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https://github.com/andwati/predictive_analysis.git
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andwati
ed2ff5b359
- Created `streamlit_app.py` for a user-friendly dashboard to predict student outcomes using a Random Forest model. - Added `requirements.txt` to specify necessary Python packages for the application. - Implemented `train_and_save_model.py` to train the Random Forest model and save it along with preprocessing information. - Included detailed user input forms for various student characteristics and academic performance metrics. - Added custom styling for better UI/UX in the Streamlit app. - Integrated prediction results display with probability metrics and model performance insig
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
"""
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Simple setup script to test if required packages are available
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Run this first to check your environment
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"""
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def check_dependencies():
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"""Check if all required packages are available"""
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required_packages = [
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'pandas', 'numpy', 'sklearn', 'streamlit', 'plotly'
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]
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missing_packages = []
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for package in required_packages:
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try:
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if package == 'sklearn':
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import sklearn
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else:
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__import__(package)
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print(f"✅ {package} is available")
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except ImportError:
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print(f"❌ {package} is missing")
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missing_packages.append(package)
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if missing_packages:
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print(f"\n📦 To install missing packages, run:")
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if 'sklearn' in missing_packages:
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missing_packages.remove('sklearn')
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missing_packages.append('scikit-learn')
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print(f"pip install {' '.join(missing_packages)}")
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else:
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print("\n🎉 All dependencies are available!")
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print("You can now run: python train_and_save_model.py")
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if __name__ == "__main__":
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check_dependencies() |