🔍 Project Objectives Predict flight ticket prices based on airline, route, stops, timings, etc.
Forecast passenger demand using time series models.
Identify key patterns and trends in flight data through visualizations.
🧠 Techniques Used Data Preprocessing: Handling nulls, feature extraction (date, time, stops), encoding
Exploratory Data Analysis (EDA): Price trends, demand fluctuations, correlation insights
Machine Learning Models:
Random Forest Regressor
XGBoost Regressor
Linear Regression (baseline)
Time Series Forecasting:
Forecasted both price and demand over time using decomposition and forecasting techniques
📁 Files Overview
File Name Description eda.ipynb Initial data exploration and visualization Price_Prediction.ipynb ML models for predicting flight ticket prices demandprediction.ipynb ML models to predict passenger demand Price_timeseries.ipynb Time series forecasting of prices Demand_timeseries.ipynb Time series forecasting of demand datasetcsv.zip Raw dataset (compressed) README.md You’re reading it :) 🧰 Tech Stack Python
Pandas, NumPy
Scikit-learn, XGBoost
Matplotlib, Seaborn
Statsmodels, TimeSeries tools
Jupyter Notebook
📈 Key Results Achieved high accuracy in price prediction with Random Forest and XGBoost
Discovered peak pricing times, demand spikes, and seasonal fluctuations
Time series models gave reliable short-term demand forecasts
🚀 Applications Can be adapted for railway demand forecasting, fare optimization, and resource planning
Useful for airlines, railways, or any transport sector dealing with large-scale passenger data