{"id":24404890,"url":"https://github.com/misaghmomenib/airport-flight-analysis","last_synced_at":"2025-04-30T14:27:56.441Z","repository":{"id":273035065,"uuid":"918527335","full_name":"MisaghMomeniB/Airport-Flight-Analysis","owner":"MisaghMomeniB","description":"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.","archived":false,"fork":false,"pushed_at":"2025-01-18T19:37:59.000Z","size":7,"stargazers_count":5,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-14T02:11:55.578Z","etag":null,"topics":["analysis","data-analysis","data-visualization","git","open-source","python","python3"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MisaghMomeniB.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-18T06:42:34.000Z","updated_at":"2025-01-31T06:32:21.000Z","dependencies_parsed_at":"2025-01-18T07:28:29.188Z","dependency_job_id":"76a88d1b-2a94-4adf-80f5-e958842d0a4d","html_url":"https://github.com/MisaghMomeniB/Airport-Flight-Analysis","commit_stats":null,"previous_names":["misaghmomenib/airport-flight-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MisaghMomeniB%2FAirport-Flight-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MisaghMomeniB%2FAirport-Flight-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MisaghMomeniB%2FAirport-Flight-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MisaghMomeniB%2FAirport-Flight-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MisaghMomeniB","download_url":"https://codeload.github.com/MisaghMomeniB/Airport-Flight-Analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243510128,"owners_count":20302294,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis","data-analysis","data-visualization","git","open-source","python","python3"],"created_at":"2025-01-20T04:11:46.847Z","updated_at":"2025-03-14T02:11:58.826Z","avatar_url":"https://github.com/MisaghMomeniB.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📊 Airport Flights Data Analysis\n\nThis 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.\n\n## 📦 Dataset\nThe dataset (`airport_flights.csv`) contains the following columns:\n- **Airport**: The name of the airport\n- **Number of Flights**: The total number of flights for the period\n- **Number of Passengers**: The total number of passengers\n- **Amount**: Revenue generated by the airport\n\n## 📈 Project Overview\n### 1. Import Libraries and Load Data\n```python\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndf = pd.read_csv('airport_flights.csv')\n```\nThe script imports necessary libraries and loads the dataset using Pandas.\n\n### 2. Display Basic Information\n```python\nprint(df.head())\nprint(df.info())\nprint(df.describe())\nprint(df.isnull().sum())\n```\n- **`head()`**: Displays the first five rows.\n- **`info()`**: Provides data types and non-null counts.\n- **`describe()`**: Summary statistics for numerical columns.\n- **`isnull().sum()`**: Checks for missing values.\n\n### 3. Key Metrics Calculation\n```python\ntotal_flights = df['Number of Flights'].sum()\ntotal_passengers = df['Number of Passengers'].sum()\ntotal_revenue = df['Amount'].sum()\n\nprint(f\"Total Flights: {total_flights}\")\nprint(f\"Total Passengers: {total_passengers}\")\nprint(f\"Total Revenue: {total_revenue}\")\n```\nCalculates the total number of flights, passengers, and revenue.\n\n### 4. Data Visualization\n#### Flights per Airport\n```python\nplt.figure(figsize=(10,6))\nsns.barplot(x='Airport', y='Number of Flights', data=df)\nplt.xticks(rotation=90)\nplt.title('Number of Flights per Airport')\nplt.show()\n```\n#### Passengers per Airport\n```python\nplt.figure(figsize=(10,6))\nsns.barplot(x='Airport', y='Number of Passengers', data=df)\nplt.xticks(rotation=90)\nplt.title('Number of Passengers per Airport')\nplt.show()\n```\n#### Revenue per Airport\n```python\nplt.figure(figsize=(10,6))\nsns.barplot(x='Airport', y='Amount', data=df)\nplt.xticks(rotation=90)\nplt.title('Revenue per Airport')\nplt.show()\n```\n### 5. Correlation Heatmap\n```python\ncorrelation_matrix = df[['Number of Flights', 'Amount', 'Number of Passengers']].corr()\nsns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\nplt.title('Correlation Heatmap')\nplt.show()\n```\nVisualizes the correlation between numeric columns to identify relationships.\n\n## 🚀 How to Run the Project\n1. Clone this repository.\n2. Make sure you have Python installed along with Pandas, Matplotlib, and Seaborn:\n   ```bash\n   pip install pandas matplotlib seaborn\n   ```\n3. Run the script:\n   ```bash\n   python analysis.py\n   ```\n\n## 📊 Results\n- Airports with the highest number of flights and passengers can be easily identified.\n- Correlation heatmap shows the relationship between flights, passengers, and revenue.\n\n## 🎯 Future Improvements\n- Add time-series analysis for flight trends.\n- Enhance visualizations with interactive plots.\n\n## 📩 Contributing\nFeel free to fork this project and submit pull requests!\n\n## 📜 License\nThis project is licensed under the MIT License.\n\n🌟 **Happy Analyzing!** 🌟\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmisaghmomenib%2Fairport-flight-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmisaghmomenib%2Fairport-flight-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmisaghmomenib%2Fairport-flight-analysis/lists"}