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a13b550 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | import pandas as pd
import numpy as np
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')
class FlightDataPreprocessor:
def __init__(self):
self.airlines_df = None
self.airports_df = None
self.label_encoders = {}
def load_data(self):
"""Load all datasets"""
try:
self.airlines_df = pd.read_csv('data/airlines.csv')
self.airports_df = pd.read_csv('data/airports.csv')
self.flights_df = pd.read_csv('data/flights.csv', low_memory=False)
self.weather_df = pd.read_csv('data/weather.csv')
# Load Indian data if available
try:
indian_airports = pd.read_csv('data/indian_airports.csv')
self.airports_df = pd.concat([self.airports_df, indian_airports], ignore_index=True)
except:
pass
try:
indian_airlines = pd.read_csv('data/indian_airlines.csv')
self.airlines_df = pd.concat([self.airlines_df, indian_airlines], ignore_index=True)
except:
pass
return True
except Exception as e:
print(f"Error loading data: {e}")
return False
def clean_airlines_data(self):
"""Clean airlines data to get proper mapping"""
# Extract airline codes and names
airlines_mapping = self.airlines_df[['IATA_CODE', 'AIRLINE']].dropna()
airlines_mapping = airlines_mapping[airlines_mapping['IATA_CODE'].notna()]
airlines_mapping = airlines_mapping[airlines_mapping['AIRLINE'].notna()]
self.airlines_mapping = dict(zip(airlines_mapping['IATA_CODE'], airlines_mapping['AIRLINE']))
return self.airlines_mapping
def clean_airports_data(self):
"""Clean airports data to get proper mapping"""
# Extract airport codes and names
airports_mapping = self.airports_df[['IATA_CODE', 'AIRPORT']].dropna()
airports_mapping = airports_mapping[airports_mapping['IATA_CODE'].notna()]
airports_mapping = airports_mapping[airports_mapping['AIRPORT'].notna()]
self.airports_mapping = dict(zip(airports_mapping['IATA_CODE'], airports_mapping['AIRPORT']))
return self.airports_mapping
def preprocess_flights_data(self):
"""Preprocess flights dataset"""
df = self.flights_df.copy()
# Select relevant columns
relevant_cols = ['YEAR', 'MONTH', 'DAY', 'DAY_OF_WEEK', 'AIRLINE', 'FLIGHT_NUMBER',
'ORIGIN_AIRPORT', 'DESTINATION_AIRPORT', 'SCHEDULED_DEPARTURE',
'DEPARTURE_TIME', 'DEPARTURE_DELAY', 'SCHEDULED_ARRIVAL',
'ARRIVAL_TIME', 'ARRIVAL_DELAY', 'DISTANCE', 'CANCELLED']
df = df[relevant_cols].copy()
# Remove cancelled flights
df = df[df['CANCELLED'] == 0]
# Create target variable (delayed if arrival delay > 15 minutes)
df['IS_DELAYED'] = (df['ARRIVAL_DELAY'] > 15).astype(int)
# Fill missing values
df['DEPARTURE_DELAY'] = df['DEPARTURE_DELAY'].fillna(0)
df['ARRIVAL_DELAY'] = df['ARRIVAL_DELAY'].fillna(0)
# Convert scheduled departure to hour
df['DEPARTURE_HOUR'] = df['SCHEDULED_DEPARTURE'] // 100
df['DEPARTURE_HOUR'] = df['DEPARTURE_HOUR'].replace(24, 0) # Handle midnight
# Create season
df['SEASON'] = df['MONTH'].apply(self._get_season)
# Map airline codes to full names
if hasattr(self, 'airlines_mapping'):
df['AIRLINE_NAME'] = df['AIRLINE'].map(self.airlines_mapping).fillna(df['AIRLINE'])
# Map airport codes to full names
if hasattr(self, 'airports_mapping'):
df['ORIGIN_AIRPORT_NAME'] = df['ORIGIN_AIRPORT'].map(self.airports_mapping).fillna(df['ORIGIN_AIRPORT'])
df['DESTINATION_AIRPORT_NAME'] = df['DESTINATION_AIRPORT'].map(self.airports_mapping).fillna(df['DESTINATION_AIRPORT'])
return df
def preprocess_weather_data(self):
"""Preprocess weather dataset"""
df = self.weather_df.copy()
# Convert datetime
df['datetime'] = pd.to_datetime(df['datetime'])
df['DATE'] = df['datetime'].dt.date
df['HOUR'] = df['datetime'].dt.hour
# Convert temperature from Kelvin to Celsius
df['TEMP_C'] = df['temperature'] - 273.15
# Create weather categories
df['WEATHER_CATEGORY'] = df['weather_description'].apply(self._categorize_weather)
# Aggregate weather by city and date-hour
weather_agg = df.groupby(['city', 'DATE', 'HOUR']).agg({
'TEMP_C': 'mean',
'humidity': 'mean',
'pressure': 'mean',
'wind_speed': 'mean',
'WEATHER_CATEGORY': lambda x: x.mode()[0] if len(x.mode()) > 0 else 'clear'
}).reset_index()
return weather_agg
def merge_flight_weather(self, flights_df, weather_df):
"""Merge flight and weather data"""
# Create date column for flights
flights_df['DATE'] = pd.to_datetime(flights_df[['YEAR', 'MONTH', 'DAY']]).dt.date
# Get weather for origin airport
# Note: This is a simplified approach. In practice, you'd need airport-city mapping
merged_df = flights_df.copy()
# Add sample weather data (in real implementation, you'd match by nearest city)
# For now, we'll add random weather features for demonstration
np.random.seed(42)
merged_df['TEMP_C'] = np.random.normal(15, 10, len(merged_df))
merged_df['HUMIDITY'] = np.random.normal(60, 20, len(merged_df))
merged_df['WIND_SPEED'] = np.random.normal(10, 5, len(merged_df))
merged_df['WEATHER_CATEGORY'] = np.random.choice(['clear', 'clouds', 'rain', 'snow'], len(merged_df))
return merged_df
def _get_season(self, month):
"""Get season from month"""
if month in [12, 1, 2]:
return 'Winter'
elif month in [3, 4, 5]:
return 'Spring'
elif month in [6, 7, 8]:
return 'Summer'
else:
return 'Fall'
def _categorize_weather(self, description):
"""Categorize weather description"""
description = str(description).lower()
if 'clear' in description or 'sunny' in description:
return 'clear'
elif 'cloud' in description:
return 'clouds'
elif 'rain' in description or 'drizzle' in description:
return 'rain'
elif 'snow' in description:
return 'snow'
elif 'storm' in description or 'thunder' in description:
return 'storm'
else:
return 'other'
def get_processed_data(self):
"""Get fully processed data ready for modeling"""
if not self.load_data():
return None, None, None, None
# Clean mappings
self.clean_airlines_data()
self.clean_airports_data()
# Preprocess datasets
flights_processed = self.preprocess_flights_data()
weather_processed = self.preprocess_weather_data()
# Merge datasets
final_data = self.merge_flight_weather(flights_processed, weather_processed)
return final_data, self.airlines_mapping, self.airports_mapping, weather_processed
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