House Price Prediction using Ridge Regression
Dataset info:
Dataset:
Using some differnt types of ploting to visualize this dataset-----
Fit a linear regression model using the longitude feature 'long' and caculate the R^2 value.
Fit a linear regression model to predict the 'price' using the feature 'sqft_living' then calculate the R^2 value.
Fit a linear regression model to predict the 'price' using the list of features: floors, waterfront, lat, bedrooms, sqft_basement, view, bathrooms, sqft_living15, sqft_above, grade, sqft_living. Using this features and price we used a linear regression model and calculate R^2 value. After that we use the list to create a pipeline object to predict the 'price', fit the object using the features in the list features, and calculate the R^2 value. After the calculation of all R^2 value, we compared the all R^2 values to know which gives the best R^2 value. Here we use a bar plot to show to different R^2 values in a graph.
Split our dataset into two parts, one is test and another is train. Here we use 85% of data in train part and 15% is used in test. After the slpiting of data we create a ridge regression object using the training data and predict the values. Then we create a second order polynomial transform on both the training data and testing data then create a ridge regression function and predict the value. After that we compare the two R^2 values using bar plot.
