Update README.me

README.me

@@ -5,143 +5,96 @@ Assignment: EDA & Dataset Analysis
 Dataset Source: Kaggle – UFC Fighters' Statistics Dataset
 Dataset Size: ~4,112 rows × 18 column
 
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-
-
-# Ski Resorts Worldwide - Exploratory Data Analysis (EDA)
-
-*Author:*  Omer Gonen 
-*Course:* Introduction to Data Science  
-*Assignment:* EDA & Dataset Analysis  
-*Dataset Source:* Kaggle – Ski Resorts Worldwide Dataset  
-*Dataset Size:* ~5,478 rows × 33 columns  
-
 ---
 
 ## Project Goal
 
-The goal of this project is to explore ski resorts worldwide to understand how *physical terrain characteristics* influence resort quality ratings.
-This analysis investigates the relationship between measurable resort features—such as altitude, trail size, and infrastructure—and user satisfaction ratings to identify the key physical factors that contribute to high-quality ski resort experiences.
+The Project Goal was answer the  question : What are the defining attributes—both physiological
+(physical) and strategic (fighting style)—that characterize the ultimate mixed martial arts fighter?
 
 ---
 
 ## Dataset Overview
 
-The dataset contains information about ski resorts worldwide with the following key columns:
-
-- *NameResort* – Name of the ski resort
-- *Country* – Country where the resort is located
-- *Continent* – Geographic continent
-- *Altitude* – Elevation of the resort (meters)
-- *Total Kms* – Total kilometers of ski trails
-- *Easy* – Number of easy trails
-- *Intermediate* – Number of intermediate trails
-- *Difficult* – Number of difficult trails
-- *Ski_resort_size* – Size category (1-5)
-- *Lifts_cable_cars* – Number of lifts and cable cars
-- *Rate* – Resort quality rating (0-5 scale)
-
-The data was cleaned, validated, and filtered to ensure analysis quality.
+The dataset contains information about UFC Fighters winrate with the following key columns:
+
+- **name**
+- **height_cm**
+- **weight_in_kg**
+- **stance**
+- **Age**                  
+- **reach_in_cm** - Arm span length (cm)
+- **stance**
+- **Strikes/Min** - significant_strikes_landed_per_minute              
+- **Strike Accuracy** - significant_striking_accuracy                     
+- **Absorb/Min** - significant_strikes_absorbed_per_minute            
+- **Strike Defense** - significant_strike_defence                       
+- **Takedowns/Fight** - average_takedowns_landed_per_15_minutes            
+- **TD Defense** - takedown_defense                                  
+- **Submissions/Fight** - average_submissions_attempted_per_15_minutes       
+- **TD Accuracy** - takedown_accuracy                                  
+- **total_fights**
+- **win_rate**
+
+The final dataset is the result of systematic cleaning, validation, and filtering procedures
 
 ---
 
 ## Data Cleaning Summary
 
-- Selected *11 relevant columns* from the original 33 columns for analysis.
-- *Removed 22 irrelevant columns* including: Mountain restaurants, ski huts, gastronomy, Families and children, Après-ski, website URLs, adult-only indicators, summer skiing availability, glacier presence, snow parks, night skiing, and other non-essential features...
-- *Note:* Some potentially relevant columns (e.g., Snow reliability) were excluded due to insufficient data availability.
-- Fixed column names by removing trailing spaces and replacing spaces with underscores (e.g., Ski resort size → Ski_resort_size, Lifts and cable cars → Lifts_cable_cars).
-- Removed resorts with *Total Kms = 0* (no skiable terrain).
-- Removed resorts where *all trail types = 0* (Easy, Intermediate, Difficult all equal zero).
-- Retained only resorts with *valid rating data* (Rate not null).
-- Cleaned *Lifts_cable_cars* column by converting string values with commas to numeric format (e.g., '5,0' → 5.0).
-- Final dataset: *2,338 valid resorts* for analysis (42.7% retention rate).
+- Selected 13 relevant columns from the original 18 columns for analysis, and subsequently engineered 3 new features.
+- Handling Non-Informative & Duplicates
+- Missing Value Handling, Removing Fighters with 3 or More Missing Columns
+- Filtering 3 Fighters Due to Missing Height Data
+- Filtering Fighters with Missing Date of Birth
+- Filling Missing 'Reach' Values with the Mean
+- Filling Missing 'Stance' Values with 'Unknown
+- Column Renaming for Readability
 
 ---
 
 ## Research Questions & Insights
 
 
-### *Q1: Which terrain characteristics show the strongest correlation with resort quality ratings?*
+### *Q1: "Is the fighter's Age a statistically significant factor in predicting the Win Rate, and what is the central tendency
+(mean) of the age distribution in the dataset?
+
 
-*Visualization: Correlation Heatmap of Key Variables*  
-![Correlation Heatmap](https://i.postimg.cc/yWK5Pd4X/Screenshot-2025-11-17-111443.png)
 
 Click on the image above to view it in full size
 
 *Insight:*  
-*Total Kilometers demonstrates the strongest correlation with resort ratings (r = 0.690)*, followed by Lifts & Cable Cars (r = 0.682) and Altitude (r = 0.629).
 
-This indicates that resort size is the most important predictor of resort quality. Larger resorts with more trail kilometers offer greater variety and options for skiers of all skill levels. Infrastructure (lifts) and elevation also play significant roles, with all three factors showing strong positive correlations with quality ratings.
 
 ---
 
-### *Q2: Does resort altitude affect quality ratings? Is there a measurable relationship between elevation and resort performance?*
+### *Q2: "Is there a correlation between a physical variable or fighting style with the Win rate?"
+
 
-*Visualization: Average Altitude by Resort Rating*  
-![Altitude by Rating](https://i.postimg.cc/wtJf5KWK/Screenshot-2025-11-17-111505.png)
 
 Click on the image above to view it in full size
 
 *Insight:*  
-There is a *clear, strong positive relationship between altitude and resort quality*.
 
-- *Lowest-rated resorts* (Rating ≤ 2.0): Average altitude ~1,300 meters
-- *Highest-rated resorts* (Rating = 5.0): Average altitude ~1,900 meters
-- *Altitude difference: Approximately **600 meters (46% increase)*
 
-Higher-elevation resorts consistently receive better ratings. This substantial elevation difference translates to better snow quality, reliability, and overall skiing conditions. The correlation between altitude and rating is strong (r = 0.629).
 
 ---
 
-### *Q3: How are high-quality ski resorts (rating > 3) geographically distributed across continents?*
+### *Q3: "Given that a fighter possesses all the 'aggressive' attributes with a positive correlation to victory, are these traits sufficient, 
+on their own, to reliably predict or guarantee a high Win Rate, or are other factors more decisive?"
 
-*Visualization: High-Rated Resorts (Rating > 3) by Continent*  
-![Geographic Distribution](https://i.postimg.cc/R3fs1rgj/Screenshot-2025-11-17-111520.png)
 
-Click on the image above to view it in full size
-
-*Insight:*  
-*Europe dominates the high-quality ski resort market, accounting for **79.1% of all top-rated resorts* (393 out of 497).
-
-- *Europe*: 393 resorts (79.1%)
-- *North America*: 85 resorts (17.1%)
-- *Asia*: 10 resorts (2.0%)
-- *Oceania*: 5 resorts (1.0%)
-- *South America*: 4 resorts (0.8%)
-
-This geographic concentration suggests that European regions possess unique advantages for ski resort development—including Alpine geography, consistent snow conditions, established ski culture, and mature infrastructure.
 
 ---
 
 ## Final Conclusions
 
 *Main Research Question:*  
-"How do physical terrain characteristics—including altitude, total trail kilometers, and number of lifts and cable cars—affect ski resort quality ratings?"
 
-*Answer: YES—physical terrain characteristics significantly and measurably affect ski resort quality ratings.*
+*Answer: 
 
 ### Key Findings:
 
-1. *Resort size is the dominant quality predictor*
-   - Total kilometers show the strongest correlation with ratings (r = 0.690)
-   - Larger resorts offer more variety and options for different skill levels
-   - More trails = better experience and higher satisfaction
-
-2. *Infrastructure quality is critically important*
-   - Number of lifts shows very strong correlation (r = 0.682)
-   - More lifts = better accessibility, reduced wait times, and improved experience
-   - Modern lift systems are essential for high ratings
-
-3. *Altitude significantly affects quality*
-   - Elevation shows strong correlation with ratings (r = 0.629)
-   - Top-rated resorts average 600m higher elevation
-   - Higher altitude = better snow quality and longer seasons
-
-4. *Geographic concentration of quality*
-   - Europe leads with 79% of high-rated resorts
-   - Location matters: certain regions have natural and infrastructural advantages
-
 
 ### Overall Insights:
 - All three physical terrain characteristics show strong positive correlations with quality
@@ -154,4 +107,3 @@ This geographic concentration suggests that European regions possess unique adva
 
 ## Video
 
-[[Video link placeholder]](https://www.loom.com/share/e6da147bbe49433f81f283b9932511db)