small changes

final.ipynb

@@ -899,7 +899,7 @@
     "\n",
     "fig, axes = plt.subplots(3, 1, figsize=(10, 20))\n",
     "\n",
-    "\n",
+    "# innacuraccy differential graph\n",
     "ax1 = axes[0]\n",
     "x = range(len(inaccuracyGroups))\n",
     "y = df.groupby(\"Inaccuracy Differential Group\", observed=True)[\"Result\"].mean()\n",
@@ -908,6 +908,7 @@
     "ax1.set_xlabel('Inaccuracy Differential Group')\n",
     "ax1.set_ylabel('Mean Result')\n",
     "\n",
+    "#mistake differential graph\n",
     "ax2 = axes[1]\n",
     "x = range(len(mistakeGroups))\n",
     "y = df.groupby(\"Mistake Differential Group\", observed=True)[\"Result\"].mean()\n",
@@ -916,6 +917,7 @@
     "ax2.set_xlabel('Mistake Differential Group')\n",
     "ax2.set_ylabel('Mean Result')\n",
     "\n",
+    "# blunder differential graph\n",
     "ax3 = axes[2]\n",
     "x = range(len(blunderGroups))\n",
     "y = df.groupby(\"Blunder Differential Group\", observed=True)[\"Result\"].mean()\n",
@@ -966,6 +968,7 @@
     "# Drop na values caused by values that don't fit the bins\n",
     "groups = df[\"EloGroup\"].dropna().unique().sort_values()\n",
     "\n",
+    "#elo differential graph\n",
     "x = range(len(groups))\n",
     "y = df.groupby(\"EloGroup\", observed=True)[\"Result\"].mean()\n",
     "plt.figure(figsize=(10,6))\n",
@@ -1642,8 +1645,6 @@
     }
    ],
    "source": [
-    "\n",
-    "\n",
     "param_grid = {\n",
     "    'n_estimators': [50, 100, 200],\n",
     "    'learning_rate': [0.01, 0.1, 0.2],\n",
@@ -2032,8 +2033,8 @@
     "\n",
     "print(f\"Accuracy of Greater ELO Wins Benchmark: {accuracy_bench1*100:.2f}%\")\n",
     "print(f\"Accuracy of Randomly Guessing Winner: {accuracy_bench2*100:.2f}%\")\n",
-    "print(f\"Accuracy of Random Forest Predictions (w/o hyperparameter): {accuracy_bench3*100:.2f}%\")\n",
-    "print(f\"Accuracy of Random Forest Predictions (w/ hyperparameter): {accuracy_bench4*100:.2f}%\")\n",
+    "print(f\"Accuracy of Gradient Boosting Predictions (w/o hyperparameter): {accuracy_bench3*100:.2f}%\")\n",
+    "print(f\"Accuracy of Gradient Boosting Predictions (w/ hyperparameter): {accuracy_bench4*100:.2f}%\")\n",
     "\n"
    ]
   },