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Linear Regression Example - Ames Housing Data

A linear regression model is trained to predict prices of houses in Ames, Iowa, USA. A number of numerical features are included as predictors

First we examine the data set. Then a linear regression model is trained. The trained model is used on a testing data to evaluate the performance of the model.

URL: The Learner-Predictor construct in KNIME https://www.youtube.com/watch?v=bKrJkdPvpeA
URL: Slides (Introduction to ML Algorithm course) https://www.knime.com/form/material-download-registration

Example: Housing Price PredictionIn this exercise we will predict the price of a house Perth (Australia) given a number of selected numerical features1. Exploring the data set-We examine basic statistics, as well as the distribution of numerical features. -We also generate a scatter plot of the target (y-axis) plotted against one of the numerical features (x-axis).2. Training the linear regression mode-The data set is partitioned into the training (70%) and testing (30%)sets-The linear regression model is trained on the training data set. Target: house price, features: all numerical features-The trained model is fitted to the testing data. -We evaluate the model performance Price Prediction: Linear Regression Clustering the position of House in perth Node 1Node 303Node 304Bin PriceColor accordingto clusterVisualize the clusterson world mapNode 310Clusteringk=5CSV Reader Partitioning Numeric Scorer Missing Value Linear RegressionLearner RegressionPredictor Linear RegressionLearner RegressionPredictor Concatenate Sorter Rule Engine Color Manager OSM Map View Math Formula k-Means Example: Housing Price PredictionIn this exercise we will predict the price of a house Perth (Australia) given a number of selected numerical features1. Exploring the data set-We examine basic statistics, as well as the distribution of numerical features. -We also generate a scatter plot of the target (y-axis) plotted against one of the numerical features (x-axis).2. Training the linear regression mode-The data set is partitioned into the training (70%) and testing (30%)sets-The linear regression model is trained on the training data set. Target: house price, features: all numerical features-The trained model is fitted to the testing data. -We evaluate the model performance Price Prediction: Linear Regression Clustering the position of House in perth Node 1Node 303Node 304Bin PriceColor accordingto clusterVisualize the clusterson world mapNode 310Clusteringk=5CSV Reader Partitioning Numeric Scorer Missing Value Linear RegressionLearner RegressionPredictor Linear RegressionLearner RegressionPredictor Concatenate Sorter Rule Engine Color Manager OSM Map View Math Formula k-Means

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2024-11-12
Last update: 2026-01-18
Created with KNIME version: v5.3.2
Tags: linear regressionmachine learningpredictioneducation

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