A Machine Learning Model API in scikit-learn using Support Vector Regressors and ensemble modeling with Gradient Boost Regressor and Cross Validation.
Key Features • How To Use • Credits • License
This machine learning model predicts the happines score of a given country. This prediction is a number between 0 and 10. The dataset is taken from the World Happiness Report Kaggle Competition. So here are the key features of this project:
- Prediction is based on this country's features:
highlowgdpfamilylifexpfreedomgenerositycorruptiondystopia: Imaginary country that has the world's least-happy people.
- Professional Modularization on this Project. Some modules are programmed using OOP.
- Built with an Rest API programmed in Flask .
- Based on Scikit-Learn modules and functions such like:
svm.SVR: Support Vector Regressor.ensemble.GradientBoostingRegressor: Gradiente Boosting Regressors Ensemble method.model_selection.GridSearchCV: Cross validation method.
To clone and run this application, follow these steps
# Clone this repository
$ git clone https://github.com/santiagoahl/world-happiness.git
# Go into the repository
$ cd world-happiness
# Install dependencies
$ pip install -r requirements.txt
# Run the app
$ python3 server.py
#View results putting the following on your browser (If port 8080 is busy change it)
http:https://127.0.0.1:8080/predictThis software uses the following packages:
MIT
Web Site santiagoal.super.site · GitHub @santiagoahl · Twitter @sahumadaloz