Project Title:Flight Price Prediction Using Machine Learning

Objective:

To build a machine learning model that can predict flight prices based on various features like departure time, arrival time, airline, and other factors.

Summary:

This project focuses on developing a machine learning model to predict the price of flights based on historical flight data. Features like departure time, arrival time, source and destination airports, airline, flight duration, and date of booking are used as input variables for training the model. The goal is to help travelers forecast flight prices or assist airlines in setting competitive pricing strategies.

The project typically involves:

Data Preprocessing: Handling missing values, encoding categorical data (e.g., airline names), and normalizing numerical features (e.g., price).

Model Selection: Using algorithms like Linear Regression, Decision Trees, Random Forest, or XGBoost for regression tasks.

Model Evaluation: Testing the model on unseen data and evaluating its performance using metrics such as Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE).

Key Steps:

Collect Dataset – Use publicly available datasets like those from Kaggle, which include flight data.

Preprocess Data – Clean and preprocess the data (handling missing values, encoding categorical features).

Feature Engineering – Select relevant features, create new features (e.g., time until flight), and handle outliers.

Train Model – Apply machine learning models to predict flight prices.

Evaluate Model – Use metrics like RMSE or MAE to assess the accuracy of your model.

Technologies Used:

Python

Pandas (for data manipulation)

Scikit-learn (for machine learning algorithms and evaluation)

Matplotlib/Seaborn (for data visualization)

XGBoost (optional for advanced models)

Applications:

Travel websites and apps (e.g., Expedia, Skyscanner)

Airline pricing optimization

Travel agencies for price forecasting

Personal finance tools for planning trips

Expected Outcomes:

A trained model that predicts flight prices based on various features.

Performance metrics to evaluate model accuracy (e.g., MAE, RMSE).

Visualizations of predicted vs. actual flight prices and feature importance.