In this technologically paced era, everything is upgrading to its true potential and making lifestyles easy. On the other hand, achieving this advancement can only be possible because of developers’ smart brains. As per the current scenario, AI and ML have been taking over almost everything, and there is no doubt that AI will become a necessity in the upcoming decades. 

Why ML projects are essential in 2024?

As ML is becoming the choice of many individuals, it opens careers in ML engineering, software development programming, data science, and research and development. Making projects is a great way to showcase your skills, and where the competition has become tough, it is now crucial to make projects stand out from the crowd.

When you consider the best part of learning ML is obviously the salary you will earn; organizations are recruiting talents at competitive salaries, and according to Glassdoor, on average, you will earn more than $150,000 annually. Please note that by having more experience and expertise, you earn exponentially, and the best way is to work on projects. So, below are some of the top 10 Machine Learning project ideas you must try out. 

List of top 10 ML project ideas.

Kindly include projects that resolve real-world issues to make life easy. Below are the ten projects that actually, fall under this category. You will be required to have an intermediate level of understanding of ML in Python. 

Stock Price Prediction

This project helps predict stock prices in the future. It is clearly based on historical data and other potential market indicators. You may find it challenging because of the volatility and unpredictability of financial markets. The objective is to predict future stock prices and help investors make investing-related decisions. You can feature historical stock prices, volumes, and several technical indicators in your project, and using all of these will give a target price. 

Sales Forecasting

Consider it to be one of the important projects that you must work on. This project aims at forecasting sales based on current and historical sales reports and other factors. This plays a vital role in inventory management, planning, and making strategies for growth, and decision-making is also one of the major outcomes in which it will be helping you. The model must help to identify key factors that affect sales trends and how promotional activities and seasonal effects will help to achieve sales goals. 

Recommendation System

Recommendations have been used by many companies to understand their potential customer’s choices and use that data to increase their sales. Being an ML expert, you can build a Movie, Music, or Book recommendation system that is based on users’ preferences and historical consumption behavior. These have been used mostly in the e-commerce and entertainment industries. You can add features like User Interactions and content features such as genre, authors, and other specifications. 

Fake News Detection

The proliferation and exaggeration of information online have increased, and you can resolve this problem by doing a project that tells whether the news is fake or authentic. In this project, you use machine learning algorithms to detect misleading information automatically on the web whenever someone enters the news headline or link of that news. Here, your objective would be to classify news articles or stories as genuine or fake based on credibility indicators like user engagement metrics. 

Chatbots

Chatbot is one of the trending Machine Learning projects that will surely shine your resume. Intelligent chatbots are in high demand and are designed to stimulate conversation by interacting with human users, helping them to get answers to their queries. The most important task of a chatbot is to provide customer support by retrieving data and information from the database or web. You can use NLP (Natural Language Processing) and machine learning to understand and generate responses. You can integrate databases or web services so that the chatbot can make dynamic responses to the user.

Object Detection

This would be a cool project to make. You can make an object detection system that localizes the position of objects in images via bounding boxes. You can use it to build a detection model for objects like cars, bikes, and people in various images and videos. Datasets like COCO and Kaggle can be used. 

Sentiment Analysis

You can build a model that analyzes sentiment or opinions. This project involves analyzing text-driven data and then determining its sentiment. This can help gauge public opinions on various topics, such as product or service reviews or opinions on government policy or bills. Sentiment labels for supervised learning and textual data review are the top features, and they describe sentiment as positive, negative, or neutral. 

Image Recognition

It involves identifying and classifying objects within images. Image recognition would require to train your ML model on a high data set and the aim is to classify the objects into human, and other items categories. You can find use cases in applications that used in security surveillance or autonomous vehicles to detect the objects to avoid potential harm. 

Resume Parser

You can create a model using ML and Python that helps the HR to filter out top resumes. In case of high demand, there are chances that an ideal candidate may not be selected just because they are not getting enough attention to their resume. You can build a system that browses through a pile of resumes and parses the required fields and reduces recruiters’ manual labor. 

Handwriting to text

You can develop a neural network that can interpret handwritings into the text. It would be a good experiment to start and you can get hands-on experience on neural networks, java, machine learning, and deep learning technologies.

How can you start a Machine learning project?

For any project that you want to make, it is important to select the relevant business use case so that the machine learning model can be built to address it. One must follow an end-to-end approach to model deployment and management. Here are the fundamental steps given that you must take before starting. 

• The scope of your project must be clear.
• Understanding of Data requirements, collection, analysis, and preparation.
• Building the model using your ML skills. 
• Deployment of model into production.