klsalcedo/water_well_classification

Machine learning classifier to predict water well functionality in Tanzania # Classifying Water Wells - Tanzania Author: Katarina Salcedo ## Motivation To aid The Water Project in their goal of providing clean water to regions of Africa, I will create a classification model that is accurately able to predict whether a water well is functional or nonfunctional. The Water Project can use this model to decide where to build new wells, if a well needs maintenance and also help them prioritize regions that are in greater need of a water source. ## Data The data is sourced from Taarifa and the Tanzanian Ministry of Water. It contains around 59,000 rows and 40 independent variables describing the well's geographical location, funder, management, surrounding population, quantity and quality of water, extraction type, water source, if payment is required, etc. The target variable describes the status of the well as either 'functional', 'non functional' or 'functional needs repair'. ## Methodology Before running any models, each independent variable was checked to ensure there was good separability amoung the three different classifications. Variables that showed good separability were used in the classificaton model and variables that showed little to no separability were dropped. Pandas get_dummies function was used on the categorical variables and class imbalance was also checked. After cleaning the data and selecting useful independent variables, four vanilla models were run. These include: Logistic Regression, Decision Tree Classifier, Random Forest Classifier and Gradient Boosting. Of these four, the two models with the best evaluation metrics - Decision Tree and Random Forest Classifier - were chosen for further hyperparameter tuning using GridSearch. ## Results Out of the Decision Tree and Random Forest Classifier, the latter had the best metrics. I was able to get this model up to 84% accuracy in the test set (89% for train set). Below is the classification report of the final model: ## Conclusions This model is able to predict wat …