🧠 Machine Learning Project Ideas (Final Year / Hackathon Suitable)

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1. Crime Data Prediction Based on Geographical Location

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📌 Description:

Predicts crime occurrence based on location-specific data using ML techniques.

✅ Highlights:

Real-world application with social impact
Can use clustering, regression or classification
Visualization of crime hotspots on map using tools like Folium / Mapbox

2. Fake News Detection

📌 Description:

Classifies whether a news article or headline is real or fake using Natural Language Processing (NLP).

✅ Highlights:

Easy to build (many public datasets like LIAR dataset on Kaggle)
No complex UI required — just a text input box works
Great to showcase NLP + social relevance

🛠️ Steps:

Preprocess text (tokenize, clean, vectorize)
Train with ML models: Naive Bayes, SVM, or Logistic Regression
Evaluate with accuracy, precision, recall

3. Driver Drowsiness Detection

📌 Description:

Uses a webcam to monitor eye movements and blink patterns to detect signs of fatigue.

🔍 ML Approach:

Uses OpenCV + Dlib for facial landmarks
CNN or thresholding logic for eye aspect ratio
Alert system (audio/visual) if drowsiness detected

✅ Highlights:

Excellent real-world application
Combines ML + Computer Vision

4. Automated Medical Notes Labelling and Classification

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📌 Description:

Automatically classifies and tags unstructured clinical notes (e.g., diagnosis, symptoms, prescription).

🔍 ML Approach:

NLP techniques for cleaning and preprocessing
Classification using Naive Bayes, SVM, or transformers (BERT)

5. Detection of Epilepsy Using Machine Learning

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📌 Description:

Detects epilepsy seizures from EEG data using ML algorithms.

🔍 ML Approach:

Dataset: EEG signals (UCI or CHB-MIT)
ML models: Random Forest, SVM, or Deep Learning (LSTM/CNN)
Signal preprocessing using FFT or filtering

6. Predict Energy Consumption

📌 Description:

Predicts daily power consumption using time-based and weather data.

🔍 ML Approach:

Regression models: Linear Regression, XGBoost, or LSTM
Use datasets like Seoul Bike Sharing Demand
Goal: Help utilities reduce energy wastage and optimize demand

✅ Highlights:

Sustainable tech + real-world impact
Great for time-series forecasting

7. Continuous Intruder Detection by Learning Usage Patterns

📌 Description:

Monitors user behavior and flags anomalous access patterns that might indicate unauthorized activity.

🔍 ML Approach:

Algorithms: Isolation Forest, Autoencoders, One-Class SVM
Data points: Login time, device used, location, frequency

💡 Example:

If a user always logs in from India at 10 AM and suddenly logs in from Russia at 3 AM, the system flags it as suspicious.

8. Stroke Detection

📌 Description:

Predicts if a person is having or is at risk of a stroke using symptoms or brain scan images.