Airline Delay Analysis

 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.