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README.md

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-1. Project Overview
-This project analyzes a decade of atmospheric data  to determine the specific environmental thresholds that dictate helicopter mission suitability. By engineering a Mission_Status target variable, we transition from raw weather observation to a predictive aviation safety model.
-2. Dataset Description
-Source: Weather in Szeged 2006-2016 (Kaggle)
-Size: 96,453 Rows | 12 Features
-Key Features: 
-Numeric: Wind Speed, Visibility, Temperature, Pressure, Humidity
-Categorical: Weather Summary, Date
-A new variable called Mission_Status was created:
-Operational → Safe conditions for flight
-Non-operational → Unsafe conditions for flight
-This variable is based on predefined thresholds:
-Low visibility
-High wind speed
-Operational: Visibility > 4km AND Wind Speed < 45km/h.
-Non-Operational: Any breach of the above safety thresholds.
-The dataset was cleaned using the following steps:
-Removed duplicate rows
-Handled missing values:
-Numeric columns → filled using median
-Categorical columns → filled with "Missing"
-Identified and removed invalid values:
-Pressure values equal to zero were treated as sensor errors
-Standardized text fields for consistency
-Converted date column to datetime format
-Created new time-based features (e.g., month)
-Identified outliers and treated accordingly
-Exploratory Data Analysis (EDA)
-Q1: The Fog Trap (Humidity vs. Visibility)
-Research Question:  To what extent does humidity serve as an indicator for non-operational mission status based on visibility?
-Finding: We identified a "Fog Trap" at 90% humidity. As humidity approaches 100%, visibility consistently crashes below our 4km safety threshold, making humidity a primary leading indicator of mission grounding.
-Q2: Atmospheric Pressure Stability
-Research Question: Does atmospheric pressure stability vary between operational and non-operational mission statuses?
-Finding: Non-operational missions are characterized by high pressure volatility and extreme anomalies. Operational flights, by contrast, occur within a significantly tighter and more stable pressure window.
-Q3: The Wind Speed Ceiling
-Research Question: To what extent can extreme wind speed variations be used to expect a transition from operational to non-operational mission status?
-Finding: There is a "Hard Ceiling" at 45 km/h. Beyond this point, the mission status is 100% Non-Operational. Interestingly, wind and visibility have a near-zero correlation, proving they are independent risk factors that must be monitored separately.
-Q4: Categorical Weather Hazards
-Research Question: To what extent does categorical weather data (Precipitation Type) serve as a reliable indicator for non-operational mission status?
-Finding: While Rain is the most frequent hazard, Snow carries a 40% higher proportional risk of grounding. This identifies Snow as a critical categorical predictor for fleet safety.
-Q5: Seasonal Risk Patterns
-Research Question: Does the risk of mission grounding follow a predictable seasonal pattern?
-Finding: The data reveals a dramatic U-shaped seasonal curve. Missions in December are 30 times more likely to be grounded than those in July, allowing for data-driven strategic fleet planning.
-Key Insights
-Wind speed and visibility are the most influential factors in mission feasibility
-Weather variables interact rather than acting independently
-Extreme conditions, although rare, are crucial for understanding operational limits
-Environmental conditions significantly impact aviation decision-making
-Limitations
-The target variable is rule-based, not naturally observed
-Results should not be interpreted as causal relationships
-Some variables may be indirectly related rather than directly influencing mission outcomes
-Conclusion
-This analysis demonstrates that aviation mission feasibility is strongly influenced by environmental conditions.
-By combining data cleaning, feature engineering, and exploratory analysis, meaningful insights can be derived from weather data to support operational decision-making.
+---
+title: Weather & Aviation Mission Feasibility Analysis
+task_categories:
+  - tabular-classification
+language:
+  - en
+tags:
+  - weather
+  - aviation
+  - EDA
+  - mission-feasibility
+  - helicopter
+size_categories:
+  - 10K<n<100K
+---
+
+# Weather & Mission Feasibility Analysis
+
+**EDA Assignment · March 2026**
+
+> A decade of atmospheric data analyzed to determine the environmental thresholds that dictate helicopter mission suitability — bridging meteorology and aviation safety.
+
+## 🎬 Video Presentation
+
+<video src="Weather Data Analysis for Mission Feasibility.mp4" controls="controls" style="max-width: 720px;"></video>
+
+| Dataset | Period | Rows | Features | Questions |
+|---|---|---|---|---|
+| Szeged Weather · Kaggle | 2006–2016 | 96,453 | 12 | 5 |
+
+---
+
+## 🎯 Main Objective
+
+**To analyze to what extent atmospheric variables — humidity, altimeter pressure, and wind speed — can evaluate the suitability of environmental conditions for helicopter flight.**
+
+This project focuses exclusively on hours with active weather conditions. There are no clear or sunny baseline days in the dataset — every row represents a moment where weather played a role. The engineered `Mission_Status` variable defines, based on standard aviation thresholds, whether conditions on any given hour were safe enough to fly.
+
+The personal motivation: as a helicopter operator serving in the reserves, multiple missions have been cancelled mid-flight due to weather. A data-driven pre-flight weather assessment framework could save time, manpower, and allow missions to be replanned rather than abandoned.
+
+### Mission Status Thresholds
+
+| Status | Condition |
+|---|---|
+| ✅ **Operational** | Visibility > 4 km **AND** Wind Speed < 45 km/h |
+| ❌ **Non-Operational** | Any breach of the above thresholds |
+
+---
+
+## 📦 Dataset Description
+
+**Source:** [Weather in Szeged 2006–2016](https://www.kaggle.com/datasets/muthuj7/weather-dataset) — Kaggle  
+**Size:** 96,453 rows · 12 features
+
+### Features
+
+**Numeric:** Wind Speed (km/h), Visibility (km), Temperature (C), Apparent Temperature (C), Humidity, Pressure (millibars), Wind Bearing (degrees)
+
+**Categorical:** Summary, Precip Type, Formatted Date
+
+**Engineered:** `Mission_Status` — Operational / Non-Operational
+
+---
+
+## 🧹 Data Cleaning
+
+| Step | Action | Result |
+|---|---|---|
+| 1 | Date parsing | Converted to UTC datetime |
+| 2 | Duplicate removal | 24 rows removed → 96,429 remaining |
+| 3 | Pressure sensor failures | 1,288 zeros (1.34%) replaced with column median |
+| 4 | Missing Precipitation Type | 517 rows (0.54%) dropped |
+| 5 | Redundant columns | `Loud Cover` (zero variance) and `Daily Summary` dropped |
+| 6 | Humidity validity | All values confirmed in [0, 1] — no action needed |
+| 7 | Visibility ceiling | All values ≤ 16.1 km confirmed — no action needed |
+| 8 | Fog/Visibility contradiction | 1,939 rows relabeled from "Foggy" → "Partly Cloudy" where visibility > 2.5 km |
+
+> **Note:** All numeric columns had zero NaN values (confirmed via `df.info()`). No general median-fill was required — only the targeted pressure zero replacement above.
+
+---
+
+## 📊 Outlier Detection & Handling
+
+Distribution shape was checked before selecting any outlier method — IQR assumes symmetry and fails on skewed variables.
+
+![Distribution Shape Check](distribution_check.png)
+
+| Column Group | Method | Decision |
+|---|---|---|
+| Temperature, Apparent Temp, Pressure | IQR (symmetric) | **Keep** — real weather extremes |
+| Wind Speed, Visibility, Humidity | 1st–99th percentile (skewed) | **Keep** — operationally critical events |
+| Wind Bearing | None (circular variable) | **Keep** — 0° and 360° are the same direction; IQR/percentiles fail on circular data |
+
+**Overall decision: Keep all outliers.** In aviation weather analysis, extreme readings are not errors — they are the most operationally significant events in the dataset.
+
+---
+
+## ⚖️ Class Balance
+
+| Class | Count | Percentage |
+|---|---|---|
+| ✅ Operational | 8,998 | 90.0% |
+| ❌ Non-Operational | 1,002 | 10.0% |
+
+**Imbalance ratio: 8.98:1** — A naive model predicting "Operational" every time would score 90% accuracy while being completely useless. Future models should use **F1-score or precision-recall**, not raw accuracy.
+
+---
+
+## 🔥 Correlation Analysis
+
+![Correlation Heatmap](correlation_heatmap.png)
+
+**Key correlations:**
+- Humidity ↔ Visibility: **–0.37** (negative — high humidity predicts low visibility)
+- Wind Speed ↔ Visibility: **+0.10** (near-zero — they are **independent** risk factors)
+- Temperature ↔ Apparent Temperature: **+0.99** (expected — nearly identical variables)
+
+---
+
+## 🔬 Research Questions & Findings
+
+---
+
+### Q1 · The Fog Trap
+
+**Research Question:** To what extent does humidity serve as an indicator for non-operational mission status based on visibility?
+
+![Humidity vs Visibility](q1_humidity_visibility.png)
+*Left: Scatter plot of Humidity vs. Visibility with 4 km safety threshold · Right: KDE density of humidity by Mission Status*
+
+**Finding:** A "Fog Trap" was identified at **90% humidity**. As humidity approaches 100%, visibility consistently crashes below the 4 km safety threshold — making humidity a primary **leading indicator** of mission grounding before conditions become critical.
+
+---
+
+### Q2 · Atmospheric Pressure Stability
+
+**Research Question:** Does atmospheric pressure stability vary between operational and non-operational mission statuses?
+
+![Pressure Stability](q2_pressure_stability.png)
+*Left: Box plot — pressure spread by mission status · Right: Overlaid pressure distributions by mission status*
+
+**Finding:** Non-operational missions show **higher pressure volatility** (std: 9.78 vs 7.29 for operational). Rapid pressure drops correspond to storm systems; high-pressure outliers indicate anticyclonic conditions. Pressure instability is a meaningful — if subtle — indicator of unsafe conditions.
+
+---
+
+### Q3 · The Wind Speed Ceiling
+
+**Research Question:** To what extent can extreme wind speed variations predict a transition from operational to non-operational mission status?
+
+![Wind Speed Violin](q3_wind_violin.png) ![Wind Speed Histogram](q3_wind_histogram.png)
+*Left: Violin plot — wind speed by mission status · Right: Full dataset wind speed distribution*
+
+**Finding:** A hard ceiling exists at **45 km/h** — beyond this, 100% of missions are Non-Operational. However, most non-operational events occur below 20 km/h, confirming **visibility is the dominant grounding factor**, not wind. Wind and visibility are independent (correlation: +0.10) — both must be monitored separately.
+
+---
+
+### Q4 · Categorical Weather Hazards
+
+**Research Question:** To what extent does categorical weather data (Precipitation Type) serve as a reliable indicator for non-operational mission status?
+
+![Precipitation Risk](q4_precipitation.png)
+*Left: Mission count by precipitation type · Right: Proportional grounding risk (%) per type*
+
+**Finding:** While **Rain** is the most frequent hazard in absolute numbers, **Snow** carries a ~40% higher proportional grounding risk. Most common Non-Operational weather: Foggy. Most common Operational weather: Partly Cloudy. Snow is a disproportionate predictor despite lower frequency.
+
+---
+
+### Q5 · Seasonal Risk Patterns
+
+**Research Question:** Does the risk of mission grounding follow a predictable seasonal pattern throughout the year?
+
+![Seasonal Risk](q5_seasonal.png)
+*Left: Monthly grounding risk % (line chart) · Right: Normalized stacked bar — Operational vs. Non-Operational ratio by month*
+
+**Finding:** The data reveals a dramatic **U-shaped seasonal curve**. January grounding rate: ~27%. December: highest risk. July: lowest risk. Winter missions are roughly **30× more likely** to be Non-Operational than summer missions — enabling data-driven strategic fleet planning.
+
+---
+
+## 💡 Key Insights
+
+| Insight | Detail |
+|---|---|
+| 💨 **Wind & Visibility are independent** | Near-zero correlation — either alone can ground a mission. Monitor both separately. |
+| 💧 **Humidity is an early warning signal** | Above 90%, visibility reliably drops below 4 km. Predict groundings before they happen. |
+| ❄️ **Snow is disproportionately dangerous** | ~40% higher grounding risk than rain despite lower frequency. |
+| 📅 **Seasonality is predictable** | December/January are ~30× riskier than July. Plan resources accordingly. |
+
+---
+
+## ⚠️ Limitations
+
+- `Mission_Status` is **rule-based**, not naturally observed — engineered from predefined thresholds that may not capture all real-world operational nuance.
+- Results reflect **associations**, not causal relationships.
+- The **90/10 class imbalance** means future classifiers need careful metric selection (F1, precision-recall) and should consider resampling techniques.
+- Some variables may be **indirectly related** through confounders — seasonality drives both temperature and humidity, which in turn influence visibility.
+
+---
+
+## 📝 Conclusion
+
+This analysis demonstrates that aviation mission feasibility is strongly governed by environmental conditions. Through systematic data cleaning, distribution-aware outlier analysis, feature engineering, and five focused research questions, meaningful and actionable insights were derived from raw weather data.
+
+Wind speed and visibility define hard safety boundaries; humidity serves as an early warning signal; pressure instability flags turbulent conditions; snow is a disproportionate categorical risk; and seasonality enables proactive operational planning. Together, these findings form a robust data-driven foundation for aviation safety decision-making.
+
+---
+
+*Weather in Szeged 2006–2016 · Kaggle Dataset · Python · Pandas · Matplotlib · Seaborn*