Apartment Price Segmentation
This project explores the application of unsupervised learning techniques, including neural networks, K-means clustering, and Principal Component Analysis (PCA), to segment apartment prices based on inherent patterns within the data. Unlike supervised learning, which requires labeled data, unsupervised learning methods can uncover hidden structures and relationships within the data without prior knowledge of the outcomes.
Here are some of the details of the project:
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Dataset
- The dataset used in this project contains information about mid-range apartments in Ho Chi Minh City.
- The dataset consists of 120 examples and 18 features.
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Model
- The model leverages K-means clustering to identify distinct price clusters in the dataset.
Technologies Used
Technology Description Python A high-level programming language used for data analysis and model development. Pandas A data manipulation library used for reading and processing datasets. Matplotlib A plotting library used for visualizing data and model performance. Scikit-learn A machine learning library used for data preprocessing and model evaluation. TensorFlow An open-source deep learning library used for building neural networks. Joblib A library used for saving and loading model parameters. OpenCV A computer vision library used for image processing and feature extraction. -
Training
- The model is trained on the dataset using unsupervised learning techniques to identify price clusters in the data.
Training Visualization
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Results
- The model successfully segments apartment prices into distinct clusters based on patterns in the data.
Results Visualization
