This project aims to build a DecisionTreeRegression model to predict certain health indicators using the World Health Report datasets. The model is trained on the 2015 and 2016 datasets and evaluated using the 2017 dataset. The World Health Report provides comprehensive data on various health-related factors from different countries around the world.
The dataset used for this project consists of three main files:
2015_dataset.csv: Contains the health indicators data for the year 2015.
2016_dataset.csv: Contains the health indicators data for the year 2016.
2017_dataset.csv: Contains the health indicators data for the year 2017.
The DecisionTreeRegression model is implemented using the scikit-learn library. It is a supervised machine learning algorithm that creates a decision tree based on the features (health indicators) to predict a target variable (e.g., life expectancy). The model is trained on the 2015 and 2016 datasets and evaluated using the 2017 dataset to assess its performance in predicting health indicators.
The DecisionTreeRegression model trained on the 2015 and 2016 datasets shows promising results in predicting health indicators for the year 2017. However, it's important to note that the model's performance may vary depending on the quality and representativeness of the dataset. Further analysis and experimentation can be done to improve the model's accuracy and robustness.
Please refer to the code and documentation for more details on the implementation, data preprocessing, and model evaluation.