#61: Scikit-learn 58:Supervised Learning 36: Project: Mushroom Classifier

The video discusses a machine learning project to build a Mushroom Classifier using real world data. The models used are mostly linear models in Scikit-learn in Python. The objective was get hands on experience in a project based on the topics discussed so far in this series and beginner series in python. Timeline (Python 3.8) 00:00 - Outline of video 00:45 - Acknowledgement: Data source citation 01:03 - Story of Mushroom 03:09 - What the data looks like? 06:07 - Open Jupyter notebook 06:10 - Data 09:24 - Data: add column names 11:06 - Data: check for missing values 12:30 - Data: split training features and output class 14:32 - Data: check proportion of each class 16:00 - Preprocessing: create dummy variables 18:04 - Preprocessing: check for collinearity 20:47 - Preprocessing: correlation heat map 25:06 - Preprocessing: remove correlated or collinear features 28:40 - * * * NOTE * * *: Because the 'r' is squared there was no need for 'rsq' leq '-0.7' 33:52 - Preprocessing: split train and test set 35:25 - Model: write a function to fit, predict and score 38:17 - Model: iterate through classifiers: LogisticRegression, RidgeClassifier, Perceptron, PassiveAggressiveClassifier, SGDClassifier, LinearDiscriminantAnalysisClassifier, QuadraticDiscriminantAnalysisClassifier 48:18 - Model: re-run with collinear features 49:30 - Ending notes ################# # Download data ################# Link: https://archive.ics.uci.edu/ml/machine-learning-databases/00507/ Description: https://archive.ics.uci.edu/ml/datasets/mushroom Source: Origin: Mushroom records drawn from The Audubon Society Field Guide to North American Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred A. Knopf Source: Donor: Jeff Schlimmer Citation-1: Schlimmer,J.S. (1987). Concept Acquisition Through Representational Adjustment (Technical Report 87-19). Doctoral disseration, Department of Information and Computer Science, University of California, Irvine. Citation-2: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. ################# # Code ################# Link: https://github.com/learndataa/projects/blob/main/i61_sk58_SL_36__project_mushroom.ipynb

Видео #61: Scikit-learn 58:Supervised Learning 36: Project: Mushroom Classifier автора Лесные тропинки