Machine Learning Final Year Projects 2024

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1. AnomaData (Automated Anomaly Detection for Predictive Maintenance)

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2. DocAssist (Building Intelligent Medical Decision Support System)

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3. Find Default (Prediction of Credit Card fraud)

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4. Fire Detection Model using Deep Learning

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5. An Intelligent Mood Detection and Music Recommendation Application

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6. Build intelligence to help customers discover products they may like & purchase

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In conclusion, In the realm of Machine Learning Final Year Projects 2024, innovation meets ingenuity. From pioneering algorithms to groundbreaking applications, students have pushed the boundaries of possibility. These projects not only showcase technical prowess but also offer real-world solutions to complex problems. As the curtain falls on this chapter, it's evident that the future of AI is in capable hands.

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Machine Learning Final Year Projects 2024

By: Engineer's Planet

1. AnomaData (Automated Anomaly Detection for Predictive Maintenance)

2. DocAssist (Building Intelligent Medical Decision Support System)

3. Find Default (Prediction of Credit Card fraud)

Credit card fraud is the unauthorized use of someone else’s credit card or credit card information to make purchases or withdraw cash. We have to build a classification model to predict whether a transaction is fraudulent or not

4. Fire Detection Model using Deep Learning

Lately, there have been many fire outbreaks which is becoming a growing issue and the damage caused by these types of incidents is tremendous to nature and human interests This project is aimed at building a Fire Detection Model using Deep Learning

5. An Intelligent Mood Detection and Music Recommendation Application

The objective of this project is to build an application that detects the mood of users using still images or videos and recommends music accordingly. The system will use image or video analysis to infer the user’s mood

6. Build intelligence to help customers discover products they may like & purchase

Your task is to develop a shopping recommender system that suggests relevant products to customers based on their preferences and browsing history, thereby increasing the likelihood of purchase and overall customer retention.

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