Top 5 Machine Learning Projects in 2022Nov 15, 2021 9437 seen
Machine Learning Introduction
Machine learning is one of the important areas of AI. It plays an important role in identifying the trends and behavior of a mass of people using a given dataset. Aces like Google, Facebook, Uber, and many other leading companies use machine learning as the backbone of their operations. Overall, machine learning is a highly sought after skill these days. The more demand for this domain and its use, the more intimidating it becomes for newbies to learn. Once you have accumulated sufficient knowledge and understanding of the ethics of machine learning, the next step is to gain hands-on experience in various projects. The more projects you cover, the more proficient you will become in machine learning.
The solutions to machine learning problems are not always the same; they vary widely according to the needs of the companies. What's more, these projects will help you gain hands-on experience solving real-world problems and hone your skills in Machine Learning, Machine Learning, Natural Programming Language, Python, Flutter, and many other best skills in the industry.
In this article, we will discuss 5 of the most popular machine learning projects that can be of great benefit.
1. Movie Recommendation System Using ML
Making a movie recommendation system is a typical project and easy to get started. A system like this would recommend movies to users by applying appropriate filters based on their preferences and browsing history. Here, user preferences are taken into account according to the data viewed and their ratings. This movie recommendation system will be the result of the implementation of the established machine learning algorithm.
It would be better if you had a dataset to work with your movie recommendation system. There are many options, such as MovieLens, TasteDrive, etc. In this case, you need dataset .csv files to get movies and rating data. Now, first of all, you will need to do some preprocessing on the data to make it usable. When the data is ready, you can implement appropriate machine learning algorithms to suggest movies and record the most watched genres on your system. Besides movie recommendation systems, you might consider creating any other recommendation system, such as a book recommendation system, a cafe recommendation system, etc. You can follow the same procedure with appropriate datasets for different recommendation systems.
2. Image Cartooning System Using ML
Machine learning is expanding its capabilities in all areas, so why should autoionization remain intact? You can use techniques such as White Box Cartoonization to convert a real photo to animated. The main idea of this system is to focus on the elements of expression extraction to make the process fully manageable and flexible when implementing machine learning. If we are talking about the white box method, it splits the image into three cartoon views, namely surface view, structure view, and texture view. In addition, the GAN (Generative Neural Networks) structure is used to optimize the desired result. You can also create emojis from your photos using this model. This project is likely to take you one step closer to deep learning and computer vision.
Imagenet, Tbi, ToonNet and many other online sites are available to provide you with a sample dataset for training and testing your machine learning based model. The dataset will contain specific details for a wide range of images.
3. Wine Quality Prediction Model
In this project, you will predict wine quality based on a set of wine quality data. You've probably heard people say that the older the wine, the better it tastes. But several other factors determine the quality of a wine. These factors include physicochemical tests such as pH value, amount of alcohol, fixed acidity, and volatile acidity, to name a few. The ML model you create in this project will analyze the quality of the wine by examining its chemical properties.
The dataset you will need for this project will include data on the chemical properties of different types of wine. It will consist of values for various physical and chemical tests fed into your machine learning model. You can browse the wine quality assurance research papers available online to collect a dataset to train and test your model.
4. Affable Mental Health Tracker
Mental health is a sensitive issue these days. Building a companion app that tracks your mental health and ensures your mental well-being is ideal. This project will highlight your machine learning abilities as well as your holistic and optimistic approach. This app will include several individual tasks and regular checkups of your mental health. You decide what other features you want to add to this app. Using Flutter is a good option for developing such applications. Your Flutter skill, combined with a machine learning model, will help you create a user-friendly and potential mental health tracking app.
You can get a free list of datasets available on the Internet for modeling mental health phenomena. You might want to follow this link to get the dataset for this project. This can be data from research papers by different authors. You can prepare your dataset based on research by many mental health authors.
5. Data Preprocessing CLI in Machine Learning
As you know, before you feed a dataset into your machine learning model, you need to process the data to transform it into a form that algorithms can understand. Feeding impure data (data with missing attributes, values containing redundancy, etc.) into your model will produce dramatic results that you never want. The more essential data preprocessing is, the more tedious the task becomes. So why not create your own system to preprocess the dataset for you every time you start a new machine learning project? This CLI tool will make your other machine learning projects less time-consuming.
However, this project is beneficial in every way. First of all, this project will significantly add value to your resume. Not only will this be useful for your future projects, but it will also help you celebrate your expertise in OOP, Pandas, and exception handling concepts.
The Yelp dataset is a regular repository as Yelp made its dataset open source. You can get all sorts of datasets for a diverse assortment of machine learning projects. You need to fill out an application, and you can use their dataset.