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BCGDV Machine learning assignment

This assignment explores the concepts of standardization via min-max scaler, k-means clustering, and logistic classifier. It also applies these techniques to a four-factor model for predicting probabilities and classifying data. The assignment considers the impact of different factors on eating preferences and transportation choices.

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BCGDV Machine learning assignment

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  1. BCGDV Machine learning assignment Abhishek Das

  2. section 1

  3. Standardization via min-max scaler scales are of similar order but distributions different

  4. Intuitive: Randomly pick centroids and move them until they are ‘center’ of data clusters Distance: Minimize within cluster variance = Euclidean2 Cohesion: How far are points from their centroid? Separation: How far are clusters from each other? K-Means Clustering

  5. Standardized data choosing the k in k-means

  6. b and C are grouped together

  7. 3 centroids is not a lot better…

  8. dbscan better but has outliers

  9. why K-means did poorly

  10. section 2

  11. The more young children you are looking after and the more hungry you are, but the less alternative eating options in your area means a higher probability of you eating dinner at McDonalds tonight. More comprehensible model Logistic Classifier

  12. Odds of getting into Class 1 over Class 0 Holding other features constant Best features

  13. Four Factor Model 70-30 Train-test split

  14. Residents of Glebelike residents of Newtown, Marrickville, Erskineville and Surry Hills are more likely to take public transport to the Central Business District Group into a class based on neighbors Also intuitive KNN Classifier

  15. Four Factor Model 70-30 Train-test split

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