KNN is a technique used to estimate new values based on the similarity of known ones.In this assignment, your company wants you to estimate the selling price of a customer's building The price you calculate will be given to the customer as the company selling price recommendation. You decide to use Data Science techniques such as the K-Nearest Neighbor.(KNN) You will need to:Import the necessary libraries from your program. (You can use the model class sklearn.neighbors.KNeighborsClassifier, part of the package sci-kit-learn 1.1.1 (Links to an external site) or any other.Train/test the model with the data included in the module (cal_housing.tgz). The house you need to estimate the value for has the following properties:longitude: 120.75latitude: 39.34housingMedianAge: 35.5total rooms: 260totalBedrooms:120 population:540households: 12medianIncome:1.8 K BuildingValue: ? What is the recommended price?You need to provide the code, properly commented. You could use the following packages (if you like): from matplotlib.pyplot import scafrom sklearn import datasets,neighborsfrom sklearn.neighbors import KNeighborsRegressor from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerimport numpy as npimport pandas as pd ✓ DetailsReading Assignment: Learn this tutorial about the K-Nearest Neighbourhood (K-NN)K-Nearest Neighbours - GeeksforGeeksKNN is a technique used to estimate new values based on the similarity of known ones.In this assignment, your company wants you to estimate the selling price of a customer's buildingThe price you calculate will be given to the customer as the company selling price recommendation.You decide to use Data Science techniques such as the K-Nearest Neighbor.(KNN)You will need to:• Import the necessary libraries from your program. (You can use the model class sklearn.neighbors.KNeighbors Classifier, part of the package sci-kit-learn1.1.1The house you need to estimate the value for has the following properties:longitude: 120.75latitude: 39.34housing MedianAge: 35.5total rooms: 260total Bedrooms:120population:540households: 12medianIncome:1.8 KBuildingValue: ?What is the recommended price?You need to provide the code, properly commented.from matplotlib.pyplot import scafrom sklearn import datasets, neighborsfrom sklearn.neighbors import KNeighborsRegressorfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerimport numpy as npimport pandas as pd

Approach to estimate the selling price of a customer's building using K-Nearest Neighbors (KNN)…

KNN is a technique used to estimate new values based on the similarity of known ones. In this assignment, your company wants you to estimate the selling price of a customer's building The price you calculate will be given to the customer as the company selling price recommendation. You decide to use Data Science techniques such as the K-Nearest Neighbor.(KNN) You will need to: Import the necessary libraries from your program. (You can use the model class sklearn.neighbors.KNeighborsClassifier, part of the package sci-kit-learn 1.1.1 (Links to an external site) or any other. Train/test the model with the data included in the module (cal_housing.tgz). The house you need to estimate the value for has the following properties: longitude: 120.75 latitude: 39.34 housingMedianAge: 35.5 total rooms: 260 totalBedrooms:120 population:540 households: 12 medianIncome:1.8 K BuildingValue: ? What is the recommended price? You need to provide the code, properly commented. You could use the following packages (if you like): from matplotlib.pyplot import sca from sklearn import datasets,neighbors from sklearn.neighbors import KNeighborsRegressor from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler import numpy as np import pandas as pd

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

Section: Chapter Questions

Problem 1PE

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Question

KNN is a technique used to estimate new values based on the similarity of known ones.

In this assignment, your company wants you to estimate the selling price of a customer's building

The price you calculate will be given to the customer as the company selling price recommendation.

You decide to use Data Science techniques such as the K-Nearest Neighbor.(KNN)

You will need to:

Import the necessary libraries from your program. (You can use the model class sklearn.neighbors.KNeighborsClassifier, part of the package sci-kit-learn 1.1.1 (Links to an external site) or any other.
Train/test the model with the data included in the module (cal_housing.tgz).

The house you need to estimate the value for has the following properties:

longitude: 120.75
latitude: 39.34
housingMedianAge: 35.5
total rooms: 260
totalBedrooms:120
population:540
households: 12
medianIncome:1.8 K
BuildingValue: ?

What is the recommended price?

You need to provide the code, properly commented.

You could use the following packages (if you like):

from matplotlib.pyplot import sca
from sklearn import datasets,neighbors
from sklearn.neighbors import KNeighborsRegressor

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

import numpy as np
import pandas as pd

✓ Details
Reading Assignment: Learn this tutorial about the K-Nearest Neighbourhood (K-NN)
K-Nearest Neighbours - GeeksforGeeks
KNN is a technique used to estimate new values based on the similarity of known ones.
In this assignment, your company wants you to estimate the selling price of a customer's building
The price you calculate will be given to the customer as the company selling price recommendation.
You decide to use Data Science techniques such as the K-Nearest Neighbor.(KNN)
You will need to:
• Import the necessary libraries from your program. (You can use the model class sklearn.neighbors.KNeighbors Classifier, part of the package sci-kit-learn
1.1.1
The house you need to estimate the value for has the following properties:
longitude: 120.75
latitude: 39.34
housing MedianAge: 35.5
total rooms: 260
total Bedrooms:120
population:540
households: 12
medianIncome:1.8 K
BuildingValue: ?
What is the recommended price?
You need to provide the code, properly commented.
from matplotlib.pyplot import sca
from sklearn import datasets, neighbors
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd

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