In our Machine Learning (CS 4641) group project, we analyzed real estate datasets to provide insights and predictions that can assist with making informed decisions about renting, buying, and selling properties.
Models Implemented:
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We implemented both supervised and unsupervised models.
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Supervised Models:
- Decision Tree: Helps users decide whether to rent a house based on its features (e.g. number of bedrooms, number of bathrooms, area)
- Regression Model: Predicts the price of a house based on its characteristics
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Unsupervised Models:
- Clustering: Groups houses by similar characteristics with regard to price, helping sellers determine a suitable price range.
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Libraries Used:
- We leveraged Python packages such as numpy, pandas, sklearn, and TensorFlow for data preprocessing/cleaning, model training and evaluation, and visualization of results.
Insights Gained:
- Through this project, we gained valuable insights into the housing market and consumer preferences. These insights can help:
- Potential renters decide if a house meets their criteria
- Buyers predict housing prices
- Sellers set competitive and fair pricing
For more information about the project, see the final report pdf.