{"id":29459755,"url":"https://github.com/theartificialdev/californiahousepricepredictor","last_synced_at":"2026-04-13T12:01:40.177Z","repository":{"id":196914076,"uuid":"697446415","full_name":"TheArtificialDev/CaliforniaHousePricePredictor","owner":"TheArtificialDev","description":"we explore California's housing market, predict property prices, and unravel the factors that shape real estate values. 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The goal is to build a regression model that can estimate the median house value based on various features. The dataset used for this project is the [California Housing Prices dataset](https://www.kaggle.com/camnugent/california-housing-prices) from Kaggle.\n\n## Table of Contents\n1. [Project Overview](#project-overview)\n2. [Dataset](#dataset)\n3. [Installation](#installation)\n4. [Usage](#usage)\n5. [Data Exploration](#data-exploration)\n6. [Data Preprocessing](#data-preprocessing)\n7. [Model Building](#model-building)\n8. [Model Evaluation](#model-evaluation)\n9. [Results](#results)\n10. [Contributing](#contributing)\n11. [License](#license)\n\n## Dataset\n- **Dataset Source:** [California Housing Prices](https://www.kaggle.com/camnugent/california-housing-prices) on Kaggle\n- **Description:** This dataset contains housing-related information for various districts in California. It includes features like population, median income, housing median age, and the target variable, median house value.\n### Understating the dataset\n![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/0c3c127b-e057-467a-ad0d-71270a20ce4d)\n![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/3145441b-73c1-4fee-9ccb-4d5985d7df3b)\n![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/01bd003d-f004-46a9-bf71-d6db87c5672c)\n![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/ceffa6eb-e218-49fb-aa58-6e6d04ccd333)\n\n\n## Installation\n1. Clone this repository to your local machine using `git clone`.\n2. Navigate to the project directory.\n3. Install the required Python packages using `pip install -r requirements.txt`.\n\n## Usage\n1. Launch Jupyter Notebook: Run `jupyter notebook` in the project directory.\n2. Open and run the `Predictor.ipynb` notebook to explore the project.\n\n## Data Exploration\n- Explore the dataset using Python and Jupyter Notebook.\n- Generate histograms, scatter plots, and correlation matrices to gain insights into the data.\n### Here are some graphs to help you gain a better understanding\n\\n*This is a heat map, showing the corelation each columns has with each other*\n![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/1e797a0b-dd07-430c-b8a9-770f3b0806ed)\n\\n*This is the histogram (similar to the one shown earlier) showing the data distribution*\n![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/b693279e-753e-4435-b057-81a39fc3c455)\n\\n*This is a scatter plot, makes it simple to spot outliers in the dataset*\n![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/7c58eb93-7764-42cd-98ba-97649d1c594f)\n\n\n## Data Preprocessing\n- Handle missing data using imputation.\n- Perform feature engineering to create new informative features.\n- Scale the data to prepare it for modeling.\n\n## Model Building\n- Build a Linear Regression model using scikit-learn.\n- Train the model on the training dataset.\n- Evaluate the model's performance using various metrics.\n\n## Model Evaluation\n- Calculate evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).\n- Visualize model predictions and compare them to actual values.\n### here are a few graphs that will help you understand the performance of the model.\n\\n*Scatter plot*\n![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/05080eff-cbab-4524-a8e7-c233560187fc)\n\\n*Residual plot*\n![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/838f8a2f-6f5e-4911-8091-3d65bc77a6aa)\n\\n*Feature importance plot*\n![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/5f0d2f85-c9f7-49cc-9f9a-3c36d3f6365a)\n\n\n## Results\n- Summarize key findings and insights from the project.\n- Discuss the model's performance and any improvements achieved through model refinement.\n- The resultant was calculated based on the following parameters\nMean Absolute Error: 0.4367338817223555\nMean Squared Error: 0.3603952607354783\nRoot Mean Squared Error: 0.6003292935843446\n- this values are very average for a model of this type, to achive more suposticated results i will be refining and rewriting parts of the code to ensure maxixmum accuracy\n\n## Contributing\nContributions are welcome! Feel free to open issues or submit pull requests.\n\n## License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheartificialdev%2Fcaliforniahousepricepredictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheartificialdev%2Fcaliforniahousepricepredictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheartificialdev%2Fcaliforniahousepricepredictor/lists"}