sentiment analysis annotation services for companies that wish to create sentiment analysis models with machine learning. It boasts an extensive network of contributors across the globe. They are, in fact, manually tagging text in more than 30 different languages.
Sentiment Analyzer is a free tool that has been developed keeping in mind the simplicity to use. You need to copy and paste the text, and click “Analyze text,” and your result will showcase a sentiment score. A sentiment score of -100 connotes a very negative mood, while a score of +100 connotes a very positive mood.
How KFC is Using Sentiment Analyzer in Real-Life Business Situations?
Earlier KFC was stuck in the past while the competition was moving ahead with times by reinventing itself. They were doing this with the narratives of healthy food and feel-good experiences.
Instead of establishing themselves in the crowded niche, KFC decided to take advantage of its illustrious brand name. KFC started promoting memes and pop culture iconography to promote its brand’s value proposition. Most recently they utilized the Robocop theme in a series of ads promoting KFC’s four $20 Fill Ups and $5 Fill Up.
This approach resulted in generating natural traction around the brand augmented by pop-culture reference. Due to this, users started to engage with the brand and ultimately this resulted in engagement with their entire product line. You need to react promptly which is where sentiment analysis comes into play.
This method combines sentiment analysis in social networks monitoring and campaign management. Due to the nature of the marketing campaign, the users are actively engaged in commenting or reacting to the ad content. The result was that KFC as a brand became present in the media landscape and due to that presence it has reached steady growth in terms of market share.
3. Personality Prediction Project
How interesting would it be to simply read the posts written by people online and understand their overall personality? It would be able to resolve lots of confusion on the internet! With the help of this ML project, you will be able to decipher the Myers Briggs personality of a person on the basis of the types of posts that are put on social media.
Myers Briggs Type Indicator is a personality identification system that divides a person into one of the 16 different personalities based on introversion, intuition, thinking, and perceiving capabilities.
Before moving forward, I would like to emphasize on the Big Five Model, popularly known as the Five-Factor Model (FFM) and OCEAN model. According to several psychological theories, it came into existence in the early 1980s. It is all about how the statistical analysis is executed to personality survey data and certain words are utilized to describe the person in question. These words then provide a summation of the overall character or personality of the person in a precise and accurate manner.
Here’s a comprehensive look at the five-factors that contribute to this model.
The openness of a person to experience new things
Here, we look at different attributes like sensitivity, attentiveness to details, imagination power, preference to a particular variety, and curiosity about a particular subject.
The conscientiousness of a person to complete a given task
This attribute showcases how curious the person is to complete a given task. It highlights whether the person is organized and efficient.
The extrovert nature of the person in question
It is a behavior trait that tells how best the person in question can interact with others. In simple words, it is all about his/her social skills.
The agreeableness of the person in terms of adjustment towards others
This attribute highlights the quality of the individual in question based on sympathy, cooperativeness, generosity, and ability to adjust with others.
The neurotic behavior of the person in question
This behavioral trait showcases the mood swings and the extreme expressive power of the person in question.
One company that has been using personality prediction projects for credit scoring is Fayrix. The company has created a machine learning solution for credit score by analyzing people’s digital footprints, extracting patterns in their behavior and then predicting their psychological traits and personality type.
I would recommend you to utilize the Personality prediction dataset available on Kaggle to create the ML project. This is one of the best machine learning project ideas for beginners.
4. Loan Prediction Project
If you have ever tried to get a bank loan, you might have undergone a tedious process. Getting a loan requires a complex set of factors and the most important one being steady income. This is one of the use cases of machine learning in banking that aims to create a model to classify how much loan the user can get. It efficiently considers factors like the user’s income, education, employment prospects, marital status, number of dependents, etc.
The Loan prediction data set provides comprehensive information about all these factors, which can help create an ML model to demonstrate the loan amount that can be approved.
A Company Using Corporate Loan Prediction in Real-Life Situation
One company that has created a corporate loan prediction project is Bisnode. With the help of the AI tool, as a corporate banker you can predict the businesses’ need for cash.
It assists in identifying business opportunities both in long and short term term. By having predictive insights about the cash needs of the customers and prospects will allow you to provide the right offer at the right time.
5. Sales Prediction Project
Imagine what could happen in case shops would estimate the products they sell every month? That’s what this project aims to accomplish. Here, you need to forecast the total number of products sold in each shop while you are provided with the daily sales data. A prime example of that is Walmart that provides datasets for 98 products across 45 outlets. This way, the developers can access information every week for sales through locations and departments.
The project is highly dynamic because the list of shops and products may vary on a monthly basis. You can avail of the sales data set to create this ML project on Kaggle. This data set comprises a training set and the test set for which you need to forecast the sales. This is one of the most amazing machine learning project ideas available for final year students. The ultimate objective of the project is to ensure that you can make better data-driven decisions in channel optimization and inventory planning.
6. Movie Ticket Pricing System
Due to the rise of OTT platforms like Netflix and Amazon Prime, people now prefer to watch content sitting on their couch. Factors like pricing, content quality, and marketing have influenced the success of these platforms.
Today, the cost of making a full-length feature film has shot up exponentially. Only 10% of the movies that are made make profits. Thanks to the stiff competition from television and OTT platforms combined with the high ticket prices, it has become difficult for films to earn profits. The rising cost of the theatre ticket along with the popcorn cost leaves the cinema halls empty.
This is where an advanced ticket pricing system can be a blessing for the movie makers and viewers. According to this machine learning project idea, the ticket prices can be kept higher with the rise in the ticket demand and vice versa. Thus, if a person books the ticket weeks before the release, the cost would be less for a highly demanded movie.
The ML system would then smartly calculate the price, contingent on the interest of the viewers, social signals, and supply-demand factors.
7. Wine Quality Predictions
It’s often said that the older the wine, the better it tastes. However, if you think that age is the only thing that determines a wine’s taste, then, you are wrong. Numerous other factors ascertain the quality of the wine, like physiological tests such as fixed acidity, alcohol quantity, volatile acidity, density, and pH level, etc.
In this machine learning project idea, it allows you to predict the wine quality using MLmodel by exploring its chemical properties. The wine quality dataset that you will be employing in this project consists of roughly 4898 observations, including 11 independent variables and a single dependent variable. By completing such machine learning projects for final year, students can turn out to be a blessing in disguise.
8. Music Recommendation System Project
The music recommendation system project is one of the most popular machine learning projects that is used across different domains. You might be aware of a recommendation system in case you have utilized any eCommerce site or Movie/Music website. In most of the eCommerce websites like Amazon, during checkout, the system recommends products that you can add to your cart list. In the same way, Netflix recommends movies that you may like to watch. A prime example of a music recommendation system is Spotify as it shows similar songs that you may like to hear.
The question is, how does this system actually work? The answer lies in the classic case of machine learning application.
Under this project, I recommend you to employ the database of Asia’s leading music streaming service, Spotify to develop a better recommendation system.
The next step is to ascertain the song or which new artist a listener might like on the basis of their past choices. The primary task is to predict the chances of a user to hear a song repetitively within a specific time duration. Once you define the dataset, the prediction is marked as 1 in case the user has listened to the same song in the same month. The dataset contains the songs heard by the specific users and at what time.
9. Building Neural Network for Handwriting Recognition
Java students love to experiment with their projects and what better way than to start experimenting by working on a neural network. Deep learning and neural networks are the buzzwords as far as AI is concerned.
Some of the sparkling innovations like image recognition, driverless cars, etc. have become possible due to these two technologies. So, as a final year student, it’s the right time to explore the area of neural networks. Commence your neural network machine learning project with the MNIST Handwritten Digit Classification Challenge. Thanks to its user-friendly interface, beginners will find it highly useful.
Vidado is one company that has developed OCR for handwriting recognition. Handwriting OCR also known as Optical Character Recognition is the process of automatically extracting handwritten data from paper, scans, and other low-quality digital documents.
10. Housing Prices Prediction Project
When you plan to purchase a house a lot of factors need to be taken into consideration. These include its location, size, number of bedrooms, etc. However, a lot of times people ignore some of these factors while purchasing or selling a house. This is where the housing prices prediction project comes into play.
It takes into account different factors for the house like its front area, street, land contour, proximities, utilities, garage quality, roof materials, etc. The ultimate objective of the project is to predict the final price of the house based on these factors. I would suggest you to get the Housing Prices Prediction Project dataset from Kaggle. Then use it to create an ML algorithm to accurately predict the prices of the house based on these factors.
One company that has been successful in creating housing price predictions is Realas. The company has created their very own search engine through which you simply need to enter the suburb, postcode, school or address, and it will provide you with the real property prices on sale without wasting your invaluable time.
11. Air Ticket Pricing Prediction Project
Prices for airline tickets are as unpredictable as the English weather. You can come across the same seat with different prices in 24 hours. It also depends on the seasonality, days and parts of a week, holidays or events. If there are fewer reservations the prices can even dip. The reason being, hospitality and transportation companies, online travel agencies, and aggregators strive to motivate customers to press on the “book” button. During the peak season like Christmas or New Year the prices go even higher.
The AltexSoft team has come up with a Price Predictor tool for Fareboom, a US-based online travel agency that advises price sensitive customers about the optimal time to purchase the flight deals. Its algorithm forecasts future price alterations on the basis of historical data and machine learning models.
The Price Predictor is a search engine which shows up in the form of a popup window to the subset of users. Once travelers search for flights, they see charts showing whether the selected travel dates are reasonably priced or not. On the basis of the results obtained, the users get recommendations to either buy now or wait. It even provides forecasts of future price changes or alternative trip days.
Final Words
All of these machine learning project ideas that I have presented in this write-up are great options, especially when you are just starting in Machine Learning (ML). Alternatively, if you know the basics and wish to practice more, try these machine learning projects for students.
So check out these machine learning project ideas and when you have exhausted each of these ideas, attempt even more projects on Kaggle and participate in active competitions. If all goes well, you can even get the first prize!
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