Create README.md

Author: Harshraj1301

README.md

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+# Analyzing and Optimizing Airplane Connectivity
+
+This project focuses on analyzing and optimizing airplane connectivity through Graph, Machine Learning, and Linear Programming algorithms to improve operational efficiency, customer satisfaction, and economic performance.
+
+
+## Table of Contents
+- [Project Overview](#project-overview)
+- [Problem Statement and Objectives](#problem-statement-and-objectives)
+- [Importance of the Study](#importance-of-the-study)
+- [Dataset](#dataset)
+- [Key Analyses](#key-analyses)
+  - [Predicting Flight Arrival Delays](#predicting-flight-arrival-delays)
+  - [Airport Profiling (Clustering Analysis)](#airport-profiling-clustering-analysis)
+  - [Shortest Path between Two Airports](#shortest-path-between-two-airports)
+  - [Optimize Airport Connectivity within a State](#optimize-airport-connectivity-within-a-state)
+  - [Optimization of Flight Schedules to Minimize Delay Penalties](#optimization-of-flight-schedules-to-minimize-delay-penalties)
+
+## Project Overview
+
+In this project, we leverage a detailed dataset of nationwide flight operations to tackle key challenges in the aviation industry, aiming to enhance operational efficiency, customer satisfaction, and economic performance.
+
+## Problem Statement and Objectives
+
+The aviation industry faces numerous challenges that directly affect operational efficiency, customer satisfaction, and economic performance. This project aims to address five key problems:
+
+1. **Predict Delays:** Develop predictive models for flight delays to improve passenger satisfaction and optimize airline operations.
+2. **Cluster Airports and Identify Hotspots:** Identify and profile hotspot airports to understand regional dynamics and propose targeted improvements.
+3. **Find the Shortest Route in Terms of Flying Time:** Enhance route planning to save fuel, reduce carbon emissions, and better allocate resources.
+4. **Optimize Routes Within a State:** Improve intra-state connectivity to boost local economies by enhancing access to markets.
+5. **Optimize Schedule by Minimizing Total Delay Penalty:** Develop strategies to minimize total delay across the network and maximize flights.
+
+## Importance of the Study
+
+1. **Economic Efficiency:** Reducing delays and optimizing routes can lower operational costs significantly.
+2. **Environmental Impact:** Optimizing flight paths and reducing delays can lessen the aviation industry's environmental footprint.
+3. **Passenger Experience:** Enhancing predictability and efficiency can increase customer satisfaction and loyalty.
+4. **Strategic Planning:** Inform long-term infrastructure and policy decisions for airport authorities and government agencies.
+
+## Dataset
+
+### Flight Data Attributes Description
+The dataset provides a comprehensive overview of flight information, including operational details, timings, and delays. Key attributes include:
+
+- **Date and Identification:** FL_DATE, OP_CARRIER_AIRLINE_ID, TAIL_NUM, OP_CARRIER_FL_NUM
+- **Airport and Location Codes:** ORIGIN_AIRPORT_ID, ORIGIN, DEST_AIRPORT_ID, DEST
+- **Timing and Delays:** CRS_DEP_TIME, DEP_TIME, DEP_DELAY, ARR_TIME, ARR_DELAY, CANCELLED
+- **Performance Metrics:** CRS_ELAPSED_TIME, ACTUAL_ELAPSED_TIME, CARRIER_DELAY, WEATHER_DELAY, NAS_DELAY, SECURITY_DELAY, LATE_AIRCRAFT_DELAY
+
+## Key Analyses
+
+### Predicting Flight Arrival Delays
+- **Objective:** Develop a predictive model for flight delays to improve scheduling reliability.
+- **Methods:** Used Gradient Boosting Classifier with feature engineering and SMOTE for class imbalance.
+- **Outcome:** Achieved an accuracy of 71.01% and an ROC AUC of 76.85%.
+
+### Airport Profiling (Clustering Analysis)
+- **Objective:** Identify patterns in airport operations related to delays and cancellations.
+- **Methods:** Used K-means clustering to group airports based on delay and cancellation characteristics.
+- **Outcome:** Identified four clusters with unique delay and cancellation patterns.
+
+### Shortest Path between Two Airports
+- **Objective:** Determine the shortest flying time between any two airports.
+- **Methods:** Implemented Dijkstra’s Algorithm using NetworkX.
+- **Outcome:** Provided actionable insights into the fastest routes based on historical data.
+
+### Optimize Airport Connectivity within a State
+- **Objective:** Enhance intra-state connectivity by finding the most efficient routes.
+- **Methods:** Used Kruskal’s algorithm to compute the Minimum Spanning Tree (MST) of the airport network.
+- **Outcome:** Achieved a significant reduction in total delay by over 93%.
+
+### Optimization of Flight Schedules to Minimize Delay Penalties
+- **Objective:** Optimize flight schedules to reduce delay penalties and maximize flights.
+- **Methods:** Used linear programming with penalty cost minimization.
+- **Outcome:** Reduced total penalty costs by 20.15% with minimal reduction in total flights.