stuart-d-king/pump_it_up_predictive

Predicting water pump functionality using a Random Forest classifier

Pump it up: Data mining the water table in Tanzania

Data used in this repository comes from DrivenData, an online web platform for data science practice competitions aimed at tackling social challenges. The datasets used are a compilation of data from Taarifa and the Tanzanian Ministry of Water. For this particular challenge, competitors are tasked with creating a statistical model that will predict which water pumps in Tanzania are functional, which are in need of repairs, and which don't work at all.

Building the model

Feature selection

My first step was to read in the data, make feature selection, and clean the resulting dataframe. To select the features used in the model, I first identified and removed redundant features (e.g. extraction_type and extraction_type_group hold the same information). I also removed features deemed to hold little predictive power (i.e. recorded_by), or features with a majority of missing values (e.g. amount_tsh and population). Once my set of features was determined, I then encoded all categorical variables and filled missing values with either the mode for categorical variables, or the mean for continuous variables. Finally, I created a pump_age feature using the date the data was collected (2013) and the construction_year for each pump.

Cross validation

The cleaned data was then split into training and test sets, and the independent variables (X values) for each set were scaled. Out-of-the-box classifiers from SciKit Learn were then implemented using the training data. Classifiers evaluated include Random Forest, K-Nearest Neighbors, XG Boost, Gradient Boost, Ada Boost, and Logistic Regression. All models were scored by taking the mean accuracy, precision, recall, and F1 scores computed from 5-fold cross validation.

Because the Random Forest classifier produced slightly better scoring metrics, I ran a hyper parameter grid search for this classifier. The gird search results were incorporated into my final model.

Before settling on the Random Forest classifier, I also constructed a simple multi-layer perceptron neural network using Keras to try and improve prediction accuracy. I performed a grid search on my neural network, but even the network using the best parameters did not predict water pump functional status as well as the Random Forest classifier.

Model training

The model can be trained by running the following code from the command line:

python taarifa_train.py

This will create train and test datasets and save them to the data directory (will be created if it does not already exist).

Make predictions

To make predictions run the following code from the command line:

python taarifa_predict.py

This will return an accuracy score for the model, as well as an array of predictions for each data observation. The predictions will also be saved to a predict_results.csv in the root directory.

Additional insights / Next steps

During my analysis, I examined the importance of my model's features. Using the leave-one-out approach, the features with the greatest importance were gps_height, quantity_group, and pump_age. Understanding this, additional analysis into how these features impact the likelihood of pump functionality would be an important next step.

• Bar charts of leave-one-out feature importance and leave-one-out OOB scores can be generated using taarifa_main.py

Related, I performed principle component analysis (PCA) on my selected features and plotted the amount of variance explained by each principle component. Based on a visual inspection of the scree plot, further dimensionality reduction using PCA would not account for a majority of the variance in my model. A scree plot can be generated using taarifa_main.py.

Finally, my model does not explore interactions between features, which would be useful to increase the interpretability of the model.