rezajahadi/house-price

Predicting the Price of Houses in Tehran city by Machine Learning

Here is a README.md for your Tehran house price prediction project:

Tehran House Price Prediction

This project aims to predict house prices in Tehran based on features like area, number of rooms, parking, etc.

Data

The data is loaded from housePrice.csv. It contains the following columns:

• Area - Total area of the house in sqft
• Rooms - Number of bedrooms
• Parking - Boolean indicating if parking spot is available
• Warehouse - Boolean indicating if storage warehouse is available
• Elevator - Boolean indicating if elevator is present
• Address - General location in Tehran
• Price - House price in Iranian Rials
• Price(USD) - House price converted to USD

Notebook

housePrice.ipynb loads the data, analyzes correlations between features, trains regression models, and evaluates predictions.

Key contents:

• Exploratory data analysis
• Feature correlation plots
• Linear regression model
• Regularized regression models (Ridge, Lasso)
• Model evaluation and comparison

Results

The regularized models like Ridge achieve lowest RMSE of around $15,000 on test data. Top positive predictors are area, number of rooms and presence of elevator. Presence of parking influences price negatively.

Requirements

Python 3.x Common data science libraries like Pandas, NumPy, Scikit-Learn, Matplotlib.

Next Steps

Some ideas for improving the model:

• Add geographical coordinates to train geospatial models
• Neighborhood specific models
• Ensemble modeling
• Automated hyperparameter tuning

Let me know if you would like me to modify or expand this README!