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e4fd44c | 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 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 | import streamlit as st
import pandas as pd
import numpy as np
import sklearn
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
from sklearn.metrics import mean_absolute_error
st.header(" :blue[Estimated Time Arrival Predictor] :car:")
st.subheader("This tool helps to predict the estimated time arrival based on the weather,hour,traffic and week conditions from the starting point to the destination based upon longitude and latitude")
df = pd.read_csv("hyderabad_eta_data.csv")
# Sliders in the middle of the page
start_lat = st.slider(
"Select Latitude",
min_value=-90.0,
max_value=90.0,
value=0.0,
step=0.1
)
start_lon = st.slider(
"Select start Longitude",
min_value=-180.0,
max_value=180.0,
value=0.0,
step=0.1
)
end_lat = st.slider(
"Select end Latitude",
min_value=-90.0,
max_value=90.0,
value=0.0,
step=0.1
)
end_lon = st.slider(
"Select end Longitude",
min_value=-180.0,
max_value=180.0,
value=0.0,
step=0.1
)
distance = st.slider(
"Select the distance (km)",
min_value = 0.0,
max_value = 700.0,
value = 100.0,
step =1.0
)
traffic = st.selectbox("Select the traffic density", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
weather = st.selectbox("Select the weather condition", ["rainy","clear","foggy"])
week_day = st.selectbox("Select the week",["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"])
week_val = {"Sunday" : 0,"Monday" : 1,"Tuesday" : 2,"Wednesday": 3,"Thursday": 4,"Friday": 5,"Saturday" : 6}
week = week_val[week_day]
hour = st.slider(
"select the hour",
min_value = 0,
max_value = 23,
value = 0,
step = 1
)
# Map user-selected weather condition
weather_map = {'rainy': 0, 'clear': 1, 'foggy': 2}
weather_val = weather_map[weather]
# Prepare dataset
df["weather_condition"] = df["weather_condition"].map(weather_map)
X = df.drop("ETA", axis=1)
y = df["ETA"]
# splitting the target variable
X = df.drop("ETA", axis =1)
y = df["ETA"]
# doing the train-test split
X_train, X_test,y_train,y_test = train_test_split(X,y , test_size= 0.2, random_state=23)
# Model Building and Model Evaluation
sv_r = SVR(kernel='rbf', C=100, epsilon=0.1)
sv_r.fit(X_train,y_train)
y_pred = sv_r.predict(X_test)
result = mean_absolute_error(y_pred,y_test)
features = np.array([[start_lat, start_lon,end_lat,end_lon,distance,weather_val,traffic,week,hour]])
estimated_arrival = sv_r.predict(features)[0]
# Display formatted output without array brackets
if st.button(" :red[Click to predict estimated time]"):
st.header(" :orange[The ESTIMATED TIME ARRIVAL IS ]")
st.markdown(f"<h2 style='color:brown; font-size:36px;'><strong>{estimated_arrival:.2f} hours</strong></h2>", unsafe_allow_html=True) |