The document provides an overview of different machine learning algorithms used to predict house sale prices in King County, Washington using a dataset of over 21,000 house sales. Linear regression, neural networks, random forest, support vector machines, and Gaussian mixture models were applied. Neural networks with 100 hidden neurons performed best with an R-squared of 0.9142 and RMSE of 0.0015. Random forest had an R-squared of 0.825. Support vector machines achieved 73% accuracy. Gaussian mixture modeling clustered homes into three groups and achieved 49% accuracy.
This project aims to determine the housing prices of California properties for new sellers and also for buyers to estimate the profitability of the deal using various regression models.
Below are the details of the models implemented and their performance score:
Linear Regression: RMSE- 68321.7051304
Decision Tree Regressor: RMSE- 70269.5738668
Random Forest Regressor: RMSE- 52909.1080535
Support Vector Regressor: RMSE- 110914.791356
Fine Tuning the Hyperparameters for Random Forest Regressor: RMSE- 49261.2835608
ABSTRACT
House Price Index is commonly used to estimate the changes in housing price. Since housing price is strongly correlated to other factors such as location, area, population, it requires other information apart from House price prediction to predict individual housing price. There has been a considerably large number of papers adopting traditional machine learning approaches to predict housing prices accurately, but they rarely concern about the performance of individual models and neglect the less popular yet complex models. As a result, to explore various impacts of features on prediction methods, this paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models. This paper will also comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction.
INTODUCTION
House price prediction is great project to learn and apply the machine learning algorithm. The basic idea behind this project is we are training the machine using the machine learning algorithm from the data set.
In this busy world it is very difficult to find a house according to our need and budget. It becomes more difficult to find the house in metropolitan cities like Mumbai, Kolkata, Delhi, etc. This project uses the data of Mumbai city in order to train and test the machine so that it become capable of predicting the price of house. Machine learning algorithm makes it easy to know the price of houses depending on the location, area, number of bedrooms, etc.
In this project Random Forest Regression, Linear Regression, and Decision Tree Machine learning algorithm has been used to compare the efficiency of the algorithm. Based on comparison we predict which algorithm best suits for the prediction of price of house in Mumbai.
CONCLUSION AND FUTURE SCOPE
The model designed accuracy depends on the dataset selected, better the dataset better will be the accuracy. Best suited model applied is Random Forest. This can be applied to datset of any city for their house price prediction. The project can be enhanced by UI designing through they can predict the price in more easier and interactive way. In this busy world it will be of immense use to search for a house at near to our workplace.
DATASET LINK
https://www.kaggle.com/
House Price Estimates Based on Machine Learning Algorithmijtsrd
Housing prices are increasing every year, necessitating the creation of a long term housing price strategy. Predicting a homes price will assist a developer in determining a homes purchase price, as well as a consumer in determining the best time to buy a home. The sale price of real estate in major cities depends on the specific circumstances. Housing prices are constantly changing from day to day and are sometimes fired rather than based on estimates. Predicting real estate prices by real factors is a key element as part of our analysis. We want to make our test dependent on all of the simple metrics that are taken into account when deciding the significance. In this research we use linear regression techniques pathway and our results are not self inflicted process rather is a weighted method of various techniques to give the most accurate results. There are fifteen features in the data collection. In this research. There has been an effort to build a forecasting model for determining the price based on the variables that influence the price.The results have proven to be effective lower error and higher accuracy than individual algorithms are used. Jakir Khan | Dr. Ganesh D "House Price Estimates Based on Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42367.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42367/house-price-estimates-based-on-machine-learning-algorithm/jakir-khan
This project aims to determine the housing prices of California properties for new sellers and also for buyers to estimate the profitability of the deal using various regression models.
Below are the details of the models implemented and their performance score:
Linear Regression: RMSE- 68321.7051304
Decision Tree Regressor: RMSE- 70269.5738668
Random Forest Regressor: RMSE- 52909.1080535
Support Vector Regressor: RMSE- 110914.791356
Fine Tuning the Hyperparameters for Random Forest Regressor: RMSE- 49261.2835608
ABSTRACT
House Price Index is commonly used to estimate the changes in housing price. Since housing price is strongly correlated to other factors such as location, area, population, it requires other information apart from House price prediction to predict individual housing price. There has been a considerably large number of papers adopting traditional machine learning approaches to predict housing prices accurately, but they rarely concern about the performance of individual models and neglect the less popular yet complex models. As a result, to explore various impacts of features on prediction methods, this paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models. This paper will also comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction.
INTODUCTION
House price prediction is great project to learn and apply the machine learning algorithm. The basic idea behind this project is we are training the machine using the machine learning algorithm from the data set.
In this busy world it is very difficult to find a house according to our need and budget. It becomes more difficult to find the house in metropolitan cities like Mumbai, Kolkata, Delhi, etc. This project uses the data of Mumbai city in order to train and test the machine so that it become capable of predicting the price of house. Machine learning algorithm makes it easy to know the price of houses depending on the location, area, number of bedrooms, etc.
In this project Random Forest Regression, Linear Regression, and Decision Tree Machine learning algorithm has been used to compare the efficiency of the algorithm. Based on comparison we predict which algorithm best suits for the prediction of price of house in Mumbai.
CONCLUSION AND FUTURE SCOPE
The model designed accuracy depends on the dataset selected, better the dataset better will be the accuracy. Best suited model applied is Random Forest. This can be applied to datset of any city for their house price prediction. The project can be enhanced by UI designing through they can predict the price in more easier and interactive way. In this busy world it will be of immense use to search for a house at near to our workplace.
DATASET LINK
https://www.kaggle.com/
House Price Estimates Based on Machine Learning Algorithmijtsrd
Housing prices are increasing every year, necessitating the creation of a long term housing price strategy. Predicting a homes price will assist a developer in determining a homes purchase price, as well as a consumer in determining the best time to buy a home. The sale price of real estate in major cities depends on the specific circumstances. Housing prices are constantly changing from day to day and are sometimes fired rather than based on estimates. Predicting real estate prices by real factors is a key element as part of our analysis. We want to make our test dependent on all of the simple metrics that are taken into account when deciding the significance. In this research we use linear regression techniques pathway and our results are not self inflicted process rather is a weighted method of various techniques to give the most accurate results. There are fifteen features in the data collection. In this research. There has been an effort to build a forecasting model for determining the price based on the variables that influence the price.The results have proven to be effective lower error and higher accuracy than individual algorithms are used. Jakir Khan | Dr. Ganesh D "House Price Estimates Based on Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42367.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42367/house-price-estimates-based-on-machine-learning-algorithm/jakir-khan
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
Predicting Moscow Real Estate Prices with Azure Machine LearningLeo Salemann
With only three months' instruction, a five-person team uses Azure Machine Learning Studio to predict Moscow real estate prices based on property descriptors, macroeconomic indicators, and geospatial data.
House Price Prediction An AI Approach.Nahian Ahmed
Suppose you have a house. And you want to sell it. Through House Price Prediction project you can predict the price from previous sell history.
And we make this prediction using Machine Learning.
Prediction of house price using multiple regressionvinovk
- Constructed a mathematical model using Multiple Regression to estimate the Selling price of the house based on a set of predictor variables.
- SAS was used for Variable profiling, data transformations, data preparation, regression modeling, fitting data, model diagnostics, and outlier detection.
Basics of machine learning including architecture, types, various categories, what does it takes to be an ML engineer. pre-requisites of further slides.
Prediction of Diamond Prices Using Multivariate RegressionMohitMhapuskar
The prices of precious diamonds are primarily determined by some sort of combination of the four C's : Carat, Color, Cut and Clarity. Our team used SAS to implement feature selection and multivariate regression to create a regression model that would allow us to predict the prices of diamonds based on those intrinsic characteristics. Our model achieved an accuracy of 94%.
Dataset: Gather a large dataset of laptops and their features, including processor speed, RAM, storage, and display size, along with their corresponding prices.
Feature engineering: Extracting meaningful features from the dataset, such as brand, model, and year, and transforming them into a format that machine learning algorithms can use.
Model selection: Choosing the most appropriate machine learning algorithm, such as linear regression, decision tree, or random forest, based on the type of data and desired level of accuracy.
Model training: Splitting the dataset into training and testing sets, and using the training data to train the machine learning model.
Model evaluation: Testing the model's performance on the testing data and evaluating its accuracy using metrics such as mean squared error or R-squared.
Hyperparameter tuning: Optimizing the model's hyperparameters, such as learning rate or regularization strength, to achieve the best performance.
Predicting rainfall using ensemble of ensemblesVarad Meru
The Paper was done in a group of three for the class project of CS 273: Introduction to Machine Learning at UC Irvine. The group members were Prolok Sundaresan, Varad Meru, and Prateek Jain.
Regression is an approach for modeling the relationship between data X and the dependent variable y. In this report, we present our experiments with multiple approaches, ranging from Ensemble of Learning to Deep Learning Networks on the weather modeling data to predict the rainfall. The competition was held on the online data science competition portal ‘Kaggle’. The results for weighted ensemble of learners gave us a top-10 ranking, with the testing root-mean-squared error being 0.5878.
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
Predicting Moscow Real Estate Prices with Azure Machine LearningLeo Salemann
With only three months' instruction, a five-person team uses Azure Machine Learning Studio to predict Moscow real estate prices based on property descriptors, macroeconomic indicators, and geospatial data.
House Price Prediction An AI Approach.Nahian Ahmed
Suppose you have a house. And you want to sell it. Through House Price Prediction project you can predict the price from previous sell history.
And we make this prediction using Machine Learning.
Prediction of house price using multiple regressionvinovk
- Constructed a mathematical model using Multiple Regression to estimate the Selling price of the house based on a set of predictor variables.
- SAS was used for Variable profiling, data transformations, data preparation, regression modeling, fitting data, model diagnostics, and outlier detection.
Basics of machine learning including architecture, types, various categories, what does it takes to be an ML engineer. pre-requisites of further slides.
Prediction of Diamond Prices Using Multivariate RegressionMohitMhapuskar
The prices of precious diamonds are primarily determined by some sort of combination of the four C's : Carat, Color, Cut and Clarity. Our team used SAS to implement feature selection and multivariate regression to create a regression model that would allow us to predict the prices of diamonds based on those intrinsic characteristics. Our model achieved an accuracy of 94%.
Dataset: Gather a large dataset of laptops and their features, including processor speed, RAM, storage, and display size, along with their corresponding prices.
Feature engineering: Extracting meaningful features from the dataset, such as brand, model, and year, and transforming them into a format that machine learning algorithms can use.
Model selection: Choosing the most appropriate machine learning algorithm, such as linear regression, decision tree, or random forest, based on the type of data and desired level of accuracy.
Model training: Splitting the dataset into training and testing sets, and using the training data to train the machine learning model.
Model evaluation: Testing the model's performance on the testing data and evaluating its accuracy using metrics such as mean squared error or R-squared.
Hyperparameter tuning: Optimizing the model's hyperparameters, such as learning rate or regularization strength, to achieve the best performance.
Predicting rainfall using ensemble of ensemblesVarad Meru
The Paper was done in a group of three for the class project of CS 273: Introduction to Machine Learning at UC Irvine. The group members were Prolok Sundaresan, Varad Meru, and Prateek Jain.
Regression is an approach for modeling the relationship between data X and the dependent variable y. In this report, we present our experiments with multiple approaches, ranging from Ensemble of Learning to Deep Learning Networks on the weather modeling data to predict the rainfall. The competition was held on the online data science competition portal ‘Kaggle’. The results for weighted ensemble of learners gave us a top-10 ranking, with the testing root-mean-squared error being 0.5878.
Predicting Moscow Real Estate Prices with Azure Machine LearningKarunakar Kotha
With only three months' instruction, a five-person team uses Azure Machine Learning Studio to predict Moscow real estate prices based on property descriptors, macroeconomic indicators, and geospatial data
Predict Backorder on a supply chain data for an OrganizationPiyush Srivastava
Performed cleaning and founded the important variables and created a best model using different classification techniques (Random Forest, Naïve Bayes, Decision tree, KNN, Neural Network, Support Vector Machine) to predict the back-order for an organization using the best modelling and technique approach.
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
This report includes information about:
1. Pre-Processing Variables
a. Treating Missing Values
b. Treating correlated variables
2. Selection of Variables using random forest weights
3. Building model to predict donors and amount expected to be donated.
2. Agenda
▪ Introduction
▪ About Dataset
▪ Linear Regression
▪ Neural Networks
▪ Random Forest
▪ SupportVector Machine
▪ Gaussian Mixture Model
▪ Algorithm Comparisons
▪ Q & A
3. Introduction
Our goal for this project was to use regression and
classification techniques in order to estimate the sale
price of a house in King County,Washington given the
feature and pricing data for around 21,000 houses sold
within one year.
4. About
Dataset
Our dataset comes from a Kaggle competition.
Our dataset contains house sale prices and its features for
homes sold in King County,Washington between May 2014
and May 2015.
King County is the most populous county inWashington and
is included in the Seattle-Tacoma-Bellevue metropolitan
statistical area.The county is considered the 13th most
populous county in the United States.
There are 21,613 observations in the dataset.
There are 25 total attributes in the dataset, four of which we
derived from current columns.We are planning to use 22
attributes in our models: all attributes except for date,
latitude and longitude.
5. Dataset-
Preprocessing
During cleaning and preprocessing,
we created four attributes derived
from other attributes: Age,
Age_revovated, Sqrt_living15_diff,
and Sqft_lot15_diff.
We chose not to use the variable data,
because it only shows us when the
data was entered into the database.
We chose not to use latitude and
longitude because the attribute,
Zipcode, contains the same
information and was easier to work
with in our models.
We checked for missing variables and
the dataset didn’t contain any.
We performed feature selection by
looking at the correlation percentage
of each attribute with price.
Attribute Percentage
Bedrooms 0.308349598
Bathroom 0.525137505
Sqft_living 0.702035055
Sqft_lot 0.089660861
Floors 0.256793888
Waterfront 0.266369434
View 0.397293488
Condition 0.036361789
Grade 0.667434256
Sqft_above 0.605567298
Sqft_basement 0.323816021
Yr_built 0.054011531
Age -0.054011531
Yr_renovated 0.126433793
Age_renovation -0.105754631
Sqft_living15 0.585378904
Sqft_living15_diff 0.405391664
Sqft_lot15 0.082447153
Sqft_lot15_diff 0.050590661
6. Outlier
Detection
using
Cook’s
Distance
We used Cook's distance for the original dataset and found one
outlier. It was removed to create dataset 2.
After applyingCook's distance to dataset 2, we found 608
outliers. They were removed to create dataset 3.
The team used dataset 2 and dataset 3 for the project.
7. Linear
Regression
Used MATLAB to create 3 models
Datasets : divided into 80% training, 20% test
Dataset 2 - one outlier removed
Dataset 3 – 608 outliers removed
Algorithm
Model 1 used dataset 2
Model 2 used dataset 3 with 7 predictors removed
Models 3 used dataset 3 with 15 predictors removed
(yhat= predict(mdl,Xnew))
8. Linear
Regression
Analytics Results:
Model 2Training Metrics:
Root Mean Squared Error: $96,800
R-squared: 0.879
Adjusted R-Squared 0.878
Model 2Test Metrics:
Root Mean Squared Error: $94,927.38
R-squared: 0.913223143
9. Neural
Networks
Used Matlab’s built in functions: fitnet and feedforwardnet
Tried two different methods: Levenberg-Marquardt and Bayesian
Normalized with mapminmax to scale the targets, where the
output of the network will be trained to produce outputs in the
range [–1,+1]
Started with 1 hidden layer and 10 neurons.
Ran each combination again with 100 hidden neurons to
determine which one would perform better.
11. Neural
Networks
Analytics Results:
R-Squared score, Root Mean Squared Error (RMSE), Mean
Absolute Error of each model were calculated to find the quality
and performance of the algorithm.
Fit Net Bayesian with 100 hidden neurons performed the best
Took nearly 2 hours to run the model
R-Squared = 0.9142
RMSE = 0.0015
MeanAbsolute Error = 0.0258
12. Random
Forest
Written in and executed in Python due to it’s extensive
sklearn package.
In this algorithm:
Transformed the target variable(price) to the log to
correct for skew
Removed outliers using a function called median
absolute deviation
Calculated the skew for each variable
Transformed variables if the skew was greater than
0.75
Applied a normalizer to each column
Ran Random Forest using 10 fold cross validation
13. Random
Forest
Feature Selection:
Outputted feature importance coefficients, mapped
them to their feature name, and sorted in decreasing
order. Given our choice of model and methods for
preprocessing data the most significant features are: 1.
Grade 2. Zipcode 3. Sqft_living 4.Yr_built
14. Random
Forest
Analytics Results:
R-Squared score, Root Mean Squared Error (RMSE), Mean Absolute
Error, and ExplainedVariance of each model were calculated to find
the quality and performance of the algorithm.
Model 1 performed the best
|R-Squared = 0.825 | Adj. R-Squared = 0.825| RMSE = 0.217 |Mean
Absolute Error | = 0.157| ExplainedVariance Score= 0.825
15. SVM
A SupportVector Machine (SVM) is a discriminative classifier
formally defined by a separating hyperplane. Given labeled
training data (supervised learning), the algorithm outputs an
optimal hyperplane which categorizes new examples.
Advantages:
SVMs produces large margin separating hyperplane, and efficient
in higher dimension
It maximizes the margin between points closest to the boundary
SVMs only consider points near the margin (support vectors) –
more robust
Disadvantages:
Due to complexity of the algorithm it requires high amount of
memory and takes long time to train the model and predict the
test data
The model is sensitive to optimal choice of kernel and
regularization parameters
16. SVM
Data Preparation
• Used Correlation to figure out which predictor
contribute more in prediction of prices
• Normalized all predictor to equal scale.
• Converted the target (Price – numerical data) to
categorical values and into three bins.
Bin 1 0-300000
Bin2 300000-700000
Bin 3 700000+
17. SVM
Creating Gram matrix from training data
Fit the model using gram matrix
Predict the New X
Building Model
18. SVM
Predicted –
Class 1
Predicted –
Class 2
Predicted –
Class 3
Total Actual
Actual –
Class 1
0 0 0 0
Actual –
Class 2
4427 12582 4205 21214
Actual –
Class 3
10 182 207 399
Total
Predicted
4437 12764 4412 21613
Linear SupportVector Machine
Accuracy = 0.58 Time taken = 10 mins
20. SVM
Predicted –
Class 1
Predicted –
Class 2
Predicted –
Class 3
Total Actual
Actual –
Class 1
1548 2881 8 4437
Actual –
Class 2
678 11473 613 12764
Actual –
Class 3
5 1572 2835 4412
Total
Predicted
2231 15376 3456 21613
Kernel - Gaussian Infinite Dimension
Class 1 Class 2 Class 3
Precision 0.53 0.69 0.57
Recall 0.69 0.52 0.35
AUC 0.86 58 0.5426 0.20
Accuracy = 0.73
Time taken = 2.5 hours
22. Gaussian
Mixture
Model
• Bucketed price values into three different groups:
• Less than or equal to 300K (21.2%);
• Greater than 300K
• Less than 700K (58.4%); 700K or greater (20.4%).
• Principle Component Analysis was performed to select the best features.
• Transformed data into a set of linearly uncorrelated variables.
• Chose the two components that accounted for the majority of data variance.
• Ran with 1-3 clusters with different levels of regularization.
• Selected clusters with lowest Negative Log-Likelihood.
• Used posterior probability as a predictor to see if this improved the accuracy.
• The best overall model was with 3 clusters and 0 regularization.
• The best model accuracy and lowest negative log-likelihood was with the posterior
probability with 0.05 regularization
23. Gaussian
Mixture
Model
Model 1 Model 2 Model 3
NLL 6.85E+04 6.98E+04 -6.25E+03
Accuracy 0.4928 0.5456 0.5483
Class One Two Three One Two Three One Two Three
Recall 0.65 0.58 0.08 0.006 0.92 0.04 0.005 0.92 0.039
Precision 0.35 0.59 0.56 0.01 0.62 0.45 0.009 0.62 0.44
AUC 0.7176 0.5517 0.7943 0.2297 0.5316 0.8001 0.2337 0.5318 0.8339
Class 1:
Class 2:
Class 3:
24. Gaussian
Mixture
Model
Class 1 Class 2 Class 3
Recall 0.65 0.58 0.08
Precision 0.35 0.59 0.56
AUC 0.7176 0.5517 0.7943
Accuracy = 0.49
Negative Log-Likelihood
= 6.85e+04 = 68544
CFM Actual–
Class 1
Actual–
Class 2
Actual –
Class 3
Total Predic
ted
Predicted –
Class 1
3009 5086 618 8713
Predicted–
Class 2
1537 7284 3436 12257
Predicted–
Class 3
24 261 358 643
Total Actual 4570 12631 4412 21613
25. Algorithm
Comparisons
Linear Regression:
R-squared: 0.913223143
Root Mean Squared Error: 0.349
Neural Networks:
R-Squared = 0.9142
Root Mean Squared Error = 0.0015
Mean Absolute Error = 0.0015
Random Forest:
R-squared = 0.825
Adjusted R-Squared = 0.825
Root Mean Squared Error = 0.217
Mean Absolute Error = 0.157
RegressionAlgorithms: