GitHub - monil37/Flight-Fare-Prediction: Developed a machine learning model to predict airline ticket prices using ensemble learning (Random Forest Regressor). Applied categorical feature encoding, outlier detection, and data preprocessing to improve data quality. Optimized model performance through hyperparameter tuning with cross‑validation.

This project focuses on predicting airline ticket prices using an ensemble learning approach. A Random Forest Regressor was trained after preprocessing the dataset with categorical feature encoding and outlier detection to improve overall data quality. Hyperparameter tuning with cross‑validation was applied to enhance prediction performance, and the model was evaluated using R‑squared and Mean Absolute Error (MAE) metrics.

Performed Data Cleaning / Data Preparation / Data Pre-processing
Data Visualization (Exploratory Data Analysis)
Performed feature engineering
- Feature encoding
- Checking outliers and impute it
- Feature selection
Build a machine learning model and dump it in pickle format
Hypertuning the model along with cross-validation

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
.ipynb_checkpoints		.ipynb_checkpoints
Data_Train.xlsx		Data_Train.xlsx
Flight Price Prediction.ipynb		Flight Price Prediction.ipynb
Readme.md		Readme.md
random_model.pkl		random_model.pkl