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This project also demonstrates initiative, you’re not using a pre-packaged dataset, you’re querying a live database and deciding what inquiries to ask. The ensuing dataset has multi-level column headers, Yes/No responses saved as strings, and ranking columns that need cautious interpretation. This requires combining two datasets with totally different buildings, standardizing inconsistent column values, and answering a particular business question from the end result.
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Forecast Walmart sales by combining ARIMA time sequence modeling with machine learning regression and hyperparameter tuning Predict telecom buyer churn using logistic regression with feature reduction, hyperparameter tuning, and a production API Build Speech Emotion Recognition systems utilizing ANNs and Music Recommendation Engines on the KKBox dataset.
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