"What projects can I do with machine learning?" This is a question frequently asked by individuals new to the field. At learns.edu.vn, our industry experts recommend immersing yourself in diverse Machine Learning Projects across various business sectors. These projects offer practical application of your acquired skills and a chance to tackle real-world challenges.
We have compiled a curated list of innovative and exciting machine learning projects with readily available source code, ideal for professionals launching their careers in machine learning. These projects are meticulously designed to mirror the complexities encountered by machine learning engineers, deep learning engineers, and data scientists, making them invaluable additions to your professional portfolio.
Table of Contents
Top 50 Machine Learning Projects with Source Code for 2025
For machine learning beginners and final-year students, selecting data science or machine learning projects that spark interest and motivation is paramount. Begin by pinpointing datasets that align with your passions, balancing complexity and scale. To enrich your portfolio, brainstorm potential machine learning project ideas, choose the most compelling ones, and commence work to strengthen your resume.
learns.edu.vn experts advise starting with projects focused on data cleaning, progressing to analytics, machine learning, and then deep learning. To guide you, we’ve assembled a categorized project list by difficulty, perfect for aspiring machine learning engineers seeking AI and ML project opportunities but struggling to find engaging ideas.
Best Machine Learning Projects for Beginners
Once you’ve grasped the essential prerequisites to learn machine learning, practical ML projects are crucial for solidifying your knowledge. This section features engaging machine learning projects tailored for newcomers. These foundational machine learning projects offer a swift learning curve.
1) Home Value Prediction: Real Estate Price Estimator
Imagine needing to buy, sell, or rent a home in a new city and seeking reliable information. The founders of Zillow, a prominent real estate platform, recognized this need and launched the “Zestimate” feature in 2006. Zestimate revolutionized the market by providing estimated home values based on public and sales data. It now covers over 97 million homes with impressive accuracy, often within 10% of the selling price.
Project Idea: In this machine learning project for students, you will utilize Zillow’s Economics dataset to develop a house price prediction model using XGBoost. This model will consider factors like average income, crime rates, and the number of hospitals and schools. Upon completion of this top ML project, you will be able to answer critical real estate questions, such as identifying states with the highest rent values, optimal locations for buying or renting, Zestimate per square foot, and median rental prices.
Industry: Real Estate
Source Code: Zillow House Price Prediction Project Solution
2) Sales Prediction: Forecasting Product Demand
To broaden your machine learning skills, it’s beneficial to explore various topics. This project introduces unsupervised machine learning algorithms using a grocery supermarket sales dataset.
Project Idea: The BigMart Sales dataset offers rich learning opportunities. It includes 2013 sales data for 1,559 products across ten stores in different cities. Your objective in this ML project is to create a regression model capable of predicting sales for each of these products in the following year for each BigMart outlet. The dataset also provides detailed attributes for each product and store, offering valuable insights into sales drivers. This project is an excellent way to understand how machine learning can aid businesses like BigMart in enhancing their sales strategies.
Industry: Retail, Consumer Goods
Source Code: BigMart Sales Prediction Machine Learning Project Solution
3) Music Recommendation System: Personalized Playlist Generator
Recommendation systems are highly popular and versatile machine learning applications across various domains. If you’ve used e-commerce sites or music/movie platforms, you’ve likely encountered a recommendation system. Sites like Amazon suggest products to add to your cart during checkout, while Netflix and Spotify recommend movies or songs based on your viewing/listening history. This is a prime example of applied machine learning.
Project Idea: This machine learning project idea leverages a dataset from a leading Asian music streaming service to build an improved music recommendation system. You will predict listener preferences for new songs or artists based on their past choices. The core task is to forecast the likelihood of a user repeatedly listening to a song within a specific timeframe. The dataset marks predictions as ‘one’ if a user listens to the same song within a month. It includes data on user-song interactions and timestamps. Employ classification ML algorithms for this problem, and challenge yourself by incorporating deep learning algorithms like neural networks.
Industry: Entertainment, Music Streaming
Source Code: Music Recommendation Machine Learning Project
4) Iris Flowers Classification: Botanical Species Identifier
The Iris Flowers Classification project is a quintessential example, often referred to as the “Hello World” of machine learning. It utilizes the Iris dataset, a straightforward machine learning dataset in classification problems. This project is excellent for beginners to learn data loading and handling numeric attributes. The Iris dataset is compact, memory-efficient, and requires minimal preprocessing.
Project Idea: You can download the Iris Dataset from the UCI ML Repository: Iris Flowers Dataset. This data science project for beginners aims to classify iris flowers into three species—Virginia, setosa, or versicolor—based on petal and sepal dimensions. For an advanced challenge, you can incorporate this project into your deep learning projects portfolio by implementing more complex algorithms.
Industry: Botany, Biology, Data Science Education
5) Stock Prices Predictor: Financial Market Forecaster
Here’s a captivating machine learning project idea for financial data scientists: building a stock price predictor. This system will forecast future stock prices by analyzing company performance and granular data like volatility indices and macroeconomic indicators. Stock prediction often involves time series analysis to identify patterns, trends, and anomalies. Model selection depends on factors like data availability and prediction timeframe.
Project Idea: Effective models for time series forecasting include moving average, exponential smoothing, and ARIMA (autoregressive integrated moving average). The moving average model is a straightforward modeling technique predicting the next value as the mean of past values. Exponential smoothing calculates the mean with less weight on distant occurrences. ARIMA models use regression analysis to track the strength of a dependent variable based on changing independent variables. Explore the source code to understand forecasting method selection and time series forecasting applications.
Industry: Finance, Investment, Data Science
Source Code: Stock Prices Predictor using Time Series Project
6) Wine Quality Prediction: Enological Assessment Model
It’s widely believed that wine quality improves with age. However, wine certification involves numerous factors beyond aging, including physiochemical tests assessing alcohol and acidity levels, density, and pH.
Project Idea: This machine learning project aims to develop a model predicting wine quality based on chemical properties. The wine quality dataset includes 4,898 observations with 11 independent variables and one dependent variable. Utilize data visualization to determine input feature variables for the machine learning model. Then, prepare a report and fine-tune the model’s hyperparameters to enhance prediction accuracy.
Industry: Viticulture, Food Science, Quality Control
Source Code: Wine Quality Prediction in Python Project
Machine Learning Projects for Intermediate Professionals
This section delves into intermediate machine learning projects for professionals aiming to deepen their expertise in a machine learning role. Before moving forward, consider this insightful tip from Daniel Lee.
7) Movie Recommender System: Personalized Entertainment Guide
With the rise of streaming platforms like Netflix and Hulu, efficient movie recommender systems have become essential to meet consumers’ demand for personalized content. The Movielens Dataset is a popular resource for learning to build recommender systems, containing approximately 1,000,209 movie ratings from 6,040 users for 3,900 movies.
Project Idea: Start by visualizing movie titles using a word cloud and then develop a movie recommender system using the Movielens dataset. This project will teach you collaborative filtering and content-based recommendation techniques.
Industry: Entertainment, Media Streaming, Personalized Services
8) House Pricing Prediction: Advanced Real Estate Valuation
The Boston Housing Prices Dataset includes housing prices across Boston, along with features like non-retail business area (INDUS), crime rate (CRIM), house owner age (AGE), and 11 other attributes, totaling 14.
Project Idea: Download the Boston Housing dataset from the UCI Machine Learning Repository. This project predicts home selling prices using machine learning on housing data. Though small (506 observations), it’s excellent for regression practice. Advanced learners can experiment with deep learning algorithms on this dataset to build a deep learning project.
Industry: Real Estate, Urban Planning, Data Analysis Education
Source Code: Housing Price Prediction
9) Sentiment Analysis: Social Media Opinion Mining
Social media platforms like Twitter, Facebook, YouTube, and Reddit generate vast big data. Mining this data helps understand trends, public sentiment, and opinions, crucial for branding, marketing, and business. Sentiment analyzers use machine learning to understand and predict sentiments in content like tweets or social media posts. Twitter data is a great starting point for sentiment analysis machine learning.
Project Idea: The Twitter dataset offers a mix of tweet content and metadata like hashtags and user locations for insightful analysis. With 31,962 tweets and 3MB size, you can analyze public opinion on movies, elections, or trending topics like sports events. Working with Twitter data provides experience in social media data mining challenges and deepens your understanding of classifiers. Start by building a model to classify tweets as positive or negative using machine learning or deep learning algorithms.
Industry: Marketing, Public Relations, Social Media Analytics, Political Science
Source Code: E-commerce product reviews – Pairwise ranking and sentiment analysis
10) Interest Rate Prediction: Rental Housing Market Analyzer
Finding a comfortable home is paramount, especially with remote work trends. Sifting through numerous rental listings can be exhausting.
Project Idea: By sentiment analysis of viewer reactions to rental listings, you can gauge interest in specific properties and predict interest levels for new listings. This benefits owners by forecasting inquiries. The challenge is grouping and interpreting past data. This analysis aids fraud control, identifies potential issues, and helps owners and agents understand renter preferences.
Industry: Real Estate, Rental Market Analysis, Customer Behavior Analysis
Source Code:Predicting Interest Levels of Rental Listings
Machine Learning Mini Projects for Final Year Students
This section features simple machine learning projects that final-year students can use for course projects.
11) Coupon Purchase Prediction: Targeted Marketing Optimizer
Coupon marketing is a common strategy to attract customers across e-commerce, travel, healthcare, and education sectors. However, its effectiveness relies on reaching the right audience.
Project Idea: By analyzing customer responses to different coupons, you can predict future behavior and coupon interest. Data Visualization tools, machine learning, and deep learning can analyze coupon usage and predict purchase behavior. This improves recommendation systems for more targeted coupon generation.
Industry: E-commerce, Marketing, Retail Analytics
Source Code: Coupon Purchase Prediction
12) Loan Eligibility Prediction: Banking Risk Assessment
Loans are central to banking, with profits primarily from loan interest. Banks have rigorous loan approval processes. Machine learning can predict loan eligibility, improving planning beyond approval or rejection.
Project Idea: Train a loan eligibility prediction model using datasets with features like sex, marital status, income, credit history, and loan amount. Use the SYL bank dataset from Australia. Employ cross-validation for training and testing, data visualization for cleaning, and handle missing values. This project is excellent for learning statistical models like Gradient Boosting and XGBoost, and metrics like ROC Curve.
Industry: Financial Services, Banking, Risk Management
Source Code: Loan Prediction Analysis
13) Inventory Demand Forecasting: Supply Chain Optimization
Accurate inventory preparation is crucial for restaurants and product companies. Demand forecasting is essential for sales, finance, production, logistics, and marketing decisions. Accurate forecasts help businesses grow by delivering the right products at the right time and avoiding resource wastage.
Project Idea: Apply algorithms like Bagging, Boosting, XGBoost, GBM, and SVMs to predict customer demand accurately. This significantly improves inventory management and overall operations.
Industry: Retail, Supply Chain, Operations Management, Manufacturing
Source Code: GitHub – bkkinfo/Inventory-Product-Demand-Forecasting
14) Passenger Survival Prediction: Titanic Disaster Analysis
Remember the Titanic movie scene listing survivors? In the 1912 tragedy, only about 1500 survived.
Project Idea: Kaggle has a Titanic challenge to predict passenger survival based on name, age, gender, and socioeconomic status. Use any machine learning model to correlate passenger characteristics with survival chances.
Industry: History, Data Analysis, Social Sciences, Demographics
Simple Machine Learning Projects for Beginners with Source Code in Python
Practical machine learning projects are the best way to learn. Here are beginner-friendly machine learning project ideas in Python.
15) Retail Price Optimization: Dynamic Pricing Strategy
Pricing is highly competitive across industries. Optimizing prices is key to profit management. Retailers must find the right price range to boost sales while maintaining profit margins. Machine learning helps build effective pricing solutions. Amazon pioneered retail price optimization using machine learning, contributing to its growth from $30 billion in 2008 to $1 trillion in 2019.
Project Idea: This retail price optimization project involves training a machine learning model to price products like humans. Price optimization machine learning models use historical sales data, product characteristics, and unstructured data like images to learn pricing rules without human input. They adapt to dynamic pricing environments, maximizing revenue and profit margins by processing numerous pricing scenarios in real-time.
Industry: Retail, E-commerce, Hospitality, Dynamic Pricing
Source Code: Retail Price Optimization
16) Customer Churn Prediction Analysis: Customer Retention Strategy
Customers are vital assets. Retaining them boosts revenue and builds lasting relationships. Acquiring new customers costs five times more than retaining existing ones. Identifying churn risk and acting quickly is crucial. Machine learning helps identify churn factors and provides tools to address them.
Project Idea: Collect and prepare data for processing. Feature engineering is key in churn prediction machine learning. Data scientists use experience and domain knowledge to tailor models to understand churn reasons. For example, in banking, track monthly balance deviations and indicators like dormant accounts to predict churn. Combine internal data with external competitor offers. Identify and remove weak predictors to reduce dimensionality.
Industry: Telecommunications, Banking, Subscription Services, Customer Relationship Management
Source Code: Customer Churn Prediction Analysis using Ensemble Learning
17) Avocado Price Prediction: Agricultural Market Analysis
Avocados are increasingly popular, with US consumption rising from 436 million pounds in 1985 to 2.6 billion pounds in 2020. Avocado prices fluctuate with season and supply. A machine learning model can monitor and predict these prices.
Project Idea: Avocado price prediction based on sales benefits vendors, producers, and companies. Price predictions inform market decisions, guiding product distribution and demand management based on geographical location, weather, and seasonality.
Industry: Food and Beverages, Agriculture, Market Forecasting
Source Code: Avocado Machine Learning Project Python for Price Prediction
18) Credit Card Default Prediction: Financial Risk Management
This project aims to predict customers likely to default on loans. Credit card defaults cause bank losses.
Project Idea: Analyze customer databases to identify potential defaulters in the next two years. Machine learning models can predict loan defaults, enabling banks to manage risk by canceling credit lines or reducing credit limits. These models also aid in screening credit card applicants.
Industry: Financial Services, Banking, Credit Risk Assessment
Source Code: Access Give Me Some Credit Kaggle ML Project Solution Solution
19) Human Activity Recognition: Smart Fitness Tracker
The smartphone dataset includes fitness activity recordings from 30 people using smartphone inertial sensors.
Project Idea: Build a classification model to accurately identify human fitness activities. This project will help you solve multi-classification problems.
Industry: Healthcare, Fitness Technology, Wearable Technology
Source Code: Human Activity Recognition using Smartphone Dataset Project
20) Plant Species Identification: Digital Botany Assistant
This project is excellent for Botany students exploring Data Science. It uses machine learning to identify 99 plant species from leaf images and features like shape, margin, and texture.
Project Idea: Even non-Botany students will find it fascinating to identify plant species by leaf characteristics. Explore the source code to implement this project from scratch. Learn about image-based features and classification machine learning algorithms in image classification. Benchmark classifier significance in image classification.
Industry: Botany, Environmental Science, Agriculture, Image Recognition
Source Code: (ML) Project- Build a plant species identification algorithm
Advanced Machine Learning Projects with Source Code in Python
These advanced machine learning projects will test your production engineering skills and theoretical machine learning knowledge.
21) Sales Forecasting: Retail Demand Planner
Sales forecasting is a common machine learning application for identifying sales drivers and predicting future sales volume. This project uses the Walmart dataset, featuring sales data for 98 products across 45 stores weekly. The project forecasts sales per store and department to improve data-driven decisions for channel optimization and inventory planning. The Walmart dataset includes markdown events impacting sales.
Project Idea: Build a predictive model using the Walmart dataset to estimate future sales.
- Import and explore data using EDA.
- Prepare data for modeling by merging datasets and grouping data.
- Plot and analyze time-series graphs.
- Fit sales forecasting models, like ARIMA, to training data.
- Compare models with test data.
- Optimize models by selecting key features for accuracy.
- Use the best supervised learning model to predict next year’s sales.
Working on this Kaggle project will demonstrate the power of machine learning models in simplifying sales forecasting. Re-use these sales forecasting machine learning models in production for any department or retail store.
Industry: Retail, E-commerce, Supply Chain Management, Business Analytics
Source Code: Walmart Store Sales Forecasting Machine Learning Project
22) Census Income Analysis: Socioeconomic Data Analyzer
Income inequality is a major concern. Census data can predict health and income based on historical records. This project uses adult census income data to predict if income exceeds $50K/year based on factors like education and work hours.
Project Idea: The Adult Census Income dataset is rich and diverse, with over 32K rows and 15 columns describing people’s attributes. It includes missing values and categorical data, perfect for building a classifier.
Source Code: Access Solution to the Adult Census Income Dataset Project
23) Speech Emotion Recognition: Voice-Based Emotion Detector
Virtual communication has become essential, making emotion detection in communication critical.
Project Idea: Speech Emotion Recognition (SER) systems analyze audio signals to identify emotions. They use acoustic, lexical, and vocal parts of speech. This project focuses on the acoustic part, including pitch and tone, to detect emotions.
Industry: Communication, Entertainment, Customer Service, Accessibility
Source Code: Speech Emotion Recognition Project using Machine Learning
24) Time Series Forecasting: Predictive Analytics with Prophet
Time series analysis examines data points over time to make future predictions. It identifies patterns, trends, and anomalies.
Project Idea: This advanced project uses Time Series Analysis with Prophet, Facebook’s open-source forecasting tool. It’s ideal for time series modeling.
Industry: Finance, Economics, Operations Research, Data Science
Source Code: Time Series Analysis with Facebook Prophet Python and Cesium
25) Store Sales Prediction: Retail Performance Forecaster
Effective inventory management balances demand and supply. Predicting store sales is key to understanding product demand for optimal stock levels, especially for perishable goods.
Project Idea: Store sales are influenced by promotions, competition, holidays, seasonality, and location. Machine learning can identify patterns and their impact on sales.
Industry: Retail, Supply Chain, Inventory Management, Business Strategy
Source Code: Sales Forecasting ML Project
26) Production Line Performance Checker: Manufacturing Quality Control
Bosch, a global engineering company, monitors production line performance to ensure quality. They collect data at each assembly line step to improve manufacturing using advanced analytics.
Project Idea: Predict manufacturing failures along Bosch’s assembly line. This project is challenging due to complex production lines and data formats, making it an interesting machine learning challenge.
Industry: Manufacturing, Industrial Engineering, Quality Assurance
Source Code: aakashveera/bosch-production-line-performance
27) Ola Bike Ride Request Demand Forecasting: Ride-Sharing Demand Analyzer
Project Idea: Predict Ola bike ride request demand using machine learning, considering factors like location and time. Choose the best forecasting method based on data availability and business needs, also considering external factors like weather.
Industry: Transportation, Ride-Sharing Services, Urban Mobility
Source Code:Ola Bike Ride Request Demand Forecast
28) Taxi Demand Prediction: Urban Transportation Planner
Ride-sharing and delivery services rely on driver availability. Predicting driver availability in specific areas helps allocate drivers efficiently and inform users about wait times.
Project Idea: Convert a time series problem to a supervised learning problem to predict driver demand. Use time series analysis, ACF, and PACF. Build and test a regression model, then use Random Forest and Xgboost ensemble models for driver demand prediction.
Industry: Transportation, Urban Planning, Logistics, Demand Forecasting
Source Code: Driver Demand Prediction ML Project
29) Market Basket Analysis: Retail Association Mining
Market basket analysis identifies product combinations frequently purchased together, enhancing sales through better store layouts and targeted promotions.
Project Idea: If customers buy item ‘A’, they are likely to buy ‘B’. Use market basket analysis for targeted promotions, personalized recommendations, and cross-selling using techniques like Fpogrowth and Apriori algorithm.
Industry: Retail, E-commerce, Marketing, Customer Analytics
Source Code: Market Basket Analysis
NLP Machine Learning Project Ideas
Explore these natural language processing projects for intermediate professionals.
30) Fake News Classification: Online Misinformation Detector
The internet facilitates global communication and rapid news dissemination but also enables misinformation. Unlike traditional media, online news often lacks rigorous editorial checks, leading to the spread of fake news through text and graphics.
Project Idea: Develop an NLP-based fake news detection system to analyze news in real-time, preventing misinformation spread. This is crucial due to the internet’s data volume and speed.
Industry: Media, Journalism, Information Technology, Social Media Monitoring
Source Code: Fake News Classification Machine Learning Project
31) Resume Parser: Automated Applicant Screening
Recruiters face challenges reviewing numerous resumes for job openings. Automated resume parsing can streamline this process, ensuring ideal candidates are not overlooked.
Project Idea: Build a resume parser using machine learning and NLP to extract key fields and categorize applicants by work history, education, and skills. This reduces manual labor and increases hiring efficiency, despite varying resume layouts.
Source Code: Access Solution to ML Project on Resume Parsing with NLP Spacy Python
32) ChatBot: Conversational AI Assistant
Chatbots, like Google Assistant and Siri, are AI applications that simulate human conversation. They are increasingly used for customer service on websites.
Project Idea: Build your own Chatbot using NLP techniques and machine learning algorithms with Python libraries like NLTK and neural networks. This beginner-friendly project introduces NLP techniques like lemmatization, POS tagging, tokenization, and bag-of-words models.
Industry: Customer Service, E-commerce, Education, Interactive AI
Source Codet NLP chatbot example application using python
33) Text Classification: Advanced NLP with Transformers
BERT is a widely used ML algorithm for NLP. This project explores advanced models like RoBERTa and XLNet, which improve upon BERT.
Project Idea: Deeply understand RoBERTa and XLNet by solving a text classification problem. Learn Transformer architecture, BERT, self-attention, and text preprocessing. Fine-tune RoBERTa and XLNet, comparing them to BERT and evaluating performance.
Libraries – datasets, NumPy, pandas, matplotlib, seaborn, ktrain, transformers, TensorFlow, sklearn
Industry: NLP Research, Text Analytics, Information Retrieval
Source Code: Text Classification with Transformers-RoBERTa and XLNet Model
34) Topic Modelling: Text Data Insight Extractor
Topic modeling is an unsupervised NLP technique for text analysis, revealing customer preferences, product review themes, and online conversation topics.
Project Idea: Apply Latent Dirichlet Allocation (LDA) Topic Modelling with Python to the RACE dataset, a large dataset of reading comprehensions, to group similar feedback and deduce customer discussion topics.
Industry: Market Research, Customer Feedback Analysis, Content Analysis
Source Code: Topic Modelling Python using RACE Dataset
Computer Vision ML Projects
Explore these computer vision projects to enhance your skills.
35) Handwritten Digit Classification: Image-Based Digit Recognizer
Deep learning and neural networks are vital in image recognition, text generation, and self-driving cars.
Project Idea: Start with the MNIST dataset for handwritten digit classification. This beginner-friendly challenge helps you work with image data and neural networks.
Industry: Image Processing, Pattern Recognition, Optical Character Recognition (OCR)
Source Code: MNIST Handwritten Digit Classification Project
36) Similar Images Finder: Visual Search Engine
E-commerce convenience increases the need for visual search. Finding similar images based on a clicked picture would enhance online shopping.
Project Idea: Develop a system to find similar images to a user-uploaded picture, focusing on accurate product recognition for e-commerce applications. Train a model to accurately detect and match similar images.
Industry: E-commerce, Image Search, Visual Content Management
Source Code: Similar Image Builder Machine Lewarning Project
37) Ultrasound Nerve Segmentation: Medical Image Analysis
Surgical procedures carry risks and post-operative pain. Ultrasound nerve segmentation can identify pain sources for targeted treatment, reducing reliance on systemic pain medication.
Project Idea: Accurately identify nerve structures in ultrasound images to guide catheter insertion for pain management. This project requires high accuracy due to patient safety implications. Compare normal and abnormal nerve images, breaking down images into matrices for analysis.
Industry: Medicine, Medical Imaging, Surgical Assistance
Source Code:Machine Learning Project with Source Code to Ultrasound Nerve Segmentation
Unique Machine Learning Projects
These unique ML projects offer a different perspective from the previous sections.
38) Music Composition: AI Music Generator
Computer-generated music dates back to 1957. Today, AI models like OpenAI’s JukeBox demonstrate advanced music composition.
Project Idea: Use generative adversarial networks (GANs) for music composition. Train a GAN model on classical music to generate lifelike compositions using LSTM and GAN neural networks. Evaluate the generated music’s quality.
Industry: Entertainment, Music Industry, Creative AI
Source Code: https://github.com/seyedsaleh/music-generator
39) Predictive Maintenance for Renewable Energy Sources: Smart Energy Management
The renewable energy IoT market is growing, integrating AI for predictive maintenance to prevent costly downtime and risks in renewable energy sources.
Project Idea: “ReneWind” needs classification models to predict generator failures in wind turbines using sensor data. With 40 predictors and 50,000 observations, tune and evaluate models to minimize maintenance costs by accurately predicting failures.
Industry: Renewable Energy, IoT, Predictive Maintenance, Energy Management
Source Code: rochitasundar/Predictive-maintenance-cost-minimization-using-ML-ReneWind
40) Seismic Activity Prediction: Earthquake Early Warning System
Earthquakes pose significant threats. Predictive models are crucial for disaster preparedness and resource allocation.
Project Idea: Forecast earthquake magnitude and probability in California using historical seismic data. Train machine learning models on the “SOCR Earthquake Dataset” (2017-2019) using time series analysis, clustering, and regression for enhanced seismic risk assessment.
Industry: Seismology, Disaster Management, Geophysics
Source Code: akash-r34/Earthquake-prediction-using-Machine-learning-models
Fun Projects on Machine Learning
These fun ML projects can further your machine learning expertise.
41) Language Detection: Multilingual Text Identifier
Language detection is essential for multilingual support, content filtering, and information retrieval, evolving from rule-based systems to machine learning.
Project Idea: Develop a language detection model using NLP and machine learning on the European Parliament Proceedings Parallel Corpus with Python 3.6 and scikit-learn. Preprocess data, extract features, train, and evaluate the model. Use techniques like tokenization and Logistic Regression. Create and deploy a language detection pipeline.
Industry: Localization, Global Content Management, Multilingual Applications
Source Code: GitHub – akhiilkasare/Language-Detection-Using-NLP-and-Machine-Learning
42) Real Estate Price Prediction: Property Valuation Tool
Online property sales are common. Real estate companies need accurate property pricing based on attributes. Machine learning can solve this.
Project Idea: Predict property prices in Pune, India, using a dataset of 200 properties. Build predictive models using data preprocessing, NLP, and machine learning libraries like pandas, numpy, and sklearn. Deploy models via APIs and a web application using FastAPI and Heroku for real-time predictions.
Industry: Real Estate, Property Technology, Fintech
Source Code: Build Real Estate Price Prediction Model with NLP and FastAPI
43) Personalized Mental Health Assistant: AI Therapy Chatbot
afiki, an AI chatbot, provides 24/7 personalized mental health support using NLP for empathetic responses.
Project Idea: Develop your own mental health assistant using AI models like Llama 2. Fine-tune Llama 2 with mental health counseling datasets to create a virtual assistant offering personalized support and guidance.
Industry: Healthcare, Mental Health, AI Therapy
Source Code: https://github.com/Cody-Lange/MentalHealthAssistant
Cool Machine Learning Projects in Python
Bonus Python machine learning projects for continuous learning.
44) Software Bug Classification: Defect Prediction System
SaaS companies need software bug classification for application quality. Defect prediction reduces development costs and improves software quality.
Project Idea: Predict software bugs using a dataset from the University of Geneva, analyzing software properties to forecast bug counts in advance, facilitating proactive defect management. Classify software data based on bug severity.
Industry: Software Engineering, Quality Assurance, DevOps
Source Code: https://github.com/YousefGh/software_bug_prediction
45) Air Quality Index Analysis: Pollution Level Forecaster
Delhi’s air pollution necessitates accurate AQI predictions, especially in winter, to issue timely alerts and enable preventive actions.
Project Idea: Forecast AQI levels using datasets from Indian cities. Process data, train algorithms, and evaluate models, addressing data imbalances to provide precise AQI forecasts for effective air pollution management.
Industry: Environmental Science, Public Health, Urban Planning
Source Code: Prediction of Air Quality Index Using Machine Learning Techniques
46) Drug Discovery: AI-Powered Pharmaceutical Research
The COVID-19 pandemic highlights the need for faster drug discovery methods. Machine learning accelerates drug discovery by analyzing vast medical data.
Project Idea: Use machine learning for drug discovery, targeting the SARS coronavirus 3C-like proteinase with ChEMBL database data. Preprocess data, explore chemical space, and build regression models to predict drug potency and identify promising drug candidates.
Industry: Pharmaceuticals, Biotechnology, Medical Research
Source Code: https://github.com/shashwat0105/Bioinformatics-Drug-Discovery
MLOps Projects
MLOps streamlines machine learning lifecycle automation. These projects enhance ML application deployment and efficiency.
47) Text Detection Model Deployment on GCP: Cloud-Based ML Deployment
Deploy a text detection model on Google Cloud Platform using Kubernetes and Kubeflow for streamlined ML workflows and automated containerized application management.
Source Code: MLOps Project on GCP using Kubeflow for Model Deployment
48) Classification Model Deployment on AWS: Banking Application Deployment
Deploy a banking classification model on AWS using Amazon EKS, EC2, and Elastic Load Balancing for marketing optimization through cloud-based ML model deployment.
Source Code: AWS MLOps Project to Deploy a Classification Model [Banking]
49) Text Analytics for Medical Search Engine: Azure-Based Search Enhancement
Enhance medical search engines using word embeddings and Azure services to create an intelligent search engine understanding medical term relationships for improved search accuracy.
Source Code: Azure Text Analytics for Medical Search Engine Deployment
50) Q&A Bot using Microsoft Azure: Azure FAQ Chatbot Development
Develop an FAQ chatbot using Microsoft Azure services, integrating QnA Maker with Azure Bot Service to create a conversational bot capable of answering user queries in natural language.
Source Code: Fundamentals of question answering with the Language Service – Training | Microsoft Learn
How to Start a Machine Learning Project
Solid planning is crucial for successful machine learning projects. Follow these steps to start your ML project effectively:
1) First Step: Machine Learning Project Scoping
Understand the business requirements and choose a relevant machine learning use case with a clear ROI.
2) Second Step: Data
Data is essential. Follow a four-step data process:
- Data Requirements: Define data needs, format, sources, and compliance.
- Data Collection: Set up data extraction from organizational or third-party sources.
- Exploratory Data Analysis: Validate data quality and accuracy.
- Data Preparation: Prepare data for ML algorithms, including cleaning, feature engineering, and encoding.
3) Third Step – Building the Model
Choose an appropriate machine learning algorithm and train the model. Define accuracy and error metrics for model selection. Evaluate the model on validation data to prevent overfitting.
4) Fourth Step -Model Deployment into Production
Deploy the model to end-users for new data input and continuous learning. Retrain and tune the model on live data to maintain performance and accuracy.
How to Showcase Machine Learning Projects on Your Resume
Real-world experience is vital. Showcase your machine learning projects on your resume to attract employers:
- List projects after work experience.
- Number projects sequentially with titles.
- Briefly describe the dataset and problem.
- Mention ML tools and technologies used.
- Link projects to GitHub, your website, or blog for detailed review.
This list provides excellent machine learning projects for your resume.
What Next? Build Your ML Portfolio with learns.edu.vn
Organizations need tailored machine learning solutions. As a data scientist or machine learning engineer, you must adapt and deliver efficient solutions, requiring hands-on experience with diverse data science tools and technologies.
learns.edu.vn offers interesting and cool machine learning projects using novel tools. Gain access to continuously updated solved projects across industries like Retail, Finance, and Manufacturing.
These projects help beginners quickly enhance applied ML skills and explore business use cases. Stay motivated, make progress, and build your portfolio with learns.edu.vn to land top ML roles.
FAQs for Machine Learning Projects
1) How do I find Machine Learning projects?
Find machine learning projects on platforms like learns.edu.vn and Kaggle. learns.edu.vn provides 50+ solved end-to-end projects to enhance your portfolio and skills.
2) What are the three key steps in a machine learning project?
Key steps in any ML project are:
Step 1: Defining the Machine Learning Process
Step 2: Building an end-to-end Machine Learning Pipeline
Step 3: Model Deployment
3) How do I start a machine learning project?
Start a machine learning project by following these steps:
- Define the Business Problem
- Data Acquisition
- Data Preparation
- Spot Check ML Algorithms
- Model Selection and Modeling
- Model Validation and Tuning
- Model Deployment
- Present the Solution to Stakeholders
4) What is the most important part of a machine learning project?
Training the machine learning model is crucial, emphasizing training data quality, feature selection, and hyperparameter tuning to maximize performance and avoid overfitting.
5) What are some good machine learning projects?
Good machine learning projects include:
- Sentiment Analysis
- Loan Default Prediction
- House Price Prediction
- Stock Price Estimation
- Store Sales Forecasting
6) Are machine learning projects difficult?
Machine learning projects can seem difficult without proper foundational skills. Learning math basics, Python/R, and key algorithms makes project implementation easier.
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