Spaces:
Build error
Build error
File size: 3,006 Bytes
5d1d846 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import streamlit as st
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import LabelEncoder
# Load the dataset with error handling
try:
df = pd.read_csv("Cleaned_Travel.csv")
except FileNotFoundError:
st.error("Dataset not found. Please upload 'Cleaned_Tour (1).csv'.")
st.stop()
# Convert categorical Yes/No columns to binary (1/0)
categorical_cols = ["adventure", "nature", "hill_station", "water_activites", "religious"]
df[categorical_cols] = df[categorical_cols].applymap(lambda x: 1 if x == "Yes" else 0)
# Encode categorical 'places' column
encoder = LabelEncoder()
df["places"] = encoder.fit_transform(df["places"])
# Define features and target
X = df[["adventure", "nature", "places", "discount_percentage", "original_price", "hill_station", "water_activites", "religious"]]
y = df["discount_price"]
# Train model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestRegressor(random_state=42)
model.fit(X_train, y_train)
# Streamlit App - Custom Title with Color & Emoji
st.markdown("<h1 style='text-align: center; color: darkblue;'>🌍✈ Travel Discount Price Predictor ✈🌍</h1>", unsafe_allow_html=True)
# User Input
place = st.selectbox("Places", encoder.classes_)
adventure = st.selectbox("Adventure", ["Yes", "No"])
nature = st.selectbox("Nature", ["Yes", "No"])
hill_station = st.selectbox("Hill Station", ["Yes", "No"])
water_activites = st.selectbox("Water Activities", ["Yes", "No"])
religious = st.selectbox("Religious", ["Yes", "No"])
discount_percentage = st.number_input("Discount Percentage", min_value=0.0, max_value=100.0, step=0.1)
original_price = st.number_input("Original Price", min_value=1.0, step=0.1)
if st.button("Predict Discount Price"):
# Validate place encoding
if place in encoder.classes_:
place_encoded = encoder.transform([place])[0]
else:
st.error("Selected place is not in training data.")
st.stop()
# Validate discount logic
if not (0 <= discount_percentage <= 100):
st.error("Discount percentage must be between 0 and 100.")
st.stop()
# Prepare input data
input_data = pd.DataFrame({
"adventure": [1 if adventure == "Yes" else 0],
"nature": [1 if nature == "Yes" else 0],
"places": [place_encoded],
"discount_percentage": [discount_percentage],
"original_price": [original_price],
"hill_station": [1 if hill_station == "Yes" else 0],
"water_activites": [1 if water_activites == "Yes" else 0],
"religious": [1 if religious == "Yes" else 0]
}, columns=X_train.columns) # Ensure column order matches training set
# Predict
prediction = model.predict(input_data)
st.success(f"Predicted Discount Price: ₹{round(prediction[0], 2)}") |