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.
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
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.
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
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.
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.
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.
Basics of machine learning including architecture, types, various categories, what does it takes to be an ML engineer. pre-requisites of further slides.
Data Science: Prediction analysis for houses in Ames, Iowa.ASHISH MENKUDALE
For the vastly diversified realty market, with prices of properties increasing exponentially, it becomes essential to study the factors which affect directly or indirectly when a customer decides to buy a house and to predict the market trend. In general, for any purchase, a potential customer makes the decision based on the value for the money.
The problem statement was taken from the website Kaggle. We chose this specific problem because it provided us an opportunity to build a prediction model for real-life problems like the prediction of prices for houses in Ames, Iowa.
• Have used and demonstrated CRISP-DM methodology throughout the project.
• Used RapidMiner tool to automatically adapt all the possible attributes and operator to provide the prediction.
• Have used different algorithms like Decision tree, Random forest, and Gradient boosted tree to predict price distribution and created the simulation of the result.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
This is a small presentation on my project , diabetes prediction using R language.The method used is knn(K nearest neighbour). it the basic Machine learning algorithm.
Discussed what is Prescriptive Analytics, comparison between Descriptive and Prescriptive Analytics, process, methods and tools. A report presentation conducted at University of East - Manila, Philippines dated July 6, 2017.
In this presentation, two different data-sets are being collected to implement the machine learning classification techniques introduced from introduction to data mining and machine learning coursework. Both data-sets are collected by analyzing their output and team members interest. Following are the data-sets named as, Electricity grid stability simulated data-set and Face Recognition on Olivetti Data set
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Statistical theory is a branch of mathematics and statistics that provides the foundation for understanding and working with data, making inferences, and drawing conclusions from observed phenomena. It encompasses a wide range of concepts, principles, and techniques for analyzing and interpreting data in a systematic and rigorous manner. Statistical theory is fundamental to various fields, including science, social science, economics, engineering, and more.
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.
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.
Basics of machine learning including architecture, types, various categories, what does it takes to be an ML engineer. pre-requisites of further slides.
Data Science: Prediction analysis for houses in Ames, Iowa.ASHISH MENKUDALE
For the vastly diversified realty market, with prices of properties increasing exponentially, it becomes essential to study the factors which affect directly or indirectly when a customer decides to buy a house and to predict the market trend. In general, for any purchase, a potential customer makes the decision based on the value for the money.
The problem statement was taken from the website Kaggle. We chose this specific problem because it provided us an opportunity to build a prediction model for real-life problems like the prediction of prices for houses in Ames, Iowa.
• Have used and demonstrated CRISP-DM methodology throughout the project.
• Used RapidMiner tool to automatically adapt all the possible attributes and operator to provide the prediction.
• Have used different algorithms like Decision tree, Random forest, and Gradient boosted tree to predict price distribution and created the simulation of the result.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
This is a small presentation on my project , diabetes prediction using R language.The method used is knn(K nearest neighbour). it the basic Machine learning algorithm.
Discussed what is Prescriptive Analytics, comparison between Descriptive and Prescriptive Analytics, process, methods and tools. A report presentation conducted at University of East - Manila, Philippines dated July 6, 2017.
In this presentation, two different data-sets are being collected to implement the machine learning classification techniques introduced from introduction to data mining and machine learning coursework. Both data-sets are collected by analyzing their output and team members interest. Following are the data-sets named as, Electricity grid stability simulated data-set and Face Recognition on Olivetti Data set
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Statistical theory is a branch of mathematics and statistics that provides the foundation for understanding and working with data, making inferences, and drawing conclusions from observed phenomena. It encompasses a wide range of concepts, principles, and techniques for analyzing and interpreting data in a systematic and rigorous manner. Statistical theory is fundamental to various fields, including science, social science, economics, engineering, and more.
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.
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
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
"Optimizing Drug Discovery (ADMET) using Machine Learning" involves leveraging advanced algorithms to enhance the drug development process. By analyzing Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) data with ML models, researchers can predict a drug candidate's properties, safety, and efficacy. This approach accelerates the identification of potential drugs, reduces costs, and minimizes the likelihood of late-stage failures. Machine learning aids in the selection of promising compounds, ultimately improving the efficiency and success of drug discovery, benefiting both pharmaceutical companies and patients by delivering safer and more effective medications.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
The Art of the Pitch: WordPress Relationships and Sales
Predicting Moscow Real Estate Prices with Azure Machine Learning
1. Predicting Real Estate Prices in Moscow
A Kaggle Competition
University of Washington Professional & Continuing Education
BIG DATA 220B SPRING 2017 FINAL PROJECT
Team D-Hawks
Leo Salemann, Karunakar Kotha, Shiva Vuppala, John Bever, Wenfan Xu
Keywords: Big Data, Kaggle, Machine Learning, Azure ML Studio, Boosted Decision Tree, Neural Network, Regression, Tableau
2. Problem Description & Datasets
Input Data Description Features Observations
Housing Data Property, neighborhood, sales date & price 292 30,473
Macroeconomics Daily commodity prices, indicators like GDP 100 2,485
Data Dictionary Feature Definitions
Shapefiles Spatial data for maps
4. Azure ML Studio Experiments - Variations
Name
Strategy Experiment Characteristics Cols Rows
Root Mean
Squared Error
RMSE /
STDEV(price)
Wenfan
Baseline
● Basic 12 real estate features
● Tried 4 regression models, kept 2
13 27,909 2,505,749.58 0.524203184
Leo
Incremental add
● Incrementally add more real estate features
● Omit macroeconomic features
● Detailed Human-in-The-Loop process
64 15,693 2,573,721.30 0.538422878
Shiva
Feature Selection Pre-processor
● Separate Experiment for Feature Selection
(Permutation Feature Importance)
● Joined Macro Data
● Added Retail-specific Features
● Added Decision Forest Regression Module
21 30,471 2,425,862.34 0.507490762
Karunakar
Filter Based Feature Selection
● Filter Based Feature Selection
● Boosted Decision tree
● Decision forest regression
38 14,853 3,054,675.32 0.639038531
John
Parallel Cleansing Paths
● Joined Macro Data
● Start with all fields, gradually remove
● Parallel cleansing paths (set to zero; set to 391 30,471 2,263,084.20 0.473437552
5. The Winning Experiment
2. Clean Missing Data - Try three Modes
a. Custom Value Substitution (a fixed value i.e. 0)
b. Replace with Mean
c. Replace using Probabilistic PCA
3. Clip, Normalize Split (same for all 3 paths)
- Handling Categorical & Continuous Variables
- Outlier clipping (per-value; not via SQL)
- Data Normalization or Feature Scaling
4. Train & Evaluate - Compare Three different models
a. Poisson Regression
b. Neural Network Regression
c. Boosted Decision Tree Regression
BA C
1. Collecting Data
11. Conclusion & Further Work
TWL (Today We Learned)
● Azure ML Studio is great for trying multiple techniques in parallel (try that in python!)
● Many ways to approach the problem.
○ Effort required varies a lot …
○ So does the quality of the results.
Next time …
● Watch those row counts … did you lose any?
● Deploy Web Service earlier and more often.
Someday/Oneday …
● Use different models for different subclasses of real estate.
14. Azure ML Studio Experiments - Variations
Name
Strategy Experiment Characteristics Regression Models Notes
Wenfan
Baseline
● Basic 12 real estate features ● Boosted Decision Tree
● Neural Network
● Bayesian Linear
● Linear
Kept Boosted Decision Tree
and Neural Network; dropped
the others.
Leo
Incremental add
● Incrementally add more real estate
features
● Omit macroeconomic features
● Boosted Decision Tree
● Neural Network
Detailed Human-In-The-Loop
(HITL) process.
Shiva
Feature Selection
Pre-processor, add
Macro & Retail
● Joined Macro Data
● Added Retail-specific Features
● Boosted Decision Tree
● Decision Forest
Regression
Separate Experiment for
Feature Selection (Permutation
Feature Importance)
Karunakar
Filter Based Feature
Selection
● Filter Based Feature Selection
● Remove features that aren’t helping
● Boosted Decision Tree
● Forest Regression
Kept Filter Based
Feature,Boosted Decision tree
and Forest regression
John
Parallel Cleansing Paths -
set to 0 vs. median vs.
Probabilistic PCA
● Joined Macro Data
● Start with all fields, gradually remove
● Parallel cleansing paths
● Multiple Boosted Decision
Tree Models
● Poisson
● Neural Network
Multiple simultaneous parallel
paths
16. Shiva’s Pre-Processor Experiment
Permutation Feature Importance algorithm to compute importance scores for each of the feature variables of dataset.
1.Load Housing and macro data; Join data
2. Select ALL columns Edit Metadata (set datatype)
3. Split Data
4. Add Permutation Feature Importance Model. Conn: L: Train Model, R: Dataset
Works only for Regression or Classification.
5. Execute Permutation Feature Importance (40 mins).
6. Result lists top most scored features in the dataset.
17. Karunakar Pre-Processor Experiment
Boosted decision tree algorithm in a decision tree ensemble tends to improve accuracy with some small risk of less coverage.
1.Load Housing data
2. Select columns, Edit Metadata (set datatype)
3. Apply SQL transformations.
4. Filter based feature selection ,normalize data and
split data.
5.choosed Boosted decision tree and decision tree regression to choose the best
predictive.
6 Apply train and score model for each decision algorithm .
7. Evaluate the data model .
18. Karunakar Variation
1. Filter Based Feature Selection (remove features
that aren’t helping)
2. Decision Forest
Filter Based Feature Selection:
1. Feature selection is the process of selecting those
attributes(Columns) in dataset that are most relevant to the
predictive modeling.
2. By choosing the right features, it can potentially improve the
accuracy and efficiency of classification.
3. Filter Based Feature Selection module to identify the columns in
your input dataset that have the greatest predictive power.
Pearson Correlation:
1. Pearson’s correlation statistics or Pearson’s correlation coefficient
is also known in statistical models as the r value. For any two
variables, it returns a value that indicates the strength of the
correlation.
2. Pearson's correlation coefficient is computed by taking the
covariance of two variables and dividing by the product of their
standard deviations. The coefficient is not affected by changes of
scale in the two variables.
19. Karunakar Variation
Decision Forest Regression Model:
Decision trees are nonparametric models that perform a sequence of
simple tests for each instance, traversing a binary tree data structure
until a leaf node (decision) is reached.
Decision trees have these advantages:
1. They are efficient in both computation and memory usage
during training and prediction.
2. They can represent non-linear decision boundaries.
3. They perform integrated feature selection and classification
and are resilient in the presence of noisy features.
This regression model consists of an ensemble of decision trees.
Each tree in a regression decision forest outputs a Gaussian
distribution by way of prediction. An aggregation is performed over the
ensemble of trees to find a Gaussian distribution closest to the
combined distribution for all trees in the model.