🚀 Top Data Science Portfolio Projects That Stand Out
📌 1. Customer Churn Prediction
Why it stands out: It’s a classic business problem relevant to many industries. Shows you understand classification and business impact.
Tech Stack: Python, pandas, scikit-learn, XGBoost
Extras: Add SHAP for interpretability and segment churn risk
Bonus: Build a Streamlit dashboard or use Flask for deployment
📌 2. End-to-End Sales Forecasting
Why it stands out: Forecasting requires time-series knowledge, which many skip.
Dataset: Retail or eCommerce data (e.g., Kaggle, UCI)
Tech Stack: Python, Prophet, ARIMA, pandas, matplotlib
Bonus: Compare multiple models and visualize confidence intervals
📌 3. NLP: Sentiment Analysis on Real Reviews
Why it stands out: Text data is common, and this project shows NLP skills.
Dataset: Amazon, Yelp, or IMDb reviews
Tech Stack: Python, NLTK/spacy, sklearn, TF-IDF, Word2Vec/BERT
Bonus: Build a web app that classifies live input
📌 4. Credit Card Fraud Detection
Why it stands out: Highly relevant to finance/tech, and involves class imbalance.
Dataset: Kaggle - Credit Card Fraud Detection
Skills: Anomaly detection, imbalanced classification, ROC-AUC, precision-recall
Bonus: Use autoencoders or isolation forests
📌 5. A/B Testing Case Study
Why it stands out: A/B testing is a must-know in product and growth roles.
Scenario: Website redesign, button color change, pricing experiment
Skills: Hypothesis testing, p-values, confidence intervals
Bonus: Simulate data if needed, and walk through statistical significance
📌 6. Image Classification with Deep Learning
Why it stands out: Demonstrates deep learning and computer vision skills.
Dataset: CIFAR-10, MNIST, or a custom dataset (e.g., medical images)
Tech Stack: TensorFlow or PyTorch, CNNs
Bonus: Use data augmentation and transfer learning
📌 7. Movie Recommendation System
Why it stands out: Shows collaborative filtering, matrix factorization, and personalization.
Dataset: MovieLens, Netflix dataset
Tech Stack: Surprise, LightFM, pandas
Bonus: Compare collaborative vs content-based approaches
📌 8. Web Scraping + Analysis Project
Why it stands out: Shows initiative and creativity.
Idea: Scrape job listings, Airbnb data, e-commerce product reviews
Tools: BeautifulSoup, Selenium, requests
Bonus: Combine scraping with NLP or visualization
📌 9. COVID-19 / Public Health Data Tracker
Why it stands out: Demonstrates time-series, visualization, and real-world data skills.
Dataset: WHO, Kaggle, Our World in Data
Tech Stack: Python, Plotly/Dash, pandas
Bonus: Build an interactive dashboard
📌 10. Your Own Kaggle Competition Solution (with explanation)
Why it stands out: Shows you can apply competitive techniques and explain them clearly.
Key: Don’t just show the code — explain your decisions, models, and feature engineering in a blog post or notebook.
✅ What Makes a Project Stand Out?
Clarity: Easy to read code, good documentation, and explanation of the problem.
Storytelling: Not just “what” you did, but why you did it and what the results mean.
Visualization: Insightful charts and dashboards.
Deployment: Turning your model into a simple app (Streamlit, Flask, or FastAPI).
GitHub ReadMe: Professional, clean README file with structure, images, and how to run the project.
🧰 Bonus Tips:
Host your projects on GitHub with clear commits.
Write blogs or Medium articles explaining your project.
Use Jupyter Notebooks with markdown to explain each step.
Make a portfolio website (GitHub Pages or Notion works too).
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