Flight Fare Prediction

Importing the required libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Training data

• Reading the data
• Checking the shape of dataset
• The dataset is too large and null values values are less than 1% so we can drop them
• Checking for the shape after dropping the null values

EDA

• Converting the datetime columns from string format into datetime format
• Creating the duration feature

Encoding

• Converting categorical features into numerical values called encoding
• Converting Nominal categorical features into numeric values by using oneHotEncoding technique
• Converting Ordinal categorical features into numeric values by using LabelEncoder technique
• Dropping un neccessary columns

Testing data

• All the above steps were repeted for testing data also

Feature Importance

• Finding the feature importance by using Heatmap and ExtratreeRegressor

Model Building

• Importing the model from sklearn
• Training the model with training data
• test the model with test data
• creating pickle file for model

App

• Creating the app using Streamlit
• Deploying the app