https://github.com/willie-conway/boston-housing-market-analysis
This repository contains an 📈analysis of the 🏘️Boston Housing Data focusing on the factors influencing housing prices. Using statistical tests and data visualizations, we explore key relationships, including the impact of proximity to the Charles River 🌊, the age of homes 🏡, the NOX concentrations 🧪, and employment center distances 🚗.
https://github.com/willie-conway/boston-housing-market-analysis
analysis anova boston-housing-dataset boston-housing-price-prediction matplotlib nox pandas pearson-correlation python regression-analysis scipy seaborn statsmodels t-test
Last synced: 15 days ago
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This repository contains an 📈analysis of the 🏘️Boston Housing Data focusing on the factors influencing housing prices. Using statistical tests and data visualizations, we explore key relationships, including the impact of proximity to the Charles River 🌊, the age of homes 🏡, the NOX concentrations 🧪, and employment center distances 🚗.
- Host: GitHub
- URL: https://github.com/willie-conway/boston-housing-market-analysis
- Owner: Willie-Conway
- License: mit
- Created: 2025-02-12T19:53:53.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-16T17:48:17.000Z (about 1 month ago)
- Last Synced: 2025-03-16T18:58:40.541Z (about 1 month ago)
- Topics: analysis, anova, boston-housing-dataset, boston-housing-price-prediction, matplotlib, nox, pandas, pearson-correlation, python, regression-analysis, scipy, seaborn, statsmodels, t-test
- Language: Jupyter Notebook
- Homepage:
- Size: 131 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🏠 **Boston Housing Data Analysis** 🏙️
Welcome to the **Boston Housing Data Analysis** project! This repository contains a comprehensive analysis of the Boston Housing dataset, using statistical methods and data visualizations to explore key factors that influence housing prices in the Boston area.
---
## 📊 **Project Overview**
This project explores various aspects of the Boston housing market, analyzing factors such as:
- **Proximity to the Charles River** 🌊
- **Age of homes** 📅
- **Nitric oxide concentration (NOX)** 🧪
- **Distance to employment centers** 🚗
- **Proportion of non-retail business acres per town** ⚙️
Through the use of statistical tests and visualizations, we aim to answer key questions about these factors and their relationship to home prices.
---
## 🔧 **Technologies Used**
- **Python** 🐍
- **Pandas** 📊
- **Seaborn** 📈
- **Matplotlib** 🎨
- **SciPy** 🧮
- **StatsModels** 📐
---
## 🛠️ **Key Analysis and Methods**
### 1. **T-test for Charles River Proximity**
We performed a T-test to determine if there is a significant difference in the median value of homes based on whether they are near the Charles River or not.
### 2. **ANOVA for Home Age (Pre-1940)**
We analyzed the impact of the proportion of homes built before 1940 on the median home values using ANOVA.
### 3. **Pearson Correlation for Nitric Oxide (NOX) and Non-Retail Acres**
We examined the relationship between nitric oxide concentrations and the proportion of non-retail business acres using Pearson's correlation.
### 4. **Regression Analysis for Employment Centers**
We explored how the distance to employment centers affects the median value of homes using regression analysis.
---
## 📥 **Getting Started**
To get started with this project locally, follow these steps:
1. Clone this repository to your local machine:
```bash
git clone https://github.com/yourusername/Boston-Housing-Analysis.git
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Open the Jupyter notebook and start analyzing:
```bash
jupyter notebook
```
---
# Boston Housing Dataset Analysis
## 📚Data Description
The dataset used in this analysis is based on the **Boston Housing dataset**, which includes the following features:
- **CRIM**: Per capita crime rate by town
- **ZN**: Proportion of residential land zoned for lots over 25,000 sq. ft.
- **INDUS**: Proportion of non-retail business acres per town
- **CHAS**: Charles River dummy variable (1 if tract bounds river; 0 otherwise)
- **NOX**: Nitric oxides concentration (parts per 10 million)
- **RM**: Average number of rooms per dwelling
- **AGE**: Proportion of owner-occupied units built before 1940
- **DIS**: Weighted distances to five Boston employment centers
- **RAD**: Index of accessibility to radial highways
- **TAX**: Full-value property tax rate per $10,000
- **PTRATIO**: Pupil-teacher ratio by town
- **LSTAT**: Percentage of lower status of the population
- **MEDV**: Median value of owner-occupied homes (in $1000s)
## 📝Contributions
Feel free to fork this repository, create a branch, and submit a pull request with any improvements or suggestions!
## 📫Contact
For any questions or further information, feel free to reach out via email: [[email protected]]
## ⭐License
This project is licensed under the MIT License - see the [LICENSE] file for details.