From 72232872e922d5a24e212854587428b6e401f490 Mon Sep 17 00:00:00 2001
From: Bobby <bobbyskhs@gmail.com>
Date: Mon, 14 Nov 2022 21:31:13 -0500
Subject: push

---
 project.ipynb | 107 +++++++++++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 102 insertions(+), 5 deletions(-)

diff --git a/project.ipynb b/project.ipynb
index 445ea22..7e3ffd4 100644
--- a/project.ipynb
+++ b/project.ipynb
@@ -1853,7 +1853,107 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1145,
+   "execution_count": 1152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>R-Sq</th>\n",
+       "      <th>RMSE</th>\n",
+       "      <th>MAE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>RidgeCV</th>\n",
+       "      <td>0.060517</td>\n",
+       "      <td>163.393393</td>\n",
+       "      <td>62.532860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DecisionTreeRegressor</th>\n",
+       "      <td>0.051456</td>\n",
+       "      <td>164.179481</td>\n",
+       "      <td>61.556500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RandomForestRegressor</th>\n",
+       "      <td>0.038417</td>\n",
+       "      <td>165.304023</td>\n",
+       "      <td>60.911719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DummyRegressor</th>\n",
+       "      <td>-0.000054</td>\n",
+       "      <td>168.578356</td>\n",
+       "      <td>66.384145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KNeighborsRegressor</th>\n",
+       "      <td>-0.086344</td>\n",
+       "      <td>175.700859</td>\n",
+       "      <td>61.922131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GradientBoostingRegressor</th>\n",
+       "      <td>-0.145187</td>\n",
+       "      <td>180.396561</td>\n",
+       "      <td>64.375011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LinearRegression</th>\n",
+       "      <td>-7.239768</td>\n",
+       "      <td>483.891107</td>\n",
+       "      <td>364.603158</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               R-Sq        RMSE         MAE\n",
+       "RidgeCV                    0.060517  163.393393   62.532860\n",
+       "DecisionTreeRegressor      0.051456  164.179481   61.556500\n",
+       "RandomForestRegressor      0.038417  165.304023   60.911719\n",
+       "DummyRegressor            -0.000054  168.578356   66.384145\n",
+       "KNeighborsRegressor       -0.086344  175.700859   61.922131\n",
+       "GradientBoostingRegressor -0.145187  180.396561   64.375011\n",
+       "LinearRegression          -7.239768  483.891107  364.603158"
+      ]
+     },
+     "execution_count": 1152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compare model performance\n",
+    "ups_model_performance_df = pd.DataFrame.from_dict(ups_model_performance_dict, orient='index', columns=['R-Sq', 'RMSE', 'MAE'])\n",
+    "ups_model_performance_df.sort_values(by='R-Sq', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1153,
    "metadata": {},
    "outputs": [
     {
@@ -1940,15 +2040,12 @@
        "LinearRegression          -23.806729  172.333979  134.354980"
       ]
      },
-     "execution_count": 1145,
+     "execution_count": 1153,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Compare model performance\n",
-    "ups_model_performance_df = pd.DataFrame.from_dict(ups_model_performance_dict, orient='index', columns=['R-Sq', 'RMSE', 'MAE'])\n",
-    "ups_model_performance_df.sort_values(by='R-Sq', ascending=False)\n",
     "\n",
     "num_comments_model_performance_df = pd.DataFrame.from_dict(num_comments_model_performance_dict, orient='index', columns=['R-Sq', 'RMSE', 'MAE'])\n",
     "num_comments_model_performance_df.sort_values(by='R-Sq', ascending=False)"
-- 
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