Atharva Joshi's Presentation on Regression.pdf
- 1. Regression - The art of predicting the future By Atharva Joshi 28 Dec 2022
- 2. Contents ● Overview ● Methodology ● Mathematical Understanding ● Data manipulation ● Steps for Data Manipulation ● Regression Analysis using Neural network ● Regression-prediction ● Significance & Application
- 3. Overview Regression is a statistical method used to predict a continuous outcome variable based on one or more predictor variables.
- 4. Methodology 01 It is based on the assumption that there is a relationship between the predictor variables and the outcome variable, and that this relationship can be quantified and used to make predictions about the outcome variable. 02 The goal of regression is to find the line of best fit that describes the relationship between the predictor variables and the outcome variable.This line of best fit is called the regression line. 03 In order to make predictions using regression, you need to have a set of data that includes both the predictor variables and the outcome variable. You can then use this data to estimate the coefficients of the regression line and use this line to make predictions about the outcome variable for new data points.
- 5. Methodology ( Continues… ) 04 It is a mathematical model that describes how the predictor variables are related to the outcome variable. 05 The regression line is determined by finding the values of the coefficients that minimize the sum of the squared errors between the predicted values and the actual values of the outcome variable.
- 6. Mathematical Understanding In this formula, y is the predicted value of the outcome variable, b0 is the intercept (the value of y when x is 0), b1 is the slope of the line (the amount that y changes for each unit change in x), and x is the predictor variable. There are also more complex regression models that can be used to model relationships between multiple predictor variables and an outcome variable. These models can be linear or nonlinear, depending on the nature of the relationship between the variables. y = b0 + b1*x
- 7. Data manipulation The process of preparing and cleaning the data for analysis. Data manipulation is an important step in the regression analysis process because it helps to ensure that the results of the analysis are accurate and reliable.
- 8. Steps for Process of Data Manipulation 01 Identifying and removing missing values 02 Identifying and removing outliers 03 Checking for multicollinearity 04 Transforming variables 05 Splitting the data into training and testing sets
- 9. Neural Network In a neural network, regression is It is based on the idea of building a model that can learn the relationship between the predictor variables and the outcome variable from training data and make predictions about the outcome variable for new data points. Regression in neural networks is a powerful tool for predicting continuous outcomes and understanding the relationships between variables. It is widely used in a variety of applications, including finance, marketing, and healthcare.
- 10. Regression - Prediction ❏ It is based on the assumption that there is a relationship between the predictor variables and the outcome variable, and that this relationship can be quantified and used to make predictions about the outcome variable. ❏ To make predictions using regression, you need to have a set of data that includes both the predictor variables and the outcome variable. ❏ You can then use this data to estimate the coefficients of the regression line and use this line to make predictions about the outcome variable for new data points.
- 11. Significance & Application Regression is a powerful tool that is widely used in many fields, including economics, finance, marketing, and psychology, to name a few. It is a valuable tool for understanding the relationships between variables and for making predictions about future outcomes.
- 12. Thank you! “ Predicting The Future isn't magic, it's some sort of calculations of ML regression in neural networks”