Access the interactive dashboards covering executive strategy, ML forecasting models, geographic analysis, and the 2030 electric vehicle transition.
This project presents a comprehensive analysis of the global automotive industry, delivered as an interactive Streamlit application with 14 specialized analysis pages, forecasts through 2030, and strategic recommendations based on machine learning models.
All pages are accessible from the sidebar of the Streamlit application.
- Description: Project introduction and overview
- Content: Key metrics, production trends, yearly overview
- Description: Strategic overview for senior management
- Content: Key performance indicators, 2030 scenario comparison
- Audience: Executive leadership, board of directors
- Description: Comparative analysis of machine learning models
- Content: Model performance (R² scores), feature importance
- Models: XGBoost, Prophet, Linear Regression, ARIMA
- Description: Regional analysis
- Content: Production trends and price comparison by region
- Regions covered: North America, Europe, Asia-Pacific, China
- Description: Analysis of the transition to electric vehicles
- Content: EV share evolution, EV production by manufacturer
- Description: Comparative analysis of major manufacturers
- Content: Production trends and average price by manufacturer
- Manufacturers covered: Toyota, Volkswagen, Ford, Hyundai-Kia, Stellantis, GM
- Description: Strategic economic analysis
- Content: Correlation with GDP growth, oil price impact on production
- Description: Competitive analysis and market positioning
- Content: Market share by manufacturer, market share evolution over time
- Description: Risk identification and analysis
- Content: Operational and regulatory risks, technological and market opportunities
- Description: Pandemic impact and industry recovery
- Content: Monthly production trend 2020–2023, pre/post-COVID comparison
- Description: In-depth analysis of the electric transition
- Content: EV share projections to 2030, battery cost evolution
- Description: Detailed strategic recommendations
- Content: Immediate and medium-term action plan, 2030 objectives
- Description: Analysis by sector and market segment
- Content: Production by vehicle category, margin analysis by segment
- Description: Long-term vision and future scenarios
- Content: 2030 scenario comparison (radar chart), technology trends (autonomy, electrification)
pip install -r requirements_streamlit.txtstreamlit run streamlit_app.pyThe app opens in your browser with a sidebar to navigate between all analysis pages: Home, Executive Dashboard, ML Models, Geographic Analysis, Electric Transition, Manufacturers, Economic Analysis, Competitive Intelligence, Risks and Opportunities, Post-COVID Analysis, Advanced Electric Transition, Strategic Recommendations, Sector Analysis, and 2030 Outlook.
Automotive-Industry-Forecasting/
├── streamlit_app.py # Main Streamlit application
├── run_streamlit.py # Streamlit launcher script
├── requirements_streamlit.txt # Streamlit app dependencies
├── regenerate_models.py # Script to retrain/regenerate ML models
│
├── code/ # Analysis scripts
│ ├── automotive_analysis_main.py
│ ├── automotive_analysis_complete.py
│ ├── run_complete_analysis.py
│ ├── run_analysis.py
│ └── requirements.txt
│
├── dashboards/ # Standalone HTML dashboards
│ ├── dashboard_executif_direction.html
│ ├── dashboard_modeles_ml.html
│ ├── dashboard_analyse_geographique_avancee.html
│ ├── dashboard_transition_electrique.html
│ ├── dashboard_fabricants_automobile.html
│ ├── dashboard_analyse_economique_strategique.html
│ ├── dashboard_intelligence_concurrentielle.html
│ ├── dashboard_risques_opportunites.html
│ └── dashboard_principal_automobile.html
│
├── data/ # Data and results
│ ├── comprehensive_automotive_data.csv
│ └── automotive_analysis_results_clean.json
│
├── models/ # Saved ML models
│ ├── xgboost_production_clean.pkl
│ ├── xgboost_price_clean.pkl
│ ├── linear_regression_production_clean.pkl
│ ├── linear_regression_price_clean.pkl
│ ├── prophet_production_clean.pkl
│ └── arima_production_clean.pkl
│
└── README.md
- Economic analysis: policy impact, costs, profitability
- Electric transition: EV adoption, infrastructure, government policies
- Risks and opportunities: risk matrix and mitigation measures
- Competitive intelligence: benchmarking and positioning
- Six machine learning models trained and validated
- Forecasts through 2030
- Multiple scenario analysis
- Interactive Streamlit application with 14 analysis pages and sidebar navigation
- Dynamic charts built with Plotly
- Standalone HTML dashboards also available in the
dashboards/folder - Interface adapted to different screen sizes
- Best-case scenario: accelerated electric vehicle transition (+10.4%)
- Most influential factor: raw material prices (28% importance)
- Recommended approach: gradual rather than abrupt transition
- Electric vehicle opportunity: estimated growth of 300% by 2030
- Accelerate the electric transition gradually
- Geographically diversify supply chains
- Develop technology partnerships
- Optimize production costs
- Improve operational efficiency
README.md: project guide- Commented and documented source code
| Item | Detail |
|---|---|
| Analysis period | 2010-2023 (14 years) |
| Forecast horizon | 2024-2030 (7 years) |
| Data volume | 12,096 observations |
| Manufacturers covered | Toyota, Volkswagen, Ford, Hyundai-Kia, Stellantis, GM |
| Regions covered | North America, Europe, Asia-Pacific, China |
| Analysis pages | 14 pages (Streamlit app) |
| Machine learning models | 6 models |