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.