This project focuses on predicting house prices in California using Deep Neural Networks (DNN). The goal is to develop a robust and accurate model that can predict housing prices based on various features, providing valuable insights for real estate stakeholders and potential buyers.
The dataset used for this project is sourced from [/content/sample_data/california_housing_train.csv]. It includes a comprehensive set of features such as location, square footage, number of bedrooms, and other relevant factors affecting house prices in California.
Python TensorFlow Keras Pandas NumPy Matplotlib Scikit-learn
Data Preprocessing: Clean and preprocess the dataset, handling missing values, outliers, and encoding categorical variables.
Feature Engineering: Explore and extract relevant features to improve the model's predictive performance.
Model Architecture: Design and implement a Deep Neural Network architecture using TensorFlow and Keras, considering the complexity of the problem.
Training: Train the model on the preprocessed dataset, optimizing hyperparameters for better performance.
Evaluation: Evaluate the model's performance using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
Visualization: Visualize key insights and trends in the data, as well as the model's predictions.
Deployment: Optionally, deploy the trained model for real-time predictions or integrate it into a web application.