https://github.com/hemangsharma/australian-real-estate-prediction-model-project

https://github.com/hemangsharma/australian-real-estate-prediction-model-project

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• Host: GitHub
• URL: https://github.com/hemangsharma/australian-real-estate-prediction-model-project
• Owner: hemangsharma
• Created: 2025-01-28T07:35:02.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-01-28T08:12:17.000Z (4 months ago)
• Last Synced: 2025-01-28T09:22:22.927Z (4 months ago)
• Language: Jupyter Notebook
• Size: 1.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Australian-Real-Estate-Prediction-Model-Project

## Project Overview

This project aims to develop an accurate prediction model to guide property investment decisions in Australia. By leveraging comprehensive real estate data, the model will provide insights into market trends, regional preferences, and property valuation. The ultimate goal is to help stakeholders make informed investment choices by understanding the factors that drive property prices and demand.

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## Dataset: "aus_real_estate.csv"

### Summary

The dataset contains detailed real estate information across major Australian cities, with 5000 entries and 10 distinct columns. It captures essential features influencing property valuation and market dynamics.

### Columns and Attributes

- **Price**: The cost of the property, ranging from $100,000 to $2,000,000.

- **Bedrooms**: Number of bedrooms (1–5).

- **Bathrooms**: Number of bathrooms (1–3).

- **SqFt**: Property size in square feet (800–4000 sq ft).

- **City**: Major cities (Sydney, Melbourne, Brisbane, Perth, Adelaide).

- **State**: Australian states (NSW, VIC, QLD, WA, SA).

- **Year_Built**: Year the property was constructed (1950–2023).

- **Type**: Property type (Apartment, House, Townhouse).

- **Garage**: Binary indicator for garage availability (1 = Yes, 0 = No).

- **Lot_Area**: Land area in square feet (1000–10,000 sq ft).

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## Objectives

1. **Data Analysis**: 

   - Explore patterns in property prices across different cities and states.

   - Analyze how attributes like size, number of bedrooms, and year built affect pricing.

2. **Prediction Model**: 

   - Develop a machine learning model to predict property prices based on the dataset.

   - Evaluate model performance using key metrics (e.g., RMSE, MAE).

3. **Investment Insights**:

   - Identify regions with high growth potential.

   - Highlight attributes most influential in determining property value.

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## Methodology

1. **Data Preparation**:

   - Clean and preprocess the dataset to handle missing values and outliers.

   - Encode categorical features for model compatibility.

2. **Exploratory Data Analysis (EDA)**:

   - Visualize data trends and correlations using plots and statistical summaries.

3. **Model Development**:

   - Experiment with machine learning models such as Linear Regression, Decision Trees, and Random Forests.

   - Perform hyperparameter tuning to optimize performance.

4. **Validation and Testing**:

   - Split the data into training and testing sets (e.g., 80/20 split).

   - Assess model accuracy on unseen data.

5. **Insights Generation**:

   - Derive actionable insights based on model predictions and feature importance.

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## Tools and Technologies

- **Python**: For data manipulation, analysis, and modeling.

- **Libraries**: 

  - **Pandas** and **NumPy**: Data handling and processing.

  - **Matplotlib** and **Seaborn**: Data visualization.

  - **Scikit-learn**: Machine learning model development.

- **Jupyter Notebook**: Interactive development environment.

- **Git**: Version control for project management.

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## Expected Outcomes

- An accurate prediction model for Australian property prices.

- Insights into regional real estate trends and investment opportunities.

- A framework for analyzing real estate datasets for other markets.

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## How to Use

1. **Install Dependencies**:

   - Use `pip install -r requirements.txt` to install necessary libraries.

2. **Run the Analysis**:

   - Execute the Jupyter Notebook for data exploration and model training.

3. **Generate Predictions**:

   - Use the trained model to predict prices for new property data.

4. **Visualize Insights**:

   - Review plots and dashboards for actionable insights.

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## Contributing

Contributions are welcome! Please submit a pull request or raise an issue to propose changes or report bugs.

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## License

This project is licensed under the MIT License. See the LICENSE file for details.