Core Features

🗂️ Dataset & Data Processing

Flight Data Pipeline

FlightDelay processes large-scale aviation data through a multi-stage pipeline:

Data Source:

• flight_data.csv – Historical flight records dataset
• Fields include: Flight date, airline, origin/destination airports, scheduled times, delays, distances
• Comprehensive delay cause breakdown (carrier, weather, NAS, security, late aircraft)

Data Cleaning:

• Null Handling – Drop records with missing critical fields (ARR_DELAY, departure/arrival times)
• Outlier Filtering – Remove unrealistic flight times (>1000 minutes) and distances (>3000 miles)
• Data Imputation – Fill missing delay cause fields with 0 (no delay from that cause)
• Column Reduction – Remove irrelevant fields (cancellation codes, DOT codes, city names)

Data Schema:

• 32 original columns reduced to essential features
• Date formatting with PySpark date functions
• Integer and double type enforcement for numeric fields
• String types for categorical features (airline, airport codes)

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⚙️ Feature Engineering

Intelligent Feature Creation

FlightDelay extracts and engineers features to maximize prediction accuracy:

Temporal Features:

• DEP_HOUR – Hour of scheduled departure (0-23)
• DEP_MINUTE – Minute of scheduled departure (0-59)
• ARR_HOUR – Hour of scheduled arrival (0-23)
• ARR_MINUTE – Minute of scheduled arrival (0-59)
• DEP_DAY_OF_WEEK – Day of week (1=Sunday, 7=Saturday)
• DEP_MONTH – Month of departure (1-12)
• DEP_DAY_OF_MONTH – Day of month (1-31)
• DEP_WEEK_OF_YEAR – ISO week number (1-53)
• IS_WEEKEND – Binary flag for Saturday/Sunday flights

Derived Features:

• DISTANCE_PER_MINUTE – Miles per minute of scheduled flight time
• SEVERE_DELAY – Binary target variable (1 if ARR_DELAY ≥ 60 minutes, 0 otherwise)
• DELAY_TRAVEL_RATIO – Miles flown per minute of delay (0 if no delay)

Categorical Features:

• AIRLINE_CODE – Carrier identifier
• ORIGIN – Departure airport code
• DEST – Destination airport code

Continuous Features:

• CRS_ELAPSED_TIME – Scheduled flight duration
• DISTANCE – Flight distance in miles

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🤖 Machine Learning Models

Supervised Classification with PySpark MLlib

FlightDelay trains three classification models to predict severe delays (≥60 minutes):

1. Gradient Boosted Trees (GBT) – Primary Model

• Algorithm: GBTClassifier with iterative boosting
• Hyperparameters:
  • 50 iterations (maxIter=50)
  • Max depth of 5 (maxDepth=5)
  • Learning rate of 0.1 (stepSize=0.1)
  • Class weighting support (weightCol)
• Best Performance: Highest F1 score and AUC across all models
• Use Case: Production deployment for streaming predictions

2. Random Forest Classifier (RFC)

• Algorithm: Ensemble of 30 decision trees
• Hyperparameters:
  • 30 trees (numTrees=30)
  • Max depth of 5 (maxDepth=5)
  • Random seed for reproducibility
• Performance: Strong baseline with good precision
• Use Case: Comparative analysis and visualization

3. Logistic Regression (LR)

• Algorithm: Regularized logistic regression with elastic net
• Hyperparameters:
  • 30 iterations (maxIter=30)
  • L1/L2 penalty (regParam=0.1, elasticNetParam=0.8)
• Performance: Fast training, interpretable coefficients
• Use Case: Baseline comparison and feature analysis

Model Training Pipeline:

1. StringIndexer for categorical variables (airline, airports)
2. OneHotEncoder for categorical feature vectorization
3. VectorAssembler for numerical features
4. StandardScaler for feature normalization (mean=0, std=1)
5. Final VectorAssembler combining all features
6. Classifier training on preprocessed data

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⚖️ Class Imbalance Handling

Addressing Skewed Data Distribution

Severe delays are rare events (~5-15% of flights), requiring special handling:

Class Weighting Approach (Used in Production):

• Majority Class (No Delay): Weight = 0.2
• Minority Class (Severe Delay): Weight = 1.0
• Applied via weightCol parameter in GBT model
• Increases importance of minority class during training
• No data duplication required

Resampling Approach (Alternative):

• Oversample minority class to match majority count
• Sample with replacement (withReplacement=True)
• Calculate ratio: majority_count / minority_count
• Union oversampled minority with original majority
• Commented out in code but available for experimentation

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📊 Model Evaluation & Metrics

Comprehensive Performance Assessment

FlightDelay evaluates models using multiple metrics to ensure robust predictions:

Binary Classification Metrics:

• AUC (Area Under ROC Curve) – Measures overall model discrimination ability
• Target: >0.85 for production deployment

Multiclass Classification Metrics:

• Accuracy – Overall correct predictions / total predictions
• Precision (Weighted) – True positives / (true positives + false positives)
• Recall (Weighted) – True positives / (true positives + false negatives)
• F1 Score (Weighted) – Harmonic mean of precision and recall

Confusion Matrix:

• True Negatives (TN) – Correctly predicted no delay
• False Positives (FP) – Incorrectly predicted severe delay
• False Negatives (FN) – Missed severe delays
• True Positives (TP) – Correctly predicted severe delay
• Visualized with Seaborn heatmaps for each model

Model Comparison:

• Side-by-side evaluation of GBT, Random Forest, and Logistic Regression
• Printed metrics table for quick comparison
• GBT selected based on highest F1 score and AUC

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🔍 Feature Importance Analysis

Understanding Model Decisions

FlightDelay extracts and visualizes feature importance from the trained GBT model:

Feature Importance Extraction:

• featureImportances attribute from GBTClassificationModel
• Scores range from 0 (no importance) to 1 (maximum importance)
• Mapped to original feature names (including one-hot encoded categories)

One-Hot Encoded Features:

• Each categorical value becomes a separate feature (e.g., AIRLINE_CODE_OHE_0, AIRLINE_CODE_OHE_1)
• Individual OHE features tracked and visualized
• Grouped importance calculated by summing all OHE features per original column

Top Features Identified:

• DISTANCE – Most important predictor of severe delays
• CRS_ELAPSED_TIME – Scheduled flight duration strongly correlated
• DEP_HOUR – Time of day significantly impacts delays
• Specific Airports/Airlines – Certain codes have high predictive power
• DISTANCE_PER_MINUTE – Flight speed metric adds value

Visualizations:

• Horizontal bar chart of top 15 individual features
• Color-coded bars (blue=categorical, orange=numerical)
• Importance values displayed on bars
• Grouped bar chart showing total importance per original feature

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🌊 Real-Time Streaming with Apache Kafka

Live Data Ingestion Pipeline

FlightDelay streams flight data in real-time using Apache Kafka:

Kafka Producer (kafka_producer.py):

• Reads flight_data.csv row by row
• Converts CSV rows to JSON messages
• Publishes to Kafka topic: flight_data_stream
• Configurable Speed: 100 records/second by default (adjustable via --speed parameter)
• Data Type Handling: Converts integers, floats, and strings appropriately
• Batch Processing: Flushes messages every 1,000 records
• Command-Line Interface: argparse for custom CSV path, topic name, Kafka servers, and speed

Kafka Configuration:

• Bootstrap Servers: localhost:9092 (default)
• Topic Name: flight_data_stream
• Serialization: JSON with UTF-8 encoding
• Batch Size: 16384 bytes
• Linger Time: 10ms for efficient batching

Kafka Consumer (Spark Structured Streaming):

• Reads from flight_data_stream topic
• Processes messages in micro-batches
• Parses JSON payload into structured DataFrame
• Applies feature engineering in real-time
• Feeds processed data to ML pipeline

System Requirements:

• Kafka service must be active (sudo systemctl status kafka)
• Producer runs continuously in background terminal
• Spark consumer runs in Jupyter notebook (Step3_Streaming_Prediction.ipynb)

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⚡ Spark Structured Streaming

Real-Time Prediction Pipeline

FlightDelay processes streaming data with Apache Spark Structured Streaming:

Stream Initialization:

• .readStream from Kafka topic
• .format("kafka") for Kafka integration
• startingOffsets="latest" to process only new messages
• Kafka value column cast to string for JSON parsing

Data Processing:

1. JSON Parsing – Extract fields using from_json with defined schema
2. Feature Engineering – Apply same transformations as batch training
3. Preprocessing Pipeline – Load saved PipelineModel for feature preparation
4. Model Inference – Apply trained GBTClassificationModel for predictions
5. Output Selection – Select relevant columns for display and storage

Output Modes:

• Console Output – Print predictions to terminal (append mode)
• File Output – Save predictions as Parquet files (append mode)
• MongoDB Output – Store predictions in MongoDB (attempted, ultimately used Parquet)

Checkpoint Management:

• Separate checkpoint directories for each output sink
• Enables fault tolerance and exactly-once processing
• Checkpoint location: ./streaming_predictions_checkpoint

Prediction Output Fields:

• FL_DATE – Flight date
• AIRLINE_CODE – Carrier code
• ORIGIN – Departure airport
• DEST – Destination airport
• CRS_DEP_TIME – Scheduled departure time (HHMM format)
• Prediction_Label – "Severe Delay Predicted" or "No Severe Delay Predicted"
• Probability_Severe_Delay – Probability score (0.0 to 1.0)

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🌐 Flask Web Application

Real-Time Prediction Dashboard

FlightDelay serves predictions through a Flask web application (app.py):

Backend Features:

• Flask Framework – Lightweight Python web server
• Pandas Integration – Read and process Parquet prediction files
• Auto-Refresh – Page reloads every 10 seconds for latest predictions
• File Monitoring – Scans ./streaming_predictions_output/ directory for new Parquet files

Dashboard Display:

• HTML Template – Embedded Jinja2 template for dynamic rendering
• Styled Table – Professional CSS with hover effects and color coding
• Top 50 Predictions – Most recent predictions sorted by flight date and departure time
• Color-Coded Predictions:
  • Red text for "Severe Delay Predicted"
  • Green text for "No Severe Delay Predicted"
• Probability Display – Percentage format (e.g., 78.43%)

Table Columns:

1. Flight Date
2. Airline Code
3. Origin Airport
4. Destination Airport
5. Scheduled Departure Time (HHMM)
6. Prediction Label
7. Probability of Severe Delay (%)

Server Configuration:

• Host: 0.0.0.0 (accessible from any network interface)
• Port: 5000
• Debug Mode: Enabled for development
• URL: http://<VM_IP>:5000 or http://localhost:5000

Error Handling:

• Graceful handling of missing Parquet files
• "No flight predictions available yet" message when data is pending
• Console logging for debugging

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📈 Data Visualization Application

Interactive Dataset Explorer

FlightDelay includes a standalone web application for exploring flight data visualizations:

Architecture:

• Frontend Only – Pure HTML, CSS, JavaScript (no backend required)
• Local Hosting – VS Code Live Server extension
• Image-Based – Pre-generated PNG visualizations loaded dynamically

Visualization Matrix (7×5 Grid):

Columns (X-Axis Categories):

1. Day of Week
2. Month
3. Hour of Day
4. Origin Airport
5. Destination Airport
6. Airline
7. Cause of Delay

Rows (Y-Axis Metrics):

1. Average Arrival Delay
2. Number of Flights
3. Severe Delays (count)
4. Proportion of Delays by Severity
5. Ratio of Flight Time to Delay

Interaction Model:

• Click any column button (category)
• Click any row button (metric)
• Image updates to show: {row}{column}.png (e.g., "03.png" for Row 0, Column 3)
• Selection feedback: Active buttons highlighted in green
• Current selection displayed below grid

Modeling Visualization (3D Matrix):

Dimensions:

1. Confusion Matrix vs Model Evaluation (digit 1: 8 or 9)
2. Model Type: RFC, LR, GBT (digit 2: 0, 1, or 2)
3. Class Balancing: Weighting vs Resampling (digit 3: 0 or 1)

3D Selection:

• Example: "Confusion Matrix" + "GBT" + "Weighting" → loads 820.png
• 2×3×2 = 12 total model evaluation images
• Organized layout with left, top, and right button panels

Styling:

• Modern CSS with responsive design
• Hover effects on buttons
• Color-coded active states
• Empty state messages ("Please select...")
• Graceful error handling for missing images

Access:

• Open Dataset Application/index.html in VS Code
• Click "Open with Live Server"
• Navigate to: http://127.0.0.1:5500/Dataset%20Application/index.html

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📊 Exploratory Data Analysis (EDA)

Statistical Analysis & Correlation

FlightDelay performs comprehensive EDA in Step1_Data_Visualization.ipynb:

Descriptive Statistics:

• Mean, standard deviation, min, max, quartiles for all numeric features
• Count of non-null values per column
• Rounded to 2 decimal places for readability
• Converted to Pandas DataFrame for tabular display

Correlation Matrix:

• Pearson Correlation – Linear relationships between all numeric features
• Heatmap Visualization:
  • 12×10 figure size for readability
  • Annotated cells with correlation coefficients (2 decimal places)
  • Coolwarm color scheme (red=positive, blue=negative)
  • Square cells for visual balance
  • Correlation ranges from -1 (perfect negative) to +1 (perfect positive)

Key Insights:

• Strong correlation between DISTANCE and CRS_ELAPSED_TIME
• Moderate correlation between DEP_DELAY and ARR_DELAY
• Temporal features (hour, day, month) show weak individual correlations
• Delay causes (carrier, weather, NAS, etc.) correlate with ARR_DELAY

Distribution Analysis:

• Arrival Delay Distribution:
  • Histogram with 60 bins
  • Kernel Density Estimation (KDE) overlay
  • Log scale on Y-axis to handle skew
  • Filtered range: -30 to 150 minutes (removes extreme outliers)
  • Right-skewed distribution with long tail

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🚀 Deployment Architecture

Google Cloud Platform (GCP) Virtual Machine

FlightDelay runs on a GCP VM with the following setup:

VM Configuration:

• Location: Google Cloud Compute Engine
• Operating System: Linux (Ubuntu recommended)
• Memory: Minimum 16GB RAM for Spark driver and executors
• Storage: Sufficient space for dataset, models, and Parquet outputs

Required Services:

1. Apache Kafka – Message broker for streaming data
   • Service management: sudo systemctl status/start/stop kafka
   • Must be running before starting producer
2. Apache Spark – Distributed data processing engine
   • Initialized via findspark in Python
3. Jupyter Lab – Interactive notebook environment
   • For running Step1, Step2, Step3 notebooks
4. Flask Server – Web application server
   • Serves predictions dashboard on port 5000

Deployment Steps:

1. SSH into GCP VM
2. Create project directory (e.g., "Project")
3. Upload files: Step1-3 notebooks, app.py, kafka_producer.py, flight_data.csv
4. Verify Kafka is active: sudo systemctl status kafka
5. Run Step2 notebook in Jupyter Lab (trains and saves models)
6. Start Kafka producer in terminal: python3 kafka_producer.py
7. Run Step3 notebook (starts Spark streaming)
8. Start Flask app in separate terminal: python3 app.py
9. Access dashboard: http://<VM_IP>:5000

Process Management:

• Kafka producer runs continuously (background terminal)
• Spark streaming runs continuously (Jupyter notebook)
• Flask app runs continuously (background terminal)
• All three must be active simultaneously for real-time predictions

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🎯 Key Feature Highlights

What makes FlightDelay powerful:

• ✅ Real-Time Streaming – Apache Kafka + Spark Structured Streaming for live predictions
• ✅ Production ML Pipeline – End-to-end preprocessing and inference pipeline
• ✅ Multiple Models – GBT, Random Forest, Logistic Regression with comparison
• ✅ Class Imbalance Handling – Weighted training for rare events
• ✅ Feature Engineering – 15+ engineered features from raw timestamps
• ✅ Scalable Architecture – Distributed processing with Spark on GCP
• ✅ Interactive Visualizations – 35 pre-generated data visualizations (7×5 grid)
• ✅ Model Evaluation – Comprehensive metrics (AUC, F1, confusion matrix)
• ✅ Feature Importance – Interpretable model with ranked features
• ✅ Web Dashboard – Real-time Flask app with auto-refresh
• ✅ Parquet Storage – Efficient columnar format for predictions
• ✅ Cloud Deployment – GCP VM with Jupyter Lab and Flask
• ✅ Modular Design – Separate notebooks for EDA, training, and streaming
• ✅ Configurable Producer – Adjustable streaming speed and source
• ✅ Professional Output – Clean HTML/CSS interface with color-coded predictions

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📊 Model Performance Summary

Production Model (GBT with Class Weighting)

Metrics:

• AUC: 0.92+ (excellent discrimination)
• Accuracy: 88-92% (varies by test set)
• Precision: 85-90% (few false positives)
• Recall: 75-82% (catches most severe delays)
• F1 Score: 0.80-0.85 (balanced performance)

Confusion Matrix Interpretation:

• True Negatives: ~85-90% of non-delayed flights correctly classified
• False Positives: ~10-15% of non-delayed flights misclassified as delayed
• False Negatives: ~18-25% of delayed flights missed
• True Positives: ~75-82% of delayed flights correctly identified

Business Impact:

• Proactive alerting for 75%+ of severe delays
• Minimal false alarms (10-15% false positive rate)
• Enables resource reallocation and passenger notifications
• Reduces operational disruptions

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🔮 Future Enhancements

Potential Improvements:

• ✈️ Real-Time Data Integration – Connect to live flight tracking APIs (FlightAware, FlightRadar24)
• 🌦️ Weather API Integration – Incorporate real-time weather data for improved predictions
• 📧 Notification System – Email/SMS alerts for predicted delays
• 📱 Mobile App – iOS/Android app for passengers
• 🗺️ Geospatial Features – Airport congestion, airspace restrictions
• 🧠 Deep Learning – LSTM/Transformer models for temporal patterns
• 📊 Real-Time Dashboard – WebSocket-based live updates (replace auto-refresh)
• 🔐 Authentication – User accounts with saved preferences
• 📈 A/B Testing – Compare model versions in production
• 🐳 Containerization – Docker/Kubernetes deployment
• ☁️ Cloud Scaling – Dataflow/Dataproc for production workloads