Heritage Housing Issues

The live link to the App can be found Here

Business Overview

The primary objective of this project is to develop a data-driven web application that enables the client to accurately predict house sale prices based on various house attributes and provides insightful visualizations of how these attributes correlate with sale prices.

This will aid the client in making informed decisions regarding the sale of four inherited properties and any future real estate investments in Ames, Iowa.

Content Table

• Business Overview
• CRISP-DM
  • CRISP-DM Workflow
  • Agile Development
• Business Requirements
• Dataset Content
• Hypothesis and Validation
• Mapping the business requirements to the Data Visualisations and ML tasks
• ML Business Case
• Dashboard Design
  • Page 1: Project Summary
  • Page 2: Sale Price Study
  • Page 3: Sale Price Prediction
  • Page 4: Hypothesis and Validation
  • Page 5: Machine Learning Model
• Unfixed Bugs
• Testing
  • Manual Testing
  • PEP 8 Compliance Testing
• Deployment
• Technologies Used
  • Development and Deployment
  • Main Data Analysis and Machine Learning Libraries
• Credits

CRISP-DM

What is CRISP-DM?

CRISP-DM, which stands for Cross-Industry Standard Process for Data Mining, is a widely adopted methodology for data mining projects. It provides a structured approach to planning and executing data mining tasks.

The CRISP-DM framework consists of six phases:

1. Business Understanding: This initial phase focuses on understanding the project objectives and requirements from a business perspective, then converting this knowledge into a data mining problem definition and a preliminary plan.

2. Data Understanding: This phase starts with data collection and proceeds with activities aimed at familiarizing with the data, identifying data quality issues and discovering initial insights.

3. Data Preparation: The data is prepared for modeling by performing tasks such as data cleaning and formatting data as necessary.

4. Modeling: Various modeling techniques are selected and applied. During this phase, models are calibrated to optimal parameter settings and tested to ensure they are appropriate for the data.

5. Evaluation: The model or models are thoroughly evaluated and reviewed to ensure they effectively meet the initial business objectives set out in the first phase.

6. Deployment: The completion of the process involves deploying the data mining solution to the business.

CRISP-DM Workflow

The development followed the Cross Industry Standard Process for Data Mining (CRISP-DM), organized into distinct phases and can be found HERE:

• Epic 1: Business Understanding:

  This stage involved extensive discussions with the client to understand their expectations and develop acceptance criteria, which are detailed in the Business Requirements section below.

• Epic 2: Data Understanding:

  This stage was dedicated to conducting an exploratory study to identify the factors influencing the sale price, using raw data to avoid introducing biases through premature data preparation.

  This approach was chosen to ensure that the insights derived from the unaltered data were genuine and reflective of the true dynamics present in the dataset.

  This phase directly addresses and fulfills the first business requirement, as detailed in the Business Requirements, which was performed in the Sale Price Study Notebook.

• Epic 3: Data Preparation:

  This critical step involved cleaning and imputing data, conducting feature engineering like transformations or scaling, and reformatting data as needed.

  These tasks were performed in the Data Cleaning and Feature Engineering Notebooks.

• Epic 4: Modeling:

  This phase focused on selecting modeling algorithms and splitting the data into training and testing sets.

  The training set was used to validate various algorithms and tune them through hyperparameter optimization and was performed in the Modeling and Evaluation Notebook.

• Epic 5: Evaluation:

  The test set was used to evaluate model performance, ensuring alignment with the business acceptance criteria.

  The evaluation was performed in the Modeling and Evaluation Notebook.

• Epic 6: Deployment:

  A Streamlit app was developed to meet the business requirements established with the client.

  The app was deployed on Heroku, with the process described in the Deployment section.

Agile Development

To effectively manage the CRISP-DM workflow for my project, I've adopted Agile development practices, as they are both iterative and flexible frameworks that can effectively complement each other.

I've aligned each stage of the CRISP-DM process with an Agile epic, breaking down the complex tasks into manageable user stories.

This structure has enabled me to adaptively add tasks as the project evolved.

Link to Epics: Epics

Link to Kanban Board: User Stories

Business Requirements

• Data Visualization Requirement:
  The client requires a dashboard that displays visualizations of house attributes correlated with sale prices to understand market trends and factors influencing house values in Ames, Iowa.

• Predictive Analysis Requirement:
  The client needs a robust predictive model integrated within the dashboard that can forecast the sale prices of her four inherited houses and any additional houses in Ames.
  This model should use historical data and house attributes to generate predictions.

Succes Metrics

Model Performance:
Achievement of an R2 score of at least 0.75 on both the training and testing datasets, indicating strong predictive accuracy of the model.

Variable Correlation Analysis:
Completion of a comprehensive study identifying and visualizing the most relevant variables that are correlated with the sale price. This includes clear documentation and presentations of these correlations through the dashboard to aid in understanding how different house attributes impact sale prices in Ames, Iowa.

Predictive Capability:
Successful implementation of the predictive model within the dashboard that can accurately forecast sale prices for the four inherited properties, as well as for any other house in Ames. The predictions should consistently align with actual market prices, demonstrating the model's effectiveness.

Dataset Content

• The dataset is sourced from Kaggle. We then created a fictitious user story where predictive analytics can be applied in a real project in the workplace.
• The dataset 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

Hypothesis and Validation

1. Hypothesis: There is a positive correlation between the size-related features of a property and its sale price.

   • Validation Method: Conduct a correlation analysis to determine the strength and direction of the relationship between property size features and sale prices.

2. Hypothesis: The year a property was built is positively correlated with its sale price.

   • Validation Method: Perform a correlation analysis to assess the relationship between the year of construction and the sale price of properties.

3. Hypothesis: Based on the identified features, it is possible to predict sale prices with an accuracy yielding an R² score of at least 0.75.

   • Validation Method: Develop a regression model using the identified features to predict property sale prices. Validate the model by calculating the R² score on a test dataset to ensure the prediction accuracy meets or exceeds 0.75.

Mapping the business requirements to the Data Visualisations and ML tasks

Business Requirement 1: Correlation Study and Data Visualization

• Objective: To analyze the data related to house sales to uncover how various house attributes correlate with the sale price.

• Tasks:

  • Data Inspection: Review and inspect the dataset containing house records to ensure a comprehensive understanding of the available data.

  • Correlation Analysis: Perform both Pearson and Spearman correlation studies to identify the relationships between various variables and the sale price.

  • Data Visualization: Create visual plots of key variables against the sale price to derive actionable insights and visually represent how house attributes impact sale prices.

• User Stories:

  • As a client, I want to review the data related to house records to explore how the house attributes influence the sale price.

  • As a client, I want to visually map the main variables against the sale price to better understand and visualize the impact of different attributes on house pricing.

Business Requirement 2: Predictive Modeling and Performance Evaluation

• Objective: To develop predictive models that accurately estimate house values and evaluate their performance.

• Tasks:

  • Model Development: Construct a regression model to predict the sale price as the dependent variable.
  • Visualization: Create visual representations to compare the predictions of the train and test sets against actual sale prices.
  • Model Evaluation: Conduct regression analysis to determine the R² Score and Mean Absolute Error, ensuring the model's accuracy and reliability.

• User Stories:

  • As a client, I want to predict the sale price of my four inherited properties using a machine learning model.
  • As a client, I want the flexibility of predicting the sale price of any other home in Ames, Iowa.

ML Business Case

Machine Learning Model Development for Predicting House Sale Prices

Project Objective:

This project aims to develop a machine learning (ML) model to predict the sale price, in dollars, of homes in Ames, Iowa. The target variable is a continuous number, indicating the sale price. The focus is on a regression model, which is supervised and uni-dimensional, to offer a robust tool for predicting the sale prices of homes, particularly for a client's inherited properties.

Success Criteria:

• Correlation Study: Deliver a comprehensive analysis identifying key variables that significantly correlate with the sale price. This study will help in understanding which features impact the sale price most.

• Predictive Accuracy: Develop a regression model capable of predicting the sale price with high reliability. The model should achieve an R² score of at least 0.75 on both training and testing datasets, ensuring it can accurately predict the sale prices of the four specific inherited properties and other homes in the region.

Model Selection:

• Based on the characteristics of the data and the nature of the target variable (sale price), a regression model is suggested.

Client Benefits:

• Maximizing Investment Returns: By accurately predicting the sale prices, the client can make informed decisions regarding when and at what price to sell the inherited properties, potentially maximizing their investment returns.

• Strategic Planning: Reliable sale price predictions will aid the client in strategic planning and management of property assets.

Model Inputs and Outputs:

• Inputs: The model will utilize a range of house attributes such as size, age and conditions inputs to predict the sale price.

• Outputs: The primary output from the model will be the predicted sale price of a home as a continous value in dollars.

Dashboard Design

Dashboard Expectations

Dashboard Overview:

The dashboard will serve as a multifunctional platform, presenting detailed insights, predictions, and analyses related to house sale prices. It will include the following key pages:

Page 1: Project summary

• Overview: Provide a concise summary of the project.
• Project Terms and Jargon: Define specific terminology used throughout the project.
• Dataset Prescription: Outline the characteristics and scope of the project dataset.
• Business Requirements: Detail the client's needs and expectations from the project.

Page 2: Sale Price Study

• Purpose: Address Business Requirement 1 by identifying and displaying key features that strongly correlate with house sale prices.

• Correlation Analysis: Discuss the methodologies used for the correlation study and their findings.

• Data Visualization: Offer visual representations of the data to highlight significant correlations.

• Sale Price Study Page Screenshots:

  • Displays the business requirement and includes a checkbox for dataset inspection.

  

  • Introduces the summary of the correlation study.
  • Indicates that there are visual aids to help explain how conclusions were reached.
  • A yellow warning message alerts users to potential waiting times due to computing demands.

  

• Pearson Correlation Study

  • Briefly introduces the Pearson correlation study.
  • Activating the checkbox reveals the heatmap visualization of the correlation study.
  • Includes a barplot for easier understanding by non-technical users.

  

  

  

• Spearman Correlation Study

  • Briefly introduces the Spearman correlation study.
  • Activating the checkbox reveals the heatmap visualization of the correlation study.
  • Includes a barplot for easier understanding by non-technical users.

  

  

  

• PPS Study

  • Provides an overview of the PPS (Predictive Power Score) study.
  • Ticking the checkbox displays the heatmap visualization of the PPS study.

  

  

• Bivariate Analysis

  • Introduces the concept of Bivariate Analysis.
  • Enables the generation of regression plots for features strongly correlated with sale price upon selection.

  

  

Page 3: Sale Price Predictions

• Objective: Fulfill Business Requirement 2 by showcasing predictions for the four inherited properties.

• Prediction Display: List the attributes of the four properties along with their predicted sale prices.

• Real-Time Prediction Widget: Include an interactive input widget that allows users to input real-time data to receive instant sale price predictions.

• Sale Price Predictor Screenshots:

  • Introduces the user to the second business requirement.
  • Explains that the features available for input through widgets have been identified by the model as the most effective for predicting sale prices.

  

• Prediction Widget

  • Allows users to enter their home data to receive a predicted sale price.
  • By clicking the "Run Predictive Analysis" button, the model calculates and displays the predicted sale price.

  

• Prediction Inherited Widget

  • As specified in Business Requirement 2, predicts the sale prices of the four inherited houses.
  • Shows a loaded dataframe with relevant features for each inherited property.
  • Upon pressing the "Run Analysis" button, displays predicted prices for each property and also calculates the total value of all properties.

  

Page 4: Hypothesis Testing and Validation

• List of Hypotheses: Enumerate the project hypotheses.

• Validation Process: Explain how each hypothesis was tested and validated throughout the project.

• Hypothesis Page Screenshot:

Page 5: Machine Learning Model

• Model Overview: Describe the machine learning pipeline used for training the model.

• Feature Significance: Discuss the importance of various features within the model.

• Performance Analysis: Provide an evaluation of the model’s performance, including metrics and insights.

• Machine Learning Model Screenshots:

  • Introduces the user to the details of the machine learning model.
  • Describes the process of preparing the data before it enters the model.
  • Outlines the pipeline used in the model

  

• Features importance

  • Introduces the most critical features that influence the model.
  • Clarifies why these features are vital and explains the significance of their values in the context of model predictions.

  

  • Displays a visual representation, in the form of a barplot, highlighting the features that have the greatest impact on the model’s predictions.

  

• Model Evaluation

  • Provides an overview of how the model's performance is assessed.
  • Describes the significance of each metric used in the evaluation, helping users understand what these numbers mean.

  

  • The user gets introduced to the evaluation plot and metric scores

  

• Conclusions

  • Presents a summary of the machine learning model's performance, reviewing key results.
  • Each metric and outcome is clearly explained, providing insight into what the results signify for the model's effectiveness and areas for improvement.

  

Unfixed Bugs

• Some logs are showing warnings related to the use of deprecated versions of libraries or methods.
• When navigating between pages in the Streamlit dashboard, the state of checkboxes does not reset as expected.

Testing

Manual Testing

The deployed app and notebooks have been extensively tested to guarantee that data visualizations appear correctly and sale price predictions function accurately.

PEP8 Compliance Testing

All Python files were checked using the CI Python Linter.

Minor issues like long lines and trailing whitespace were corrected.

Notably, one line in the page_1_summary.py, specifically line 65, exceeds 79 characters due to containing a GitHub link, which cannot be split.

Ultimately, no other errors were found.

Deployment

Heroku

• The App live link is: https://housing-sale-price-predictor-3fd3a72156e8.herokuapp.com/
• Set the runtime.txt Python version to a Heroku-22 stack currently supported version.
• The project was deployed to Heroku using the following steps.

1. Log in to Heroku and create an App
2. At the Deploy tab, select GitHub as the deployment method.
3. Select your repository name and click Search. Once it is found, click Connect.
4. Select the branch you want to deploy, then click Deploy Branch.
5. The deployment process should happen smoothly if all deployment files are fully functional. Click the button Open App on the top of the page to access your App.
6. If the slug size is too large then add large files not required for the app to the .slugignore file.

Technologies Used

Development and Deployment

Tool	Description
GitHub	A web-based platform for version control and collaboration, used to host and manage the project's repository.
Gitpod	A cloud-based integrated development environment (IDE) that facilitated the creation of this project.
Jupyter Notebooks	Interactive computing environments that enable users to create and share documents with code, visualizations, and text. They were extensively utilized for data analysis, as well as the development and evaluation of the machine learning pipeline in this project.
Kaggle	An online community and platform for open-source data, which served as the primary data source for this project.
Heroku	A cloud platform service that supports several programming languages and is used to deploy, manage, and scale modern apps.
Streamlit	An open-source app framework for Machine Learning and Data Science projects, used to quickly create and share data apps.
Python	A high-level programming language known for its readability and flexibility, used extensively for all programming tasks in this project including data manipulation, analysis, and machine learning model development.

Main Data Analysis and Machine Learning Libraries

Library/Tool	Usage Description
NumPy	Employed for mathematical operations such as calculating means, modes, and standard deviations.
Pandas	Used for reading and writing data files, as well as inspecting, creating, and manipulating series and dataframes.
Pandas Profiling	Utilized to generate comprehensive Profile Reports of the dataset, providing detailed data analysis.
PPScore	Applied to determine the predictive power score of data features, assessing their predictive relationship.
Matplotlib & Seaborn	Used for creating plots to visualize data analysis, including heatmaps, correlation plots, and histograms of feature importance.
Feature Engine	Deployed for various data cleaning and preparation tasks such as dropping features, imputing missing variables, ordinal encoding, numerical transformations, outlier assessment, and smart correlation assessments.
Scikit-Learn	Central to numerous machine learning tasks, including splitting train and test sets, feature processing and selection, grid search for optimal regression models and hyperparameters, model evaluation using R2 score, and Principal Component Analysis.
XGBoost	Used specifically for the XGBoostRegressor algorithm, enhancing the predictive modeling process.

Credits

The development of this project extensively utilized resources and methodologies from the CI Churnometer Walkthrough Project and CI course content. These resources provided a foundational framework and code for various functions and classes that were integral during the project's creation. Key components sourced include:

• Hyperparameter Optimization Search: Techniques for optimizing model parameters to enhance performance.
• Feature Importance Analysis: Methods to assess and rank the influence of different features on model predictions.
• Evaluation of Train and Test Sets: Processes to evaluate model performance across training and testing datasets to ensure generalizability.
• PPS and Correlation Analysis: Tools for analyzing predictive power and relationships between variables.
• Missing Data Evaluation: Strategies to assess and handle missing data within the dataset.
• Data Cleaning Effect: Approaches to measure and improve the impact of data cleaning on model accuracy.
• Streamlit: Inspiration from the CI Churnometer Walkthrough helped in utilizing Streamlit for creating the app pages and the prediction widget.
• Predictive Analytics Course: Helped shape the analysis and modeling methods used in this project.

These components were employed within the Jupyter Notebooks throughout the project's lifecycle to ensure robust development and analysis.

README file content has been inspired from Van-essa and Vasi

My mentor, Marcel, guiding me through this project.