Flight Cancellation Predictor

Python

FastAPI

A machine-learning web app that predicts flight cancellations using six different classifiers (Naive Bayes, KNN, Logistic Regression, Decision Tree, MLP, Random Forest), with batch evaluation and a real-time inference UI.

End-to-end data-science project from the Data Science course at IST. The goal: turn raw flight data into actionable predictions, the right way — with a real preprocessing pipeline, multiple model options, and a serving layer.

What I built

Preprocessing pipeline

A reusable transformation pipeline applied identically at training and inference time:

Missing-value imputation: mean for numeric, mode for categorical.
Type enforcement: numeric coercion, type standardization.
Cyclic encoding: sine/cosine transforms for temporal features (Month, DayOfWeek, Quarter, Time blocks) so the model sees Sunday→Monday as adjacent rather than 6→0.
Ordinal encoding for categorical features.
MinMax scaling to [0, 1].
Feature selection down to 37 retained features.

A JSON manifest (prediction_objects.json) keeps the pipeline reproducible and the inference path consistent with training.

Six classifiers, side-by-side

Algorithm	Library	Use case
Naive Bayes (Gaussian)	scikit-learn	Baseline, fast
K-Nearest Neighbors	scikit-learn	Non-parametric reference
Logistic Regression	scikit-learn	Linear baseline
Decision Tree	scikit-learn	Interpretable
Multi-Layer Perceptron	scikit-learn	Non-linear
Random Forest	scikit-learn	Best overall trade-off

A focus on imbalanced-class scenarios (cancellations are rare) shaped the metrics and the resampling strategy.

Serving layer

A FastAPI web app with two endpoints:

POST /predict — single flight, instant prediction.
POST /evaluate — upload a CSV, get accuracy / precision / recall / F1 across all six models.

A vanilla HTML/CSS/JS frontend wraps the endpoints with a usable UI, and joblib-persisted models keep the server cold-start cheap.

Companion work: time series

In parallel I worked on traffic-volume forecasting on high-frequency time series, comparing classical statistics (ARIMA) with deep learning (LSTM) — explored as a separate experiment but on the same data-science core.