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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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from pickle import load, dump |
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import random |
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import shap |
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import os |
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with open('preprocessing.pkl', 'rb') as file: |
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preprocessor = load(file) |
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with open('model.pkl', 'rb') as file: |
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model = load(file) |
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df = pd.read_csv('Flight_Price.csv') |
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df = df[['departure_time', 'stops', 'arrival_time', 'class','airline', 'flight', 'source_city', 'destination_city', 'duration', 'days_left']] |
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cols_names = df.columns.tolist() |
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df_pre = preprocessor.transform(df) |
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df_pre = pd.DataFrame(df_pre, columns=cols_names) |
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def input_features(): |
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st.sidebar.title('Input Features') |
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airline = st.sidebar.selectbox('Airline', df['airline'].unique().tolist()) |
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source_city = st.sidebar.selectbox('Source City', df['source_city'].unique().tolist()) |
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destination_city = st.sidebar.selectbox('Destination City', df['destination_city'].unique().tolist()) |
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departure_time = st.sidebar.selectbox('Departure Time', df['departure_time'].unique().tolist()) |
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arrival_time = st.sidebar.selectbox('Arrival Time', df['departure_time'].unique().tolist()) |
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stops = st.sidebar.selectbox('Stops', df['stops'].unique().tolist()) |
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class_reservation = st.sidebar.selectbox('Class', df['class'].unique().tolist()) |
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days_left = st.sidebar.slider('Days Left', df['days_left'].min(), df['days_left'].max(), 10) |
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duration = st.sidebar.slider('Duration', df['duration'].min(), df['duration'].max(), df['duration'].mean()) |
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flight = random.choice(df[df['airline'] == airline]['flight'].unique().tolist()) |
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data = { |
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'departure_time': departure_time, |
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'stops': stops, |
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'arrival_time': arrival_time, |
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'class': class_reservation, |
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'airline': airline, |
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'flight': flight, |
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'source_city': source_city, |
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'destination_city': destination_city, |
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'duration': duration, |
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'days_left': days_left |
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} |
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features = pd.DataFrame(data, index=[0]) |
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return features |
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def predict(features, preprocessor, model): |
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features = preprocessor.transform(features) |
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prediction = model.predict(features) |
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return np.round(np.exp(prediction), 3) |
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@st.cache_data |
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def summary_plot(): |
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explainer = shap.TreeExplainer(model) |
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shap_values = explainer.shap_values(df_pre) |
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st.write('Feature Importance') |
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fig = plt.figure() |
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plt.title('Feature Importance based on SHAP values') |
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shap.summary_plot(shap_values, df_pre) |
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st.pyplot(fig, bbox_inches='tight') |
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curr_dir = os.getcwd() |
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image_path = os.path.join(curr_dir, 'assets/flight.jpg') |
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features = input_features() |
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st.write('# Flight Price Prediction') |
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st.image(image_path) |
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st.write('---') |
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st.write('## Specified Input Parameters') |
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st.write(features) |
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st.write('---') |
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if st.button('Predict'): |
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price = predict(features, preprocessor, model) |
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st.markdown(f""" |
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<div style="border: 2px solid #4CAF50; padding: 20px; text-align: center; background-color: #f9f9f9; border-radius: 10px;"> |
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<strong style="font-size: 30px; color: #333;">Price Predicted for this trip is: {price[0]:,.2f}</strong> |
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</div> |
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""", unsafe_allow_html=True) |
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summary_plot() |
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st.write('---') |
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