Welcome to the House Prediction in Buenos Aires repository! This project focuses on predicting house prices in Buenos Aires using various machine learning techniques.
The goal of this project is to build a predictive model to estimate house prices in Buenos Aires. By leveraging machine learning algorithms, this project aims to provide accurate price predictions based on features such as location, size, number of rooms, and more.
- Data Preprocessing: Clean and prepare data for modeling.
- Feature Engineering: Create new features to enhance model performance.
- Model Training: Train multiple machine learning models for comparison.
- Model Evaluation: Assess model performance using various metrics.
- Visualization: Visualize data and prediction results.
The dataset used includes features related to houses in Buenos Aires, such as:
- Location
- Size (in square meters)
- Number of Rooms
- Type of Property (e.g., apartment, house)
- Year of Construction
- Ensure that the dataset is placed in the correct directory as specified in the scripts.
This project uses several machine learning models, including:
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Linear Regression: A basic model to understand relationships between features and prices.
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Decision Tree Regression: A non-linear model for capturing complex patterns.
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Random Forest Regression: An ensemble method to enhance prediction accuracy.
You can adjust the models and their parameters in the train_models.py script.
Model Performance is evaluated using the following metrics :
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- R^2 Score
These metrics help in assessing the accuracy and reliability of the predictions.
For any questions or suggestions, please contact:
Name: Ujjwal Bisht GitHub: ujjwalbisht74