moraar222/land-python

House Price Prediction

This project provides a simple Python script to predict house prices based on various features such as the number of bedrooms, bathrooms, square footage, and location. The model is built using a linear regression algorithm from the scikit-learn library.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Dataset
• Model Training
• Evaluation
• Prediction
• Contributing
• License

Introduction

House price prediction is a common task in the real estate industry. By analyzing past data, we can build models to predict the price of a house based on its features. This project aims to create a simple linear regression model that can be trained on a dataset of houses and then used to predict the prices of new houses.

Features

• Data Preprocessing: Handles missing values and encodes categorical variables.
• Model Training: Trains a linear regression model on the provided dataset.
• Model Evaluation: Evaluates the model using the Root Mean Squared Error (RMSE) metric.
• Prediction: Provides an example to predict the price of a house based on input features.

Installation

Requirements

• Python 3.6+
• pandas library
• scikit-learn library

You can install the required Python libraries using pip:

pip install pandas scikit-learn

Files

• house_price_prediction.py: The main script for training the model and making predictions.
• README.md: Project documentation (this file).
• house_data.csv: Example dataset (you need to provide your own dataset).

Usage

1. Prepare your dataset: Ensure you have a CSV file named house_data.csv in the same directory as the script. The CSV file should include columns like bedrooms, bathrooms, square_footage, location, and price.

2. Run the script: Execute the Python script to train the model and evaluate its performance.

python house_price_prediction.py

3. Evaluate the Model: The script will output the Root Mean Squared Error (RMSE) of the model's predictions on the test data.

4. Predict House Prices: You can modify the script to predict the price of a new house by providing its features in the new_data section.

Dataset

The dataset should be in CSV format with the following columns:

• bedrooms: Number of bedrooms in the house.
• bathrooms: Number of bathrooms in the house.
• square_footage: Total square footage of the house.
• location: Location of the house (categorical variable).
• price: Price of the house (target variable).

Example:

bedrooms	bathrooms	square_footage	location	price
3	2	1500	New York	500000
4	3	2000	San Francisco	1000000

Model Training

The model is trained using the LinearRegression class from the scikit-learn library. The data is split into training and testing sets using the train_test_split function. The model learns the relationship between the features and the target variable (price) during training.

Evaluation

The model's performance is evaluated using the Root Mean Squared Error (RMSE) metric, which gives an indication of how well the model's predictions match the actual prices.

Prediction

To predict the price of a new house, you can modify the new_data variable in the script with the features of the house you want to predict.

Example:

new_data = [[3, 2, 1500, 1, 0, 0]]  # Adjust based on your dataset's encoding
predicted_price = model.predict(new_data)
print(f'Predicted House Price: ${predicted_price[0]:,.2f}')

Contributing

Contributions are welcome! If you have any suggestions or improvements, feel free to create an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.