This project aims to predict house prices using machine learning techniques. The dataset used for this project contains various features related to residential properties, such as the number of bedrooms, the size of the property, and the location. The goal is to develop a model that can accurately predict the sale price of a house based on these features.
The dataset used for this project consists of two main parts: a training set and a test set. The training set contains information on various houses, including their features and the corresponding sale prices. The test set contains similar information but does not include the sale prices, which are to be predicted using the developed model.
The project follows a structured approach to building and evaluating machine learning models for house price prediction. The key steps involved in the methodology include:
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Data Preprocessing: This step involves cleaning the data, handling missing values, and encoding categorical variables.
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Feature Engineering: New features are created from the existing dataset to improve the predictive power of the models. This may include deriving new variables or transforming existing ones.
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Model Selection: Several machine learning algorithms are evaluated to determine which one performs best for the given task. Common algorithms include linear regression, decision trees, random forests, and gradient boosting.
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Model Evaluation: The performance of each model is evaluated using appropriate evaluation metrics, such as mean squared error (MSE), mean absolute error (MAE), and R-squared.
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Hyperparameter Tuning: The hyperparameters of the selected model are tuned to optimize its performance further.
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Final Model Training: The final model is trained on the entire training dataset using the optimized hyperparameters.
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Prediction and Submission: The trained model is used to make predictions on the test dataset, and the results are submitted for evaluation.
The performance of the developed models is evaluated based on their predictive accuracy and generalization ability. The chosen model is expected to achieve competitive results compared to other participants in the competition.
House price prediction is a challenging task that requires careful data analysis, feature engineering, and model selection. By following a systematic approach and leveraging machine learning techniques, it is possible to build accurate predictive models that can provide valuable insights for real estate stakeholders.
This project is licensed under the MIT License.