Datasets:

MichaelYitzchak
/

Weather_Conditions

@@ -20,7 +20,7 @@ size_categories:
 > 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>
@@ -30,7 +30,7 @@ size_categories:
 ---
-## 🎯 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.**
@@ -42,12 +42,12 @@ The personal motivation: as a helicopter operator serving in the reserves, multi
 | 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
@@ -62,7 +62,7 @@ The personal motivation: as a helicopter operator serving in the reserves, multi
 ---
-## 🧹 Data Cleaning
 | Step | Action | Result |
 |---|---|---|
@@ -79,7 +79,7 @@ The personal motivation: as a helicopter operator serving in the reserves, multi
 ---
-## 📊 Outlier Detection & Handling
 Distribution shape was checked before selecting any outlier method — IQR assumes symmetry and fails on skewed variables.
@@ -95,18 +95,18 @@ Distribution shape was checked before selecting any outlier method — IQR assum
 ---
-## ⚖️ 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)
@@ -117,7 +117,7 @@ Distribution shape was checked before selecting any outlier method — IQR assum
 ---
-## 🔬 Research Questions & Findings
 ---
@@ -176,18 +176,18 @@ Distribution shape was checked before selecting any outlier method — IQR assum
 ---
-## 💡 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.
@@ -196,7 +196,7 @@ Distribution shape was checked before selecting any outlier method — IQR assum
 ---
-## 📝 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.

 > 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>
 ---
+#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.**
 | 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
 ---
+##  Data Cleaning
 | Step | Action | Result |
 |---|---|---|
 ---
+##  Outlier Detection & Handling
 Distribution shape was checked before selecting any outlier method — IQR assumes symmetry and fails on skewed variables.
 ---
+##  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)
 ---
+##  Research Questions & Findings
 ---
 ---
+##  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.
 ---
+##  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.