NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Machine Learning > Data Machine Learning: Iowa House Prices Predicting

Data Machine Learning: Iowa House Prices Predicting

Qifan Wang, heqian and Weixing Yang
Posted on Mar 17, 2019

Project GitHub | LinkedIn:   Niki   Moritz   Hao-Wei   Matthew   Oren

The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Introduction

There are many factors involved in real estate pricing. In reality, it is often hard for us to tell which factors are more important and which factors are not. In this project, we are going to predict house prices of Ames, Iowa with supervised predictive modeling techniques. The dataset is from a Kaggle competition, it has a total number of 80 features for each observation. During this project, we learning the entire machine learning process from data cleaning to advanced model ensembling techniques like stacking.

Data Cleaning

The data cleaning is always the first step of any data science project. After inspecting the dataset, the first thing we found out is that the target variable is not following the normal distribution.

The violation in the normality will cause the linear model to be influenced more by the tails, so we used the log data transformation to transform the target variable back to normal distribution.

After the first step, we started to work on exception handling. We found two obvious outliers after we checked the relationship between the target variable and other independent variables like ground living areas. There are two observations that are obvious outliers, so we decided to remove them.

Processing of missing data is the next step of our data manipulation process. First, found out missing data. After inspection, we chose to separate the imputation into three groups. We used None to replace missingness at category group and utilized 0 for the numeric data. After these two groups, we found out several variables in these two groups are not meaningful impute with 0 or none.

Then we chose other ways to handle missing data in these variables. One of these variables is LotFrontage variable means linear feet of street connected to the property. According to the meaning of this variable, 0 is not a good way to impute, so we decided to use the mean. For dealing with Other variables are all in the category group, we chose to fill the missing with the most frequent patterns.

Feature Engineering & Modeling

For the modeling, our team used linear models including Lasso, Ridge and ElasticNet to predict the price of selling houses. Lasso and Ridge are used to decrease the model complexity which is the number of predictors, and they both add a hyperparameter lambda to tell you how much your model weighs on the penalization. The difference between Lasso and Ridge is Ridge penalizes sum of the squared coefficients and Lasso penalizes the sum of their absolute value. Therefore, Lasso actually forced some coefficient estimates to be exactly zero. ElasticNet is the combination of Lasso and Ridge.

In order to get the best model for all of them, we used GridSearchCV with ten fords to find the optimal lambda for each of them. With the Alpha ranged in 0 to 0.001, we have the lambda for the best model equal to 0.0001, and it comes with the best CV score 0.9143.

For Ridge, with the Alpha ranged in 0.2 to 0.3, we have the lambda for the best model equal to 0.2474, and the best CV score comes with it equal to 0.9107.

For ElasticNet, our Alpha ranged in 0 to 0.001, the lambda for the best model equal to 0.0001, and L1r_ratio = 0.5333 which means it place emphasis on Lasso slightly more, and the best CV score is equal to 0.9107.

Beside linear models, our team also tried Tree Based Model, including random Forest, Gradient Boosting and Extreme Gradient Boosting. The idea of them is to use a combination of learning models to increase the overall result. The difference is each tree is generated by using information from previous trees.

The result of our modeling:

After trying above six models, our team wants to see if we compare the feature importance in each model, and filter out some features that do not contribute to any of models will it improve the performance of our models.  The following are the top important features in each model after removing the duplication as following:

Lasso:

KitchenAbvGr, BsmtFinSF1, TotalBsmtSF, Foundation, BsmtFullBath, HeatingQC, Fireplaces, SaleType, SaleCondition, YearBuilt, YearRemodAdd, MSZoning

Ridge:

OverallQual, OverallCond, Neighborhood, PoolQC, RoofMatl, Exterior1st, Functional, CentralAir, HouseStyle, Condition1, Street, BsmtExposure, MasVnrType, LotConfig, MSSubClass, GarageType, Heating, Exterior2nd, HalfBath, BsmtCond

ElasticNet:

FullBath, 1stFirSF, GarageCars, BsmtQual, KitchenQual, LotArea,  GrLivArea

Random Forest:

GarageQual, BsmtFinType1, Electrical, MiscVal, Condition2, RoofStyle, MiscFeature, FireplaceQu, LotShape, GarageFinish, BedroomAbvGr, PavedDrive, LandContour, ExterCond, Fence, BldgType, 3SsnPorch, LandSlope, GarageCond

Gradient Boosting:

Alley, BsmtFinType2, YrSold, 1stFlrSF, GarageArea

Extreme Gradient Boosting:

ScreenPorch, WoodDeckSF, GarageYrblt, BsmtUnfSF, TotRmsAbvGrd, OpenPorchSF, LotFrontage, 2ndFlrSF

After compare with all of the above models, we decided to eliminate the following features: BsmtFinSF2,  BsmtHalfBath, EnclosedPorch, MasVnrArea, MoSold. And re-run all above six models with fewer features in the data frame. Unfortunately, this does not help us with improving scores for any of the models, it ends up with decreasing them. The comparison can be found below:

After the reduced feature modeling experiment, we decided not to reduce our features but creating more features. So we created the TotalSF that adding up the GrLivSF and BsmtSF, and the YrBtwRemod is the number of years between the last remodeling and the year sold of the house. These 2 extra features gave us another 10% improvement in the public leaderboard. We have created many other features and used the lasso and other tree models to test the feature importance but unfortunately, the first 2 features are the two with the strongest predicting power.

Model Stacking

By finishing up with the model selection and feature engineering, we were doing some extra experiment to further improve our model accuracy. By doing some research online, we have noticed this common Kaggle technique called stacking. It is basically by combining different models and get a better result.

Our first attempt was taking the average between lasso and XGBoost which is our top 2 most accurate models, and it got us a test error of 0.11963 right away which is top 22% in the leaderboard. After that, we have also tried the regular stacking method, which is taking the prediction of other models as input and use a meta-model to predict base on the base modelโ€™s output. Unfortunately, the result is closed to the best base model but it did not beat the average of XGBoost and lasso.

The final result we were taking is the weighted average of lasso, GBM and GXBoost. The weighted assigned to each model is 0.5, 0.25, 0.25.

Summary

In summary, during this machine learning project, we have learned the basic general process of a machine learning project from data cleaning, missingness imputation to cross-validation and predictive modeling. Out key get away from this project is feature engineering is always the most important thing during the machine learning.

The largest improvement we gained is by adding extra features, comparing with other methods like models selection and model ensemble. In the feature, feature engineering will definitely be one of the steps that we pay more attention to, in order to obtain a better predicting result.

About Authors

Qifan Wang

Recent NYU graduate with MS in Management Information Systems, with previous experiences in business analytics and marketing industry, Qifan is passionate about applying Data Science on the field of business. With 12 weeks of intensive training in the...
View all posts by Qifan Wang >

heqian

View all posts by heqian >

Weixing Yang

Data scientist with a background in big data analytics and intensive programming. I am currently seeking a position within a creative and dynamic work environment that gives me the opportunity to contribute my abilities and skill set gained...
View all posts by Weixing Yang >

Related Articles

Data Analysis
Injury Analysis of Soccer Players with Python
Machine Learning
Ames House Prices Predictions
Python
US Honey Production Analysis With Python (1998-2012)
Machine Learning
The Ames Data Set: Sales Price Tackled With Diverse Models
Python
EDA and machine learning Ames housing price prediction project

Leave a Comment

Cancel reply

You must be logged in to post a comment.

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application