Graphic design python turtle 🐢

By Kajal Vishwakarma -
from turtle import * import colorsys bgcolor('black') pensize(0) tracer(50) h=0 for i in range(300): c=colorsys.hsv_to_rgb(h,1,1) h+=0.9 color(c) forward(300) left(100) fd(i) goto(0,0) down() rt(90) begin_fill() circle(0) end_fill() rt(10) for j in range(5): rt(30) done() Please follow my blog and subscribe my channel for more videos and newly updates 👍👍👍👍👍 import turtle as t import colorsys t.bgcolor('black') t.tracer(100) h=0.4 def draw(ang,n): t.circle(5+n,60) t.left(ang) t.circle(5+n,60) for i in range(200): c=colorsys.hsv_to_rgb(h,1,1) h+=0.005 t.color(c) t.pensize(2) draw(90,i*2) draw(120,i*2.5) draw()
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Regression in Machine Learning

By Kajal Vishwakarma -

 

Regression

Regression models are used to predict a continuous value. Predicting prices of a house given the features of house like size, price etc. is one  of the common examples of regression. It is a supervised technique.

Types of Regression:-

  •  Simple Linear Regression 
  • Polynomial regression
  • Support Vector Regression
  •  Decision Tree Regression
  •  Random Forest Regression



1.       Simple Linear Regression:-  This  si one of most common and interesting type of regression technique. Here we predict a target variable Y based on the input variable X. A linear relationship should exist between target variable and predictor and so comes the name linear regression.

2.       Polynomial Regression:-  In polynomial regression, we transform the original features into polynomial features of a given degree and then apply linear regression on it.

3.       Support Vector Regression:-  In SVR, we identify a hyper plane with maximum margin such that maximum numbers of data points are within that margin. SVRs are almost similar to SVM classification algorithm.

4.       Decision Tree Regression :-  Decision tree can be used for classification as well as regression. In decision trees, at each level we need to identify the splitting attribute.

5.       Random Forest Regression:-  Random forest is an ensemble approach where we take into account the predictions of several decision regression trees.

1)      Select k random points.

2)      Identify n where n is the number of decision tree regressors to be created. Repeat step 1 and 2 to create several regression trees.

3)      The average of each branch is assigned to leaf node in each decision tree.

4)      To predict output for a variable, the average of all the predictions of all decision trees are taken into consideration.

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