Data Science Research Topics
1. Predictive Analytics for Patient Readmission in Hospitals Using Machine Learning
Aim:
To develop a predictive model for identifying patients at high risk of hospital readmission.
Objectives:
- To collect and preprocess patient data including demographics, diagnosis, and readmission history.
- To build and compare classification models such as Random Forest, Logistic Regression, and XGBoost.
- To evaluate model performance using standard metrics like accuracy, AUC, and recall.
- To apply explainability techniques (e.g., SHAP) for interpreting model predictions
2. Sentiment Analysis of Consumer Reviews Using NLP and Deep Learning
Aim:
To analyze consumer sentiments using deep learning techniques on text reviews.
Objectives:
- To gather product or service review data from e-commerce or social platforms.
- To preprocess and vectorize textual data using NLP techniques.
- To train deep learning models such as LSTM or BERT for sentiment classification.
- To evaluate and visualize sentiment trends for business insights.
3. Fraud Detection in Financial Transactions Using Anomaly Detection
Aim:
To identify fraudulent financial transactions using machine learning models.
Objectives:
- To obtain and prepare a dataset of financial transactions.
- To implement unsupervised techniques like Isolation Forest and Autoencoders.
- To compare the accuracy and false-positive rates of different models.
- To develop a framework for real-time fraud detection.
4. Air Quality Prediction Using Spatiotemporal Data and Machine Learning
Aim:
To forecast air pollution levels using time-series and geospatial data.
Objectives:
- To collect historical air quality and weather data from open sources.
- To train forecasting models such as LSTM and ARIMA for PM2.5 prediction.
- To integrate spatial features using geolocation data.
- To visualize predicted air quality trends on interactive maps.
5. Real-Time Traffic Flow Forecasting Using Edge-Compatible Data Science Models
Aim:
To forecast urban traffic congestion using real-time sensor data and edge computing.
Objectives:
- To source real-time traffic datasets from smart city APIs or simulators.
- To design and implement time-series models compatible with edge devices.
- To evaluate model latency, accuracy, and resource usage.
- To deploy and test the system in a simulated edge environment.
6. Energy Consumption Forecasting in Smart Homes Using IoT Data
Aim:
To predict energy usage patterns in smart homes for better energy management.
Objectives:
- To collect and preprocess energy consumption data from IoT devices.
- To develop models using regression and time-series forecasting techniques.
- To assess performance of models like Gradient Boosting and Prophet.
- To recommend actionable strategies for reducing consumption during peak hours.
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