Linear Regression With Multiple Variable

 

Linear Regression with Multiple Variable


Here we predict the house price by its area,bedroom & age.

first we import necessary library


Now we read CSV file by using Pandas library's read_csv method


Here you can see Nan value for bedroom so we have to fill something there . so we find the median of bedroom column and replace the Nan value with median. for fill the Nan value we use fillna method of pandas library


Now we build the Linear Regression model using sklearn librabry


Here we pass the independent variable area,bedroom and age by using df.drop('price') which means all column except price and then we pass dependent variable in fit method which is price column.Fit method train our linear Regression model.

Lets predict the price of house. Here we predict the price of house which has a 3000 sqft area , 3 bedroom and 40  year old.


You can verify your answer by simple maths equation 
y = m1(coef_)+m2(coef_)+m3(coef_)+intercept   where y is dependent variable 




You can download csv file by click here

you can download the code by download my repository by click here








Comments

Post a Comment

Popular posts from this blog

Multivariate logistic regression in Python

Image
 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...
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

K Means Cluster Algorithm

Image
 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.
Read more