driangregory / machinelearning-multilinearregressionwithgradientdescent Goto Github PK

• Python script to estimate coefficients for multilinear regression using gradient descent algorithm.
• Linear regression implemented from scratch.
• Using simulated data of job prospects given AI and machine learning skills.

is the intercept (bias value)

W is a vector of parameter (weights) to be estimated.

X is a matrix of 1's and K feature weights and N data points of given inputs

and is a vector of estimation errors denoted

• The loss function chosen is minimum mean square error given by:

• With partial derivatives

• With weight updates given by:

• Where is the "learning weight".

python mulitpleLinearRegression.py

=======================================================================
MULTI LINEAR REGRESSION USING GRADIENT DESCENT TERMINATION RESULTS
=======================================================================
Initial Weights were:             0.0, 0.0, 0.0.
   With initial cost:          3281.9.
        # Iterations:       2,500,000.
       Final weights:    w0:+24.94, w1:+0.32, w2:+0.483.
          Final cost:            +8.1.
                RMSE:            +4.0, R-Squared:         +0.7
=======================================================================
Finished

Python (>2.7), Numpy and Pandas.