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README.md

@@ -58,27 +58,19 @@ This sample size provides sufficient statistical power to detect meaningful patt
 ## Exploratory Data Analysis
 
 Key findings from our initial data exploration:
-1. Hero pick/ban patterns in professional matches
-   - Most banned heroes across tournaments
-   - Hero synergy analysis
-   - Pick order impact on win rates
-
-2. Win rate distribution across different player ranks
-   - Professional vs high-ranked public matches
-   - Role-specific performance metrics
-   - Team composition win rates
-
-3. Correlation between early game indicators and match outcomes
-   - First blood impact
-   - Lane dominance metrics
-   - Resource distribution patterns
-
-4. Impact of team composition on victory probability
-   - Hero counter relationships
-   - Draft phase analysis
-   - Role combination effectiveness
-
-Detailed visualizations and analysis can be found in our [EDA Notebook](notebooks/exploratory_analysis.ipynb).
+1. Hero pick patterns in matches:
+
+![](./imgs/heroes_pick_distribution.png)
+
+2. Hero ban patterns in matches:
+
+![](./imgs/heroes_ban_distribution.png)
+
+3. Hero win rates in matches:
+
+![](./imgs/heroes_winrate_distribution.png)
+
+Detailed visualizations and analysis can be found in our [Repository](https://github.com/howl-anderson/game-oracle/blob/main/EDA/EDA.py).
 
 ## Project Repository