diff --git a/README.md b/README.md index 521abf8..743ec5a 100644 --- a/README.md +++ b/README.md @@ -1,40 +1,101 @@ -# House Price Prediction +# 🏠 House Price Prediction -The "House Price Prediction" project focuses on predicting housing prices using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, and XGBoost, this project provides an end-to-end solution for accurate price estimation. +This project helps **predict house prices** using **machine learning**. We use tools like Python, Pandas, Scikit-learn, Matplotlib, Seaborn, and XGBoost to build a model that can guess house prices based on different factors like income, age of the house, and number of rooms. -## Project Overview +--- -The "House Price Prediction" project aims to develop a model that can accurately predict housing prices based on various features. This prediction task is of great significance in real estate and finance, enabling informed decision-making for buyers, sellers, and investors. By employing machine learning algorithms and a curated dataset, this project provides a powerful tool for estimating house prices. +## πŸ“Œ Project Overview -## Key Features +The goal of this project is to build a model that can **accurately predict housing prices** using real-world data. This kind of prediction is useful for **buyers, sellers, and real estate investors** who want to make better decisions. -- **Data Collection and Processing:** The project utilizes the "California Housing" dataset, which can be directly downloaded from the Scikit-learn library. The dataset contains features such as house age, number of rooms, population, and median income. Using Pandas, the data is processed and transformed to ensure it is suitable for analysis. +We use the **California Housing dataset**, which comes built into Scikit-learn. It includes information like: -- **Data Visualization:** The project employs data visualization techniques to gain insights into the dataset. Matplotlib and Seaborn are utilized to create visualizations such as histograms, scatter plots, and correlation matrices. These visualizations provide a deeper understanding of the relationships between features and help identify trends and patterns. +* Median income in the area +* Number of rooms +* Population +* House age +* And more! -- **Train-Test Split:** To evaluate the performance of the regression model, the project employs the train-test split technique. The dataset is split into training and testing subsets, ensuring that the model is trained on a portion of the data and evaluated on unseen data. This allows for an accurate assessment of the model's predictive capabilities. +--- -- **Regression Model using XGBoost:** The project utilizes the XGBoost algorithm, a popular gradient boosting framework, to build the regression model. XGBoost is known for its ability to handle complex relationships between features and achieve high predictive accuracy. The Scikit-learn library provides an implementation of XGBoost that is utilized in this project. +## 🌟 What This Project Does -- **Model Evaluation:** The project assesses the performance of the regression model using evaluation metrics such as R-squared error and mean absolute error. R-squared error measures the proportion of the variance in the target variable that can be explained by the model, while mean absolute error quantifies the average difference between the predicted and actual house prices. These metrics provide insights into the model's accuracy and precision. Additionally, a scatter plot is created to visualize the predicted prices against the actual prices. +### βœ… 1. **Loads and Prepares Data** -## Getting Started +We use **Pandas** to clean and prepare the data, making it ready for machine learning. -To run this project locally, follow these steps: +### πŸ“Š 2. **Visualizes the Data** -1. Clone the repository: `gh repo clone MYoussef885/House_Price_Prediction` -2. Install the required libraries: `If you're using Google Colab, you don't need to pip install. Just follow the importing the dependencies section.` -3. Launch Google Colab: `https://colab.research.google.com/` -4. Open the `House_Price_Prediction.ipynb` file and run the notebook cells sequentially. +With **Matplotlib** and **Seaborn**, we make charts and graphs to better understand patterns in the data, like: -## Conclusion +* Histograms +* Scatter plots +* Correlation maps -The "House Price Prediction" project provides a practical solution for estimating housing prices based on various features. By leveraging data collection, preprocessing, visualization, XGBoost regression modeling, and model evaluation, this project offers a comprehensive approach to addressing the price prediction task. The project utilizes the "California Housing" dataset from Scikit-learn, ensuring a reliable and widely accessible data source. +### πŸ§ͺ 3. **Splits Data for Training and Testing** -## License +We divide the dataset into **training data** (to teach the model) and **testing data** (to see how well it learned). -This project is licensed under the MIT license. See the [LICENSE](LICENSE) file for more information. +### βš™οΈ 4. **Builds a Machine Learning Model** -## Acknowledgements +We use **XGBoost**, a powerful machine learning algorithm, to build a **regression model** that predicts prices. -This project is made possible by the contributions of the open-source community and the powerful libraries it provides, including NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, and XGBoost. I extend my gratitude to the developers and maintainers of these libraries for their valuable work. In addition, the mentor Siddhardan, visit his channel here : https://www.youtube.com/@Siddhardhan +### πŸ“ˆ 5. **Evaluates the Model** + +We check how good the model is using: + +* **R-squared**: How much of the data the model can explain +* **Mean Absolute Error (MAE)**: How far off the predictions are, on average +* **Scatter plot**: To compare predicted prices with real ones + +--- + +## πŸš€ How to Run This Project + +You don’t need to install anything if you're using **Google Colab**! + +1. **Clone the repository:** + + ``` + gh repo clone MYoussef885/House_Price_Prediction + ``` + +2. **Open Google Colab:** + [Google Colab](https://colab.research.google.com/) + +3. **Upload the notebook:** + Open the file `House_Price_Prediction.ipynb` and run each cell one by one. + +--- + +## 🧠 What You’ll Learn + +By doing this project, you'll learn how to: + +* Work with real datasets +* Clean and prepare data using Pandas +* Visualize data with Matplotlib and Seaborn +* Use XGBoost to build regression models +* Evaluate machine learning models + +--- + +## πŸ“œ License + +This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for more details. + +--- + +## πŸ™ Acknowledgements + +Special thanks to the open-source community and tools like: + +* **NumPy** +* **Pandas** +* **Scikit-learn** +* **Matplotlib** +* **Seaborn** +* **XGBoost** + +And big thanks to **Siddhardhan** for his helpful tutorials! +πŸ‘‰ [Check out his YouTube channel](https://www.youtube.com/@Siddhardhan)