https://github.com/estnafinema0/housing-price-analysis
Predicting housing prices with regression models and visual analytics. Includes preprocessing, custom pipelines, and visualized performance metrics.
https://github.com/estnafinema0/housing-price-analysis
custom-models data-preprocessing data-visualization eda exploratory-data-analysis housing-prices jupyter-notebook machine-learning pipelines regression-models seaborn sklearn
Last synced: about 1 month ago
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Predicting housing prices with regression models and visual analytics. Includes preprocessing, custom pipelines, and visualized performance metrics.
- Host: GitHub
- URL: https://github.com/estnafinema0/housing-price-analysis
- Owner: estnafinema0
- Created: 2025-01-28T22:10:40.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-30T11:55:02.000Z (over 1 year ago)
- Last Synced: 2025-06-28T09:43:56.133Z (12 months ago)
- Topics: custom-models, data-preprocessing, data-visualization, eda, exploratory-data-analysis, housing-prices, jupyter-notebook, machine-learning, pipelines, regression-models, seaborn, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 1.76 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Housing Price Analysis
A research project inspired by Yandex ML trainings. It's for predicting **house prices** in Ames with **exploratory analysis (EDA)** and **visual comparisons** of regression models.
The project includes:
- **Preprocess continuous and categorical features**
- **Custom regression models**
- **Πne-hot encoding** for categorical variables
- **Training pipelines**
- **Visualize data and model performance** via metrics such as MAE, RMSLE, etc
---
## Table of Contents
- [Housing Price Analysis](#housing-price-analysis)
- [Table of Contents](#table-of-contents)
- [Overview](#overview)
- [Data description](#data-description)
- [Installation](#installation)
- [Usage](#usage)
- [Where is the project?...](#where-is-the-project)
- [Scripts](#scripts)
- [Classes and Models](#classes-and-models)
- [Data Preprocessing](#data-preprocessing)
- [Custom Models](#custom-models)
- [Pipelines](#pipelines)
- [π Visualizations](#-visualizations)
- [Data Analysis](#data-analysis)
- [Feature Correlations](#feature-correlations)
- [Location Analysis](#location-analysis)
- [Model Performance](#model-performance)
- [Training Dynamics](#training-dynamics)
- [Prediction Accuracy](#prediction-accuracy)
- [Results](#results)
---
## Overview
This project builds a regression model to predict housing prices in [Ames, Iowa](https://www.openml.org/d/41211).
I use:
- **Continuous data transformations**: scaling.
- **Categorical data encoding** via `OneHotEncoder`.
- **Custom linear regression** using stochastic gradient descent, with optional L2 regularization.
- **Hyperparameter tuning**: grid search and cross-validation.
- **Logarithmic transformations** of the target variable for improved model stability.
---
## Data description
The data is split into **training** and **testing** sets.
The **Ames Housing** key columns include:
1. **Continuous features**: `Year_Built`.
2. **Categorical features**: `Overall_Qual`.
3. **Target variable**: `Sale_Price`.
---
## Installation
1. **Clone the repository**:
```bash
git clone https://github.com/estnafinema0/Housing-Price-Analysis.git
cd Housing-Price-Analysis
```
2. **Create a virtual environment**:
```bash
python -m venv venv
source venv/bin/activate
```
Working on Linux.
3. **Install requirements**:
```bash
pip install -r requirements.txt
```
---
## Usage
### Where is the project?...
The complete analysis is in `notebook.ipynb`.
Just open and run cells in sequence!
### Scripts
If you want to check particular parts of the project, look at the `scripts/` folder.
## Classes and Models
### Data Preprocessing
1. **`BaseDataPreprocessor`**
- Picks out number-based columns you want to use
- Makes all numbers work on the same scale
2. **`SmartDataPreprocessor`**
- Adds helpful new data like distance to city center
- Fixes missing data by using middle values
- Makes numbers ready for the model to use
- Makes predictions better by using real-world knowledge
3. **`OneHotPreprocessor`**
- Built on top of `BaseDataPreprocessor`
- Turns text data (like house zones and sale types) into numbers the model can use
### Custom Models
1. **`ExponentialLinearRegression`**
- A special version of the `Ridge` model
- Changes house prices to a better format while learning
- Changes them back when making predictions
2. **`SGDLinearRegressor`**
- A model that learns step by step
- Keeps track of how well it's learning
- Shows you how it improves over time
### Pipelines
We have several ready-to-use pipelines to make predictions:
1. **`make_base_pipeline()`**
- The simple version that works with just numbers
- Uses `BaseDataPreprocessor` and basic `Ridge` model
2. **`make_onehot_pipeline()`**
- Our best performer!
- Handles both numbers and categories (like house zones)
- Uses `OneHotPreprocessor` to turn text into numbers
3. **`make_smart_pipeline()`**
- Uses `SmartDataPreprocessor` to add helpful new data
- Good for when you want to use location data
Each pipeline combines data preparation and model training into one easy step. Just use `fit()` and `predict()`! π
---
## π Visualizations
Step-by-step visualizations.
### Data Analysis
The distribution of house prices shows a clear right-skew pattern. We found that log-transformation makes the data more normally distributed, which helps our models perform better.
### Feature Correlations
For 'Sale_Price' could be important location-based features (Longitude, Latitude). Also they show weak correlations with other features.
### Location Analysis
We see interesting neighborhood patterns:
- Higher-priced clusters in northern areas
- Price variations more tied to neighborhood than distance to center
### Model Performance
Our model comparison shows:
- OneHot Pipeline leading with lowest MAE (~18,000)
- Clear performance ranking: OneHot > Exponential > Base > SGD
### Training Dynamics
The SGD Regressor's training shows:
- Loss stabilization around 800 iterations
- After 800 iterations, the model's performance starts to decrease, because of the overfitting.
### Prediction Accuracy
The scatter plots show that the OneHot Pipeline follows the ideal prediction line most closely.
> π‘ Check out `notebook.ipynb` to recreate these visualizations.
---
## Results
| Model | MAE | RMSLE | Notes |
|-------------------------|--------|---------|------------------------------------------------|
| **OneHot Pipeline** | 18,000 | 0.155 | Best performer! Great with categorical features |
| **Base Pipeline** | 23,000 | 0.190 | Simple but stable baseline |
| **Exponential Pipeline**| 20,500 | 0.182 | Good with price distribution |
| **SGD Regressor** | 26,000 | 0.200 | Shows instability after 800 iterations |
> π‘ **Key results:**
> - OneHot Pipeline shows best results across both metrics
> - SGD Regressor needs more tuning to compete with other models
---
Thanks for checking out, guys!