https://github.com/simran2911/flight-price-pridiction
This github repositiory contains the Flight Price Prediction project aims to develop a machine learning model to predict flight ticket prices based on various factors such as departure and arrival locations, dates, airlines, and other relevant features.
https://github.com/simran2911/flight-price-pridiction
accuracy-score catboostregressor machine-learning matplotlib numpy pandas r2-score random-forest-regression seaborn xgboost-regression
Last synced: about 2 months ago
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This github repositiory contains the Flight Price Prediction project aims to develop a machine learning model to predict flight ticket prices based on various factors such as departure and arrival locations, dates, airlines, and other relevant features.
- Host: GitHub
- URL: https://github.com/simran2911/flight-price-pridiction
- Owner: Simran2911
- Created: 2024-07-14T15:47:58.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-15T16:53:30.000Z (almost 2 years ago)
- Last Synced: 2025-02-26T16:49:41.601Z (over 1 year ago)
- Topics: accuracy-score, catboostregressor, machine-learning, matplotlib, numpy, pandas, r2-score, random-forest-regression, seaborn, xgboost-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 920 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## 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.