Machine Failure Prediction · Decision Tree
Professor Review
A rubric-aligned executive summary of the Machine Failure Prediction project, with evidence links to every page that produced each result.
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Rubric coverage
01
Business Understanding
Problem framing, cause-chain, and key questions.
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02
Data Understanding
Schema, sample size, class imbalance audit.
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03
Data Preparation
Stratified train/test split (70/30, seeded).
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04
Exploratory Data Analysis
Univariate distributions + multivariate correlations.
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05
Modeling
CART Decision Tree with live hyperparameter tuning.
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06
Model Evaluation
Accuracy, precision, recall, F1, ROC/AUC, confusion matrix.
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07
Insights & Recommendations
Feature-importance-driven maintenance policy.
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