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README.md

@@ -1,19 +1,36 @@
+---
+license: mit
+language:
+- en
+library_name: scikit-learn
+pipeline_tag: tabular-classification
+tags:
+- predictive-maintenance
+- classification
+- scikit-learn
+- tabular-data
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+---
 
 # Predictive Maintenance Model
 
 ## Overview
-This repository contains the best-performing machine learning model for the predictive maintenance project.
+This repository contains the best-performing machine learning model developed for the predictive maintenance project.
 
-## Problem Statement
-The objective of the model is to classify whether an engine is operating normally or may require maintenance based on sensor readings.
+## Business Problem
+The objective of this model is to classify whether an engine is operating normally or is likely to require maintenance based on sensor readings.
 
 ## Input Features
-- Engine_RPM
-- Lub_Oil_Pressure
-- Fuel_Pressure
-- Coolant_Pressure
-- Lub_Oil_Temperature
-- Coolant_Temperature
+- Engine_rpm
+- Lub_oil_pressure
+- Fuel_pressure
+- Coolant_pressure
+- lub_oil_temp
+- Coolant_temp
 
 ## Selected Model
 AdaBoost
@@ -21,5 +38,8 @@ AdaBoost
 ## Evaluation Summary
 {'Model': 'AdaBoost', 'Best_Parameters': "{'learning_rate': 0.05, 'n_estimators': 100}", 'CV_Best_F1': 0.7752, 'Test_Accuracy': 0.6304, 'Test_Precision': 0.6304, 'Test_Recall': 1.0, 'Test_F1': 0.7733}
 
-## Notes
-This model was selected after comparing multiple tree-based classification algorithms using cross-validation and test-set evaluation.
+## Model Interpretation
+The selected model was identified after comparing multiple tree-based algorithms using cross-validation and test-set evaluation.
+
+## Limitation
+Although the selected model achieved the highest test F1-score, its confusion matrix shows that it predicted all observations as class 1. This means the model was very strong in identifying maintenance-required cases but weak in distinguishing normal operating cases.