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@@ -228,17 +228,19 @@ Gradient Boosting delivered the most balanced predictions, with the best stabili
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+ # Conculation
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+ This project brought together real-world data exploration, feature engineering, clustering, and predictive modeling into one complete workflow.
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+ Through exploratory data analysis, I found the main factors that influence ride prices.
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+ Feature engineering and clustering uncovered hidden patterns in the data and improved how the model learned.
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+ I trained several regression and classification models, compared how they performed, and chose the best ones based on clear metrics.
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+ Overall, this process deepened my understanding of practical machine-learning pipelines, from raw data to clear insights.
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+ It also showed me the value of experimentation, iteration, and careful evaluation.
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+ # Video Link: https://www.loom.com/share/6a96400976ba486c89091b9d75738884
 
 
 
 
 
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