Housing data generated from Tehran Divar website will be analyzed in this notebook. I use different types of regression for evaluating model and compare the results of those types. Predicting housing price in Tehran and also choosing a good regression algorithm is the goal of this notebook.
This dataset is available in kaggle and you can also check the scrap project for generating this dataset here.
I use some famous python libraries like numpy, pandas, sklearn and seaborn. In order to do some specific activies, I have to use unidecode,bidi.algorithm and arabic_reshaper.
Tip The instructio of !pip install ... is written in the notebook to ensure that all libraries are installed in the destination machine.
- Analysis.ipynb: Contains the entire python code of project. Data analysis, regression result and choose the best model is included in the file.
- Data.csv: It is the dataset of housing in Tehran and is the input of this project. It is available in kaggle too.
- TehranHousingPriceBackground.csv: This file contains the monthly cost of housing in Tehran during 5 years (from 1395 to 1399).