Skip to content
0

Understanding flight delays ✈️

📖 Background

You work for a major airline operating flights across the USA. Flight delays are a significant challenge for both the airline and passengers, causing disruptions, financial losses, and dissatisfaction. As part of the airline’s data analytics team, your goal is to analyze historical flight data to uncover delay patterns, identify operational inefficiencies, and predict delays before they occur. By identifying delay patterns, predicting delays, and uncovering the factors that contribute most to delays, you’ll be able to drive operational efficiency and enhance the overall passenger experience. Your insights will help the airline make data-driven decisions to optimize scheduling, improve on-time performance, and enhance passenger satisfaction.

Can you crack the code behind flight delays and revolutionize air travel?

💾 The data

Your team provided you with 2 files with the following information (source):

flights.csv

  • id - Id number of the flight
  • year - Year of Flight
  • month - Month of Flight
  • day - Day of Month
  • dep_time - Time of departure (24h format)
  • sched_dep_time - Scheduled departure time
  • dep_delay - Delay in departure (minutes)
  • arr_time - Time of arrival (24h format)
  • sched_arr_time - Scheduled arrival time
  • arr_delay - Delay in arrival (minutes)
  • carrier - Airline company code
  • flight - Flight number
  • tailnum- Aircraft identifier number
  • origin - Origin Airport - 3 letter code
  • dest - Destination Airport - 3 letter code
  • air_time - Duration of the flight (minutes)
  • distance - Flight distance (miles)
  • hour - Hour component of scheduled departure time
  • minute - Minute component of scheduled departure time

airlines_carrier_codes.csv

  • Carrier Code - Airline company code
  • Airline Name - Airline Name
import pandas as pd
flight_data = pd.read_csv('data/flights.csv')
airlines_codes = pd.read_csv('data/airlines_carrier_codes.csv')
flight_data.head()
airlines_codes.head()

💪 Challenge

Create a report summarizing your insights. Your report should explore the following questions:

  1. How do different airlines compare in terms of their departure and arrival times? Are there noticeable trends in their on-time performance over the year? A well-structured visualization could help uncover patterns.
  2. Are there particular months/weeks/time of day where there is a general trend of greater delays in flights across all carriers? If so, what could be the reasons?
  3. Some airports seem to operate like clockwork, while others are notorious for disruptions. How do different airports compare when it comes to departure and arrival punctuality? Could location, traffic volume, or other factors play a role? Are there patterns that emerge when looking at delays across various airports?
  4. [Optional 1] Predict whether a flight will have a delay of 15 minutes or more at departure.
  5. [Optional 2] What underlying factors influence flight delays the most? Are some routes more prone to disruptions than others? Do external variables like time of day, distance, or carrier policies play a significant role? By analyzing the relationships between different features, you might discover unexpected insights.

🧑‍⚖️ Judging criteria: your vote, your winners!

This is a community-driven competition, your votes decide the winners! Once the competition ends, you'll get to explore submissions, celebrate the best insights, and vote for your favorites. The top 5 most upvoted entries will win exclusive DataCamp merchandise - so bring your A-game, impress your peers, and claim your spot at the top!

✅ Checklist before publishing

  • Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.ipynb.
  • Remove redundant cells like the introduction to data science notebooks, so the workbook is focused on your story.
  • Check that all the cells run without error.

⌛️ Time is ticking. Good luck!

Flight Delays Analysis: Uncovering Patterns in Air Travel 🛫

Executive Summary

This comprehensive analysis examines flight delay patterns across major US airlines to identify operational inefficiencies, predict delays, and provide actionable insights for improving on-time performance and passenger satisfaction.

📊 Table of Contents

  1. Data Import & Initial Exploration (Invalid URL)
  2. Data Preprocessing & Feature Engineering (Invalid URL)
  3. Airline Performance Comparison (Invalid URL)
  4. Temporal Patterns Analysis (Invalid URL)
  5. Airport Performance Analysis (Invalid URL)
  6. Predictive Modeling (Invalid URL)
  7. Root Cause Analysis (Invalid URL)
  8. Key Insights & Recommendations (Invalid URL)
# Import required libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import warnings
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelEncoder
import datetime as dt

# Set style for better visualizations
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")
warnings.filterwarnings('ignore')

# Configure pandas display
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 100)

print("📊 Libraries imported successfully!")

1. Data Import & Initial Exploration {#data-import}

Load the datasets

try:
    flight_data = pd.read_csv('data/flights.csv')
    airlines_codes = pd.read_csv('data/airlines_carrier_codes.csv')
    print("✅ Data loaded successfully!")
except FileNotFoundError:
    print("⚠️ Data files not found. Please ensure 'flights.csv' and 'airlines_carrier_codes.csv' are in the 'data' folder.")
    # Create sample data structure for demonstration
    print("Creating sample data structure for demonstration...")

# Display basic information
print(f"\n📈 Flight Data Shape: {flight_data.shape}")
print(f"📈 Airlines Data Shape: {airlines_codes.shape}")

Quick overview of the data

print("🔍 FLIGHT DATA OVERVIEW")
print("="*50)
flight_data.info()

print("\n" + "="*50)
print("📊 STATISTICAL SUMMARY")
print("="*50)
flight_data.describe()
# Check for missing values
missing_data = flight_data.isnull().sum()
missing_percentage = (missing_data / len(flight_data)) * 100

missing_df = pd.DataFrame({
    'Column': missing_data.index,
    'Missing Count': missing_data.values,
    'Missing %': missing_percentage.values
}).sort_values('Missing %', ascending=False)

print("🚨 MISSING VALUES ANALYSIS")
print("="*50)
print(missing_df[missing_df['Missing %'] > 0])
‌
‌
‌