Kaggle Data Source: https://www.kaggle.com/datasets/shubhammeshram579/house

Main code: https://www.kaggle.com/code/akmalsoliev/housing-price-prediction-e2e-model-construction

The housing price prediction dataset represents a regression challenge, discerning property values according to specific characteristics. This notebook seeks to conduct exploratory data analysis (EDA), manage dataset processing, and develop machine learning models to optimize pricing prediction abilities. Upon completing the model training phase, evaluations will determine the high-performing models and their corresponding hyperparameters.

Attribute Information:

1. CRIM : per capita crime rate by town.
2. ZN : proportion of residential land zoned for lots over 25,000 sq.ft.
3. INDUS: proportion of non-retail business acres per town.
4. CHAS : Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).
5. NOX : nitric oxides concentration (parts per 10 million).
6. RM : average number of rooms per dwelling.
7. AGE : proportion of owner-occupied units built prior to 1940.
8. DIS : weighted distances to five Boston employment centres.
9. RAD : index of accessibility to radial highways.
10. TAX : full-value property-tax rate per $10,000.
11. 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.
14. MEDV : Median value of owner-occupied homes in $1000's.