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Dummy Variables & One Hot Encoding

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  Dummy Variables & One Hot Encoding Dummy Variables vs One Hot Encoding Dummy variable: You replace the categorical variable by different boolean variables (taking value 0 or 1) to encode whether or not the categorical value had a certain value. For encoding a categorical variable that can take k values, you only need  k-1  dummy variables. Often used in more statistical domains as it uses the “correct number of degrees of freedom”. One-hot encoding : You replace the categorical variable by a vector indicating “in which dimension” your variables lives. This vector will have  k  dimensions. Often used in CS domains. Lets code  First of all we importing Pandas library Now we read the data from csv file using read_csv method of panda library Here you can clearly see that our data set has a categorical data which is town column . so we know that  categorical  data  can not acceptable in machine learning so we have to handle this problem an...
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Decision Tree Classification in Machine Learning

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 What is Decision Tree Classification? A  decision tree  is a graphical representation of all the possible solutions to a  decision  based on certain conditions. It's called a  decision tree  because it starts with a single box (or root), which then branches off into a number of solutions, just like a  tree . Lets see one example : is person salary >>> 100 K $ ? if we make a decision using  Decision Tree classification  then it look like above. lets say if person work in google and he/her has done master in computer then definitely his/her salary > 100 K $.    Decision Tree classification in Python : Import library first  Read the data sets  Now we create two data frame which contain our independent and dependent variable. Because we have to pass this variable as x and y in our model.   Now we all know ml does not  contain any categorical data but in our data frame columns name company job a...
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what is Logistic Regression ?

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  Implementation of  Logistic Regression  Logistic regression is the appropriate regression analysis to conduct when the dependent variable is binary. Basically logistic regression is working on this formula : Now the question is there is already linear regression available then why we need logistic regression?  Logistic regression vs linear regression : Lets understand the concept of why we need logistic regression. consider below graph and fit line using leaner regression.  so you can clearly saw that it's a very inappropriate way to fit line and our ml model accuracy look bad.  Lets now fit the line using Logistic regression  You can clearly see the difference which method is useful and accurate.  Logistic regression in python : Now lets do some coding We will predict the person have insurance or not on the basis of his/her age. First we import the library Lets read the data from the csv file. visualising data using scatter plot Lets ...
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