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Project Overview:

The goal of this project is to develop a predictive model that estimates future flight prices based on historical data and various influencing factors.

Objectives/Buisness Goals:

  • Predict flight prices for specific routes.
  • Analyze factors influencing flight price fluctuations.
  • Provide actionable insights for travelers.

Data Preprocessing:

  • Data Cleaning: Handle missing values, outliers, and inconsistencies.

  • Feature Engineering: Create features such as:

    1. Day of the week.

2. Time of booking (lead time).

3. Airline Preffered.

4. Number of stops.

Exploratory Data Analysis (EDA):

  • Visualize price trends With number of stops.
  • Analyze correlation between features and price.
  • Identify seasonal patterns and price volatility.
  • Identify Price variation with Source and Destination.

Model Selection:

  • Regression Models

     1. Extratree Regression
 2. Random Forest.
 3. Catboost Regression

Gradient Boosting (XGBoost, LightGBM).

Model Evaluation:

  • Split data into training and test sets (e.g., 80/20).
  • Use metrics like RMSE, MAE, and R² to evaluate model performance.

Accuracy:

Model Name Accuracy
Random Forest Regression 0.8532074703106208
Extratree Regressor 0.7890353681268577
Catboost Regressor 0.8596289688357996

Deployment:

  • Create a web application Flask serve predictions.

Future Work:

  • Incorporate real-time data for ongoing price updates.
  • Explore deep learning models for improved accuracy.
  • Develop a user interface for travelers to input parameters and get price predictions.

Tools and Technologies:

  • Programming Languages: Python
  • Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.
  • Database: SQL for storing historical data.
  • Web Framework: Flask, Django for the application.

Conclusion:

This project aims to empower travelers with predictive insights, helping them make informed decisions and potentially save on flight costs.