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Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value

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Logistic-regression-in-python

01_LR_Introduction (Theory)

  • Predict categories based on MLE

02_Odd_LogOdd_OddRatio (Theory)

  • Probability : Something Happening / Everything that could Happen
  • Odds : Something Happening / Something Not Happening
  • Log(Odds) : To make Odds output symmetry

03_Logit_Model (Theory)

  • Indepth Logistic Output explained

04_Likelihood_Probability (Theory)

05_MLE (Theory)

06_LR_Assumptions (Theory)

  • Assumption 1 - Appropriate outcome type (Must be categorical)
  • Assumption 2 - Linearity of independent variables and log odds
  • Assumption 3 - No strongly influential outliers
  • Assumption 4 - Absence of multicollinearity
  • Assumption 5 - Independence of observations
  • Assumption 6 - Sufficiently large sample size

07_LR_Assumptions (Python Code)

  • Python Code for Logistic Regression Assumptions

08_AIC_BIC (Theory)

  • Akaike Information Criterion
  • Bayesian Information Criterion
  • Choose the lowest score

09_Logistic_Regression (Python Code)

  • Python Code for Logistic Regression

10_Multiclass_Classification (Theory)

  • One vs All (OvA) also known as One vs Rest (OvR)
  • One vs One (OnO)

11_Multi_Class_Classification (Python Code)

  • Python Code for Multi Class Classification

12_Regularization (Theory)

  • L1 Lasso
    • SSR + lamda * (slope)^2
    • Useless variable become 0
  • L2 Ridge
    • SSR + lamda * |slope|
    • Useless variable tends to become 0 but never = 0
  • Elastic Net : Combination of L1 & L2

13_LR_Regularization (Python Code)

  • Python Code of Regularization (L1 Lasso,L2 Ridge & Elastic Net)

14_WOE_IV (Theory)

  • Weight of Evidence : Predictive power of Independent Variables
  • Information Value : Technique to select important Variables

15_LR_WOE_IV (Python Code)

  • Python Code for WOE and IV

16_LR_Revision (Theory)

  • Logistic Regression Revision

17_LR_1_Interview_Questions (Theory)

  • Logistic Regression Interview quesion bank

18_LR_2_Interview_Questions (Theory)

  • Indepth Logistic Output explained

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Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value

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