diff --git a/2-Regression/3-Linear/notebook.ipynb b/2-Regression/3-Linear/notebook.ipynb
index 2902cce8..aa1dc7b0 100644
--- a/2-Regression/3-Linear/notebook.ipynb
+++ b/2-Regression/3-Linear/notebook.ipynb
@@ -16,9 +16,209 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "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>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
@@ -32,9 +232,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_7488\\2637987050.py:9: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n"
+     ]
+    },
+    {
+     "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>Month</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>281</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
+       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
+       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "\n",
+       "    High Price      Price  \n",
+       "70        15.0  13.636364  \n",
+       "71        18.0  16.363636  \n",
+       "72        18.0  16.363636  \n",
+       "73        17.0  15.454545  \n",
+       "74        15.0  13.636364  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
     "\n",
@@ -71,9 +391,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1ae64410440>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "plt.scatter('Month','Price',data=new_pumpkins)"
@@ -81,18 +422,382 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1ae644ea490>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "\n",
     "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_7488\\3995499251.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n"
+     ]
+    }
+   ],
+   "source": [
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = None\n",
+    "colors = ['red', 'blue', 'green', 'yellow']\n",
+    "for i, var in enumerate(new_pumpkins['Variety'].unique()):\n",
+    "    df = new_pumpkins[new_pumpkins['Variety'] == var]\n",
+    "    ax = df.plot.scatter('DayOfYear', 'Price', ax=ax, c=colors[i], label=var)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Variety'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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//34ZOXKkVK9eXWJiYpyuHQAABEL4+Oabb+Tee+/13M7s1+jTp49MmjRJtm3bJjNmzJDTp0+bjcg6duwob7zxhpleAQAAuObw0aZNG8nIyMj18WXLlt1oTQAAwI9xtgsAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAA8O7wsXbtWuncubNERUVJUFCQfPnll9kez8jIkFdffVUqVKggJUqUkPbt28v333/vZM0AACCQwkdKSoo0aNBAJk6cmOPj48aNk/fff18mT54sX3/9tdx0000SExMjFy5ccKJeAADg44pc63/QqVMnc+VERz3Gjx8vo0aNki5dupj7Zs6cKeXLlzcjJI8++uiNVwwAAHyaoz0fBw4ckCNHjpiplkxhYWHSrFkz2bhxY47/TWpqqiQnJ2e7AACA/3I0fGjwUDrSkZXeznzsSnFxcSagZF7R0dFOlgQAALyM66tdYmNj5cyZM54rKSnJ7ZIAAICvhI/IyEjz8ejRo9nu19uZj10pODhYQkNDs10AAMB/ORo+qlSpYkLGypUrPfdpD4euemnRooWT3woAAATKapdz587Jvn37sjWZJiYmSnh4uFSqVEmGDh0qf/zjH6VGjRomjLzyyitmT5CuXbs6XTsAAAiE8PHNN9/Ivffe67k9fPhw87FPnz4yffp0GTlypNkL5JlnnpHTp0/L3XffLUuXLpXixYs7WzkAAAiM8NGmTRuzn0dudNfT119/3VwAAABet9oFAAAEFsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMC3w8cf/vAHCQoKynbVqlXL6W8DAAB8VJGC+KK33367rFix4r/fpEiBfBsAAOCDCiQVaNiIjIzM13NTU1PNlSk5ObkgSgIAAP7c8/H9999LVFSUVK1aVXr16iUHDx7M9blxcXESFhbmuaKjowuiJAAA4K/ho1mzZjJ9+nRZunSpTJo0SQ4cOCD33HOPnD17Nsfnx8bGypkzZzxXUlKS0yUBAAB/nnbp1KmT5/P69eubMFK5cmWZN2+eDBgw4KrnBwcHmwsAAASGAl9qW7p0abnttttk3759Bf2tAACADyjw8HHu3DnZv3+/VKhQoaC/FQAACMTwMWLECImPj5cff/xRNmzYIA8//LAULlxYHnvsMae/FQAA8EGO93z8/PPPJmicPHlSypYtK3fffbckJCSYzwEAABwPH3PmzHH6SwIAAD/C2S4AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAP8IHxMnTpRbb71VihcvLs2aNZNNmzYV1LcCAACBHj7mzp0rw4cPl9dee022bt0qDRo0kJiYGDl27FhBfDsAABDo4ePPf/6zPP3009KvXz+pU6eOTJ48WUqWLCkff/xxQXw7AADgQ4o4/QUvXrwoW7ZskdjYWM99hQoVkvbt28vGjRuven5qaqq5Mp05c8Z8TE5OFjekp56XQOTW/2+38XoHFl7vwMLr7c73zcjIsB8+Tpw4IWlpaVK+fPls9+vt77777qrnx8XFyejRo6+6Pzo62unSkIew8W5XAJt4vQMLr3dgCXP59T579qyEhYXZDR/XSkdItD8kU3p6uvzyyy9SpkwZCQoKkkChiVEDV1JSkoSGhrpdDgoYr3dg4fUOLIH6emdkZJjgERUV9ZvPdTx8RERESOHCheXo0aPZ7tfbkZGRVz0/ODjYXFmVLl1aApX+RQ2kv6yBjtc7sPB6B5ZAfL3DfmPEo8AaTosVKyaNGjWSlStXZhvN0NstWrRw+tsBAAAfUyDTLjqN0qdPH2ncuLE0bdpUxo8fLykpKWb1CwAACGwFEj569uwpx48fl1dffVWOHDkiDRs2lKVLl17VhIr/0qkn3Rflyiko+Cde78DC6x1YeL1/W1BGftbEAAAAOISzXQAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AKCA/fTTT7Jr1y6z5xEAwgcAOEZP7tZTvbN65plnpGrVqlKvXj2pW7eu2XIb/mHcuHHy66+/em6vX78+20GputX4c88951J13o3wAQAO+fDDD+Xmm2/23Nb9jaZNmyYzZ86UzZs3m6MjcjpIE75JzybTgJGpU6dO8u9//9tz+/z58zJlyhSXqvNuhA+X3H///XLmzBnP7bFjx8rp06c9t0+ePCl16tRxqTo4jdc7MHz//fdmZ+dMixYtki5dukivXr3kzjvvlDfffDPb0RPwbVduk8W2WflH+HDJsmXLsg3P6Q8lPc030+XLl2XPnj0uVQen8XoHBh2Cz3qQ2IYNG6RVq1ae2zr9ors+A4GO8OESEnNg4fUODJUrV5YtW7aYz0+cOCE7d+6Uli1beh7X4JHfUz8Bf1YgZ7sAQCDSAzUHDx5sQseqVaukVq1a5pTvrCMh2nQK/zF16lQJCQnxjGBOnz5dIiIizO2s/SDIjvDhkqCgIHNdeR/8E693YBg5cqRpMlywYIFERkbK/Pnzsz2uqyEee+wx1+qDsypVqiR/+ctfPLf1Nf/kk0+ueg6uxsFyLilUqJDpjM489XDx4sXStm1buemmm8xt7Q/QTvm0tDSXK4UTeL0B4L8IHy7p169fvp6ny/Tg+3i9A8OmTZvMNEvhwoVzfFxDpq6AeeSRR6zXhoKhv0L37dsnFy9elJo1a0qRIkwo5AfhAwAcoqHj8OHDUq5cOXNbV74kJiaaVS7q6NGjEhUVxQiXnzhw4IA89NBDZvdadcstt8gXX3whTZo0cbs0r8dqFy/13XffyW233eZ2GbDo888/d7sEWFjVxPs9//H73//eNJl++umn5t9vdHS0DBo0yO2yfALjQ15Kh2f379/vdhlwkP6Q0lBZrFixbMFSh+FfffVV81iPHj1crREFj0Zj/7Fu3ToTOu6++25zu3nz5lKxYkVJSUnx9HMhZ4x8ABbs2LFDqlevLg0aNJDatWtLt27dzBB869atpX///qYZlbAJ+JZjx45JjRo1PLcrVKggJUqUMPcjb4x8ABa8+OKLJnxMmDBBZs+eba7du3fLgAEDzCoX/YEF/6Dz/5m7mOoUi45onTt3zrPxGPxrFEtf26z/fnVlm+7vkZyc7Lkv6663+A8aTr3Ut99+a86CoDHNP2gD4j/+8Q9p2LChOeNFDx+bMWOGPPnkk26XBgfpLx79hZTTj9XM+/Uj/6796/XOKvM1zvo5r/fVGPlwif7yyWvuV/sD4D/0Ha+uclC6vbbOB+v8MPxv9QMCx+rVq90uwWcRPlwyfvx4t0uARRo0dSi2ePHinndDeghZ1qFZxfCs75/tgsCh/R6ZbypwbZh28WI6VJfbZkXw7eHZrEOzWW8zPOvbtm3blq/n1a9fv8BrgZ0R7IkTJ8rjjz/udik+h5EPL7R371756KOPZObMmWbDIvg+hmcDg/b05NbzkYmQ6T/GjBkjAwcOlIULF8rkyZOlTJkybpfkMxj58BJ6GNXcuXPl448/lo0bN0rjxo2le/fuZhMbAL7hp59+ytfzmJ7xrz4fXbWmq5z0kLnOnTu7XZJPIHy4LCEhwRzJrKdf6umHuvxS3yXfc889bpcGC8Px2nyqrzsbT/mH119/XUaMGCElS5Z0uxRYpsvohw0bZvbxufJ8l61bt7pWl7di2sUl77zzjhnl0GWXesT22rVrzQZURYsWZegugIbj9T5tQh06dKj5xUWPj28bPXq02V6b8BF4I14LFiwwPSBdunThcLl84P+Qi5tO6cUvnMBegnn69GnZsmWLvPLKK+YHl75rhu9iIDnw6FTL7373O2nfvr3s3LlTypYt63ZJPoFpF5fExcWZ49MvXLhgRj50s6m6deuakQ/dYKxOnTpulwiL9HwIfde8fft2t0vBDa5q0m3z+QUUGO677z7ZtGmT2Tqhd+/ebpfjUxj5cElsbKy54uPjzfRLs2bNzPbbmgVPnTrldnmwrFGjRmxQ5Sf00MDf6uH55ZdfrNWDgqOrlrSfSw+Tw7Vh5MNL6AZUs2bNMkFEh+GbNm1qTjgdPny426XBAl3hpHsFEEB8f+RD3wVrI3Fe+vTpY60mFBydMtftEPT4BFwbwodLqlatKps3b86xuVSH3nWfDw0jnI7o/44fP26m3nTVi4ZP+Hb40EPl+GUUGHi9rx/hw4v/0l66dMn0gMD33XHHHTkOxetqp59//llq1qxpDp6LjIx0pT44g3fCgYXwcf3o+fBiBA//0bVr1xzv17NcNHjExMSw6skP8F4u8Og+TSEhIXk+54UXXrBWj69g5MPFxKxHqv/W3PBDDz1krSYUHN3H5a677mL9f4DTH7c6zcY7Zf/5Oa7Npnm9cdARzx9++MFqXb6A8OHiX9rfwhkQ/oPh+MCgm4vphlOZS20feOAB8864QoUK5rYuw9VTUPl37R+Ydrl+v/0bEAVG/9Kmp6fnevEDyn+Q8QOD7tuT9bXWEa9ff/0123P4u+A/OBbh+hE+AEv4QQXF3wP/QZC8fkxAA5b07dtXgoOD83yOng8BwDe89tprv9lsipwRPlzSrl07KVGihNtlwKJSpUrxmgfAqEbWkY0rb8P/wgeuDw2nLtFDxCZOnGh2tYT/ozEtcF5nXcGWGTj04EBdTp3ZYK4/bpOTk+nnQsBj5MMlY8aMkYEDB8rChQtlypQpEh4e7nZJKEC8+w0MelgkgN/GyIeL9ByPAQMGyK5du8yxzJ07d3a7JLg08rF7926zpf7bb79tvTYAsI3w4QUmTJggw4YNk9q1a1+1CdXWrVtdqwvO0dOLW7Zsme31TUlJkTlz5pjQkZCQIHXq1JEdO3a4WieA/NOzt/KaSr18+bL5Ga4HhSI7pl1cphsS6QoH7QHp0qULO2D6qdatW3s+X79+vQkc8+bNM3tAaPDUA+Vq1arlao24cfrvOD9TbL/88ouVelCwdPO4rJsH1qtXT/7+979LdHS0uX3y5Elp0aIFPT454Dedi3Sq5Xe/+520b99edu7c6dkVEf75Dmn69OkmZOhhcnqK7Zo1a8wPpv79+xM8/MT48ePdLgEWXTlx8OOPP5oDQfN6Dv6D8OGS++67TzZt2mSmXHr37u12OShglStXlh49esh7770nHTp0yNf2+vA9ffr0cbsEeBmazXPGT0CX6DDctm3b8gwe33zzjdWaULDhY926dWa77b1797pdDlyiQ/TPP/+822UAriN8uGT58uXmNMRz585ddfZDYmKiWfnSrFkz1+qDs7777jv59NNPzS+fJk2aSKNGjeTdd981j/HOyL/oFKqOaH744Ydmnw914sQJ09tTtWpVWb16tdslwiH6b/fs2bNm7xadTtXb+jNdb2deyIWudoF9SUlJGc2bN88oVKhQRtGiRTOGDRuWkZKSkvHkk09mFCtWLKNnz54ZCQkJbpeJAnD27NmMDz/8MKNFixYZQUFBGW3atDG3jx075nZpuEGLFi0y/571ddWrWrVqGatWrcqIiIjIiImJyViyZInbJcJB+hrrz/DMK7fbuBpLbV3y6KOPyp49e8w+H7raRZdi3nnnnWa046WXXjKjIvB/mft7fPLJJ2YFxJXNavAtuqRSl1S/8cYbMnXqVBk+fLjcfvvtptFYR7zgX/Tn9rWudsN/ED5cEhUVZUJH8+bNzUqIyMhI+fOf/yxDhw51uzS4QEPH4sWLpVu3bm6XghugW6tv2bJFqlevbvq69CDBpUuXmhVtAP6L1S4uOXr0qFSpUsV8rmvES5YsKZ06dXK7LBSQ/Mz98gvK9+n8v57logoXLmwOEtQ+D/in/PZ0ZP6dwH8RPlyUdbmlfl6sWDFX60HBKV26dJ6NpToAqY+zGZHvW7ZsmRkBUenp6bJy5cqrdq596KGHXKoOTuLf9fVj2sVLT7/MxE6I/oG54cCQn/1b+GXkP/h3ff0IHy6ZMWNGvp7HpkUAAH9D+AAAS3QaRs/+ePDBB90uBQ69nn/605/kr3/9q1y8eFHatWsnr732mun1Qd7YZAywNByvDYh5XRwq6L/27dsnL7/8sllC//DDD7tdDhwyZswY87qGhITILbfcYo5PGDx4sNtl+QRGPlzC6ZeBZdGiRbk+tnHjRnn//ffNu6gLFy5YrQsFR3cunj9/vtnvQ08yvueee8z+Pho+ypcv73Z5cECNGjVkxIgRMnDgQHN7xYoV8sADD5jXnvOb8kb4cAk9H9BN5nRDOd3fo1evXvL666+bM2Dg2zZv3mwCx5w5c6RatWrmtX3xxRfNWU516tRxuzw4SPdx0VGt6Ohoz33Fixc397FRZN4Y53VJfkIFHfH+6dChQ2ZeWANoTEyMOcunbt26bpcFB9SvX9/s/fD444/Lhg0bzO6mSkMm/M/ly5dN2MiqaNGi7FScD4QPL6SnnuqW2zNnzjQHkcE/6MFTb775pvzv//6vNGzY0Oz/oEPx8K/RrJ49e8q9997LKEcA0ImDvn37mhGQTDp1OmjQILnppps89+lu1siO8OElzp8/L3PnzjVnQGgPQOPGjc25EPAP48aNk7feestsoz979mzp0qWL2yWhAPzwww8yffp0efbZZ828/2OPPWamXTi5OHBGsJ944glXavE19Hy4LCEhwcwPa2NapUqVzEFjeuQ274j9izaf6fI73UJdV7bkhndI/mPVqlXmzYS+pvpuWBsTn3rqKbntttvcLg1wHSMfLnnnnXfMDyYditd3R2vXrpUGDRqY+cIyZcq4XR4c1rt3b979Bpi2bduaS/+Nf/bZZ+bf+9tvv236e7T5FAhkjHy4RPd00A54XeGQ9Z2who9vv/2W+WLAB/Xo0cOMbmgjcU5hU5uLNYTo0mogkLEQ2SVvvPGGmWrRk201hFx58BQA33Pq1Cmzz4NOob766qumByQrbTQmeACMfHjFwUT6Tujzzz+X6tWry86dO819LVu2dLs0OKhbt275eh49H77vp59+kmnTppnVavq5HiqmoyHdu3fPtioCCGSED5foOyId9cgcmj179qzMmjXLBJEtW7ZI06ZNzRAuK178Q79+/fL1PP2lBf9rOl24cKEJHtrf1b9/f2nUqJHbpQGuIny4RPs8dA+PcuXKmdu6N4AOx+q2y9u3bzf7fGgYOXbsmNulArhBmW8u9BwQbUDVzamAQEb4cHHp5ZEjRzzho1SpUqbRtGrVqp7n6C552oAK36fvdn+LjoJp6IR/OXDggNn7Q69///vfZrn10qVL3S4LcBVLbb0YwcN/6C8ePbfljjvuMLsiwr/pvh7ax6VTLrqMXs/+GDBggJl+y3oOCBCoCB8u0Xe5Vy7FYx8I/6U7XurOpvouWH8B6S6I4eHhbpcFh23atMkEDt2tWAOInmCroxzt2rXj3zeQBdMuLk67dOrUydP9rieb6oZEWc8DUKx+8B+pqanm9dRfTnromC7J1HfDHTt25BeTH/271s0C9XXVbdVvvvlmt0sCvBLhwyWsfghsugRTp2J0OaY2H+oS65CQELfLwg3aunWr3HnnnW6XAXg9pl1cQqgIbPoOWUc7NPunpaW5XQ4cQvAA8ocdTgGL0y7a99GhQwdzuJguqZ4wYYIcPHiQUQ8AAYWRD8CC5557TubMmWNWOuiyWw0hERERbpcFAK6g5wOwNM2i533oUtu8mktpMAYQCBj5ACzo3bs3K1oA4P8x8gEADtHl8vk98wUIZIx8AIBD1qxZY3ay1T1c2KEYyB0jHwDgkD/96U9mGf3JkyfNJmPaXFy3bl23ywK8DkttAcAhv//972XXrl3y5ZdfmpNsW7ZsKU2bNpXJkydLcnKy2+UBXoORDwAoIOfPn5f58+fLxIkTTSg5dOiQhIaGul0W4DpGPgCgALdbj4+Pl927d5vpF/pAgP8gfACAg3R048033zS72Pbo0cOcXvz1119LQkKClChRwu3yAK/AtAsAOOT++++X1atXm5OKtdlUV70UKcKiQuBKhA8AcHAn2woVKki5cuXy3FROp2OAQEYkBwCHvPbaa26XAPgERj4AAIBVjHwAQAHYtm2b7N2713yuzaf169d3uyTAaxA+AMBBmzZtkgEDBph9PTIHlrX/4/bbb5ePPvpImjRp4naJgOtYagsADtHA0a5dO7Ok9tNPPzWNpXp98sknEhwcbB7T5wCBjp4PAHDII488IpcvX5YvvvjiqtUu+qO2W7duZqOxefPmuVYj4A0IHwDgkLJly8qSJUukcePGOT6+efNmsxfI8ePHrdcGeBOmXQDAIXqYXPny5XN9PDIy0jwHCHSEDwBwSOXKlU3DaW50m3V9DhDoCB8A4JBHH31Uhg8fLjt27Ljqse3bt8uIESOkZ8+ertQGeBN6PgDAIRcuXDArWnSEo0OHDlK7dm3TaKqn2q5YsUKaNm0qq1atkuLFi7tdKuAqwgcAOOjixYvy7rvvyuzZs7NtMqajIsOGDTNLboFAR/gAAABWscMpADjs119/leXLl3tGPmrWrCnt27c3m48BIHwAgKP++te/ylNPPSUnTpzIdn9ERITZXr1z586u1QZ4C1a7AIBDNmzYID169JBWrVrJ+vXr5ZdffjHXunXr5J577jGPJSQkuF0m4Dp6PgDAIbp7aXR0tEyZMiXHxwcOHChJSUny97//3XptgDchfACAQ8LDwyU+Pl7q1auX4+Pbtm2T1q1by6lTp6zXBngTpl0AwMFG09DQ0FwfDwsLM3uBAIGO8AEADqlRo4bZRCw3K1euNM8BAh3hAwAc0q9fP7OFek49HV999ZWMHDlS+vbt60ptgDeh5wMAHJKenm7Obvniiy/M3h5Zt1f//vvvpWvXrjJ//nwpVIj3fQhshA8AcNjcuXNz3F5dLwCEDwCw5ocffpBBgwbJP/7xD7dLAVzF2B8AWHL27FnTdAoEOsIHAACwivABAACsInwAAACrONUWABxyxx13SFBQUK6Pnz9/3mo9gLcifACAQ3QfDwC/jaW2AADAKno+AMASPVTu7bffdrsMwHWMfACAg44fPy5ff/21FCtWTNq1ayeFCxeWS5cuyQcffCBxcXFy+fJlOXHihNtlAq4ifACAQ9atWycPPvigJCcnm8bTxo0by7Rp00wvSJEiReSFF16QPn36SIkSJdwuFXAV4QMAHNKmTRuJioqSl19+WWbMmCHvvPOO1KhRQ8aMGSM9evRwuzzAaxA+AMAhZcqUkX/+859Sp04d+fXXXyUkJEQWLFggXbp0cbs0wKvQcAoADjl16pRERESYz3VqpWTJklK3bl23ywK8Dvt8AICDdu3aJUeOHDGf68Dynj17JCUlJdtz6tev71J1gHdg2gUAHFKoUCHTaJrTj9XM+/VjWlqaK/UB3oKRDwBwyIEDB9wuAfAJjHwAAACrGPkAAIds27YtX8+j5wOBjpEPALDQ85GJng+AkQ8AcAw9H0D+MPIBAACsYpMxAHDIuHHjzM6mmdavXy+pqame22fPnpXnnnvOpeoA78HIBwA4RE+wPXz4sJQrV87cDg0NlcTERKlataq5ffToUXP2Cz0fCHSMfACAQ658L8d7OyBnhA8AAGAV4QMAAFjFUlsAcNDUqVMlJCTEfH758mWZPn2656RbbTgFQMMpADjm1ltvNZuI/Rb2A0GgI3wAAACr6PkAAABW0fMBAA55//338/W8F154ocBrAbwZ0y4A4JAqVapku52UlCQVKlSQIkX++z5Pe0J++OEHF6oDvAfhAwAKSKlSpeTbb7/17HAK4D/o+QAAAFYRPgAAgFWEDwAAYBWrXQDAIcnJydlua3PpuXPnrrpfT7sFAhkNpwDgkEKFCmXb4VR/vOZ0Oy0tzaUKAe/AyAcAOGT16tVulwD4BEY+AACAVTScAgAAqwgfAADAKsIHAACwivABAACsInwAQAHQXv4TJ07IyZMn3S4F8DqEDwBw0JEjR6R3795y8803S/ny5aVcuXLm8/79+8vRo0fdLg/wCiy1BQCH6E6mDRs2NLua9urVS2rVqmVGQHbt2iWzZ882IWTr1q0SEhLidqmAq9hkDAAc8t5770nhwoVl586dUrZs2WyPjRo1Slq2bCnvv/++vPzyy67VCHgDpl0AwCFfffWVCRZXBg+l0y+xsbGyePFiV2oDvAnhAwAcsnfvXrnrrrtyfVwf27Nnj9WaAG9E+AAAB3s+Spcunevj+tiVJ9wCgYjwAQAO0eZSPdk2N3qiLT3+AKtdAMAxGjzCwsJMyMiJ/rjVkY+0tDTrtQHehNUuAOCQadOmuV0C4BMY+QAAAFbR8wEADtm0aVOeUyqpqakyb948qzUB3oiRDwBwiG4wdvjwYbOnhwoNDZXExESpWrWqua3bq0dFRdHzgYDHyAcAOOTK93I5vbfj/R5A+AAAq3JbCQMEEsIHAACwiqW2AOAgPcH2yJEjnimW7777zpxyq06cOOFydYB3oOEUABzcZCy3XUwz79ePNJwi0DHyAQAOOXDggNslAD6BkQ8AAGAVDacA4JBXXnlFLl++nOvjBw8elA4dOlitCfBGhA8AcMiMGTOkSZMmsmPHjqsemzJlitStW1eKFGG2GyB8AIBDNHTUq1dPGjduLHFxcZKenm5GO9q3by8jR46Ut99+W5YsWeJ2mYDr6PkAAIctWrRIBg4cKJGRkaYJtWnTpjJ16lSpXLmy26UBXoGRDwBwWPPmzc0IyLZt28zox6hRowgeQBaEDwBw0OzZs6VOnTomdOzevVueffZZ6dixowwbNkwuXLjgdnmAV2DaBQAc0r17d1m2bJnp9xgyZIjn/g0bNki/fv3M59OnT5cWLVq4WCXgPtquAcAhuq36v/71L6lRo0a2+++66y5JTEyUl156SVq3bi0XL150rUbAGzDyAQAO0akW3WI9L2vXrpVWrVpZqwnwRoQPAABgFdMuAOCQbt265et5CxYsKPBaAG9G+AAAh4SFhbldAuATmHYBAABWsc8HAACwimkXAHBI//798/W8jz/+uMBrAbwZ0y4A4BBdZqvbqN9xxx2S14/WhQsXWq0L8DaMfACAQ3Qrdd1eXQ+T0x1Nn3jiCQkPD3e7LMDrMPIBAA5KTU01S2l1akW3VX/ggQdkwIAB5nyXoKAgt8sDvALhAwAKyE8//WTOcpk5c6ZcvnxZdu7cKSEhIW6XBbiO1S4AUIA9IDraoe/x0tLS3C4H8BqEDwBweNpF+z46dOggt912m2zfvl0mTJggBw8eZNQD+H80nAKAQ5577jmZM2eOREdHm2W3GkIiIiLcLgvwOvR8AICD0yyVKlUyS23zai7lbBcEOkY+AMAhvXv3ZkULkA+MfAAAAKtoOAUAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AFijy1C//PJLt8sA4DLCB4BsOnfuLPfdd1+Oj/3zn/80AWLbtm3X9bUPHz4snTp1yvfz+/btK127dr2u7wXAexE+AGSjx78vX75cfv7556semzZtmjRu3Fjq169/TV/z4sWL5mNkZKQEBwc7VisA30T4AJDNgw8+KGXLljVHwWd17tw5mT9/vhmJeOyxx+SWW26RkiVLSr169cwZJlm1adNGnn/+eRk6dKg52yQmJibHaZekpCR55JFHpHTp0hIeHi5dunSRH3/80Tz2hz/8QWbMmCGLFi0y/51ea9askbZt25qvndXx48elWLFisnLlygL8PwPAKYQPANkUKVLEbBOu4SPrBsgaPPRY+CeeeEIaNWokX331lezYsUOeeeYZefLJJ2XTpk3Zvo4GBw0E69evl8mTJ1/1fS5dumRCSalSpcx0jj5PT33VKR8dKRkxYoQJJnpbp2v0uuuuu+Spp56SWbNmmdNjM3366acmDGkwAeD9CB8ArqInsu7fv1/i4+OzTbl0795dKleubIJBw4YNpWrVqjJkyBATEObNm5fta9SoUUPGjRsnNWvWNNeV5s6dK+np6TJ16lQzelK7dm3zPfToeR3h0CBSokQJM02j0zV6aZjp1q2b+e91RCSTBiXtD+FcFcA3ED4AXKVWrVpmlOHjjz82t/ft22dGJ7QfREc/3njjDRMYdKpEQ8KyZctMaMhKR0fy8u2335qvqyMf+jX00q934cIFE3xyU7x4cTPSklnb1q1bzQiMhg8AvoFTbQHkSIOGjmpMnDjRjEhUq1ZNWrduLW+99Za89957Mn78eBNAbrrpJtPbkdlUmknvz4v2kGhA+eyzz656THtO8qJTLzryok2xWptOt+iIDADfQPgAkCPtt/if//kf018xc+ZMefbZZ820hvZmaGOo9n4onTrZu3ev1KlT55q+/p133mmmXsqVKyehoaE5PkenWXSk5UoaenTVzV/+8hdT34QJE67zTwnADUy7AMiRToP07NlTYmNjTbNn5rSG9nLoUtwNGzbI7t27ZeDAgXL06NFr/vq9evUyK2E0yOiUzoEDB0yvxwsvvOBZ5nvrrbeaPUX27NkjJ06cME2qWUc/xo4da5piH374YQf/5AAKGuEDQJ5TL6dOnTKrUqKiosx9o0aNMqMWep8uqdVG0OvZCEyX6a5du1YqVapkmki14VS/n/Z8ZI6EPP3006ZZVUc5dCpGR10y6XJfXZmjH7UPBIDvCMrIupYOAHyE7geifSibN282YQiA7yB8APApOvVy8uRJs9xXp2qyjoYA8A1MuwDwKRo2KlSoYEY8ctq8DID3Y+QDAABYxcgHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAQGz6P9C/g5HeFYelAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety'] == 'PIE TYPE']\n",
+    "pie_pumpkins.plot.scatter('DayOfYear', 'Price', c='red', label='PIE TYPE')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation between Day of Year and Price for PIE TYPE: -0.2669192282197318\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Calculating the Correlation\n",
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety'] == 'PIE TYPE']\n",
+    "correlation = pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price'])\n",
+    "print('Correlation between Day of Year and Price for PIE TYPE:', correlation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 144 entries, 70 to 1630\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column      Non-Null Count  Dtype  \n",
+      "---  ------      --------------  -----  \n",
+      " 0   Month       144 non-null    int32  \n",
+      " 1   DayOfYear   144 non-null    int64  \n",
+      " 2   Variety     144 non-null    object \n",
+      " 3   City        144 non-null    object \n",
+      " 4   Package     144 non-null    object \n",
+      " 5   Low Price   144 non-null    float64\n",
+      " 6   High Price  144 non-null    float64\n",
+      " 7   Price       144 non-null    float64\n",
+      "dtypes: float64(3), int32(1), int64(1), object(3)\n",
+      "memory usage: 9.6+ KB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Paskal Sunari\\AppData\\Local\\Temp\\ipykernel_7488\\3144308612.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  pie_pumpkins.dropna(inplace=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pie_pumpkins.dropna(inplace=True)\n",
+    "pie_pumpkins.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Coefficients: [-0.01751876]\n",
+      "Intercept: 21.133734359909326\n",
+      "Score on test set: 0.10220539781539772\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "x = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1, 1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train, y_train)\n",
+    "print('Coefficients:', lin_reg.coef_)\n",
+    "print('Intercept:', lin_reg.intercept_)\n",
+    "print(\"Score on test set:\", lin_reg.score(X_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.77 (17.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model determination:  0.04460606335028361\n"
+     ]
+    }
+   ],
+   "source": [
+    "score = lin_reg.score(X_train,y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1ae7381f9d0>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, y_test, color='blue', label='Actual Prices')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1ae73a71a90>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, pred, color='red', label='Predicted Prices')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Polynomial Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score on test set: 0.12719946902474621\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "print(\"Score on test set:\", pipeline.score(X_test, y_test))\n",
+    "pred = pipeline.predict(X_test)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -106,7 +811,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.13.3"
   },
   "orig_nbformat": 2
  },