Logistic-regression-in-python

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Logistic-regression-in-python

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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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