Goal:
To contruct machine learning model to predict the flight prices.
Process:
- Clean dataset with null values.
- Extract time features from the dataset.
- Perform EDA with matplotlib, seaborn and plotly.
- Transform categorical features with either one-hot encoding or target guided ordinal encoding.
- Detect outliers with IQR approach and replace them with median value.
- Select features based on mutual information approach.
- Construct Random Forest machine learning model with hyperparameters tuning for price prediction.
- Build machine learning pipeline for future automation.