✈️ Flight Price Prediction – End-to-End Machine Learning Pipeline

From raw flight data → EDA → Feature Engineering → Model Training → Evaluation → Deployment-ready pipeline.

📌 Business Problem

Airline ticket prices fluctuate based on:

• Airline brand
• Number of stops
• Flight duration
• Time of departure
• Seasonal demand

The objective is to build a robust regression model that predicts flight ticket prices with high accuracy.

🔎 1️⃣ Exploratory Data Analysis (EDA)

Dataset Overview

The dataset includes:

• Airline
• Source
• Destination
• Date of Journey
• Duration
• Total Stops
• Additional Info
• Price (Target)

Key Insights

• ✈️ Non-stop flights generally cost more.
• 🕒 Longer duration flights are often cheaper.
• 📅 Month & season significantly impact pricing.
• 🏷 Premium airlines maintain higher base fares.
• 🌙 Early departures can influence ticket cost.

🧠 2️⃣ Feature Engineering

Engineered features include:

• Journey Month
• Journey Day
• Departure Hour
• Arrival Hour
• Duration in Minutes
• Weekend Indicator
• Peak Season Flag

Categorical Encoding:

• One-Hot Encoding (Nominal)
• Ordinal Encoding (Stops)

Outlier Handling:

• IQR-based filtering
• Log transformation on price (optional)

🏗 3️⃣ Machine Learning Pipeline

Implemented using sklearn Pipeline:

Pipeline([
    ('preprocessing', ColumnTransformer(...)),
    ('model', XGBRegressor())
])

Pipeline handles:

• Missing values
• Encoding
• Scaling (if needed)
• Model training
• Cross-validation

🤖 4️⃣ Model Training

Models Tested

Model	R² Score	RMSE
Linear Regression	0.62	Medium
Random Forest	0.83	Low
XGBoost	0.88	Lowest

Best model: XGBoost Regressor

Training Strategy

• Train/Test Split (80/20)
• 5-Fold Cross Validation
• Hyperparameter tuning via GridSearchCV
• Early stopping (for boosting models)

📊 5️⃣ Model Evaluation

Metrics Used:

• R² Score
• RMSE
• MAE

Error Analysis:

• Slight underprediction for premium airlines
• Higher variance for rare routes
• Model generalizes well across most routes

🚀 6️⃣ Deployment-Ready Structure

Project organized for scalability:

├── data/
├── notebooks/
├── src/
│   ├── preprocessing.py
│   ├── train.py
│   ├── evaluate.py
│   ├── predict.py
├── models/
├── api/
│   └── app.py
└── README.md

🌐 7️⃣ Optional Production Extension

Can Be Extended To:

• REST API (FastAPI)
• Dockerized deployment
• Streamlit dashboard
• CI/CD integration
• MLflow experiment tracking
• Model monitoring

🛠 Tech Stack

• Python
• Pandas
• NumPy
• Seaborn / Matplotlib
• Scikit-learn
• XGBoost