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Data analysis and comparing different regression algorithms on dataset about Tehran housing price.

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Regression_TheranHousing

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.

Prerequisites

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.

Files

  1. Analysis.ipynb: Contains the entire python code of project. Data analysis, regression result and choose the best model is included in the file.
  2. Data.csv: It is the dataset of housing in Tehran and is the input of this project. It is available in kaggle too.
  3. TehranHousingPriceBackground.csv: This file contains the monthly cost of housing in Tehran during 5 years (from 1395 to 1399).

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Data analysis and comparing different regression algorithms on dataset about Tehran housing price.

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