Machine Failure Detection with PCA and Classification 🚀

1. Exploratory Data Analysis (EDA) and Data Preprocessing

• Handling missing values: Imputation techniques were used to replace missing values in key features.
• Encoding categorical variables: One-Hot Encoding was applied to categorical variables.
• Normalization and scaling: PCA (Principal Component Analysis) was used for dimensionality reduction and feature scaling.

2. Model Selection Using Multiple Classifiers (e.g., Logistic Regression, RandomForest, GradientBoosting, XGBoost)

• Logistic Regression: Used for classifying machine status.
• Random Forest Classifier: Utilizes multiple trees to handle complex feature relationships.
• Gradient Boosting Classifier: Boosting model to improve accuracy.
• XGBoost Classifier: An advanced boosting model optimized for speed and efficiency.

PCA was used to reduce feature dimensionality and improve model performance.

3. Metrics Calculation for Each Model

• Precision: The proportion of correctly predicted positive cases.
• Recall: The proportion of true positives over the total actual positive cases.
• F1 Score: The harmonic mean of precision and recall.
• Accuracy: The proportion of correct predictions overall.
• Confusion Matrix: To visualize true positives, false positives, etc.