KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#typescript #ai-agents #cli #claude-code #codex #developer-tools #ai #dsh-plugin #ai-agent #mcp #deepseek-harness #windows

K-mean-House-Price-Prediction

★ 41 Open GitHub ↗

No description available.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# K-mean-House-Price-Prediction

## Housing Data Analysis Notebook
This Jupyter Notebook contains code for analyzing and visualizing housing data. The notebook uses Python and popular data analysis libraries such as pandas and matplotlib to load, explore, and visualize the dataset.

## Notebook Content
### Loading the Data:

The notebook starts by loading the housing data from a CSV file named housing.csv using the pandas library.

The dataset includes various features such as longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, median_house_value, and ocean_proximity.

### Data Exploration:

The first few rows of the dataset are displayed using the head() function to get an initial look at the data.

A subset of the dataset is created by selecting specific columns (longitude, latitude, median_house_value) for further analysis.

### Data Visualization:

The notebook includes code to create a scatter plot of the housing data, using longitude and latitude as the x and y axes, respectively. The median_house_value is used to color the points, providing a visual representation of housing prices across different locations.

### Further Analysis:

Additional analysis and visualizations can be added to explore relationships between different features, such as the distribution of median_income or the correlation between total_rooms and median_house_value.

## Libraries Used
### Pandas:
For data manipulation and analysis.

### Matplotlib:
For creating visualizations.

How to Use
### Clone the Repository:

git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name
Install Dependencies:
Ensure you have the required libraries installed. You can install them using pip:

pip install pandas matplotlib
### Run the Notebook:
Open the Jupyter Notebook and run the cells to see the analysis and visualizations:

jupyter notebook house.ipynb
### Dataset
The dataset used in this notebook is housing.csv, which contains information about housing in California. The dataset includes various features that describe the properties and their locations.

### Contributing
Feel free to contribute to this project by forking the repository and submitting pull requests. Any contributions, whether they are improvements to the code, additional analyses, or new visualizations, are welcome.

K-mean-House-Price-Prediction

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README