In ML Algorithms we have :
Supervised Learning Unsupervised Learning In Supervised Learning we have :
Regression Classification Firstly, we discuss about different types of Regressions
i. Linear Regression
a. Simple Linear Regression b. Multi-linear Regression
ii. Logistic Regression
iii. Lasso Regression
iv. Ridge Regression
In this repository we discuss about Simple Linear Regression
Simple Linear Regression:
It is applicable when relationship between input variable and output variable is linear, that is it should have positive or negative correlation between input and output variable. That can be known using scatter plot.
Here X is independent variable and Y is dependent variable, both X and Y are continuous.
After getting scatter plot we will get a best fit line using OLS (ordinary least squares) method
OLS method: we will find distance between actual and predicted value on the line and this is the error (e1), similarly for all data points e2,e3,.....
e1^2+e2^2+e3^2+...........+en^2=(error value)
which line will get this 'error value' as less that is best fit line.
Now using this best fit line we will build a model.
How can we say that model is best?
1. R^2 - coefficient of determination
R^2 = explaines variation/total variation
0<=R^2<=1
if R^2 is nearly 1 => Good model
R^2 is nearly 0 => Bad model
2. RMSE (Root Mean Square Error) should be less
To further improve the model we tried different transformations such as log transformation, Exponential Transformation, Quadratic Transformation Data Used :-
50 Startup dataset :- Build a prediction model for the dataset Toyota Corolla dataset:- prepare a prediction model for predicting Price Programming language: Python
The Codes regarding this Multiple Regression model with two different business problems 50 Startup dataset, Toyota Corolla dataset are present in this Repository in details.