Linear Discriminant Analysis (LDA) for Classification
This project implements Linear Discriminant Analysis (LDA) to classify samples as "Good" or "Bad" based on sensor readings. The dataset is preprocessed, normalized, and projected onto an LDA component for visualization and analysis.
•Data Preprocessing: Handles dataset loading, balancing, and normalization.
•LDA Implementation: Uses sklearn.discriminant_analysis.LinearDiscriminantAnalysis.
•Visualization: Generates histograms to visualize class separation.
•Feature Importance Analysis: Determines feature contributions using LDA coefficients.
Install dependencies using:
pip install pandas numpy matplotlib scikit-learn
1.Load dataset and assign labels based on conditions.
2.Normalize features using StandardScaler.
3.Perform LDA and project data onto a single component.
4.Visualize results using histograms.
5.Analyze feature contributions using LDA coefficients.
Histogram Plot: Displays class separation.
Feature Importance Table: Ranks feature contributions.
Open-source under the MIT License. Created by Radhika Goel