https://github.com/divakarkumarp/boston-house-prices-prediction

https://github.com/divakarkumarp/boston-house-prices-prediction

Last synced: 2 months ago
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• Host: GitHub
• URL: https://github.com/divakarkumarp/boston-house-prices-prediction
• Owner: divakarkumarp
• License: apache-2.0
• Created: 2023-12-19T07:13:19.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-23T12:09:03.000Z (11 months ago)
• Last Synced: 2025-01-22T08:13:18.485Z (4 months ago)
• Language: Jupyter Notebook
• Size: 1.14 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Boston House Prices Prediction

Each record in the database describes a Boston suburb or town. The data was drawn from the Boston Standard Metropolitan Statistical Area (SMSA) in 1970.CRIM: per capita crime rate by town

![Boston](https://github.com/divakarkumar424/Boston-House-Prices-Prediction/assets/32620288/b566eeb2-a302-405a-8190-e7cce225c3d4)

## Data Description

1. ZN: proportion of residential land zoned for lots over 25,000 sq.ft.

2. INDUS: proportion of non-retail business acres per town

3. CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)

4. NOX: nitric oxides concentration (parts per 10 million)

5. RM: average number of rooms per dwelling

6. AGE: proportion of owner-occupied units built prior to 1940

7. DIS: weighted distances to five Boston employment centers

8. RAD: index of accessibility to radial highways

9. TAX: full-value property-tax rate per $10,000

10. PTRATIO: pupil-teacher ratio by town 12. B: 1000(Bk−0.63)2 where Bk is the proportion of blacks by town 13.LSTAT: % lower status of the population

11. MEDV: Median value of owner-occupied homes in $1000s

## Overview:

Software And Tools Requirements

1. [Github Account](https://github.com)

2. [HerokuAccount](https://heroku.com)

3. [VSCodeIDE](https://code.visualstudio.com/)

4. [GitCLI](https://git-scm.com/book/en/v2/Getting-Started-The-Command-Line)

Technology and tools wise this project covers,

1. Python

2. Numpy and Pandas for data cleaning

3. Data visualization

4. Sklearn for model building

5. Google Colab Notebook

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### Technologies Used:

![](https://forthebadge.com/images/badges/made-with-python.svg)

[](https://numpy.org)    [](https://pandas.pydata.org)  [](https://seaborn.pydata.org) [](https://matplotlib.org)   [](https://colab.research.google.com/)

[](https://scikit-learn.org/stable/index.html)

[](https://flask.palletsprojects.com/en/3.0.x/)

[](https://www.docker.com/)