Lab Programs Timeline
Follow the complete chronological progression of all 10 ML Lab Programs, from basic Exploratory Data Analysis to advanced K-means Clustering.
EXP1: Data Exploration + Outlier Scan
Histograms, box plots, and IQR-based outlier detection for California Housing features.
EXP2: Correlation Mapping
Correlation heatmap and pair plot to reveal feature relationships.
EXP3: PCA Dimensionality Reduction
Reduce Iris dataset from 4D to 2D and visualize class separation.
EXP4: Find-S Concept Learning
Learns the most specific hypothesis consistent with positive examples.
EXP5: Semi-Supervised KNN Labeling
Labels unlabeled data using KNN and compares accuracy across k values.
EXP6: Locally Weighted Regression
Fits non-parametric curves with varying bandwidth values.
EXP7: Linear vs Polynomial Regression
Compares linear and polynomial regression on housing and auto MPG data.
EXP8: Decision Tree Classification
Builds a decision tree to classify breast cancer samples.
EXP9: PCA + Naive Bayes Faces
Classifies Olivetti faces using PCA + Gaussian Naive Bayes.
EXP10: K-means Clustering + PCA
Clusters Wisconsin breast cancer data and visualizes in PCA space.