House Sales Prediction in King County USA

Data Analysis with Python

This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.

The following questions were explored in this analysis:

Data Wrangling

1. Drop the columns "id" and "Unnamed: 0" from axis 1 using the method drop() , then use the method describe() to obtain a statistical summary of the data.
2. replace the missing values of the column 'bedrooms' with the mean of the column 'bedrooms' and 'bathrooms' with the mean of the column 'bathrooms'using the method replace() .

Exploratory Data Analysis

3. Use the method value_counts to count the number of houses with unique floor values, use the method .to_frame() to convert it to a dataframe.
4. Use the function boxplot in the seaborn library to determine whether houses with a waterfront view or without a waterfront view have more price outliers.
5. Use the function regplot in the seaborn library to determine if the feature sqft_above is negatively or positively correlated with price.
6. use the Pandas method corr() to find the feature other than price that is most correlated with price.

Model Development

7. Fit a linear regression model using the longitude feature 'long' and caculate the R^2.
8. Fit a linear regression model to predict the 'price' using the feature 'sqft_living' then calculate the R^2.
9. Fit a linear regression model to predict the 'price' using the list of features. Then calculate the R^2.
10. Create a list of tuples, the first element in the tuple contains the name of the estimator: 'scale' 'polynomial' 'model' The second element in the tuple contains the model constructor StandardScaler() PolynomialFeatures(include_bias=False) LinearRegression()
11. 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.
12. split the data into training and testing sets (test_size=0.1)
13. Create and fit a Ridge regression object using the training data, set the regularization parameter to 0.1, and calculate the R^2 using the test data.
14. Perform a second order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data, set the regularisation parameter to 0.1, and calculate the R^2 utilising the test data provided.