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You are working as a data scientists and you have received data on house prices in the Boston region. The data set contains the following variables: • crim: per capita crime rate by town • zn: proportion of residential land zoned for lots over 25,000 sq.ft. • indus: proportion of non-retail business acres per town • chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) • nox: nitric oxides concentration • rm: average number of rooms per dwelling •age: proportion of owner-occupied units built prior to 1940 • dis: weighted distances to five Boston employment centers • rad: index of accessibility to radial highways • tax: full-value property-tax rate per $10,000 • ptratio: pupil-teacher ratio by town • b: 1000(Bk – 0.63)² where Bk is the proportion of blacks by town • Istat: % lower status of the population • medv: Median value of owner-occupied homes in $1000s Given this information: 1. Download the dataset boston.csv and open it as a PANDAS dataframe. 2. Using 'medv' as the response variable and per capita crime rate by town, proportion of owner-occupied units built prior to 1940, and nitric oxides concentration as predictors, fit a linear model (OLS), and a k-nearest neigherbour model (using the 5 nearest neighbour). Which one has better prediction properties using k-fold cross validation (k=5)? Explain why.

You are working as a data scientists and you have received data on house prices in the Boston region. The data set contains the following variables: • crim: per capita crime rate by town • zn: proportion of residential land zoned for lots over 25,000 sq.ft. • indus: proportion of non-retail business acres per town • chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) • nox: nitric oxides concentration • rm: average number of rooms per dwelling •age: proportion of owner-occupied units built prior to 1940 • dis: weighted distances to five Boston employment centers • rad: index of accessibility to radial highways • tax: full-value property-tax rate per $10,000 • ptratio: pupil-teacher ratio by town • b: 1000(Bk – 0.63)² where Bk is the proportion of blacks by town • Istat: % lower status of the population • medv: Median value of owner-occupied homes in $1000s Given this information: 1. Download the dataset boston.csv and open it as a PANDAS dataframe. 2. Using 'medv' as the response variable and per capita crime rate by town, proportion of owner-occupied units built prior to 1940, and nitric oxides concentration as predictors, fit a linear model (OLS), and a k-nearest neigherbour model (using the 5 nearest neighbour). Which one has better prediction properties using k-fold cross validation (k=5)? Explain why.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
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You are working as a data scientists and you have received data on house prices in the Boston region. The data set contains the following variables: • crim: per capita crime rate by town • zn: proportion of residential land zoned for lots over 25,000 sq.ft. • indus: proportion of non-retail business acres per town chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) • nox: nitric oxides concentration ● • rm: average number of rooms per dwelling •age: proportion of owner-occupied units built prior to 1940 • dis: weighted distances to five Boston employment centers • rad: index of accessibility to radial highways • tax: full-value property-tax rate per $10,000 • ptratio: pupil-teacher ratio by town • b: 1000(Bk - 0.63)² where Bk is the proportion of blacks by town • Istat: % lower status of the population • medv: Median value of owner-occupied homes in $1000s Given this information: 1. Download the dataset boston.csv and open it as a PANDAS dataframe. 2. Using 'medv' as the response variable and per capita crime rate by town, proportion of owner-occupied units built prior to 1940, and nitric oxides concentration as predictors, fit a linear model (OLS), and a k-nearest neigherbour model (using the 5 nearest neighbour). Which one has better prediction properties using k-fold cross validation (k=5)? Explain why.
Transcribed Image Text:You are working as a data scientists and you have received data on house prices in the Boston region. The data set contains the following variables: • crim: per capita crime rate by town • zn: proportion of residential land zoned for lots over 25,000 sq.ft. • indus: proportion of non-retail business acres per town chas: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) • nox: nitric oxides concentration ● • rm: average number of rooms per dwelling •age: proportion of owner-occupied units built prior to 1940 • dis: weighted distances to five Boston employment centers • rad: index of accessibility to radial highways • tax: full-value property-tax rate per $10,000 • ptratio: pupil-teacher ratio by town • b: 1000(Bk - 0.63)² where Bk is the proportion of blacks by town • Istat: % lower status of the population • medv: Median value of owner-occupied homes in $1000s Given this information: 1. Download the dataset boston.csv and open it as a PANDAS dataframe. 2. Using 'medv' as the response variable and per capita crime rate by town, proportion of owner-occupied units built prior to 1940, and nitric oxides concentration as predictors, fit a linear model (OLS), and a k-nearest neigherbour model (using the 5 nearest neighbour). Which one has better prediction properties using k-fold cross validation (k=5)? Explain why.
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