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Regression Techniques in Machine learning including topics from Assumption, Simple and Multiple Linear Regression. Both theory and python codes are included.
simple-multiple-regression-in-python's Introduction
Simple-Multiple-Regression-in-Python 01 LR Introduction (Theory)
Linear relationship between Input and Output
02 OLS, Simple & Multiple Regression (Theory)
Simple : One Independent Variable
Multiple : More than One Independent Variables
OLS :
Ordinary Least Square
Sum of all [(Acutal - Observed)^2] = Total Error
Steps to build Regression Model
Select all variable
Stepwise Regression - Backward & Forward
Model Score comparision
03 Regression Assumptions (Theory)
Linearity : X linear to Y
Constant Error Variance : Homoscedacity
Independent Error Term : Auto Correlation
Normal Error : Normal distribution of Error
No multicollinearity : Independent X variables
Exogenity : Omitted Variance Bias
04 Residual Plot (Theory) 05 Homoscedasticity & Hetroscedasticity (Theory)
Homoscedasticity : Same Variance
Hetroscedasticity : Different Variance
06 Covariance & Correlation (Theory)
Covariance : Direction of a relationship between variables
Correlation : Strength & Direction of a relationship between variables
07 Correlation & Causation (Theory)
Why colliearity a Problem ?
Check Collinearity
Multi collinearity
09 Auto Correlation (Theory)
Similarity between observations as a function of time lag between them
Detects multicollinearity in Regression
11 Regression Assumptions in Python (Code)
Step by steps checking Regression Assumptions
12 Metrics in Regression (Theory)
Asses Model Performance
Mean Absolute Error (MAE)
Mean Square Error (MSE)
Root Mean Square Error (RMSE)
Mean Absolute Percentage Error (MAPE)
Mean Percentage Error (MPE)
R Square
13 Sum of Square & Adjusted R Square (Theory)
Total Variation = Explain Variation + Unexplained Variation
SST = SSR + SSE
SST = Sum of Square Total
SSR = Sum of Square Residual = Actual - Mean = explained error
SSE = Sum of Square Error = Actual - Predicted = unexplained error
14 R, R Square & Adjusted R Square (Theory)
R = Correlation Value is known as R
R Square = SSR / SST
R Square is variation explained by the Data
15 Hypothesis Testing (Theory)
Evalutes 2 or more exclusive statements
Null Hypothesis is always neutral (no relationship between variables)
Alternate Hypothesis is always neutral (there is a relationship between variables)
Probability for the hypothesis to be True
All statistical package give P Value of Alternate Hypothesis
So P value for alternate hypothesis to be True
17 Level of Significance (Theory)
Level of Significance is Probability with which we will reject the Null Hypothesis denoted by (alpha)
Confidence Level is Probability with which we will accept the Null Hypothesis denoted by (1 - alpha)
18 Normal Distribution (Theory) 19 Confidence Interval & CLT (Theory) 20 Standard Error (Theory)
Meausre of Uncertainity in Sample Mean
Population Mean != Sample Mean
Akaike Information Criterion
Bayesian Information Criterion
Both Penalize the complex model in nature
22 Regression Output Explained Part 1 (Theory)
Overall output is explained in depth
23 Regression Output Explained Part 2 (Theory)
Python Stats Model output explained in depth
24 Simple & Multiple Regression in Python (Code)
Step by step code of Simple & Multiple Regression
25 Interview Questions: Simple & Multiple (Theory)
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