Predictive Analytics - House Sale Prices in Ames, Iowa

Link to deployed application

Table of Contents

• Project Terminology

• Project Dataset

• Business Requirements

• Project Hypothesis

• ML Business Case

• Dashboard Design

• Unfixed Bugs

• Deployment

• Cloning

• Main Data Analysis and Machine Learning Libraries

• Credits

Project Terminology

Correlation Analysis

The examination of the relationship or association between variables, in this case, house attributes and sale prices.

House Attributes

The characteristics or features of a house that can influence its value, such as the number of bedrooms, square footage, location, etc.

House Prices

The monetary value or sale price of residential properties.

Property Appraisal

The process of estimating the value of a property.

Sales Price

The amount at which a property is sold or offered for sale.

Project Dataset

The dataset from Kaggle has almost 1.5 thousand rows and represents housing records from Ames, Iowa, indicating house profile (Floor Area, Basement, Garage, Kitchen, Lot, Porch, Wood Deck, Year Built) and its respective sale price for houses built between 1872 and 2010.

Variable	Meaning	Units
1stFlrSF	First Floor square feet	334 - 4692
2ndFlrSF	Second-floor square feet	0 - 2065
BedroomAbvGr	Bedrooms above grade (does NOT include basement bedrooms)	0 - 8
BsmtExposure	Refers to walkout or garden level walls	Gd: Good Exposure; Av: Average Exposure; Mn: Minimum Exposure; No: No Exposure; None: No Basement
BsmtFinType1	Rating of basement finished area	GLQ: Good Living Quarters; ALQ: Average Living Quarters; BLQ: Below Average Living Quarters; Rec: Average Rec Room; LwQ: Low Quality; Unf: Unfinshed; None: No Basement
BsmtFinSF1	Type 1 finished square feet	0 - 5644
BsmtUnfSF	Unfinished square feet of basement area	0 - 2336
TotalBsmtSF	Total square feet of basement area	0 - 6110
GarageArea	Size of garage in square feet	0 - 1418
GarageFinish	Interior finish of the garage	Fin: Finished; RFn: Rough Finished; Unf: Unfinished; None: No Garage
GarageYrBlt	Year garage was built	1900 - 2010
GrLivArea	Above grade (ground) living area square feet	334 - 5642
KitchenQual	Kitchen quality	Ex: Excellent; Gd: Good; TA: Typical/Average; Fa: Fair; Po: Poor
LotArea	Lot size in square feet	1300 - 215245
LotFrontage	Linear feet of street connected to property	21 - 313
MasVnrArea	Masonry veneer area in square feet	0 - 1600
EnclosedPorch	Enclosed porch area in square feet	0 - 286
OpenPorchSF	Open porch area in square feet	0 - 547
OverallCond	Rates the overall condition of the house	10: Very Excellent; 9: Excellent; 8: Very Good; 7: Good; 6: Above Average; 5: Average; 4: Below Average; 3: Fair; 2: Poor; 1: Very Poor
OverallQual	Rates the overall material and finish of the house	10: Very Excellent; 9: Excellent; 8: Very Good; 7: Good; 6: Above Average; 5: Average; 4: Below Average; 3: Fair; 2: Poor; 1: Very Poor
WoodDeckSF	Wood deck area in square feet	0 - 736
YearBuilt	Original construction date	1872 - 2010
YearRemodAdd	Remodel date (same as construction date if no remodelling or additions)	1950 - 2010
SalePrice	Sale Price	34900 - 755000

Business Requirements

As a good friend, you are requested by your friend, who has received an inheritance from a deceased great-grandfather located in Ames, Iowa, to help in maximizing the sales price for the inherited properties.

Although your friend has an excellent understanding of property prices in her own state and residential area, she fears that basing her estimates for property worth on her current knowledge might lead to inaccurate appraisals. What makes a house desirable and valuable where she comes from might not be the same in Ames, Iowa. She found a public dataset with house prices for Ames, Iowa, and will provide you with that

• The client is interested in discovering how the house attributes correlate with the sale price. Therefore, the client expects data visualizations of the correlated variables against the sale price to show that.
• The client is interested in predicticing the sale prices of her four inherited houses, and any other house in Ames, Iowa.

Project Hypothesis and Validation

• The distribution of sale prices exhibits a right-skewness, which can pose difficulties in accurately predicting higher sale prices.
• In order to validate the project hypothesis regarding the distribution shape - a combined boxplot and histogram of the sale prices is plotted.

The rationale to map the business requirements to the Data Visualisations and ML tasks

Business Requirement 1: Correlation Study and Data Visualization

1. The client desires to examine the house records data to gain insights into the variables' significance concerning the sale price.
2. The client requests a heatmap displaying the Spearman correlation coefficients to facilitate the ordering of variables based on their importance in relation to the sale price.
3. The client intends to visualize the correlation between the important variables and the sale price by plotting them together.

Business Requirement 2: Target Prediction & Data Analysis with Machine Learning

1. The client seeks to access the inherited house records data for easy identification of specific house attributes.
2. The client wishes to employ an ML model to predict the prices of their four inherited houses located in Ames, Iowa.
3. The client aims to utilize the ML model for predicting the price of any other house in Ames, Iowa.

ML Business Case

1. What are the business requirements?

• The client is interested in understanding the correlation between house attributes and sale prices through data visualizations.
• The client wants to predict sale prices for their inherited houses and other houses in Ames, Iowa.

2. Is there any business requirement that can be answered with conventional data analysis?

• Yes, conventional data analysis can be used to explore the correlation between house attributes and sale prices.

3. Does the client need a dashboard or an API endpoint?

• The client specifically requires a dashboard.

4. What does the client consider as a successful project outcome?

• The client expects a comprehensive study revealing the most relevant variables correlated to sale price.
• The client desires the ability to accurately predict sale prices for the inherited houses and other houses in Ames, Iowa.

5. Can you break down the project into Epics and User Stories?

• Epic: Information gathering and data collection.
• Epic: Data visualization, cleaning, and preparation.
• Epic: Model training, optimization, and validation.
• Epic: Dashboard planning, designing, and development.
• Epic: Dashboard deployment and release.

6. Ethical or Privacy concerns?

• No, as the client has obtained a public dataset.

7. Does the data suggest a particular model?

• The data implies the suitability of a regressor model with the sale price as the target variable.

8. What are the model's inputs and intended outputs?

• The inputs for the model are house attribute information, and the intended output is the predicted sale price.

9. What are the criteria for the performance goal of the predictions?

• The agreed performance goal is to achieve an R2 score of at least 0.75 on both the train and test sets.

10. How will the client benefit?

• The client will be able to maximize the sales price for their inherited properties, making informed decisions based on the most influential variables correlated to sale price.

Dashboard Design

Page 1: Project Summary

• Overview of the dataset
• Overview of the business requirements

Page 2: Project Findings

• Explains the client's expectations
• Presents the most relevant features and their meanings

Page 3: Target Prediction

• Presents the inherited houses' attributes, the predicted sale prices and the total price for all four houses combined
• Contains the interactive input widget for running predictive analysis of the target variable

Page 4: Project Hypothesis

• Presents the distribution shape of the target variable
• Presents the range for outliers
• Presents the hypothesis for the limitations of the ML model

Page 5: Model & Pipeline

• Pipeline Structure
• Model Performance Evaluation
• The two most important house attributes

Unfixed Bugs

No bugs found.

Deployment

To deploy the project to Heroku, follow these instructions:

1. Add the following four files into the project's root directory:

• Procfile: Specifies the commands to be executed by Heroku's server when your application is deployed.
• runtime.txt: Specifies the version of the programming language or framework required by your application.
  • Heroku's supported runtimes.
• setup.sh: Contains commands to set up the necessary environment and dependencies for your application.
• requirements.txt: Lists the Python packages and their versions required by your application.

2. Create an app on the official Heroku website or through the CLI:

• If using the website:

  • Log in to your Heroku account.
  • Create a new app and give it a unique name.
  • Choose the deployment region that is closest to your target audience.
  • Connect your app to your GitHub repository.

• If using the CLI:

  • Open your preferred terminal or command prompt.
  • Log in to your Heroku account using the following command:

    heroku login
    

  • Create a new Heroku app using the following command:

    heroku create
    

  • Link your app to your GitHub repository using the following command:

    heroku git:remote -a your-app-name
    

3. Deploy your application:

• If using the website:

  • Enable automatic deploys or manually deploy the latest changes from your GitHub repository.

• If using the CLI:

  • Push your code to the Heroku remote repository using the following command: git push heroku main

4. Once the deployment process is complete, Heroku will build and run your application.

Your project is now deployed on Heroku. You can access your app using the provided URL or the custom domain you set up.

Cloning

Instructions

To clone this project repository to your local machine, follow these steps:

1. Open your preferred Git client or Git command line.
2. Navigate to the directory where you want to clone the project.
3. Run the following command: git clone https://github.com/Sirak-K/my-project-5.git
4. This will create a local copy of the project on your machine.
5. Once the cloning process is complete, you can navigate into the project directory using the following command: cd my-project-5 .

You have successfully cloned the project repository to your local machine and you now have access to the project files on your local machine.

Main Data Analysis and Machine Learning Libraries

• numpy==1.18.5

  • Numerical computing library for efficient array operations.

• pandas==1.4.2

  • Data manipulation and analysis tool for structured data.

• matplotlib==3.3.1

  • Data visualization library for creating static, animated, and interactive plots.

• seaborn==0.11.0

  • Statistical data visualization library for creating informative and attractive graphics.

• ydata-profiling==4.3.1

  • Automated exploratory data analysis tool for generating data profiles.

• streamlit==1.0.0

  • Python framework for building interactive web applications for data science.

• feature-engine==1.0.2

  • Library for feature engineering tasks such as encoding, imputation, and scaling.

• scikit-learn==0.24.2

  • Machine learning library for classification, regression, and clustering tasks.

• plotly==4.12.0

  • Interactive data visualization library for creating interactive plots and dashboards.

• ppscore==1.2.0

  • Library for calculating predictive power scores to measure feature importance.

• xgboost==1.21

  • Gradient boosting library for optimized and high-performance machine learning models.

Credits

Content

Coding Resources:

The work for this project was inspired by the content in Code Institute's course modules:

• Scikit-Learn
• Machine Learning Algorithms
• ML Walkthrough Projects

Media

Icons for Dashboard - Twemoji

Acknowledgements

I want to express my gratitude towards my mentor Precious for helping me plan my project.