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	`1`	`+Boston Housing Price Prediction with CatBoost`
	`2`	`+`
	`3`	`+This project focuses on predicting housing prices in the Boston area using the CatBoost regression algorithm. The dataset used in this project contains various attributes related to housing, and the goal is to develop a model that can accurately predict housing prices based on these attributes using the powerful CatBoost regressor.`
	`4`	`+`
	`5`	`+Dataset`
	`6`	`+`
	`7`	`+The dataset used in this project is the [Boston Housing Dataset](http://lib.stat.cmu.edu/datasets/boston). It contains information about various housing attributes such as crime rate, zoning, industry proportion, and more. The target variable is the median value of owner-occupied homes in 1000ドルs.`
	`8`	`+`
	`9`	`+Requirements`
	`10`	`+`
	`11`	`+- Python 3.x`
	`12`	`+- pandas`
	`13`	`+- numpy`
	`14`	`+- scikit-learn`
	`15`	`+- catboost`

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