This project uses various technologies to predict housing prices in California. The project involves data preprocessing, visualization, and machine learning model training to make accurate predictions. Below are the main components and technologies used in the project:
- Python: The programming language used to write data processing and analysis scripts.
- Pandas: A Python library for data manipulation and analysis.
- Matplotlib and Seaborn: Libraries for data visualization.
- Scikit-learn: A machine learning library for model training and evaluation.
The goal of this project is to process and analyze housing data to predict housing prices in California. The data includes various features such as location, number of rooms, and median income. The project involves data cleaning, exploratory data analysis (EDA), and building predictive models using machine learning algorithms.
- housing.csv: The dataset containing housing data for California.
- the-ultimate-end-to-end-prediction.ipynb: The Jupyter notebook containing the data preprocessing, visualization, and model training scripts.
- images/: Contains images used for visualizations.
CA.png: Map of California.SFM.png: San Francisco map.