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TEMost of my early data science learning didn’t come from courses.
It came from building small projects and noticing what actually changes model behavior.
🎬 Netflix Show Clustering
This was my first unsupervised learning project.
Most of the effort went into cleaning the dataset and encoding features like genre and ratings.
Also learned that clustering algorithms break easily if features aren’t scaled properly
🧠 Diabetes Prediction with Model Comparison
This was one of the first projects where I compared multiple models on the same dataset.
The interesting part wasn’t the model, it was the preprocessing.
Also realized accuracy doesn’t say much unless you look at the confusion matrix and other metrics.
🎧 Spotify Song Popularity Predictor
This project was my first time working with API data instead of a static dataset.
Collecting and preparing the data took more effort than training the model.
Explaining predictions with SHAP made the model easier to understand.
📉 Customer Churn Predictor
This one felt closest to a real business problem.
The biggest realization was that accuracy can be misleading when churn cases are rare.
You start thinking more about recall, precision, and the cost of missing a churn prediction.
Turning the model into a small interactive app made the project feel much more practical.
These kinds of projects teach you more than just running models.
They change how you think about data.
💾 Save this if you’re looking for beginner data science project ideas.
💬 Comment **PROJECTS** if you want project links with full explanation.
🔁 Follow for more practical AI & data learning notes.
@techwithprateek
