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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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Split available dataset into training and test

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 How to  split data set into training and test data set   We can train the model using data which we call as training data or training set. The training data is the one which already has the actual value that the model should have predicted and thus the algorithm changes the value of parameters to account for the data in the training set. But how do we know after training the model is overall good ? For that, we have test data/test set which is basically a different data for which we know the values but this data was never shown to the model before. Thus if the model after training is performing good on test set as well then we can say that the Machine Learning model is good. If the model is not tested and is made such that it just perform good on training data then parameters will be such that they are only good enough to predict the value for data which was in training set. That is not general. This is called overfitting. So we don’t land making a useless model which is...
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