Flight Fare Prediction Project

A Flight Fare Prediction project involves creating a model that can predict the cost of airline tickets based on various factors.
Project Overview
The goal of the Flight Fare Prediction project is to build a machine learning model that can predict the price of airline tickets. This can help travelers find the best deals, assist travel agencies in providing competitive pricing, and aid airlines in adjusting their pricing strategies.

Objectives
1.Data Collection: Gather historical flight fare data from various sources such as airline websites, travel agencies, and APIs.
2.Data Preprocessing: Clean the data by handling missing values, encoding categorical variables, and scaling numerical features.
3.Exploratory Data Analysis (EDA): Analyze the data to identify trends and patterns that influence flight fares, such as seasonality, holidays, and economic factors.
4.Feature Engineering: Create new features that might impact the fare, such as the time until departure, day of the week, and whether the flight is direct or has layovers.
5.Model Selection: Choose appropriate machine learning algorithms for the prediction task, such as Linear Regression, Decision Trees, Random Forest, or Gradient Boosting.
6.Model Training: Train the selected models on the preprocessed data and tune hyperparameters to improve performance.
7.Model Evaluation: Evaluate the models using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.
8.Deployment: Deploy the best-performing model using a web application or API, allowing users to input flight details and receive fare predictions.

Data Features:
Typical features used in the model might include:
Airline: The airline operating the flight.
Date of Journey: The date on which the journey is scheduled.
Source and Destination: The starting and ending points of the journey.
Route: The flight path taken, including layovers.
Departure and Arrival Time: Scheduled times for departure and arrival.
Duration: The total duration of the flight.
Total Stops: The number of stops or layovers.
Additional Information: Any other relevant information such as in-flight amenities or services.

Tools and Technologies:
Programming Language: Python Web scraping (BeautifulSoup, Scrapy), APIs (e.g., Skyscanner API)
Data Collection: Data Analysis and Preprocessing: Pandas, NumPy.
Visualization: Matplotlib, Seaborn.
Machine Learning: Scikit-learn, XGBoost, LightGBM.

Project Workflow:
Data Collection: Collect flight fare data.
Data Preprocessing: Clean and preprocess the data.
EDA: Perform exploratory data analysis to understand the data better.
Feature Engineering: Create additional features that can improve the model's performance.
Modeling: Train and evaluate multiple machine learning models.
Model Selection: Choose the best-performing model based on evaluation metrics.
Deployment: Deploy the model to a production environment.

Conclusion:
This will gain insights into the factors that influence flight pricing and develop a practical tool that can help various stakeholders in the travel industry.