Best Machine Learning Project for Engineering Students in 2023

Here is the list of the Best Machine Learning Project Ideas for final-year engineering students in 2023. IT & Software Project Ideas for B-Tech, M-Tech & Ph.D. students.

Machine learning (ML) is a subset of artificial intelligence (AI) that enables software programs to grow increasingly effective at predicting outcomes without explicitly programming them to do so.

Machine learning algorithms estimate new output values by using past data as input. The goal of this essay is to quickly explain and get through the process of learning about the top Machine Learning project ideas.

• Deep Learning-Based Intelligent Video Surveillance:

This technology identifies aberrant actions such as violence or fraud in video or real-time. The user must upload a video for detection, which may be done from the device. In addition, the system can identify actions in real-time.

It will begin detecting with a single press of a button on the user’s smartphone camera. As a result, whenever an irregularity is recognized, the video frames are recorded as an image.

• Classification of Music Genres Using the KNN System:

To sign in to this service, the user must first register an account. They can access the system by entering their login and password.

The technology will recognize and categorize music genres. The findings will be shown to the user once the system has detected and identified the music. All the user needs to do is upload an audio file from their smartphone.

The front end is made up of HTML, CSS, and JavaScript, while the back end is made up of Python. Django is the framework in use, while MySQL is the database.

• CNN Traffic Sign Recognition System:

The users can upload a picture to the system for it to identify and recognize traffic signs. The technology is capable of recognizing uploaded videos.

To begin recognizing real-time traffic signs, the user must switch on their device’s camera and click on the camera button. The display of the names of the recognized traffic signs has been completed. It will also show the likelihood of the anticipated result being correct.

The front end uses HTML, CSS, and JavaScript, while the back end uses Python. Django is the framework utilized, while MySQL is the database.

• Python Human Detector and Counter:

This system employs methods like the HOG Descriptor and the Support Vector Machine. For this system to recognize persons, the user must supply a picture or video. A camera may be used to detect persons in real-time. While detecting, the number of persons counted will be presented.

• Facial Emotion Music Recommendation System:

In this method, the user may communicate with the chatbot. The mood will be identified, as well as the music player would begin playing certain music. So that the recognition is clear, the individual should be in a light-filled location.

The primary goal of our music recommender systems based on users’ facial expressions is to present consumers with choices that fit their tastes.

• Python Parkinson’s Detector System:

The issue may be rectified with a low mistake rate using the Parkinson Detection Method and the provided predictive analytics methodology.

Parkinson’s disease affects everyone differently. Early symptoms are minor and go unrecognized. Symptoms usually begin on one side of the body, develop there, and then transfer to the other. Inputs will be sent to this system to determine if or not Parkinson’s disease occurs.

• App for Vehicle Maintenance and Mileage Tracking:

Vehicle maintenance is an essential operational component that influences automotive performance and service life. It’s one of the things that’s easy to miss until there’s an issue and your car has to be repaired right away.

Our Maintenance Task and Usage Tracking App is a straightforward and practical tool that allows customers to keep track of all previous vehicle maintenance. The user may examine the vehicle count as well as any extensions or maintenance in this section.

• Python Machine Learning for Cryptocurrency Price Prediction:

This method is adequate for price forecasting, and the results acquired from forecasting pricing using machine learning attain accuracy under all scenarios of a technical trade signal, decreasing its price prediction throughout the assessment.

• Python-based Depression Detection System:

The Depression Detection System determines the kind of melancholy (anxiety, PTSD, or bipolar) & refers users to local clinics where they may speak with a psychiatrist.

Moreover, an individual must speak about themselves for 1 minute while their facial features are captured. The user must complete a test and answer all questions. The quiz in this system is based on Naive Bayes.

For Image & Video, CNN and a specific database for Depression features will be produced. This algorithm determines the kind of depression and suggests that patients in neighboring clinics visit a psychiatrist.

• NumPy OpenCV Python Car Lane Detection:

As the initial operation in this method, Canny Edge Detection is used. It reads and detects boundaries in the frames, after which the frame (i.e., picture) is transformed to grayscale using cv2’s convert color function and filtered using Gaussian Blur to decrease the number of edges.

The second procedure is to crop our lane’s region of interest. The canny edge detection output picture is provided to the area of concern function, which recovers the canny image’s height and breadth and applies a filter to eliminate everything but the mask.

• Python Sign Language Recognition:

In this system, the user will execute hand motions or gestures by switching on their webcam, and the system will recognize and show the sign to the user.

The sign detector recognizes numbers and may be readily adapted to detect a broad range of additional signs and hand signals, including the alphabet.

A machine learning algorithm called CNN is employed in this system. Convolutional Neural Networks (CNNs) may learn complex objects and processes because they include an input layer, an output layer, several hidden layers, and millions of variables.

• Python Signature Verification System:

By decreasing human mistakes, our Signature Verification System will aid in the authenticity of a handwritten signature. A machine learning model is constructed to categorize the signature classification model.

CNN will be utilized for this purpose. To test and examine the results, a sample signature (approximately 100 sample images of the same signature) would be obtained and compared to other random signatures.

• Using Decision Tree to Predict Home Prices:

The Home Prices Prediction System forecasts housing prices using multiple Data Mining approaches, and the models with the greatest accuracy score are chosen. The admin can connect to the network using a password and a username in this system. The admin can control the data for training and can add, edit, remove, and inspect data. The administrator has access to a list of registered members and their information.

• Python for Detecting Brain Tumours and Alzheimer’s:

This Brain Tumour and Alzheimer’s Detection System uses CNN to determine the presence of tumors or the development of Alzheimer’s disease.

The user must first decide whether they want to identify a brain tumor or Alzheimer’s disease. They must next submit their brain scans as well as their personal information.

This python-based method applies numerous convolutional layers to the image when uploaded. The results are accessible to only the admin.

• Google API Text Translation App:

The Translation App makes use of the Google Translate API to convert input text or messages into a specified language. For example, if a user enters content in English and chooses Marathi as the translation language, the Google Translation API will convert it into Marathi.

The detection engine for the Neural Machine Translation (NMT) model in the Google Translation API supports over a hundred languages. Language code options are used to define these languages inside a recognition request.

• AI-Powered Image Translation App:

To detect the item, the system employs Object Detection from Google Cloud Vision API, which is a component of Machine Learning (ML). Text Detection is utilized by Google Cloud Vision API to detect text, and it is also part of ML.

It converts the text or item into the desired language. For example, if a user picks a computer picture and Hindi as the language of choice, Google Translation API will interpret the detected image and show the message/output in Hindi.

• Flutter NLP Mental Health Check app:

This system allows users to chat with bots. There are no specific questions that monitor how users respond. Based on the set of categories created using Google NLP, it recognizes emotions and AI hits them.

Provide the user with a video link. Empowered with yoga, exercises, and motivational videos to help improve your mental health from one’s current mood.

• System for Detecting Fake Logos Online:

This Logo Detection software seeks to assist buyers in distinguishing forgeries from genuine products. A customer can use this approach to determine whether a commodity is genuine.

This tool can also assist brands in their fight against counterfeit goods. Counterfeit items typically have the poor build quality and, in addition to stealing sales, harm a brand’s reputation over time.

• Appointment with disease prediction app:

The administrator may control or examine the details of physicians, patients, and diseases in this system, as well as get comments from patients.

It also controls all of the doctor’s scheduling information, doctor fees, and appointments. The doctor can communicate directly with the patients to stay on top of their health situation.

Patients can estimate the condition by inputting the indications they are experiencing, and they can also stay up to speed on emerging diseases and their symptoms. If the subscriber is afflicted with one of the ailments, they can schedule an appointment.

• Collaborative Filtering Tour Recommender App:

In this approach, the user may arrange his day by picking from a list of venues based on his food and place preferences. There may be an unlimited number of plans, and all locations are retrieved using Google Place API and sorted by highest rating.

The Plan took into account your total number of hours contributed so that it could compute your trip time + time spent at a certain site. The user may manually sort the final locations or use auto-sort to find the best path.

Based on the plans of other users, the system will recommend locations where and when they are needed.

• Driver Drowsiness Detection System to Prevent Accidents:

The goal of this project is to detect the driver’s fatigue level and use image processing to detect whether the driver is feeling fatigued or sleepy.

Using image processing, the person’s eyes are scanned and detected how long the driver’s eyes are closed. If the eyes are closed for more than 20 seconds, the system’s speaker will sound alert and wake him/her up to prevent an accident.

• Chatbot for Android General Knowledge:

This system responds to the requests of the Users. The user only needs to type his question, and the system will evaluate the important phrases and respond.

The user may ask questions on many categories such as sports, education, health, and so on. The system will answer the user’s inquiry using a highly optimized algorithm.

• Monitoring & Removal of Fake Product Reviews for Genuine Rating Php:

By recognizing the IP address as well as review posting trends, this system will detect false reviews created by submitting bogus remarks about a product. The user will access the system with their user id and password, see various items, and provide feedback on the product.

To determine if a review is false or real, the system will determine the user’s IP address. If the system notices many bogus reviews sent from the same IP address, it will notify the administrator and request that the review is removed from the system.

This system employs data mining techniques. This approach assists the user in determining the proper product review.

• Data Mining-Based Real Estate Search:

This initiative assists users in making sound judgments while purchasing or selling valuable property. Previously, this procedure required a significant amount of travel and searching time.

Because of this technology, the user no longer has to drive far and can now search for the property they are looking for online, based on their specifications. This system offers property information such as an address, square footage (sq ft), number of bedrooms, floor, property seller name, contact number, and email address.

The user may search for properties based on the location they wish to live in, and the number of washrooms, bedrooms, halls, and kitchens.

• Chat Bot for College Enquiries:

To respond to the inquiry, the system employs built-in artificial intelligence. The replies are relevant to the user’s query. The technology allows the user to query any college-related activity. The consumer is not required to directly visit the college to inquire.

The system evaluates the query and then responds to the user. The system responds to the inquiry as if it were answered by a human. The system responds to the pupils’ questions using artificial intelligence. The system responds with an excellent graphical user interface, as though a real person is speaking to the user.