Airline Delay Analysis
1. Introduction
Objective: Analyze and visualize flight delays across different airlines to
identify trends and patterns in delay occurrences.
Purpose: Provide insights for improving airline operations, enhancing customer
experience, and optimizing scheduling processes.
2. Project Workflow
1. Problem Definition:
- Understand flight delay trends
across airlines and airports.
- Key questions:
- Which airlines have the most
delays?
- What are the common causes of
delays?
- How do delays vary by time of year
or day of the week?
2. Data Collection:
- Source: FAA, BTS, or Kaggle datasets
on flight delays.
- Example: A dataset containing
attributes like `Airline`, `Flight Number`, `Date`, `Delay Duration`, and
`Cause`.
3. Data Preprocessing:
- Clean and preprocess data for
analysis.
- Handle missing values and
standardize delay reasons.
4. Analysis and Visualization:
- Summarize trends using descriptive
statistics and create visualizations for insights.
5. Insights and Recommendations:
- Provide actionable recommendations
for stakeholders.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn,
Plotly
- Time-Series Analysis (optional):
Statsmodels
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn plotly statsmodels
```
Step 2: Load and Explore Dataset
Load the flight delays dataset:
```
import pandas as pd
df = pd.read_csv('flight_delays.csv')
```
Explore the dataset:
```
print(df.head())
print(df.info())
```
Step 3: Data Cleaning and Preprocessing
Clean and preprocess the data:
```
df.dropna(inplace=True)
df['Delay Duration'] = pd.to_numeric(df['Delay Duration'], errors='coerce')
df['Airline'] = df['Airline'].str.upper().str.strip()
```
Convert date column to datetime format:
```
df['Date'] = pd.to_datetime(df['Date'])
df['Month'] = df['Date'].dt.month
df['Weekday'] = df['Date'].dt.weekday
```
Step 4: Analyze and Visualize Data
1. Average Delay by Airline:
```
avg_delay_airline = df.groupby('Airline')['Delay Duration'].mean()
avg_delay_airline.plot(kind='bar', title='Average Delay Duration by Airline')
```
2. Delays by Month:
```
import matplotlib.pyplot as plt
monthly_delays = df.groupby('Month')['Delay Duration'].mean()
plt.plot(monthly_delays.index, monthly_delays.values)
plt.title('Average Delays by Month')
plt.xlabel('Month')
plt.ylabel('Average Delay (minutes)')
plt.show()
```
3. Causes of Delays:
```
import seaborn as sns
sns.countplot(data=df, y='Cause', order=df['Cause'].value_counts().index)
plt.title('Frequency of Delay Causes')
plt.show()
```
Step 5: Time-Series Analysis (Optional)
Analyze delay trends over time:
```
from statsmodels.tsa.seasonal import seasonal_decompose
delay_trends = df.groupby('Date')['Delay Duration'].mean()
result = seasonal_decompose(delay_trends, model='additive')
result.plot()
plt.show()
```
Step 6: Generate Reports
Export summarized data and visualizations:
```
with pd.ExcelWriter('airline_delay_analysis_report.xlsx') as writer:
avg_delay_airline.to_excel(writer,
sheet_name='Average Delays')
monthly_delays.to_excel(writer,
sheet_name='Monthly Delays')
```
Save visualizations as images for reporting.
5. Expected Outcomes
1. Clear identification of airlines with high or low delay durations.
2. Insights into seasonal patterns and frequent causes of delays.
3. Actionable recommendations for improving airline performance.
6. Additional Suggestions
- Advanced Analysis:
- Explore correlations between delay
duration and factors like weather or peak travel times.
- Interactive Dashboards:
- Build an interactive dashboard using
Streamlit or Dash for real-time insights.
- Predictive Modeling:
- Use regression models to predict
future delays based on historical data and external factors.