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README.md

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+
+# Predictive Maintenance Model
+
+## Overview
+This repository contains the best-performing machine learning model 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.
+
+## Input Features
+- Engine_RPM
+- Lub_Oil_Pressure
+- Fuel_Pressure
+- Coolant_Pressure
+- Lub_Oil_Temperature
+- Coolant_Temperature
+
+## Selected Model
+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.

best_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4c93788dcb2a026a9b78690d25140233dc908baae3a2c21232dad97051b09c55
+size 65796

best_model_metrics.json

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+{
+    "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
+}

best_model_params.json

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+{
+    "Model": "AdaBoost",
+    "Best_Parameters": "{'learning_rate': 0.05, 'n_estimators': 100}"
+}

experiment_tracking_log.csv

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+Model,Best_Parameters,CV_Best_F1,Test_Accuracy,Test_Precision,Test_Recall,Test_F1
+AdaBoost,"{'learning_rate': 0.05, 'n_estimators': 100}",0.7752,0.6304,0.6304,1.0,0.7733
+Gradient Boosting,"{'learning_rate': 0.05, 'max_depth': 2, 'n_estimators': 100, 'subsample': 0.8}",0.7684,0.6642,0.6857,0.8628,0.7641
+Random Forest,"{'max_depth': 10, 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 100}",0.7625,0.665,0.6904,0.8494,0.7617
+Decision Tree,"{'criterion': 'gini', 'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 2}",0.7603,0.6598,0.6895,0.8376,0.7564