Discussions
What are some good data science projects?
Here are some good data science projects—suitable for learners and professionals alike—that cover key concepts like data cleaning, visualization, machine learning, and deployment:
- Customer Churn Prediction
What it covers: Classification, feature engineering, model evaluation.
Use case: Predict which customers are likely to leave a service using historical data.
Tools: Python, scikit-learn, pandas, seaborn.
- Sales Forecasting
What it covers: Time series analysis, regression, visualization.
Use case: Forecast future sales based on past trends.
Tools: Python, Prophet, ARIMA, Excel, Power BI.
- Sentiment Analysis of Tweets or Reviews
What it covers: Natural Language Processing (NLP), text preprocessing, classification.
Use case: Analyze public sentiment about products, politics, or brands.
Tools: NLTK, TextBlob, spaCy, Python.
- Movie Recommendation System
What it covers: Collaborative filtering, content-based filtering, matrix factorization.
Use case: Suggest movies to users based on past ratings or content.
Tools: Python, scikit-learn, Surprise library.
- Credit Card Fraud Detection
What it covers: Anomaly detection, imbalanced datasets, precision-recall tradeoffs.
Use case: Identify fraudulent transactions from real ones.
Tools: Python, scikit-learn, XGBoost.
- Healthcare Analysis (e.g., Diabetes Prediction)
What it covers: Classification, medical datasets, ROC/AUC.
Use case: Predict whether a patient is at risk based on medical data.
Tools: Python, pandas, scikit-learn.
- Resume Screening Automation
What it covers: NLP, topic modeling, classification.
Use case: Automate filtering resumes for relevant roles.
Tools: Python, spaCy, BERT.
- Exploratory Data Analysis (EDA) on a Public Dataset
What it covers: Data cleaning, visualization, statistical summaries.
Use case: Draw insights from datasets like Titanic, Iris, or global development stats.
Tools: Python, matplotlib, seaborn, pandas.