This project analyzes the housing prices in Boston. The following steps have been performed:

1. Data loading and cleaning
2. Data exploration and visualization
3. Modeling
4. Model selection
5. Model accuracy evaluation
6. Presentation of model predictions

The project includes the following files:

Boston ev fiyatları notebook (1).ipynb: Jupyter Notebook file containing data exploration, modeling, and results.

housing.csv: Boston housing price data set.

The following software and libraries are required to run the analysis:

• Python 3.x
• Jupyter Notebook
• Pandas
• Matplotlib
• Seaborn
• Sklearn

The data set includes information about various aspects of houses in Boston, such as number of rooms, age, and neighborhood.

Regression models were used to predict the housing prices in Boston. The best model was selected based on its performance.

The results of the analysis, including the accuracy of the selected model and the predictions made by the model, are presented in the Jupyter Notebook file.

The Boston housing price data set was obtained from the UCI Machine Learning Repository.