This project aims to predict real estate prices based on various features such as total square footage, number of bathrooms, number of balconies, location, etc. It utilizes machine learning techniques, specifically linear regression, to analyze historical real estate data and make predictions about future prices.
The motivation behind this project is to assist both buyers and sellers in making informed decisions regarding real estate transactions. By accurately predicting prices, buyers can determine whether a property is within their budget, while sellers can set appropriate listing prices to maximize returns.
The dataset used in this project consists of historical real estate listings along with their corresponding features and prices. Features include total square footage, number of bathrooms, number of balconies, location, etc. The dataset is preprocessed to handle missing values, outliers, and categorical variables before being used for model training.
- Data Collection: Gather historical real estate listing data from reliable sources or APIs.
- Data Preprocessing: Clean the dataset, handle missing values, encode categorical variables, and perform feature scaling if necessary.
- Exploratory Data Analysis (EDA): Explore the dataset to gain insights into the distribution of features, correlations, etc.
- Model Selection: Choose an appropriate machine learning model for the task. In this project, linear regression is used due to its simplicity and interpretability.
- Model Training: Train the linear regression model on the preprocessed dataset.
- Model Evaluation: Evaluate the performance of the trained model using metrics such as mean squared error, R-squared, etc.
- Prediction: Use the trained model to make predictions on new real estate listings.
The trained linear regression model achieves a certain level of accuracy in predicting real estate prices. Evaluation metrics such as mean squared error and R-squared are used to assess the model's performance.
To use this project, follow these steps:
- Install the required dependencies listed in the
requirements.txtfile. - Run the Jupyter Notebook or Python script to preprocess the data, train the model, and make predictions.
- Analyze the model's predictions and evaluate its performance using appropriate metrics.
- Explore other machine learning algorithms and compare their performance with linear regression.
- Incorporate additional features such as crime rates, school ratings, proximity to amenities, etc., for more accurate predictions.
- Implement a web application or API to allow users to interactively predict real estate prices.
This project is licensed under the MIT License - see the LICENSE file for details.
If you have any suggestions for improving the project or spot any bugs, feel free to open an issue on the GitHub repository. Additionally, if you'd like to contribute directly to the project, you can fork the repository, make your changes, and submit a pull request. Your contributions are greatly appreciated and help make the project better for everyone!