How abhishek2305995/california_housing_price_prediction_using_machine_learning works
This is a California housing machine-learning project with a Streamlit interface for entering housing features and receiving a price estimate, a value category, and a cluster-based region label. The sampled app shows the runtime inference path and loads regression, classification, and clustering artifacts. The README also documents model training/evaluation capabilities—SVM, PCA, neural networks, and algorithm comparison—but their implementation details are not present in the sampled source. The graph represents those as documented project capabilities rather than asserting unverified runtime connections.
Main entry points: app.py Predict Action · regression_model.pkl Price Regression · kmeans_model.pkl KMeans Regions · scaler.pkl Feature Scaler
Components 16
Streamlit Application
- Feature Inputs
app.py - Model Choice
app.py - Predict Action
app.py - Prediction Results
app.py
Prediction Runtime
- Feature Scaler
scaler.pkl - Price Regression
regression_model.pkl - Value Classification
app.py - Logistic Model
logistic_model.pkl - Decision Tree
decision_tree_model.pkl - Cluster Scaler
cluster_scaler.pkl - KMeans Regions
kmeans_model.pkl
Modeling Capabilities
- SVM Comparison
ADL_PROJECT.ipynb - PCA Clustering
ADL_PROJECT.ipynb - Neural Network
neural_network_model.pkl - Model Evaluation
ADL_PROJECT.ipynb
Other components
- Housing User
Connections 15
- Housing User → Feature Inputs: enters features
app.py - Housing User → Model Choice: selects model
app.py - Housing User → Predict Action: requests prediction
app.py - Predict Action → Feature Scaler: scales features
app.py - Predict Action → Price Regression: predicts price
app.py - Predict Action → Value Classification: selects and predicts
app.py - Value Classification → Logistic Model: uses when selected
app.py - Value Classification → Decision Tree: uses when selected
app.py - Predict Action → Cluster Scaler: scales for clustering
app.py - Predict Action → KMeans Regions: predicts cluster
app.py - Predict Action → Prediction Results: displays predictions
app.py - Model Choice → Value Classification: chooses classifier
app.py - Price Regression → Prediction Results: supplies price
app.py - Value Classification → Prediction Results: supplies category
app.py - KMeans Regions → Prediction Results: supplies region
app.py