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Coupon Acceptance Prediction

Complete · Group project (2 people), data preparation and logistic regression

A university machine learning project (QUT IFN580) using a dataset of 12,684 driving scenarios to predict whether a driver accepts a coupon. I handled data preparation, which meant dropping unusable columns, imputing missing values, parsing mixed text and number fields, one-hot encoding and a correlation analysis. I also built the logistic regression model, tuned with GridSearchCV. Luke built the decision tree and neural network. For my individual part, I compared all three saved models on a new test split using accuracy, ROC curves and AUC, and the neural network came out as the best model.

UniversityMachine LearningPython

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Most of machine learning is cleaning the data

For a uni machine learning project, we predicted whether a driver would accept a coupon offer, using a dataset of 12,684 driving scenarios with 26 variables. I owned data preparation and the logistic regression model. Data prep took far longer than the modelling.

What the data threw at me:

  • A column that was almost empty. One variable was missing in about 99% of rows. No imputation rescues that, so it went.
  • Small gaps worth filling. Several “how often do you visit X” columns were missing 1 to 2% of values. Those were worth imputing rather than throwing rows away.
  • Numbers that aren’t numbers. Age mixed values like 21 and 26 with text like below21 and 50plus. Time was stored as strings like 10AM. Both needed converting before a model could use them.
  • Variables that repeat each other. Some binary columns were very highly correlated. A correlation heatmap made those easy to find and drop.

Then the models. I built and tuned the logistic regression with GridSearchCV, and my teammate Luke built the decision tree and neural network. For my individual part, I compared all three saved models on a fresh test split using accuracy, ROC curves and AUC. The neural network came out on top.

What I’d tell myself at the start: read every column before touching a model. Classifying each variable as binary, ordinal, nominal or numeric up front saved me from encoding mistakes later.

pythonpandasmachine-learningdata-cleaning