SUPERVISED LINEAR REGRESSION USING OLS MODEL

REGRESSION ANALYSIS

Purpose of Regression analysis

  1. To predict which independent variables have an impact on dependent variable.
  2. To estimate the effect of some explanatory variable on the dependent variable.

LINEAR REGRESSION

Variance
COVARIANCE -TOGETHER SPREAD OF X AND Y
CORRELATION (R): COV(x)/SIGMA (x) IS ZSCALED FORMULA SO IT IS DOING SCALING

Linear Regression two methods as shown below:

ORDINARY LEAST SQUARE

Simple Linear Regression — one independent and one dependent variable.

Random error component:

Error term=Actual value — predicted value

OLS OBJECTIVE

BEST FIT LINE

INTERPRETATION OF BETA COEFFICIENTS (b0 and b1):

slope (b1)
Intercept (b0)

MEASURES OF VARIATION:

#OLS MODEL CODE

Explainability

MATH BEHIND OLS

LOSS FUNCTION / COST FUNCTION/ERROR FUNCTION

MODEL EVALUATION METRICS

Rsquared and Adjusted Rsquared:

F-STATISTIC

OPTIMIZATION-GRADIENT DESCENT

LOSS FUNCTION / COST FUNCTION/ERROR FUNCTION

MEAN SQUARED FUNCTION

  1. Find the difference between actual value and the predicted value.

GRADIENT DESCENT ALGORITHM

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Vignesh S

Data scientist Aspirant passionate in learning new technologies and sharing my thoughts to others .