Machine Learning Project Ideas for Beginners

If you are a Machine Learning student, everyone will be having a thought of, “What project do I have to do?. Our project showcases our innovative ideas, our talents, and our knowledge of the particular subject. But you can’t master or have confidence in technology until you’ve assessed projects in real-time. Real-time projects will give experience in technology and a particular topic or topic. The project diversifies the fields of activity to gain practical experience of the skills acquired in machine learning. For starters, machine learning projects will be a perfect mix of experiments and various types of challenges, and they will be working as machine learning engineers. But struggling hard to get the right ideas on the subject of the project and cool ideas to work with. It is an important thing as a student of machine learning to find the idea that drives you and excites you.

When you decide to take up the machine learning certification program or PG program in Machine learning, start creating a machine learning portfolio; you should have ideas for interesting, fun, and innovative machine learning projects. Brainstorm all possible ML project ideas; they should look like a futuristic concept. Here we are listing down the few cool tips which will help you to choose and work on real-time projects.

Sales forecasting

Sales forecasting is not 100% possible, businesses can come close to Machine learning, but they cannot give 100% accurate results. But forecasting the sales will help them to achieve their target successfully. Taking the data sets for a particular product, where the developers can access information on a weekly, daily, or monthly basis, will help them to channel the optimization and inventory planning. The goal of this project is to make better data-driven decisions.

Stock price predictions

Stock forecasting is impossible. Stock prices and predictions are based on the datasets; the predictions work on the past databases, prices, volatility indices, and fundamentals. As a beginner, you can start a small project which will help to predict the stock prices by using stock market datasets. You can make a project which will predict the prices of stocks over the next few months. With efficient learning models, your model will use the information to predict whether a stock’s price will fall or rise at any given time. It is an open-source forecasting tool, and you can do this project with Python, R, and Prophet. This project will be a great way to become familiar with creating predictions based on massive datasets, as you will be solving a financial-related problem, and at the same time, you will be learning the basic concepts of machine learning; the project will be worth your time.

Recognition of human activity with smartphones

Cell phones these days are designed in such a way that they automatically detect when we are busy. A specific activity like doing exercise, sleeping, or getting involved with mobile phones for a long time. This will happen only through Machine learning. Through the accurate dataset, which contains fitness activities and daily activities, you can record data of people. You can build classification models which will predict future activities accurately. If you are good at solving multi-classification problems, then this project will be yours.

Handwritten documents in digitized versions

This is one of the perfect projects to showcase your deep learning and neural network skillsets. Image recognition is essential for machine learning. You can learn how to turn pixel data into images and how to use logistic regression and MNIST datasets.

Sign language recognition system

Sign language recognition can execute in various ways. The project will also make the lives of disabled people a little simpler. Many disabled people struggle to communicate with others, and it is one of the biggest problems these people confront. A sign language recognition system can assist them, particularly in the area of improving accessibility. You need to employ computer vision in this system to evaluate and recognize human gestures and issue commands to a system or application. You can create sign language-assisted apps, sign language assistants, or games using sign languages and many more.

Loan Prediction

This project will be one of the major clicks in the current industry as many lending institutions and banking apps are incorporating the loan eligibility models. This will be an inspiring angle to start your fintech knowledge. If you are not likely to scale this up for app incorporation, though, you will learn most of the enterprise’s apps that decide whether someone is eligible for a loan or not. You should have a dataset that contains some financial information, earnings, and spending trends. You will train your models to learn specific patterns, and you can predict whether the person is eligible for a loan or not or the eligibility of the loan amount.

Sentiment Analysis

Sentimental analysis is one of the trending things in the Artificial Intelligence and Machine learning industry. If you have a knack for playing around with written words, then sentiment analysis will be the perfect project for you. The sentiment analysis will involve test classification or clustering by a machine; it will be positive and negative perceptions. You can do this project through Natural Language Projects; analyzing sentiments in texts often starts with text mining to study the patterns of the texts in question. If you use the appropriate classification algorithms like Naive Bayes or the decision tree to train your ?model, this project will give you a hold on the basic concepts of text manipulation and how spam detection works.

Few tips for choosing the Project topic

Choosing the right project topic will decide your future in your Machine learning career. So, you need to do a lot of homework before deciding on the topic of your project.

  • Search for a problem that you can solve easily or which will excite you.
  • Find a suitable database and refine the questions.
  • Import database in JSON, XML, and CSV formats based on your analysis.
  • Clear your database by removing null or nonsensical values.
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