50 Machine Learning Projects With Resources

Last Updated on: 17th October 2023, 06:09 am

Machine Learning (ML), an integral part of Artificial Intelligence, has revolutionized various industry segments by making intelligent machines that learn from experience. To master these advanced technologies, it’s imperative to work on diverse projects that address different problems and methodologies.

1. Predicting Boston Housing Prices: This project uses a public dataset to predict Boston Housing prices using regression analysis. (Resource: Kaggle – Boston Housing Dataset)
2. Iris Flowers Classification: This project aims to classify iris flowers among three species based on the features provided in the dataset. (Resource: Kaggle – Iris Dataset)
3. Titanic Survival Prediction: This project aims to predict whether a passenger on the Titanic would have survived or not. (Resource: Kaggle – Titanic Dataset)
4. Digit Recognition: This project aims to identify handwritten digits using the MNIST dataset and classification techniques. (Resource: Kaggle – Digit Recognizer)
5. Stock Prices Prediction: Utilize the historical stock prices data to predict the future stock prices using time-series analysis. (Resource: Yahoo Finance)
6. Breast Cancer Detection: Predict whether a tumor is malignant or benign through the training model on a Breast Cancer Wisconin dataset. (Resource: UCI Machine Learning Repository)
7. Customer Segmentation: Analyze customer data to cluster them into distinct groups showing specific behaviors in purchasing habits via K-Means algorithm. (Resource: Mall Customers Dataset – Kaggle)
8. Sentiment Analysis: This project aims to classify the sentiment of a given text, be it an online review, tweet, or a movie review. (Resource: Sentiment140 – Kaggle)
9. Credit Card Fraud Detection: The project aims to detect fraudulent transactions by applying classification techniques to highly imbalanced credit card datasets. (Resource: Kaggle – Credit Card Fraud Detection)
10. Movie Recommendation System: Construct a movie recommendation system using the Item-Based Collaborative Filtering method. (Resource: MovieLens Dataset)
11. Web Traffic Time Series Forecasting: Predict future web traffic for a set of Wikipedia pages. (Resource: Kaggle – Web Traffic Forecasting)
12. Image Classification: Implement an image classifier that categorizes everyday objects. Use the CIFAR-10 dataset. (Resource: Kaggle – CIFAR-10)
13. Hand Gesture Recognition: Apply deep learning to recognize hand gestures. (Resource: Hand Gesture Recognition Database)
14. Wine Quality Prediction: Predict wine quality based on physicochemical data using ML algorithms. (Resource: UCI Machine Learning Repository)
15. Diabetes Prediction: Predict the onset of diabetes within five years in Pima Indian heritage females using their medical details. (Resource: UCI Machine Learning Repository)
16. Human Activity Recognition: Use smartphone sensor data to recognize human activities like Walking, Running, etc. (Resource: Kaggle – Human Activity Recognition)
17. Spam Email Detection: Classify emails as Spam or not spam based on text data and some relevant features. (Resource: UCI Machine Learning Repository)
18. Anomaly Detection in Network Traffic with K-means Clustering: Detect unusual network traffic with Unsupervised Machine Learning techniques. (Resource: CIDDS – OpenStack – CSV – A series of CSV files)
19. Predict Default Credit Card Clients: Predict whether a client will default next month or not using the default of credit card clients dataset. (Resource: UCI Machine Learning Repository)
20. Loan Prediction: Predict if a loan will get approved or not based on the applicant’s details. (Resource: Analytics Vidhya)
21. Newsgroups Text Classification: Classify text from 20 different newsgroups. (Resource: 20 Newsgroups Data Set)
22. Music Genre Classification: Classify music into different genres by analyzing audio data. (Resource: GTZAN Genre Collection)
23. Chatbot Development: Develop a chatbot that can understand and respond to specific user requests. (Resource: ChatterBot Language Training Corpus)
24. Object Detection: Implement an object detection system that identifies and locates objects in images or videos. (Resource: COCO Dataset)
25. Sales Forecasting: Predict future sales for a firm using historical sales data. (Resource: Kaggle – Rossmann Store Sales)
26. Flight Delay Prediction: Forecast whether a flight will be delayed or not based on historical records. (Resource: Bureau of Transportation Statistics)
27. Sports Victory Prediction: Predict whether a team will win or not based on historical game data. (Resource: Basketball Dataset – Kaggle)
28. Time-Series Prediction: Predict future data points based on past and present data, often used in weather forecasting. (Resource: NOAA- National Centers for Environmental Information)
29. Text Summarization: Automate the process of creating synopses for major news articles, stories, or other documents using NLTK. (Resource: BBC News Archives)
30. Earthquake Prediction: Use seismic signals to predict how much time remains until the next earthquake. (Resource: Kaggle – LANL Earthquake Prediction)
31. Predict Employee Attrition: Understand why and when employees leave by identifying patterns and predictive factors. (Resource: Kaggle – IBM HR Analytics Employee Attrition)
32. Human Resource Analytics: Analyze employee performance and identify the key factors influencing it. (Resource: Kaggle – Human Resources Analytics)
33. Sports Video Analysis: Use deep learning for sports video classification. (Resource: Sports-1M Dataset)
34. Image Colorization: Convert grayscale images into a colored version using deep learning. (Resource: Kaggle – Black and White Image Colorization)
35. Fake News Detection: Train a model to distinguish real news from fake news. (Resource: Kaggle – Fake News)
36. Video Game Sales Prediction: Predict the sales of video games based on factors like platform, genre, etc. (Resource: Kaggle – Video Game Sales with Ratings)
37. Stock Market Clustering: Cluster similar stocks together based on historical stock market data. (Resource: Yahoo Finance)
38. Sales Conversion Optimization: Optimize conversion rate using historical sales data along with website user experience data. (Resource: Kaggle – Conversion Rate)
39. Predict Ad Clicks: Predict whether a user will click an ad or not based on certain features like type, size, etc. (Resource: Kaggle – Display Advertising Challenge)
40. Predict Forest Fires: Predict the area of forest fire based on a spatial, temporal, and weather variables dataset. (Resource: UCI Machine Learning Repository)
41. Reddit Flair Detection: Identify the flair of the post in the r/India subreddit using NLP and ML algorithms. (Resource: Kaggle – Reddit India Flare Detected)
42. Crop Yield Prediction: Predict the crop yield based on weather data and remote sensing data. (Resource: USDA Economic Research Service)
43. Recognizing Traffic Signs: Develop a system that can recognize traffic signs. (Resource: GTSRB – German Traffic Sign Recognition Benchmark)
44. Image Segmentation: Segment an image into its constituent parts. (Resource: PASCAL VOC Challenge)
45. Language Translation: Develop a machine that translates one language to another using sequence-to-sequence LSTM models. (Resource: ManyThings.org language dataset)
46. Emotion Detection: Develop an emotion detection model from face images. (Resource: Kaggle – FER2013)
47. Skin Cancer Detection: Classify skin lesions into seven categories to predict skin cancer. (Resource: ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection)
48. Rainfall Prediction: Predict whether it will rain tomorrow or not by analyzing past data. (Resource: Kaggle – Rain in Australia)
49. Automatic License Number Plate Recognition: Use object detection techniques to identify and recognize vehicles’ number plates. (Resource: Kaggle – Automatic License Number Plate Recognition)
50. Churn Prediction: Predict customer churn using historical customer data and utilizing predictive modeling. (Resource: Kaggle – Telco Customer Churn)

These 50 machine learning projects offer varied challenges that will allow one to cement their ML knowledge and gain practical experience.

As you move along these projects, you will strengthen your problem-solving ability and be able to understand the applicability of Machine Learning in today’s world.