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https://github.com/misaghmomenib/airport-flight-analysis
Flight Data Analysis Project Aimed at Exploring and Visualizing Airport Operations, Flight Patterns, and Delay Trends Using Python. This Project Involves Data Cleaning, Preprocessing, and Statistical Analysis With Tools Like Pandas, Matplotlib, and Scikit-learn to Uncover Insights and Improve Operational Efficiency.
https://github.com/misaghmomenib/airport-flight-analysis
analysis data-analysis data-visualization git python python3
Last synced: 13 days ago
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Flight Data Analysis Project Aimed at Exploring and Visualizing Airport Operations, Flight Patterns, and Delay Trends Using Python. This Project Involves Data Cleaning, Preprocessing, and Statistical Analysis With Tools Like Pandas, Matplotlib, and Scikit-learn to Uncover Insights and Improve Operational Efficiency.
- Host: GitHub
- URL: https://github.com/misaghmomenib/airport-flight-analysis
- Owner: MisaghMomeniB
- Created: 2025-01-18T06:42:34.000Z (15 days ago)
- Default Branch: main
- Last Pushed: 2025-01-18T07:01:09.000Z (15 days ago)
- Last Synced: 2025-01-18T07:28:26.665Z (14 days ago)
- Topics: analysis, data-analysis, data-visualization, git, python, python3
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📊 Airport Flights Data Analysis
This project provides a comprehensive analysis of flight data from various airports using Python libraries such as Pandas, Matplotlib, and Seaborn. The analysis includes data exploration, visualization, and correlation assessment.
## 📦 Dataset
The dataset (`airport_flights.csv`) contains the following columns:
- **Airport**: The name of the airport
- **Number of Flights**: The total number of flights for the period
- **Number of Passengers**: The total number of passengers
- **Amount**: Revenue generated by the airport
## 📈 Project Overview
### 1. Import Libraries and Load Data
```python
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('airport_flights.csv')
```
The script imports necessary libraries and loads the dataset using Pandas.
### 2. Display Basic Information
```python
print(df.head())
print(df.info())
print(df.describe())
print(df.isnull().sum())
```
- **`head()`**: Displays the first five rows.
- **`info()`**: Provides data types and non-null counts.
- **`describe()`**: Summary statistics for numerical columns.
- **`isnull().sum()`**: Checks for missing values.
### 3. Key Metrics Calculation
```python
total_flights = df['Number of Flights'].sum()
total_passengers = df['Number of Passengers'].sum()
total_revenue = df['Amount'].sum()
print(f"Total Flights: {total_flights}")
print(f"Total Passengers: {total_passengers}")
print(f"Total Revenue: {total_revenue}")
```
Calculates the total number of flights, passengers, and revenue.
### 4. Data Visualization
#### Flights per Airport
```python
plt.figure(figsize=(10,6))
sns.barplot(x='Airport', y='Number of Flights', data=df)
plt.xticks(rotation=90)
plt.title('Number of Flights per Airport')
plt.show()
```
#### Passengers per Airport
```python
plt.figure(figsize=(10,6))
sns.barplot(x='Airport', y='Number of Passengers', data=df)
plt.xticks(rotation=90)
plt.title('Number of Passengers per Airport')
plt.show()
```
#### Revenue per Airport
```python
plt.figure(figsize=(10,6))
sns.barplot(x='Airport', y='Amount', data=df)
plt.xticks(rotation=90)
plt.title('Revenue per Airport')
plt.show()
```
### 5. Correlation Heatmap
```python
correlation_matrix = df[['Number of Flights', 'Amount', 'Number of Passengers']].corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()
```
Visualizes the correlation between numeric columns to identify relationships.
## 🚀 How to Run the Project
1. Clone this repository.
2. Make sure you have Python installed along with Pandas, Matplotlib, and Seaborn:
```bash
pip install pandas matplotlib seaborn
```
3. Run the script:
```bash
python analysis.py
```
## 📊 Results
- Airports with the highest number of flights and passengers can be easily identified.
- Correlation heatmap shows the relationship between flights, passengers, and revenue.
## 🎯 Future Improvements
- Add time-series analysis for flight trends.
- Enhance visualizations with interactive plots.
## 📩 Contributing
Feel free to fork this project and submit pull requests!
## 📜 License
This project is licensed under the MIT License.
🌟 **Happy Analyzing!** 🌟