Using PYTHON Download the "boston.txt" dataset from the following link: http://lib.stat.cmu.edu/datasets/boston The Bost
Posted: Thu Jul 14, 2022 2:28 pm
Using PYTHON
Download the "boston.txt" dataset from the following link:
http://lib.stat.cmu.edu/datasets/boston
The Boston House Price Dataset involves predicting a house pricein thousands of dollars given details of the house and itsneighbourhood. The dataset is small in size with only 506 cases. The details ofeach variable are available at the beginning of the file.
Perform the following tasks:
P1: Load the dataset and perform data preprocessing tasks tosave the dataset as csv format in the working directory, then loadthe csv file from the working directory.
P2: Display the first 5 and last 5 records., and applydescribe() to compute aggregates for each column and display theresult.
P3: Use pairplot() function to plot multiple pairwise bivariatedistributions for 'RM', 'PTRATIO', 'LSTAT', 'MEDV'
P4: Calculate and show correlation matrix using heatmap() for'RM', 'PTRATIO', 'LSTAT', 'MEDV'.
P5: Predict the nitrous oxide level (NOX) (apply linearregression for RM vs NOX, PTRATIO vs NOX, LSTAT vs NOX, andMEDV
P6: Predict the price of a home (apply linear regression for RMvs MEDV, PTRATIO vs MEDV, LSTAT vs MEDV, and NOX vs MEDV).
P7: Calculate the coefficient of determination, 𝑅2R2, toquantify the model's performance.
Download the "boston.txt" dataset from the following link:
http://lib.stat.cmu.edu/datasets/boston
The Boston House Price Dataset involves predicting a house pricein thousands of dollars given details of the house and itsneighbourhood. The dataset is small in size with only 506 cases. The details ofeach variable are available at the beginning of the file.
Perform the following tasks:
P1: Load the dataset and perform data preprocessing tasks tosave the dataset as csv format in the working directory, then loadthe csv file from the working directory.
P2: Display the first 5 and last 5 records., and applydescribe() to compute aggregates for each column and display theresult.
P3: Use pairplot() function to plot multiple pairwise bivariatedistributions for 'RM', 'PTRATIO', 'LSTAT', 'MEDV'
P4: Calculate and show correlation matrix using heatmap() for'RM', 'PTRATIO', 'LSTAT', 'MEDV'.
P5: Predict the nitrous oxide level (NOX) (apply linearregression for RM vs NOX, PTRATIO vs NOX, LSTAT vs NOX, andMEDV
P6: Predict the price of a home (apply linear regression for RMvs MEDV, PTRATIO vs MEDV, LSTAT vs MEDV, and NOX vs MEDV).
P7: Calculate the coefficient of determination, 𝑅2R2, toquantify the model's performance.