Top 15 Machine Learning Projects for Beginners

Machine learning is a simple study of how to educate a computer program or algorithm to gradually improve on a specific job presented at a high level. Image identification, fraud detection, recommendation systems, and other machine learning applications have already proven to be popular. 

ML jobs make human work simple and efficient, saving time and ensuring a high-quality result. Even Google, the world’s most popular search engine, uses machine learning. 

From analyzing the user’s query and altering the result based on the results to showing trending topics and adverts in relation to the query, there are a variety of options available. 

Technology that is both perceptive and self-correcting is not far off in the future. 

One of the greatest ways to get started is to get hands-on and design a project. Therefore, we’ve compiled a list of 15 top machine learning projects for beginners to get you started.

1. Titanic

This is often considered to be one of the greatest and most enjoyable tasks for anyone interested in learning more about machine learning. The Titanic challenge is a popular machine learning project that also serves as a good way to get acquainted with the Kaggle data science platform. The Titanic dataset is made up of genuine data from the sinking of the ill-fated ship.

It includes details such as the person’s age, socioeconomic status, gender, cabin number, departure port, and, most importantly, whether they survived!

The K-Nearest Neighbor technique and the decision tree classifier were determined to produce the best results for this project. If you’re looking for a fast weekend challenge to improve your Machine Learning abilities, this one on Kaggle is for you.

2. Irish Flower Classification

Beginners love the iris flower categorization project, and it’s a great place to start if you’re new to machine learning. The length of sepals and petals distinguishes iris blooms from other species. This project’s purpose is to separate the blooms into three species: Virginia, setosa, and Versicolor.

For classification exercises, the project employs the Iris flower dataset, which aids learners in learning the fundamentals of dealing with numeric values and data. The iris flower dataset is a tiny one that can be stored in memory without the need for scaling.

3. Boston House Price Prediction

Another well-known dataset for novices in machine learning is the Boston Housing data. Its goal is to forecast home values in various Boston neighborhoods. It includes vital statistics such as age, property tax rate, crime rate, and even closeness to job centers, all of which might affect housing pricing.

The dataset is simple and tiny, making it simple to experiment with for novices. To figure out what factors influence the property price in Boston, regression techniques are heavily employed on various parameters. It’s a great place to practice regression techniques and assess how well they work.

4. Wine Quality Testing

Wine is an unusual alcoholic beverage that requires years of fermenting. As a result, the antique bottle of wine is a pricey and high-quality wine. Choosing the ideal bottle of wine requires years of wine tasting knowledge, and it can be a hit-or-miss process.

The wine quality test project evaluates wines using physicochemical tests such as alcohol level, fixed acidity, density, pH, and other factors. The project also determines the wine’s quality criteria and quantities. As a result, wine purchasing becomes a breeze.

5. Stock Market Prediction

This initiative is intriguing whether or not you work in the financial sector. Stock market data is studied extensively by academics, businesses, and even as a source of secondary income. A data scientist’s ability to study and explore time series data is also vital. Data from the stock market is a great place to start.

The essence of the endeavor is to forecast the future value of a stock. This is based on current market performance as well as statistics from prior years. Kaggle has been collecting data on the NIFTY-50 index since 2000, and it is currently updated weekly. Since January 1, 2000, it has contained stock prices for over 50 organizations.

6. Movie Recommendation

I’m sure you’ve had that feeling after seeing a good movie. Have you ever felt the impulse to titillate your senses by binge-watching similar films?

We know that OTT services such as Netflix have improved their recommendation systems significantly. As a machine learning student, you’ll need to understand how such algorithms target clients based on their preferences and reviews.

The IMDB data set on Kaggle is likely one of the most complete, allowing recommendation models to be inferred based on the movie title, customer rating, genre, and other factors. It’s also an excellent method to learn about Content-Based Filtering and Feature Engineering.

7. Load Eligibility Prediction

The world revolves around loans. Banks’ major source of profit comes from interest on loans. Hence they are their fundamental business.

Individuals or groups of individuals can only expand economies by investing money in a firm in the hopes of seeing it rise in value in the future. It is sometimes important to seek a loan to be able to take risks of this nature and even partake in certain worldly pleasures.

Before a loan may be accepted, banks normally have a fairly strict process to follow. As loans are such a crucial aspect of many people’s life, predicting eligibility for a loan that someone applies for would be extremely beneficial, allowing for better planning beyond the loan being accepted or refused.

8. Sentiment Analysis using Twitter Data

Thanks to social media networks like Twitter, Facebook, and Reddit, extrapolating opinions and trends have gotten considerably easy. This information is used to eliminate opinions on events, people, sports, and other topics. Opinion mining-related machine learning initiatives are being applied in a variety of settings, including political campaigns and Amazon product evaluations.

This project will look fantastic in your portfolio! For emotion detection and aspect-based analysis, techniques such as support vector machines, regression, and classification algorithms can be used extensively (finding facts and opinions).

9. Future Sales Prediction

Big B2C businesses and merchants want to know how much each product in their inventory will sell. Sales forecasting aids business owners in determining which items are in high demand. Accurate sales forecasting will significantly decrease wastage while also determining the incremental impact on future budgets.

Retailers such as Walmart, IKEA, Big Basket, and Big Bazaar use sales forecasting to estimate product demand. You must be familiar with various techniques of cleansing raw data in order to construct such ML projects. Also, a good grasp of regression analysis, particularly simple linear regression, is required.

For these kinds of tasks, you’ll need to employ libraries like Dora, Scrubadub, Pandas, NumPy, and others.

10. Fake News Detection

It’s another cutting-edge machine learning effort aimed at schoolchildren. Fake news is spreading like wildfire, as we all know. Everything is available on social media, from connecting individuals to reading the daily news.

As a result, detecting false news has gotten increasingly difficult these days. Many big social media networks, such as Facebook and Twitter, already have algorithms in place to detect bogus news in postings and feeds.

To identify false news, this type of ML project needs a thorough understanding of multiple NLP approaches and classification algorithms (PassiveAggressiveClassifier or Naive Bayes classifier).

11. Coupons Purchase Prediction

Customers are increasingly contemplating online buying when the coronavirus attacked the planet in 2020. As a result, shopping establishments have been compelled to shift their business online.

Customers, on the other hand, are still seeking great offers, just as they were in stores, and are increasingly hunting for super-saving coupons. There are even websites dedicated to creating coupons for such clients. You can learn about data mining in machine learning, producing bar graphs, pie charts, and histograms to visualize data, and feature engineering with this project.

To generate predictions, you can also look into data imputation approaches for managing NA values and cosine similarity of variables.

12. Customer Churn Prediction

Consumers are a company’s most important asset, and keeping them is vital for any business aiming to boost revenue and build long-term meaningful connections with them.

Furthermore, the cost of acquiring a new client is five times higher than the cost of sustaining an existing one. Customer Churn/Attrition is a well-known business problem in which customers or subscribers cease doing business with a service or a company.

They will ideally no longer be a paying customer. A customer is deemed churned if it has been a particular amount of time since the customer last interacted with the company. Identifying whether a client will churn, as well as swiftly giving relevant information aimed at customer retention, are crucial to lowering churn.

Our brains are incapable of anticipating customer turnover for millions of clients; here is where machine learning can help.

13. Wallmart Sales Forecasting

One of the most prominent applications of machine learning is sales forecasting, which involves detecting characteristics that influence product sales and anticipating future sales volume.

The Walmart dataset, which contains sales data from 45 locations, is used in this machine learning study. Sales per store, by category, on a weekly basis are included in the dataset. The purpose of this machine learning project is to anticipate sales for each department in each outlet so that they can make better data-driven channel optimization and inventory planning decisions.

Working with the Walmart dataset is difficult since it contains chosen markdown events that have an impact on sales and should be considered.

14. Uber Data Analysis

When it comes to implementing and integrating machine learning and deep learning in their apps, the popular ride-sharing service is not far behind. Every year, it processes billions of trips, allowing commuters to travel at any time of day or night.

Because it has such a large client base, it needs exceptional customer service to address consumer complaints as quickly as possible.

Uber has a dataset of millions of pick-ups that it can use to analyze and display client trips to uncover insights and improve the customer experience.

15. Covid-19 Analysis

COVID-19 has swept the globe today, and not simply in the sense of a pandemic. While medical experts are concentrating on generating effective vaccinations and immunizing the world, data scientists are not far behind.

New cases, daily active count, fatalities, and testing statistics are all being made public. Forecasts are made on a daily basis based on the SARS outbreak of the previous century. For this, you can use regression analysis and support vector machine-based prediction models.

Conclusion

To summarize, we have discussed some of the top ML projects that will assist you in testing Machine Learning programming as well as grasping its ideas and implementation. Knowing how to integrate Machine Learning can help you advance in your profession as the technology takes over in every industry. 

While learning Machine Learning, we recommend that you practice your concepts and write all of your algorithms. Writing algorithms while learning is more important than performing a project, and it also provides you an advantage in understanding the subjects properly.