diff --git a/2-Regression/2-Data/notebook.ipynb b/2-Regression/2-Data/notebook.ipynb
index c9b9925b..31840d2f 100644
--- a/2-Regression/2-Data/notebook.ipynb
+++ b/2-Regression/2-Data/notebook.ipynb
@@ -1,5 +1,364 @@
 {
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>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": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name            0\n",
+       "Type               406\n",
+       "Package              0\n",
+       "Variety              0\n",
+       "Sub Variety        167\n",
+       "Grade              415\n",
+       "Date                 0\n",
+       "Low Price            0\n",
+       "High Price           0\n",
+       "Mostly Low          24\n",
+       "Mostly High         24\n",
+       "Origin               0\n",
+       "Origin District    396\n",
+       "Item Size          114\n",
+       "Color              145\n",
+       "Environment        415\n",
+       "Unit of Sale       404\n",
+       "Quality            415\n",
+       "Condition          415\n",
+       "Appearance         415\n",
+       "Storage            415\n",
+       "Crop               415\n",
+       "Repack               0\n",
+       "Trans Mode         415\n",
+       "Unnamed: 24        415\n",
+       "Unnamed: 25        391\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "price = new_pumpkins.Price\n",
+    "month = new_pumpkins.Month\n",
+    "plt.scatter(price, month)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 36,
+     "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": [
+    "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel(\"Pumpkin Price\")"
+   ]
+  }
+ ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -10,24 +369,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.12.0"
   },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3",
-   "language": "python"
-  }
+  "orig_nbformat": 2
  },
  "nbformat": 4,
- "nbformat_minor": 2,
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ]
-}
\ No newline at end of file
+ "nbformat_minor": 2
+}