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Continuous Value Prediction
Meet the Author : Mr. Bharani Kumar
Bharani Kumar Depru is a well known IT personality from Hyderabad; He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence, and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
Table of Content
Scatter Diagram - Visual representation of the relationship between two continuous variables
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Correlation Analysis - Measures the correlation between two variables
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Linear Regression
Equation of straight line that we have learnt in our school days
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Ordinary Least Squares
Ordinary Least Squares Technique to find the best fit line.
Common Least Squares Method for locating the best-fitting line.
The line with the lowest square deviations from each data point to the line is the best fit line.
Transformations may be used to increase accuracy, ensuring that the data has a linear pattern with the least amount of dispersion.
The measure of the predictability of Y (the dependent variable) when Xs (the independent variable) are supplied is the coefficient of determination R2, also known as goodness of fit.
It may be understood as the percentage of output variability (Y) that can be explained by variance in input variability (X).
Where,
Model Assumptions
L - Linearity
I - Independent variable
N - Normal Distribution
E - Equal variances
Problems arise while linear regression model training:
Autocorrelation : Errors are dependent on each other
Heterogeneity : Errors have non-constant variance
Collinearity : Independent variable pair are linearly dependent on each other
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