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 K Means Cluster  Its a distance based algorithm. The k-means algorithm is a  clustering algorithm . That means that you have a bunch of points in some space, and you want to guess what groups they seem to be in.  K-Means cluster in Python : First of all import necessary  library and read the csv file using panda library Lets visualize data    Now we build the KMeans  cluster Now we find the center of group  You can clearly saw that center is not perfectly fit in group because we didn't performing scaling  on over data set. so first we do the scaling on over data set. Now again we build KMeans cluster model Now we again find center of our data groups Now question is How we know the value of K ?  We can find the value of K using elbow method. In this method we plot the data and where you find the major difference in graph that point is your value of K. You can download data set by click here.

  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.

 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...

 Multivariate logistic regression Difference between single variate and multivariate regression :  Suppose we have data of the customer will buy insurance or not? Here you can see your prediction or target is only yes or no. you can also say that 0 or 1 .  now suppose you have to  build model in which you have to decide which party a person is going to vote for? so there are many target value for this like democratic,republic,independent and many more,Here you have more then two possible outcome so its called  multivariate regression. Multivariate logistic regression in Python: Here problem is  given that we have hand written digit(0 to 9) data set and we have to build machine learning model that predict the which digit is it. So you can clearly notice that our target value is 0 to 9 which means total 10 possible outcome. so it is the example of Multivariate regression. We will use logistic regression for this. First of all import library Now we load t...

  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 ...

How does Gradient Descent and Cost Function work in ML? In ML all we have to find minimum weights. In above image you can see that we find global minima but how we can find it. for this we take small step which is learning rate.you find the tangent at every point and you find the next point. you can see in image --->--> become small and small . Now question is how we know that what learning rate we need? basically it's trial and error method so you try different learning rate and you will find global minima.  If your learning rate is very big then it may be miss the global minima. Our linear regression line equation is : y = mx + b  where  m and B is  Where     So how you find the step size is given in below image Where mse is cost function Gradient Descent and Cost Function in python : First we import the library  Now we define  gradient_descent function Now we define X and Y lets call the function and see the output OUTPUT :...