Quick Logistic Regression Tutorial in Google Colab

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To get the Python Notebook:
https://colab.research.google.com/drive/1spC1U5r7iEpeZJOWQB45CMvyaaP7tXHv?usp=sharing

To know more about the dataset:
https://vincentarelbundock.github.io/Rdatasets/doc/DAAG/dengue.html

Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression). Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled "0" and "1". In the logistic model, the log-odds (the logarithm of the odds) for the value labeled "1" is a linear combination of one or more independent variables ("predictors"); the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value).

Intro (0:00)
Importing Libraries (0:32)
Importing Dataset (0:53)
Isolating X and y (2:36)
Logistic Regression (3:31)
Regression Summary (4:22)
Outro (5:10)