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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:

1. 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.

2. 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.

3. 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.

4. 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. Also explore Data Quality Management

5. 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.

6. 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.

7. Resume Screening Automation

  • What it covers: NLP, topic modeling, classification.

  • Use case: Automate filtering resumes for relevant roles.

  • Tools: Python, spaCy, BERT.

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