If you’re just starting out in machine learning, these beginner-friendly projects will give you the confidence you need to take on more complex tasks.
An algorithm such as logistic regression can be used to build your first ML model. It predicts discrete event probabilities. It’s one of the most basic classification algorithms.
1. Forecasting Housing Prices
Real estate is one of the most volatile markets in the country, so predicting prices can be difficult. Experts are making educated predictions about the future.
Many analysts predict less buyer demand, lower prices and higher borrowing rates in the next few years. Many buyers have been forced to sit out of the market by rising interest rates and a low home inventory.
Despite these predictions, there are some positive trends that have emerged in the housing market. Mortgage rates will be steady by 2024, for example. This will help keep the average homeowner’s monthly mortgage payments down.
The housing price index (HPI), which is now expected to increase, will do so at a slower pace than in previous years. Zillow also predicts that the majority of regional housing markets will see house prices increase in 2023, while a small number of markets are projected to experience price declines.
To predict house prices, you need to use a variety of data sources. You can use HPI, house level features, property quality, age and location to predict the price of your home.
These factors are known to affect housing prices, but they are often overlooked by hedonic price models that take structural characteristics into account. This is because these models ignore the context of the house and its surrounding environment.
But by incorporating nontraditional data, machine learning algorithms can overcome these limitations. Adding indicators such as localized crime, school quality, social venues, and more can improve the accuracy of predictions.
Machine learning algorithms can even be used to identify lucrative opportunities for real estate investors. Skyline AI has recently published a case study that shows these data are capable of predicting a 3-year rent per sq foot in multifamily buildings in Seattle. This accuracy rate is over 90 percent.
2. Predicting Web Search Trends
Predicting web search trends is a fun and easy way to start learning Machine Learning, and it’s also a great tool for building confidence. You’ll be able to experiment with different models and see which one performs best on your data.
You will also learn how to use the various types of data you can incorporate into your model. You will need to be able to deal with categorical features and ensure that models can withstand missing data.
You can predict flu outbreaks by tracking the number of searches for “flu” or “fever”. In 2010, Google Flu Trends was able to predict the 2010 influenza outbreak (based on trends for common symptoms) one or two weeks before the CDC made an official announcement.
A similar study shows that it’s possible to predict stock market volatility by analyzing the trends in web searches. The researchers found that counts of web searches for specific keywords correlate with the current value of stocks in a given country.
The same strategy has shown promise in predicting the performance of music songs and movies. It was found that counting web searches and using Billboard Top 100 listing for a song performed just as well, but critics’ reviews for movies weren’t nearly as accurate.
This project used data from five sources including Google Trends, a popular search engine. You can filter these data by time period and category.
3. Predicting Driver Demand
A machine learning algorithm that predicts driver demand is a fun project for data scientists who are just starting out. You must provide labeled data to the algorithm so that it can identify trends. This is called supervised machine learning.
It’s a great way to build confidence in your ability to use a machine learning algorithm and understand how it works. However, it is not meant to replace human knowledge in all situations.
Predicting driver demand can help you make more informed decisions about your business. This can include choosing the right time to deploy surge pricing, identifying areas that are underserved by your service and making sure you have enough drivers available to cover peak traffic times.
If you’re working in the ridesharing industry, predicting demand is essential to your success. It helps you find the best drivers and routes for your business, set your prices correctly, avoid getting stuck in traffic and maintain a profitable fleet of vehicles.
You can predict demand for drivers by looking at local events. With this type of data, you can predict which concerts or sports events are most likely to have high demand, and see if there is any way to optimize your business by putting drivers in the best position possible to meet that demand.
This information can be used to help you plan your driving route based on peak demand. You can use this information to determine which route is best and where to take a rest or fuel before you return to grab more riders.
Ultimately, predicting driver demand is a big step in leveraging the power of machine learning. But it can be intimidating for beginners to try to implement this type of technique on their own, so it’s important to get started by choosing a problem that is well-suited to the technology.
4. Fake News: How to Detect It
Fake news is spreading on social media. There are some obvious fake news stories, such as crazy stories people recognize. But there is also subtle misinformation.
It’s important to know how to spot fake news before you get swept up in it. Here are some tips to help you identify fake news:
1. Look at the website that is hosting the story (it should be real). This could mean a newspaper or a blog. These should have good reviews and be well-written.
2. Read the article in full and check that it has a proper address, is not a phishing site or scam. To verify if the site is legit, you can use tools such as ‘Red Flag.
3. Look at other sites that are reporting on the same topic and compare them.
Fake news articles are often created and shared in order to push a political agenda or to make people angry. Fake news articles are often based on false information but can have as much impact as real information.
4. Detecting Fake News with Machine Learning
Fake news detection is a vital skill, no matter if you are a Machine Learning beginner or an expert. You’ll be able to recognize fake news online, and social media.
5. Develop a Text Classification Model for Detecting Fake News
Because machine learning relies on analysing patterns and words, it can help identify fake news. A good Machine Learning model will have a high accuracy and will be able to distinguish between fake and real news. Before you can use your machine learning model to detect fake news, however, you need to know how it should be trained and what features to look for.
5. Creating Your Own Video Game
It can be a rewarding and exciting experience to create your own video games. However, it also takes a lot of time and effort to complete. In order to make your project a success, it is important to set a deadline and stick to it. This will ensure that you are able to get the most out of your efforts and avoid spending unnecessary amounts of time on something that is not going to be good enough to release.
Once you have a clear idea of what you want to create, you should start the design process. You will need to think about the design of your game and which features you want it to have. This is an important step for a beginner because it will help them determine whether they have the skills to build their own video game or not.
You can learn the basic design skills for game creation through online courses available on edX, Coursera, and Udacity. These courses will teach you how to design your own game and make it a reality!
Next is programming. There are many programming languages available for use, but you should choose one that is easy to understand and that you enjoy working with. You should learn Unity and Unreal game engines to optimize your game’s design.
Remember that coding your game can be a tedious process. It could take you hours or even days. You need to be able to handle any difficulties that might arise in the course of development.
It can help you build your confidence as a machine-learning developer by creating your own games. It is a great way to learn how to implement complex ideas and concepts into your work. You can test different algorithms under various conditions and it is a good way to see their performance.