Contact Us

* - required fields

Comments

Post a Comment

Popular posts from this blog

Dummy Variables & One Hot Encoding

Image
  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...
Read more

Decision Tree Classification in Machine Learning

Image
 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...
Read more

Decision tree for titanic dataset in Python

Image
  Build decision tree model to predict survival based on certain parameters : import library Read data set using panda library's read_csv method.   In this file using following columns build a model to predict if person would survive or not  1.Pclass 2.Sex 3.Age 4.Fare so we will drop unnecessary columns :   Now we separate dependent and independent   data frame for pass into our model Gender column contain categorical data so we have too convert it into numerical for this we will use map method of pandas. and also it contain missing value which we will handle using fillna method.   Now train test split : Building Decision tree model for titanic dataset in Python Confusion matrix : if you want to download data set then click here.
Read more