sandipanpaul21 / logistic-regression-in-python

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

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