Machine Learning Project For Final Year

Does my project need ML?

“To the man with only a hammer, every problem looks like a nail.” — Charlie Munger

In any given project, identifying what not to do is about as important as knowing what to do.

Expertise and Knowledge of machine learning can sometimes lead to a tendency of applying cutting-edge technologies  Deep Learning algorithms where a simple Linear Regression ,Random Forest or any other traditional machine learning methods of classification techniques and regression algorithms could have provided similar performance.

Many tasks where Machine learning algorithms is applied, are better suited to be efficiently done by traditional programming and heuristic-based rules and conditionals, some examples of these are:

Home Automation (Light Control):

• Scenario: A homeowner wants to automate their lights based on the time of the day and presence in the room.
• Overuse of ML: Developing an ML model to predict light usage patterns based on historical data.
• Simpler Solution: Using a rule-based system with motion sensors and scheduled timers can achieve the desired functionality with less complexity and more reliability.

‍Customer Support for Common Inquiries:

• Scenario: A homeowner wants to automate their lights based on the time of the day and presence in the room.
• Overuse of ML: Developing an ML model to predict light usage patterns based on historical data.
• Simpler Solution: Using a rule-based system with motion sensors and scheduled timers can achieve the desired functionality with less complexity and more reliability.

Generally, tasks with the following characteristics can be done using traditional coding rather than ML models:

Simple, Well-Defined Rules:

Characteristics: The task can be clearly defined with straightforward rules or logic.
Example: Determining if a number is even or odd can be done with a simple modulo operation rather than an ML model.

Low Complexity:

Characteristics: The task does not involve a high degree of variability or complexity.
Example: Basic arithmetic calculations or straightforward data manipulations like sorting a small list.

Small Scale or Low Volume:

Characteristics: The problem domain involves a limited amount of data or a small number of instances.
Example: Information retrieval and querying in the database of a small company can probably be done using faster and cheaper methods by using DSA-based searching and sorting methods without the need for an ML-based vector database.

High Costs of Implementation and Maintenance:

Characteristics: The cost, both in terms of money and time, to implement and maintain an ML solution is disproportionately high compared to the benefits it provides.
Example: Developing an ML model to predict employee attendance in a small office when a simple time-tracking system would suffice.

The goal of this article is not to discourage us from using ML but to use it where it is truly worth the effort and resources, further, we will discuss some areas where ML projects can be built, and a step-by-step guide for making the project along with some common pitfalls and bottlenecks which we should be aware about.

ML products and applications should deliver a valuable experience either by automating a repetitive tasks, or performing a challenging tasks like Image Segmentation, or tasks which can lie in both be a  complex task  and repetitive in nature, machine learning projects like statistical modeling of crime rate data to make predictive models like logistic regression based model for forecasting future crime rates and classifying the threats as severe, low or moderate based upon historical data.

Implementing a successful ML projects requires a understanding of machine learning and theoretical knowledge of it to evaluate machine learning project ideas  across the wide range of available methods

Some metrics to consider for evaluating and selecting final year machine learning project ideas :

Relevance and Impact:

The topic should address a real-world problem that has the scope for improvement with the use of ML.
Examples include developing an ML model to predict disease outbreaks and healthcare and safety-related topics.

Data Availability:

The project should have sufficient and accessible data for training and testing.

Introduction to Machine Learning is mostly done with projects for beginners and emphasizing  feature extractions,featuring scaling and other  feature engineering techniques  to  extarct key features  done using Exploratory Data Analysis and  dealing with categorical variables  for being comprehensible for statistical modelling by the code.

All of this is done for a simple reason "Garbage In ,Garbage Out","data is the  new oil" , but quality matters over just brute quantity .Impressive machine learning projects and deep learning projects have been reaching  at record milestones in model evaluation and learning outcomes outperforming  complex  deep learning architectures  by understanding of machine learning algorithm to devise  optimum  feature extraction fand feature scaling, retaining only the essential features and extract more information.

Machine Learning projects which can be done using Supervised Machine Learning frameworks for NLP  sentiment analysis to guage customer satisfaction  using publicly available social media datasets, and financial or other numerical data that is available on public forums and government websites.

For a task like image classification or classifying facial expressions done for  deep learning projects should have quality data with image processing techniques and image processing pipelines for feature engineering techniques to extract key features so that its compatible with deep learning models like Convolutional Neural Network and split in a good ratio of train and testing sets accordingly.

Even in many open-sourced datasets, sensitive data like medical data, data of social media engagement of individuals, and data related to defense sectors are only provided to those with legitimate reasons to use it and have to be approved by the sourcing authority of the data, which can be time-consuming and there is no certainty on whether the data will be approved, these kinds of datasets should be avoided when the project is under time constraints especially by final-year students  

Technical Feasibility:

The project should be technically feasible within the constraints of skills, resources, and timeframe..
Example: Building a recommendation system for a specific domain (e.g., movies, books) if you have access to the necessary datasets and computational resources.

The feasibility of a project is easier Building a rec-sys for books can be easy as it only contains text data and images whereas building a real-time movie recommendation system or stock recommender system will be more difficult as there are many more dimensions on which the rec-sys model will have to work upon.

This doesnt mean that the topic should be a simple hardcoded  linear regression model for statistical modeling  on a small numerical dataset while that might be a good option in ideas for beginners, but final-year projects need to have good learning outcomes

The list of machine learning project ideas which are application based projects for students should have mature  machine learning frameworks ,libraries and forums about errors faced and theoretical knowledge behind the architecture  like those which are present in the case for Supervised Machine Learning  in the forums and repos of  Tensorflow  and Pytorch for deep learning algorithms, such must be readily  accesible for deep learning projects.

Novelty and Innovation in the subject matter topics, improving existing S.O.T.A tech:

The project should involve a novel application of ML or aim to improve existing solutions through innovative approaches.

Although a completely new innovation can be a great  result, it should not be the goal of every project since experiments generally tend to have high levels of failure and can be time and resource-consuming.

One needs to undergo a comprehensive learning experience to get a deep understanding  of dependent variables and independent variables for complex tasks  and architecture of  machine learning models and model evaluation .

Example: Exploring new architectures for neural networks to improve image recognition accuracy., or making a previously made algorithm more efficient such that it can be trained faster and with fewer resources, or an algorithm that can help in simulating stress tests on mechanical structures by predicting locations of cracks, defects and suggesting better designs.

Scalability and Implementation (Technical Scalability, Industry Demand and relevance, and ethical concerns)

The project should be deployable on a large scale at which it can be deployed and utilized in real-time and it increases the efficiency or the revenue of the industry by an amount which is greater than its cost of implementation. An LLM can be deployed to create chatbots, but to train it and deploy it on scale would be significantly costly for a small business owner, a heuristic-based chatbot or chatbot based on RNN would be significantly cheaper.

Ethical concerns are important especially when deploying on a large scale, the data used for training the model should be legally and consensually obtained from the source, it should be balanced and proportionally representative of all the classes, especially in cases of demographic data it should be representative of all ethnicities, genders, and other features. It should not be biased against any classes or varieties even when deployed on large scales, increasingly this is an important metric of concern as bias in algorithms can amplify discrimination based on existing social differences.

An example of this can be: ML algorithms for targeting systems in weapons, these algorithms should be deployable on small machines and large quantities, at the same time, they shouldn't leak any sensitive information or be biased against certain communities and should be reliable enough that it won't hurt un-intended targets.

Unique Topics which can be considered across different sectors:

Healthcare:

Access to healthcare is essential for the well-being of people, but the cost factor of healthcare has been an impediment, ML can be applied in this field to reduce in-efficiencies of resource usage and help in driving down costs, it will also help in devising solutions which are either able to predict diagnosis of diseases at an earlier stage with lesser equipments and also help doctors in prescribing cures to the patient by drawing inferences using patient data. Some projects that could be done in this domain are:

1. Intelligent Medication Management System

Develop a machine learning-based system that can assist healthcare providers in managing patient medications, such that it can monitor and remind them of medication adherence, and be able to warn the user about potentially harmful drug interactions with other drugs or certain types of foods and alcoholic beverages and warn doctors about contraindications in advance, for example predicting the risks of asthma medications for pregnant women with thyroid.

It can help in comparing and verifying the medications from the least expensive marketplaces to source the specific drug and verify its authenticity, it can suggest the optimum amount of drug dosages which will be decided by the current condition of the patient, for example recommending the optimum number of insulin doses required for patients based on the patient’s blood sugar levels and help save costs.

Pros:

Improve patient safety by reducing medication errors
Enhance medication management efficiency for healthcare providers
Can be integrated into electronic health record (EHR) systems

Cons:

Requires access to comprehensive medication and patient data, which may be difficult to obtain
Ensuring data privacy and security is critical
Regulatory and ethical considerations around automated medication management

Similar Projects:

Adverse Drug Reaction Prediction Using Machine Learning
An-Attentive-Neural-Model-for-labeling-Adverse-Drug-Reactions

Datasets:

FAERS (FDA Adverse Event Reporting System)
Hypertension Medication Adherence Dataset

2. Hospital Triage and Resource Management System

 Triage is the sorting of patients (as in an emergency room) by assessing their conditions according to the urgency of their need for care, and allocating the resources to the patients as per the order of priorities assigned. The task of triage can be ambiguous and requires good accuracy with few condition details and in a short amount of time, it can make the difference between the life and death of a patient, hence ML algorithms to use the health metrics of the patient assess the urgency and preliminary treatments required, such a system can supplement the decisions of doctors and help them in conducting triage much faster and more efficiently which can be helpful in times of large epidemics or mass disease outbreaks.

Pros:

Improve the efficiency and accuracy of the triage process, leading to better patient outcomes
Reduce the workload of healthcare providers and enable faster triage decisions
It can be integrated into hospital information systems and electronic health records

Cons:

Requires access to large, high-quality datasets of historical patient triage data, which can be difficult to obtain due to data privacy concerns
Ensuring data privacy and security is critical, as the system will be handling sensitive patient information
Regulatory and ethical considerations around automated triage decision-making

Similar Projects:

Machine Learning and Initial Nursing Assessment-Based Triage System for Emergency Department
Medical emergency department triage data processing using a machine-learning solution

Datasets:

German Emergency Rooms Emergency Severity Index Dataset.
Hospital Emergency Department Triage Data

3. Early Prediction and Diagnosis of Chronic Diseases

Some diseases in their early stage do not show their complete symptoms, or appear as symptoms of seemingly less severe diseases, but this can be harmful and prevent the patient from seeking treatment which worsens their condition, an ML model capable of detecting diseases at an early stage can help in reducing this problem, such a model would need to be trained on vast amounts datasets containing the vital metrics of the patient, pathological report metrics of the patient and in case of multi-modal models, the x-ray and CT-Scan images of the patients from all classes of data, with a good representation of all possible diseases amongst various demographics.

Pros:

Early detection can lead to timely intervention and better patient outcomes
It helps healthcare providers allocate resources more efficiently
Can be integrated into clinical decision support systems

Cons:

Requires access to large, high-quality healthcare datasets which can be challenging to obtain,
Ensuring data privacy and security is critical, many open-source datasets take time to be approved because hospitals are reluctant to give access to the data because of privacy concerns.
Model performance may be limited by data quality and availability

Similar Projects:

Analysis and tracking of Metrics for Disease Spread Monitoring Prediction Using Machine Learning
Heart Disease Prediction Using Machine Learning

Datasets:

Diabetes Dataset
Heart Disease Dataset
Cancer Dataset

4. Personalized Rehabilitation Recommendation System

Create a machine learning system to recommend patients' individualized rehabilitation plans based on their health, physical capabilities, and personal preferences. To maximize the likelihood of recovery of patients or for outcomes related to reducing the dependency on addiction, the system will recommend exercises, and therapy modalities, and monitor the vitals of the patient to alert doctors before potential situations of danger, and progress tracking.

Pros:

Enhance the effectiveness of rehabilitation programs by tailoring them to individual patient needs
Improve patient engagement and adherence to rehabilitation plans
Can be integrated into telehealth platforms and remote patient monitoring systems

Cons:

Requires access to comprehensive datasets of patient medical records, rehabilitation outcomes, and exercise/therapy effectiveness, which may be difficult to obtain
Ensuring data privacy and security is critical, as the system will be handling sensitive patient information
Complexity in modeling the various factors that contribute to successful rehabilitation outcomes
Any wrong suggestion by the algorithm can potentially be life-threatening hence, any such project

Similar Projects:

Personalized Rehabilitation Recommendation using Machine Learning
Predicting Rehabilitation Outcomes Using Machine Learning

Datasets:

Mental Health Rehabilitation Dataset
Dataset on Alcohol and Drug Treatment Services in Australia

5. Intelligent Prosthetics Assistance system

Creating a system based on machine learning that can improve the use, comfort, and functionality of prosthetic devices for individuals who have lost or are with amputated limbs. The system should be able to:

By evaluating sensor data to identify problems such as bad fit, pressure sites, and volume variations and suggesting suitable modifications, prosthetic fitting and comfort can be improved.

Enhance prosthetic device comfort, efficacy, and integration with the rehabilitation process to promote long-term usage and adoption.

Enable more natural, multi-degree-of-freedom motions by learning from sensor data and muscle impulses. This will improve prosthetic control and functioning.

Pros:

Improving lives: This technology aims to enhance the quality of life and independence for individuals with limb differences. By providing functional and comfortable prosthetic devices, it offers them a chance to engage in daily activities with greater ease and confidence.
Boosting acceptance: With an alarming rejection rate of nearly 50% for prosthetic devices, this innovation strives to increase long-term usage. By integrating machine learning, the goal is to create prosthetics that feel like a natural extension of the user's body, improving acceptance and fostering a positive rehabilitation journey.
Seamless rehabilitation: The system is designed to work in tandem with the rehabilitation process. By providing real-time data and insights, it can enhance user training and accelerate the adaptation process. This integration can lead to better outcomes and a faster return to everyday activities for individuals with prosthetic limbs.

Cons:

Data acquisition: One of the challenges is accessing comprehensive datasets. Gathering sufficient data on prosthetic sensor readings, user feedback, and rehabilitation outcomes is crucial for training effective models. However, obtaining such data may prove difficult due to privacy concerns and the sensitive nature of patient information.
Privacy and security: As the system handles sensitive patient data, ensuring data privacy and security is paramount. Protecting user information and preventing unauthorized access require robust measures and continuous vigilance due to the sensitive nature of the data.
Modeling complexity: Successful prosthetic use and user satisfaction are influenced by various factors, including physiological, psychological, and environmental aspects. Modeling these intricate relationships and personal preferences can be complex and may require a highly nuanced approach to machine learning model design.

Similar Projects:

Prosthetic Limb Control using Machine Learning and EEG signals
Prosthetic Fit Optimization using Machine Learning

Datasets:

Prosthetic Limb Sensor Data
Prosthetic User Feedback and Outcomes

ML Projects in Finance

1. Credit Card Transaction Fraud Detection using ML

By examining transaction patterns, customer behavior, and other pertinent data, an ML-based system can be developed to precisely identify fraudulent credit card transactions and activities in real-time,

Advantages

Improve the ability to prevent fraud, minimizing monetary losses and harm to one's reputation.
Through proactive fraud detection and mitigation, increase client happiness and trust.
By automating the fraud detection process, you may optimize the allocation of resources.

Cons:

Requires access to extensive, superior datasets of past customer transactions and fraud instances. Since the system will handle sensitive financial data, ensuring data privacy and security is essential.
complexity in simulating the ever-changing consumer behavior and the dynamic character of fraud tactics.

Similar Projects and Datasets:

Machine Learning for the Identification of Credit Card Fraud
Identification of Anomalies in Fraud Prevention Datasets: Financial Transaction Training and Testing sets (Dataset for Credit Card Fraud Detection)

2. Anomaly Detection in Financial Statements

Introduction: Develop a model to detect anomalies and irregularities in financial statements, which can indicate errors, fraud, or economic distress, this can also  be done for detecting anomaly in  blockchain technology based transactions.

Pros:

Enhances financial transparency and accuracy.
Helps in the early detection of financial issues.
It can be integrated into financial auditing processes.

Cons:

Requires detailed financial statement data.
High false positive rates can be problematic.
Ethical and legal considerations in anomaly reporting.

Similar Projects & Datasets:

Anomaly Detection in Financial Data

Datasets: Yahoo Finance Data, NSE-TATA-GLOBAL dataset compared with timelines for past frauds to notice anamoly in prices.

3. Customer Lifetime Value Prediction

This research aims to use transaction history, demographic information, and behavioral data to forecast the lifetime value of clients for financial institutions.

Pros:

Helps in targeted marketing and customer retention strategies.
Enhances customer segmentation and personalization.
Provides long-term business insights.

Cons:

Requires extensive customer data.
Model complexity and interpretability challenges.
Ethical concerns with customer data usage.

Similar Projects & Datasets:

Customer Lifetime Value Prediction

Datasets: Online Retail Dataset

4. Predictive Maintenance for Financial Infrastructure

Create a model that uses real-time monitoring and maintenance data from the past to forecast when the financial infrastructure of systems (servers, ATMs, etc.) is likely to fail and in need of upgradation and maintenance, and if and how they can use recent technolgies like blockchain technology to make their systems more secure and prudent.

Advantages:

Lowers maintenance expenses and downtime.
increases the efficiency and dependability of financial services.
Predicts outcomes using real-time data and the Internet of Things.

Disadvantages :

Such a model requires integration with both IoT devices and physical infrastructure.
High setup and ongoing maintenance costs at first.
Intricate feature engineering and data preparation.

Similar Projects & Datasets:

Predictive Maintenance using Machine Learning

Datasets: NAB (Numenta Anomaly Benchmark)

5. Portfolio Optimization Using Reinforcement Learning

Using RL to find optimum management of financial portfolio which can be trained and tested with years of historic numeric time-series data, DL algorithms like LSTM are good for time-series datasets,they are a part of great list of ideas for beginners  but they have limitations causing errors even in small scale such as in  projects for students in accuracy which is why transformers can help deal with these limitations and give and adjust to scenarios it might deal with better than other algorithms.

They can also be used for interpretation of news and financial statements and their impact of the price of financial assets.

Pros:

Provides a dynamic and adaptive investment strategy.
Can handle multiple assets and constraints. Uses advanced ML techniques for portfolio management.

Cons:

Requires significant computational resources.
Complex to implement and tune.
Performance is highly dependent on market conditions.
Similar Projects & Datasets:

Portfolio Optimization with Deep Reinforcement Learning

Datasets: Yahoo Finance API, NSE-TATA-GLOBAL dataset

ML Projects in the Domain of Material Science and Engineering

(Useful for students in Core Fields like Chemical, Civil, Mechanical and Aeronautics) :

1. Defect Detection in Materials

This can be another one of those good ideas for beginners in the field. Using X-ray diffraction patterns or microscopic images, convolutional neural networks (CNNs) can be applied to automatically identify and categorize material flaws.

Pros:

Enhances accuracy and speed of defect detection.
Reduces human error and subjectivity in defect identification.
Can be integrated into automated inspection systems.

Cons:

Requires high-quality labeled image datasets.
Model performance might be limited by image resolution and quality.
Needs to handle variations in defect types and appearances.

Datasets:

A public repository of X-ray diffraction patterns.
Various microscopic image databases from research labs and institutions.

Code Repositories:

crack-pore-detection
Deep Defect Classification

2. Microstructure Analysis

Applying machine learning techniques to analyze and classify material microstructures from images, aiding in understanding the relationship between microstructure and properties.

Pros:

Provides detailed insights into microstructural features.
Automates the analysis process, saving time and resources.
Can correlate microstructural features with material properties.

Cons:

Requires high-resolution and well-annotated images.
Complex microstructures might be challenging to classify accurately.
Needs domain expertise to interpret results.

Datasets:

Open Access Microstructure Dataset (OAMD): nims.go.jp
Public microstructure image databases from academic research.

Code Repositories:

Transfer Learning for Microstructure Segmentation
MicrostructPy Library

3.Optimization of Manufacturing Processes

Use reinforcement learning (RL) to optimize manufacturing processes, such as additive manufacturing or casting, to achieve desired material properties with minimal defects.

Pros:

Can adapt to complex and dynamic process environments.
Reduces waste and improves product quality.
Automates process optimization, reducing reliance on trial and error.

Cons:

Requires detailed process models and simulation environments.
RL algorithms can be computationally expensive and time-consuming.
Needs careful tuning and validation.

Datasets:

Manufacturing process data from industry partners or research institutions.
Simulated datasets from process simulations.

Code Repositories:

Reinforcement Learning for Manufacturing
Deep Reinforcement Learning for Additive Manufacturing

4. Using Graph Neural Networks for predicting properties of new material compositions.

This project involves developing machine learning models to predict various material properties such as tensile strength, hardness, and thermal conductivity based on compositional and processing parameters.

Pros:

Can significantly speed up the material selection and design process.
Reduces the need for extensive experimental testing.
Provides insights into the relationships between composition, processing, and properties.

Cons:

Requires extensive and high-quality datasets.
Model accuracy depends on the complexity of the material system.
The model might not capture all exceptions and varieties of a material’s behavior under different conditions.

Datasets:

Materials Project Database: materialsproject.org
‍Open Quantum Materials Database (OQMD): oqmd.orgCitrination: citrination.com

Code Repositories:

Matminer
‍modnet
M3GNET

5. Accelerated Aging Studies of Materials

Create models based on data from accelerated aging tests to forecast the long-term aging and deterioration of materials.

Pros:

Provides insights into material longevity and performance over time.
Helps in predicting maintenance schedules and product lifespans.
Reduces the need for lengthy real-time aging studies.

Cons:

Requires high-quality accelerated aging data.
Models need to account for various aging mechanisms.
Needs validation through real-time aging data.

Datasets:

Accelerated aging data from industry and research institutions.
Public datasets on material degradation and aging.

Code Repositories:

Aging Prediction Models
Degradation Analysis Toolkit

Applications of ML in the Agriculture Industry

1. Precision Agriculture and Crop Yield Prediction

Develop ML models to predict crop yields based on various factors like soil quality, weather conditions, and farming practices.

Pros:

Enhances agricultural productivity.
Helps farmers make informed decisions on crop management.
Can optimize the use of resources like water and fertilizers.

Cons:

Requires large amounts of data from diverse sources.
Models need to be tailored to specific crops and regions.
Prediction accuracy can be affected by unexpected weather events.

Datasets:

USDA Crop Data: usda.gov
‍World Bank Agriculture Data: worldbank.org
‍FAO Data: fao.org

Code Repositories:

AgroML
Crop Prediction Models

2.Autonomous Farm Machinery

Implement ML algorithms to enable autonomous operation of farm machinery like tractors and harvesters.

Pros:

Reduces labor costs and increases efficiency.
Can operate continuously without fatigue.
Enhances precision in farming operations.

Cons:

High development and implementation costs.
Requires robust and reliable sensors and control systems.
Safety and regulatory compliance need to be addressed.

Datasets:

Public datasets on machinery operation and farm conditions.
Sensor data from autonomous vehicle research.

Datasets and APIs which are verified with information from Google Local map guides and contributors  who give  reviews of places and roads along with data used for targetting ads using location can be extracted to train for navigation and foreseeing the problems that might have to be dealt with in that geographic location.

Code Repositories:

Autonomous Tractor
Farm Machinery Automation

3. Crop Quality Assessment

Introduction: Implement computer vision and ML techniques to assess crop quality and grade produce automatically.

Pros:

Provides consistent and objective quality assessment.
Reduces labor costs and speeds up the grading process.
Enhances marketability of produce by ensuring quality standards.

Cons:

Requires high-quality images and labeled data.
Needs to handle variability in crop appearance.
Deployment in the field can be challenging due to lighting and environmental conditions.

Datasets:

Public datasets on crop images and quality metrics.
Grace Note Multi-Crop datasets

Code Repositories:

Crop Quality Assessment
AgriBrain

4.Smart Irrigation Systems

Introduction: Develop ML-driven smart irrigation systems that optimize water use based on soil moisture, weather forecasts, and crop requirements.

Pros:

Saves water and reduces costs.
Ensures optimal watering for crops, enhancing growth.
Can be integrated with IoT devices for real-time monitoring and control.

Cons:

Requires accurate weather and soil moisture data.
Initial setup costs can be high.
Needs reliable connectivity for real-time data transmission.

Datasets:

Public weather datasets: weather.com
Soil moisture data from agricultural research institutions.

Code Repositories:

Smart Irrigation
Irrigation Optimization

5. Weather Prediction for Farming

Introduction: Use ML to develop localized weather prediction models tailored to agricultural needs, helping farmers make better decisions based on accurate forecasts.

Pros:

Enhances farming decisions related to planting, irrigation, and harvesting.
Reduces the risk of weather-related crop damage.
Provides localized and more accurate forecasts than general weather models.

Cons:

Requires historical weather data and local environmental data.
Model accuracy can be affected by sudden weather changes.
Needs continuous updates and validation.

Datasets:

Public weather datasets: weather.com
Historical weather data from meteorological institutions.

Code Repositories:

Weather Prediction
Agricultural Weather Forecasting

‍
‍E-Commerce and Entertainment Companies

1. Video Summarization and Clip Editing

Introduction: Develop algorithms to automatically summarize long videos into short, engaging clips, highlighting the key moments.

Pros:

Enhances user experience by providing quick previews of content.
Increases engagement with long-form content.
Can be used for creating promotional material.

Cons:

Requires advanced video analysis and understanding.
Summarization quality can be subjective.
Needs substantial computational resources.

Datasets:

YouTube-8M Dataset: YouTube-8M
TVSum Dataset: TVSum

Code Repositories:

CLIP4Clip
Youtube to Tik-Tok Clip extractor

2.AI-Driven Chatbots and Virtual Assistants

Introduction: Develop a generative AI model to create intelligent chatbots or virtual assistants capable of carrying on natural and engaging conversations.

Pros:

Enhances customer service and user experience.
Can handle a wide range of queries and tasks.
Reduces workload on human support teams.

Cons:

Requires extensive training data and fine-tuning.
Needs to handle a variety of conversation contexts and tones.
Risk of generating inappropriate or inaccurate responses.

Datasets:

Public chatbot and conversation datasets.
Custom datasets created from chat logs and support interactions.

Code Repositories:

Rasa
ChatterBot

3.AI-Generated Scripts and Storylines

Introduction: Develop a generative AI model to create scripts and storylines for movies, TV shows, or video games based on given themes and character prompts, along with it, models to compose original music and soundtracks for films, games, or other media

Pros:

Provides creative assistance to writers and producers, music composers.
Can generate multiple versions of a story quickly.
Inspires new and innovative content ideas.
Saves time and costs associated with traditional music production.
Can generate music in various styles and genres.

Cons:

Generated content may lack coherence and depth in terms of emotional understanding.
Requires extensive training data from existing scripts and stories.
Needs human oversight to refine and polish the output.

Datasets:

Movie and TV scripts from public repositories.
MC3 Script Compilation: Custom datasets created from various storytelling platforms.
https://github.com/asigalov61/Los-Angeles-MIDI-Dataset

Code Repositories:

Script Generation: Done using Scripts of the sitcom ‘Seinfeld’ using RNN, for novelty this can be extended to transformers and more generalized script generation.
Story AI : Uses GPT-2 to generate stories across wide range of Genres.
https://github.com/gwinndr/MusicTransformer-Pytorch
https://github.com/openai/jukebox by Open AI.

4.Sentiment Analysis, and Recommender Systems based on Products and Content Review.

Using NLP to analyze reviews and feedback for movies, music, books, or other content to gauge audience sentiment and improve recommendations.

Pros:

Provides insights into audience and consumers’ opinions and preferences.
Helps in improving content recommendations and production.
Identifies potential issues and areas for improvement.

Cons:

Requires large volumes of multimodal data for accuracy, copyright disputes from the content on which the model is trained.
Sentiment analysis models can sometimes misinterpret sarcasm or nuanced language.
Needs continuous updates as language and trends evolve.

Datasets:

IMDb Reviews: IMDb Dataset
‍Rotten Tomatoes Reviews: Rotten Tomatoes Dataset
Recommender System Personalization Datasets

Code Repositories:

Sentiment Analysis of Product Reviews
Transformers4Rec Sys by NVIDIA

5.Automated Generation and Metrics Analyzer for User Interface

Introduction: Develop a generative AI model that can automatically design and generate user interfaces (UIs) for web and mobile applications. Alongside this, create a metrics analyzer to evaluate the usability, accessibility, and aesthetic quality of the generated UIs.

Pros:

Speeds up the UI design process.
Ensures consistent design quality.
Can generate multiple design variations for A/B testing.
Enhances usability and accessibility through automated evaluation.

Cons:

Requires high-quality training data on UI designs.
Generated UIs may need human refinement.
Metrics analyzer needs robust criteria for evaluation.
Evaluation models need continuous updates to stay relevant.
Might miss subjective human-connect design aspects of design quality.

Datasets:

Public UI design datasets (e.g., Dribbble, Behance).
Custom datasets created from design repositories and wireframes, Like UIBert
UI Automation for IOS
UI Grid based on React for Adjusting UI as per User Needs and data analysis.
FigmaChain : HTML/CSS Code Generation from Figma Designs

"Ideas are worthless..Execution is everything": Concluding Notes

"People who did great things often did so at very surprisingly young ages. (They were grayhaired when they became famous... not when they did the work.) So, hurry up! You can do great things."                                                                    
                                                                                                         -Patrick Collison
                                                                                             CEO of Stripe Payments

• Choosing the right machine learning project for your final year can significantly impact your career trajectory. Whether you're drawn to the world of e-commerce with personalized recommendations and dynamic pricing models, or you're excited by the creative potential of AI-generated scripts and sentiment analysis, each project offers valuable skills and practical experience. By tackling these projects, you not only enhance your technical abilities but also position yourself as a forward-thinking and innovative problem solver ready to make a mark in the rapidly evolving field of machine learning. So dive in, experiment, and let your final year project be the springboard to a successful future in AI and data science.

‍

Author

This article was written by Sahil Shenoy, and edited by our writers team.