https://github.com/ngangawairimu/decision-tree-model
Analyzed global population data using decision tree regression, calculating growth rates and evaluating model performance with RMSLE for insights.
https://github.com/ngangawairimu/decision-tree-model
matplotlib python sckiit-learn seaborn
Last synced: 2 months ago
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Analyzed global population data using decision tree regression, calculating growth rates and evaluating model performance with RMSLE for insights.
- Host: GitHub
- URL: https://github.com/ngangawairimu/decision-tree-model
- Owner: ngangawairimu
- Created: 2024-05-04T05:57:37.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-29T06:51:37.000Z (over 1 year ago)
- Last Synced: 2025-03-23T08:23:39.751Z (over 1 year ago)
- Topics: matplotlib, python, sckiit-learn, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 10.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
### Decision Tree Regression Analysis on Population Data
Objective: To analyze population data from various countries using decision tree regression, aiming to predict future population growth based on historical trends.
## Expected Outcomes:
- Trained Model: A trained Decision Tree regression model capable of predicting population growth rates.
- Predictions: The model should provide population growth predictions for the year 2017 (or any specified year).
- Model Performance: An RMSLE score that helps quantify how close the model's predictions are to the actual growth rates. A lower RMSLE indicates better predictive performance.
#### Model Training and Evaluation:
Split the dataset into training and testing sets to ensure a robust evaluation of the model's performance.
Train a decision tree regression model using the training set.
Evaluate the model's performance using Root Mean Squared Logarithmic Error (RMSLE) to understand its accuracy.