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Application of unsupervised learning and dimensionality reduction towards multiple problem sets.

Python 100.00%
independent-component-analysis kmeans-clustering principal-component-analysis machine-learning-algorithms expectation-maximization extremely-randomized-trees random-projections logistic-regression

unsupervised-learning's Introduction

03 - Unsupervised Learning and Dimensionality Reduction

Clustering Algorithms

  1. K-Means
  2. Expectation Maximization

Dimensionality reduction Algorithms (feature selection)

  1. Principal component analysis (PCA)
  2. Fast Independent Component Analysis (ICA)
  3. Random Projections Gaussian
  4. Extremely Randomized Trees

pca

Classification Algorithms

  1. Multi-layer Perceptron (Neural Network)
  2. Logistic Regression

Problems

  1. Wholesale customer segments
  2. Raisins class

Metrics

  1. Accuracy
  2. Recall
  3. Log Loss

Plots

  1. Learning curve
  2. Validation curve

Instructions

  1. URL
  2. Click on "Code"
  3. Click on "Download ZIP"
  4. Unzip the files
  5. Run each of the python files individually using Python 3.8.

Results

  1. All results will be printed to the console including metrics scores and execution times.
  2. All the 100 plus graphs will be generated directly in the repository once the files are run successfully.

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