https://github.com/sudothearkknight/californiahousepricepredictor

we explore California's housing market, predict property prices, and unravel the factors that shape real estate values. Let's dive into data-driven insights and discover the power of predictive modeling.
https://github.com/sudothearkknight/californiahousepricepredictor

jupyter-notebook kaggle learning-by-doing linear-regression machine-learning python

Last synced: 8 days ago
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we explore California's housing market, predict property prices, and unravel the factors that shape real estate values. Let's dive into data-driven insights and discover the power of predictive modeling.

• Host: GitHub
• URL: https://github.com/sudothearkknight/californiahousepricepredictor
• Owner: sudoTheArkKnight
• License: mit
• Created: 2023-09-27T18:42:05.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-30T10:26:53.000Z (about 1 year ago)
• Last Synced: 2024-08-01T13:10:33.707Z (4 months ago)
• Topics: jupyter-notebook, kaggle, learning-by-doing, linear-regression, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 869 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# California Housing Prices Prediction

## Project Overview

This project focuses on predicting housing prices in California districts using machine learning. 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.

## Table of Contents

1. [Project Overview](#project-overview)

2. [Dataset](#dataset)

3. [Installation](#installation)

4. [Usage](#usage)

5. [Data Exploration](#data-exploration)

6. [Data Preprocessing](#data-preprocessing)

7. [Model Building](#model-building)

8. [Model Evaluation](#model-evaluation)

9. [Results](#results)

10. [Contributing](#contributing)

11. [License](#license)

## Dataset

- **Dataset Source:** [California Housing Prices](https://www.kaggle.com/camnugent/california-housing-prices) on Kaggle

- **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.

### Understating the dataset

![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/0c3c127b-e057-467a-ad0d-71270a20ce4d)

![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/3145441b-73c1-4fee-9ccb-4d5985d7df3b)

![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/01bd003d-f004-46a9-bf71-d6db87c5672c)

![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/ceffa6eb-e218-49fb-aa58-6e6d04ccd333)

## Installation

1. Clone this repository to your local machine using `git clone`.

2. Navigate to the project directory.

3. Install the required Python packages using `pip install -r requirements.txt`.

## Usage

1. Launch Jupyter Notebook: Run `jupyter notebook` in the project directory.

2. Open and run the `Predictor.ipynb` notebook to explore the project.

## Data Exploration

- Explore the dataset using Python and Jupyter Notebook.

- Generate histograms, scatter plots, and correlation matrices to gain insights into the data.

### Here are some graphs to help you gain a better understanding

\n*This is a heat map, showing the corelation each columns has with each other*

![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/1e797a0b-dd07-430c-b8a9-770f3b0806ed)

\n*This is the histogram (similar to the one shown earlier) showing the data distribution*

![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/b693279e-753e-4435-b057-81a39fc3c455)

\n*This is a scatter plot, makes it simple to spot outliers in the dataset*

![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/7c58eb93-7764-42cd-98ba-97649d1c594f)

## Data Preprocessing

- Handle missing data using imputation.

- Perform feature engineering to create new informative features.

- Scale the data to prepare it for modeling.

## Model Building

- Build a Linear Regression model using scikit-learn.

- Train the model on the training dataset.

- Evaluate the model's performance using various metrics.

## Model Evaluation

- Calculate evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).

- Visualize model predictions and compare them to actual values.

### here are a few graphs that will help you understand the performance of the model.

\n*Scatter plot*

![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/05080eff-cbab-4524-a8e7-c233560187fc)

\n*Residual plot*

![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/838f8a2f-6f5e-4911-8091-3d65bc77a6aa)

\n*Feature importance plot*

![image](https://github.com/The-Ark-Knight/CaliforniaHousePricePredictor/assets/90926369/5f0d2f85-c9f7-49cc-9f9a-3c36d3f6365a)

## Results

- Summarize key findings and insights from the project.

- Discuss the model's performance and any improvements achieved through model refinement.

- The resultant was calculated based on the following parameters

Mean Absolute Error: 0.4367338817223555

Mean Squared Error: 0.3603952607354783

Root Mean Squared Error: 0.6003292935843446

- 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

## Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.