Repository containing the masterworks of the superstar group known as "Explosive Shells".
The Explosive Shells team offers Python-based data analysis, visualization, and predictive modeling tools to explore and forecast the efficiency of University of Iowa Solar Panels.
Just open the Hackathon_Engie_Challenge_2023_BUSCAR.ipynb and Hackathon_Engie_Challenge_2023_Electric_Vehicle.ipynb files and take a peak!
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Data Retrieval and Integration:
- Fetches data from multiple remote sources using web APIs.
- Integrates various datasets, including float efficiency, daily totals, weather conditions, and more, for comprehensive analysis.
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Data Preprocessing and Cleaning:
- Conducts thorough data cleaning to ensure data quality.
- Handles missing values and outliers to maintain data integrity.
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Data Transformation and Time-Series Handling:
- Transforms and standardizes timestamps to facilitate consistent analysis.
- Calculates statistical metrics to gain insights into the data, such as rolling averages and standard deviations.
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Exploratory Data Analysis (EDA):
- Generates visualizations to explore the dataset's characteristics.
- Includes Bollinger Bands and moving averages for trend analysis.
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Machine Learning Modeling:
- Utilizes machine learning techniques, specifically linear regression.
- Implements predictive models to understand and forecast the efficiency of explosive shells.
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Model Evaluation and Performance Metrics:
- Evaluates the predictive models using industry-standard metrics like Mean Squared Error (MSE) and R-squared.
- Assesses model accuracy and reliability for decision-making.
Train 90% and Test 10%
| Electrical Vehicle | Bus Car |
|---|---|
| Features | Features |
| *Ridge Regression: r=78.26 score=0.27 | *Ridge Regression: r=38.03 score=0.34 |
| *Bayes Regression: r=78.31 score 0.27 | *Bayes Regression: r=38.08 score=0.34 |
| *Random Forest: r=24.55 score=0.77 | *Random Forest: r=18.59 score=0.68 |
| *Decision Tree: r=48.83 score=0.55 | *Decision Tree: r=35.43 score=0.38 |
| Removed Features | Removed Features |
| *Ridge Regression: r=81.98 score=0.24 | *Ridge Regression: r=40.61 score=0.29 |
| *Bayes Regression: r=81.94 score=0.24 | *Bayes Regression: r=40.58 score=0.29 |
| *Random Forest: r=52.55 score=0.51 | *Random Forest: r=30.31 score=0.47 |
| *Decision Tree: r=84.47 score=0.22 | *Decision Tree: r=56.21 score=0.02 |
