House Prices Impact Analysis

This Jupyter Notebook provides an in-depth analysis of the factors impacting house prices. The analysis aims to identify key variables that influence property prices and uses statistical and machine learning methods to create a predictive model. This project is useful for understanding real estate trends, evaluating the importance of various property features, and potentially guiding pricing decisions.

Project Overview

This notebook includes:

1. Data Loading and Preprocessing: Import and clean the dataset, handling any missing values and outliers.
2. Exploratory Data Analysis (EDA): Visualize relationships between key features and house prices using correlation matrices, scatter plots, and distribution graphs.
3. Feature Engineering: Construct new variables, transform features, or encode categorical variables to improve model performance.
4. Model Training and Evaluation: Build and assess the accuracy of different machine learning models, such as linear regression or decision trees, to predict house prices.
5. Result Interpretation: Analyze model outputs and key metrics to determine the most impactful variables on house prices.

Requirements

To run this notebook, you will need the following packages:

• pandas: For data manipulation and analysis
• numpy: For numerical computations
• matplotlib and seaborn: For data visualization
• scikit-learn: For machine learning modeling and evaluation

Install these dependencies using:

pip install pandas numpy matplotlib seaborn scikit-learn

Getting Started

1. Load the Data: Update the notebook with the path to your house price dataset.
2. Run Each Cell Sequentially: Each cell performs a specific step in the data analysis and modeling workflow.
3. Interpret the Results: Use the visualization and model output sections to interpret how various factors impact house prices.

Example Outputs

Key outputs include:

• Correlation Heatmaps: Visual representation of the correlation between different features and the target variable (house prices).
• Scatter and Box Plots: Visual insights into data distributions and relationships.
• Predictive Model Accuracy: Evaluation metrics to assess model performance, including RMSE, MAE, and R^2 score.

License

This project is licensed under the MIT License.

Author

This analysis was conducted by [Rasha Alzaher]. Feel free to reach out with questions or suggestions.

House Prices Impact Analysis

This Jupyter Notebook provides an in-depth analysis of the factors impacting house prices...