diff --git a/challenges/01 - Flights Challenge.ipynb b/challenges/01 - Flights Challenge.ipynb
index 6f216da..e35e971 100644
--- a/challenges/01 - Flights Challenge.ipynb	
+++ b/challenges/01 - Flights Challenge.ipynb	
@@ -13,9 +13,207 @@
   },
   {
    "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>Year</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayofMonth</th>\n",
+       "      <th>DayOfWeek</th>\n",
+       "      <th>Carrier</th>\n",
+       "      <th>OriginAirportID</th>\n",
+       "      <th>OriginAirportName</th>\n",
+       "      <th>OriginCity</th>\n",
+       "      <th>OriginState</th>\n",
+       "      <th>DestAirportID</th>\n",
+       "      <th>DestAirportName</th>\n",
+       "      <th>DestCity</th>\n",
+       "      <th>DestState</th>\n",
+       "      <th>CRSDepTime</th>\n",
+       "      <th>DepDelay</th>\n",
+       "      <th>DepDel15</th>\n",
+       "      <th>CRSArrTime</th>\n",
+       "      <th>ArrDelay</th>\n",
+       "      <th>ArrDel15</th>\n",
+       "      <th>Cancelled</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>9</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>DL</td>\n",
+       "      <td>15304</td>\n",
+       "      <td>Tampa International</td>\n",
+       "      <td>Tampa</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>12478</td>\n",
+       "      <td>John F. Kennedy International</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>1539</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1824</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>9</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>WN</td>\n",
+       "      <td>14122</td>\n",
+       "      <td>Pittsburgh International</td>\n",
+       "      <td>Pittsburgh</td>\n",
+       "      <td>PA</td>\n",
+       "      <td>13232</td>\n",
+       "      <td>Chicago Midway International</td>\n",
+       "      <td>Chicago</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>710</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>740</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS</td>\n",
+       "      <td>14747</td>\n",
+       "      <td>Seattle/Tacoma International</td>\n",
+       "      <td>Seattle</td>\n",
+       "      <td>WA</td>\n",
+       "      <td>11278</td>\n",
+       "      <td>Ronald Reagan Washington National</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>DC</td>\n",
+       "      <td>810</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1614</td>\n",
+       "      <td>-7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>7</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1</td>\n",
+       "      <td>OO</td>\n",
+       "      <td>13930</td>\n",
+       "      <td>Chicago O'Hare International</td>\n",
+       "      <td>Chicago</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>11042</td>\n",
+       "      <td>Cleveland-Hopkins International</td>\n",
+       "      <td>Cleveland</td>\n",
+       "      <td>OH</td>\n",
+       "      <td>804</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1027</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>5</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "      <td>DL</td>\n",
+       "      <td>13931</td>\n",
+       "      <td>Norfolk International</td>\n",
+       "      <td>Norfolk</td>\n",
+       "      <td>VA</td>\n",
+       "      <td>10397</td>\n",
+       "      <td>Hartsfield-Jackson Atlanta International</td>\n",
+       "      <td>Atlanta</td>\n",
+       "      <td>GA</td>\n",
+       "      <td>545</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>728</td>\n",
+       "      <td>-9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year  Month  DayofMonth  DayOfWeek Carrier  OriginAirportID  \\\n",
+       "0  2013      9          16          1      DL            15304   \n",
+       "1  2013      9          23          1      WN            14122   \n",
+       "2  2013      9           7          6      AS            14747   \n",
+       "3  2013      7          22          1      OO            13930   \n",
+       "4  2013      5          16          4      DL            13931   \n",
+       "\n",
+       "              OriginAirportName  OriginCity OriginState  DestAirportID  \\\n",
+       "0           Tampa International       Tampa          FL          12478   \n",
+       "1      Pittsburgh International  Pittsburgh          PA          13232   \n",
+       "2  Seattle/Tacoma International     Seattle          WA          11278   \n",
+       "3  Chicago O'Hare International     Chicago          IL          11042   \n",
+       "4         Norfolk International     Norfolk          VA          10397   \n",
+       "\n",
+       "                            DestAirportName    DestCity DestState  CRSDepTime  \\\n",
+       "0             John F. Kennedy International    New York        NY        1539   \n",
+       "1              Chicago Midway International     Chicago        IL         710   \n",
+       "2         Ronald Reagan Washington National  Washington        DC         810   \n",
+       "3           Cleveland-Hopkins International   Cleveland        OH         804   \n",
+       "4  Hartsfield-Jackson Atlanta International     Atlanta        GA         545   \n",
+       "\n",
+       "   DepDelay  DepDel15  CRSArrTime  ArrDelay  ArrDel15  Cancelled  \n",
+       "0         4       0.0        1824        13         0          0  \n",
+       "1         3       0.0         740        22         1          0  \n",
+       "2        -3       0.0        1614        -7         0          0  \n",
+       "3        35       1.0        1027        33         1          0  \n",
+       "4        -1       0.0         728        -9         0          0  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -74,19 +272,925 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "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>DepDelay</th>\n",
+       "      <th>DepDel15</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>545</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>554</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271410</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271607</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271634</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271671</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>271885</th>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2761 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        DepDelay  DepDel15\n",
+       "171            0       NaN\n",
+       "359            0       NaN\n",
+       "429            0       NaN\n",
+       "545            0       NaN\n",
+       "554            0       NaN\n",
+       "...          ...       ...\n",
+       "271410         0       NaN\n",
+       "271607         0       NaN\n",
+       "271634         0       NaN\n",
+       "271671         0       NaN\n",
+       "271885         0       NaN\n",
+       "\n",
+       "[2761 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code to explore the data\n",
+    "#1.Clean Data\n",
+    "df_flights[df_flights.isnull().any(axis=1)][['DepDelay', 'DepDel15']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year                 0\n",
+       "Month                0\n",
+       "DayofMonth           0\n",
+       "DayOfWeek            0\n",
+       "Carrier              0\n",
+       "OriginAirportID      0\n",
+       "OriginAirportName    0\n",
+       "OriginCity           0\n",
+       "OriginState          0\n",
+       "DestAirportID        0\n",
+       "DestAirportName      0\n",
+       "DestCity             0\n",
+       "DestState            0\n",
+       "CRSDepTime           0\n",
+       "DepDelay             0\n",
+       "DepDel15             0\n",
+       "CRSArrTime           0\n",
+       "ArrDelay             0\n",
+       "ArrDel15             0\n",
+       "Cancelled            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_flights.DepDel15 = df_flights.DepDel15.fillna(0)\n",
+    "df_flights.isnull().sum()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Statistics of DepDelay and ArrDelay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum: -63.00\n",
+      "Mean: 10.35\n",
+      "Median: -1.00\n",
+      "Mode: -3.00\n",
+      "Maximum: 1425.00\n",
+      "\n",
+      "Minimum: -75.00\n",
+      "Mean: 6.50\n",
+      "Median: -3.00\n",
+      "Mode: 0.00\n",
+      "Maximum: 1440.00\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Baljo\\AppData\\Local\\Temp\\ipykernel_20244\\1062817051.py:28: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "  fig.show()\n",
+      "C:\\Users\\Baljo\\AppData\\Local\\Temp\\ipykernel_20244\\1062817051.py:28: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "  fig.show()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def show_stats(data):\n",
+    "    from matplotlib import pyplot as plt\n",
+    "    min_val = data.min()\n",
+    "    max_val = data.max()\n",
+    "    mean_val = data.mean()\n",
+    "    med_val = data.median()\n",
+    "    mod_val  = data.mode()[0]\n",
+    "    \n",
+    "    print(\"Minimum: {:.2f}\\nMean: {:.2f}\\nMedian: {:.2f}\\nMode: {:.2f}\\nMaximum: {:.2f}\\n\".format(min_val,\n",
+    "                                                                                                    mean_val,\n",
+    "                                                                                                    med_val,\n",
+    "                                                                                                    mod_val,\n",
+    "                                                                                                    max_val))\n",
+    "\n",
+    "    fig, ax = plt.subplots(2, 1, figsize=(10,4))\n",
+    "    ax[0].hist(data)\n",
+    "    ax[0].set_ylabel(\"Frequency\")\n",
+    "\n",
+    "    ax[0].axvline(x=min_val, color='gray', linestyle='dashed', linewidth=2)\n",
+    "    ax[0].axvline(x=mean_val, color='cyan', linestyle='dashed', linewidth=2)\n",
+    "    ax[0].axvline(x=med_val, color='red', linestyle='dashed', linewidth=2)\n",
+    "    ax[0].axvline(x=mod_val, color='yellow', linestyle='dashed', linewidth=2)\n",
+    "    ax[0].axvline(x=max_val, color='gray', linestyle='dashed', linewidth=2)\n",
+    "\n",
+    "    ax[1].boxplot(data, vert=False)\n",
+    "    ax[1].set_xlabel('Value')\n",
+    "    fig.suptitle(data.name)\n",
+    "    fig.show()\n",
+    "\n",
+    "\n",
+    "field = ['DepDelay','ArrDelay']\n",
+    "for col in field:\n",
+    "    show_stats(df_flights[col])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clearing Outliers in DepDelay and ArrDelay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Minimum: -11.00\n",
+      "Mean: -0.92\n",
+      "Median: -2.00\n",
+      "Mode: -3.00\n",
+      "Maximum: 16.00\n",
+      "\n",
+      "Minimum: -32.00\n",
+      "Mean: -5.03\n",
+      "Median: -6.00\n",
+      "Mode: 0.00\n",
+      "Maximum: 37.00\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Baljo\\AppData\\Local\\Temp\\ipykernel_20244\\1062817051.py:28: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "  fig.show()\n",
+      "C:\\Users\\Baljo\\AppData\\Local\\Temp\\ipykernel_20244\\1062817051.py:28: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "  fig.show()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ArrDelayq01 = df_flights.ArrDelay.quantile(.01)\n",
+    "ArrDelayq90 = df_flights.ArrDelay.quantile(.90)\n",
+    "df_flights = df_flights[df_flights.ArrDelay < ArrDelayq90]\n",
+    "df_flights = df_flights[df_flights.ArrDelay > ArrDelayq01]\n",
+    "\n",
+    "DepDelayq01 = df_flights.DepDelay.quantile(.01)\n",
+    "DepDelayq90 = df_flights.DepDelay.quantile(.90)\n",
+    "df_flights = df_flights[df_flights.DepDelay < DepDelayq90]\n",
+    "df_flights = df_flights[df_flights.DepDelay > DepDelayq01]\n",
+    "\n",
+    "for col in field:\n",
+    "    show_stats(df_flights[col])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mean of Departure and Arrival Delay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean of DepDelay: -0.92\n",
+      "Mean of ArrDelay: -5.03\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_flights.describe()\n",
+    "#Mean\n",
+    "print('Mean of DepDelay: {:.2f}\\nMean of ArrDelay: {:.2f}\\n'.format(df_flights.DepDelay.mean(), df_flights.ArrDelay.mean()))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Carrier Performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Baljo\\AppData\\Local\\Temp\\ipykernel_20244\\12332509.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
+      "  print(df_flights.groupby(df_flights.Carrier)['DepDelay','ArrDelay'].mean())\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         DepDelay  ArrDelay\n",
+      "Carrier                    \n",
+      "9E      -2.852246 -7.496322\n",
+      "AA      -0.850789 -5.879219\n",
+      "AS      -3.078139 -3.583845\n",
+      "B6      -2.357754 -5.810046\n",
+      "DL      -1.575584 -6.611133\n",
+      "EV      -2.312638 -6.813485\n",
+      "F9      -0.512635 -0.502527\n",
+      "FL      -0.385143 -3.954649\n",
+      "HA      -2.960716 -2.285384\n",
+      "MQ      -1.730151 -4.303297\n",
+      "OO      -2.525729 -4.289238\n",
+      "UA      -0.338885 -7.606653\n",
+      "US      -2.424986 -3.911790\n",
+      "VX      -0.926741 -5.303501\n",
+      "WN       1.892396 -2.884136\n",
+      "YV      -2.440316 -3.818073\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(df_flights.groupby(df_flights.Carrier)['DepDelay','ArrDelay'].mean())\n",
+    "for col in field:\n",
+    "    df_flights.boxplot(column=col, by=\"Carrier\", figsize=(10,8))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Difference in Days of the week"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Baljo\\AppData\\Local\\Temp\\ipykernel_20244\\1347591794.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
+      "  print(df_flights.groupby(df_flights.DayOfWeek)['DepDelay','ArrDelay'].mean())\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           DepDelay  ArrDelay\n",
+      "DayOfWeek                    \n",
+      "1         -0.839550 -4.918798\n",
+      "2         -1.227523 -5.571680\n",
+      "3         -1.124567 -4.750124\n",
+      "4         -0.539928 -3.954965\n",
+      "5         -0.609149 -4.448380\n",
+      "6         -1.162744 -6.244790\n",
+      "7         -0.962510 -5.509348\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(df_flights.groupby(df_flights.DayOfWeek)['DepDelay','ArrDelay'].mean())\n",
+    "for col in field:\n",
+    "    df_flights.boxplot(column=col, by='DayOfWeek',figsize=(8,8))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Airports with the Highest Average of Delays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "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>DepDelay</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OriginAirportName</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Chicago Midway International</th>\n",
+       "      <td>2.365960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dallas Love Field</th>\n",
+       "      <td>2.148798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>William P Hobby</th>\n",
+       "      <td>1.561927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Metropolitan Oakland International</th>\n",
+       "      <td>0.964853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Denver International</th>\n",
+       "      <td>0.807272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Memphis International</th>\n",
+       "      <td>-2.962737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tucson International</th>\n",
+       "      <td>-2.989154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ted Stevens Anchorage International</th>\n",
+       "      <td>-3.149758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Richmond International</th>\n",
+       "      <td>-3.198073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Long Beach Airport</th>\n",
+       "      <td>-3.447844</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>70 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     DepDelay\n",
+       "OriginAirportName                            \n",
+       "Chicago Midway International         2.365960\n",
+       "Dallas Love Field                    2.148798\n",
+       "William P Hobby                      1.561927\n",
+       "Metropolitan Oakland International   0.964853\n",
+       "Denver International                 0.807272\n",
+       "...                                       ...\n",
+       "Memphis International               -2.962737\n",
+       "Tucson International                -2.989154\n",
+       "Ted Stevens Anchorage International -3.149758\n",
+       "Richmond International              -3.198073\n",
+       "Long Beach Airport                  -3.447844\n",
+       "\n",
+       "[70 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "airport_flights = df_flights.groupby(df_flights.OriginAirportName)\n",
+    "sorted_airport_flights = airport_flights.DepDelay.mean()\n",
+    "sorted_airport_flights = pd.DataFrame(sorted_airport_flights).sort_values('DepDelay', ascending=False)\n",
+    "sorted_airport_flights"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your code to explore the data"
+    "### Arival Time Based on Delay Time"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'ArrDelay'}, xlabel='DepDel15'>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_flights.boxplot(column=\"ArrDelay\", by=\"DepDel15\", figsize=(12,10))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Route With the Most Late Arrivals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "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>ArrDel15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Route</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>San Francisco International -&gt; Los Angeles International</th>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Los Angeles International -&gt; San Francisco International</th>\n",
+       "      <td>69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LaGuardia -&gt; Hartsfield-Jackson Atlanta International</th>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Los Angeles International -&gt; John F. Kennedy International</th>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LaGuardia -&gt; Charlotte Douglas International</th>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Logan International -&gt; Austin - Bergstrom International</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Logan International -&gt; Memphis International</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Logan International -&gt; Port Columbus International</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>San Diego International -&gt; Cincinnati/Northern Kentucky International</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Louis Armstrong New Orleans International -&gt; San Francisco International</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2479 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    ArrDel15\n",
+       "Route                                                       \n",
+       "San Francisco International -> Los Angeles Inte...        90\n",
+       "Los Angeles International -> San Francisco Inte...        69\n",
+       "LaGuardia -> Hartsfield-Jackson Atlanta Interna...        68\n",
+       "Los Angeles International -> John F. Kennedy In...        52\n",
+       "LaGuardia -> Charlotte Douglas International              51\n",
+       "...                                                      ...\n",
+       "Logan International -> Austin - Bergstrom Inter...         0\n",
+       "Logan International -> Memphis International               0\n",
+       "Logan International -> Port Columbus International         0\n",
+       "San Diego International -> Cincinnati/Northern ...         0\n",
+       "Louis Armstrong New Orleans International -> Sa...         0\n",
+       "\n",
+       "[2479 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "destination_route = pd.Series(df_flights['OriginAirportName'] + \" -> \" + df_flights['DestAirportName'])\n",
+    "df_flights = pd.concat([df_flights, destination_route.rename(\"Route\")], axis = 1)\n",
+    "route = df_flights.groupby(df_flights.Route)\n",
+    "pd.DataFrame(route['ArrDel15'].sum()).sort_values('ArrDel15', ascending=False)\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "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>ArrDelay</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Route</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Louis Armstrong New Orleans International -&gt; Ronald Reagan Washington National</th>\n",
+       "      <td>24.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cleveland-Hopkins International -&gt; Palm Beach International</th>\n",
+       "      <td>18.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>John F. Kennedy International -&gt; Louisville International-Standiford Field</th>\n",
+       "      <td>18.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cleveland-Hopkins International -&gt; Philadelphia International</th>\n",
+       "      <td>12.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Memphis International -&gt; Denver International</th>\n",
+       "      <td>9.758621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lambert-St. Louis International -&gt; Cleveland-Hopkins International</th>\n",
+       "      <td>-20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Eppley Airfield -&gt; LaGuardia</th>\n",
+       "      <td>-20.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Denver International -&gt; Kahului Airport</th>\n",
+       "      <td>-22.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Jacksonville International -&gt; Chicago Midway International</th>\n",
+       "      <td>-24.125000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Indianapolis International -&gt; Logan International</th>\n",
+       "      <td>-26.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2479 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                     ArrDelay\n",
+       "Route                                                        \n",
+       "Louis Armstrong New Orleans International -> Ro...  24.500000\n",
+       "Cleveland-Hopkins International -> Palm Beach I...  18.000000\n",
+       "John F. Kennedy International -> Louisville Int...  18.000000\n",
+       "Cleveland-Hopkins International -> Philadelphia...  12.800000\n",
+       "Memphis International -> Denver International        9.758621\n",
+       "...                                                       ...\n",
+       "Lambert-St. Louis International -> Cleveland-Ho... -20.000000\n",
+       "Eppley Airfield -> LaGuardia                       -20.750000\n",
+       "Denver International -> Kahului Airport            -22.666667\n",
+       "Jacksonville International -> Chicago Midway In... -24.125000\n",
+       "Indianapolis International -> Logan International  -26.000000\n",
+       "\n",
+       "[2479 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(route['ArrDelay'].mean()).sort_values('ArrDelay', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.6 - AzureML",
+   "display_name": "my-rdkit-env",
    "language": "python",
-   "name": "python3-azureml"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -98,7 +1202,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.10.4 | packaged by conda-forge | (main, Mar 24 2022, 17:32:50) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "807168bb732b5387acfde91eb5446be5c6704514c5e9b40bc6c26e7b4ed452d6"
+   }
   }
  },
  "nbformat": 4,
diff --git a/challenges/02 - Real Estate Regression Challenge.ipynb b/challenges/02 - Real Estate Regression Challenge.ipynb
index 4f17228..c980e93 100644
--- a/challenges/02 - Real Estate Regression Challenge.ipynb	
+++ b/challenges/02 - Real Estate Regression Challenge.ipynb	
@@ -23,11 +23,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
-   "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>transaction_date</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>transit_distance</th>\n",
+       "      <th>local_convenience_stores</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>price_per_unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2012.917</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>84.87882</td>\n",
+       "      <td>10</td>\n",
+       "      <td>24.98298</td>\n",
+       "      <td>121.54024</td>\n",
+       "      <td>37.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2012.917</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>306.59470</td>\n",
+       "      <td>9</td>\n",
+       "      <td>24.98034</td>\n",
+       "      <td>121.53951</td>\n",
+       "      <td>42.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2013.583</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>561.98450</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.98746</td>\n",
+       "      <td>121.54391</td>\n",
+       "      <td>47.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2013.500</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>561.98450</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.98746</td>\n",
+       "      <td>121.54391</td>\n",
+       "      <td>54.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012.833</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>390.56840</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.97937</td>\n",
+       "      <td>121.54245</td>\n",
+       "      <td>43.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   transaction_date  house_age  transit_distance  local_convenience_stores  \\\n",
+       "0          2012.917       32.0          84.87882                        10   \n",
+       "1          2012.917       19.5         306.59470                         9   \n",
+       "2          2013.583       13.3         561.98450                         5   \n",
+       "3          2013.500       13.3         561.98450                         5   \n",
+       "4          2012.833        5.0         390.56840                         5   \n",
+       "\n",
+       "   latitude  longitude  price_per_unit  \n",
+       "0  24.98298  121.54024            37.9  \n",
+       "1  24.98034  121.53951            42.2  \n",
+       "2  24.98746  121.54391            47.3  \n",
+       "3  24.98746  121.54391            54.8  \n",
+       "4  24.97937  121.54245            43.1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -64,8 +170,579 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "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>transaction_date</th>\n",
+       "      <th>house_age</th>\n",
+       "      <th>transit_distance</th>\n",
+       "      <th>local_convenience_stores</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>price_per_unit</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2012.917</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>84.87882</td>\n",
+       "      <td>10</td>\n",
+       "      <td>24.98298</td>\n",
+       "      <td>121.54024</td>\n",
+       "      <td>37.9</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2012.917</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>306.59470</td>\n",
+       "      <td>9</td>\n",
+       "      <td>24.98034</td>\n",
+       "      <td>121.53951</td>\n",
+       "      <td>42.2</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2013.583</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>561.98450</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.98746</td>\n",
+       "      <td>121.54391</td>\n",
+       "      <td>47.3</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2013.500</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>561.98450</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.98746</td>\n",
+       "      <td>121.54391</td>\n",
+       "      <td>54.8</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2012.833</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>390.56840</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.97937</td>\n",
+       "      <td>121.54245</td>\n",
+       "      <td>43.1</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>2013.000</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>4082.01500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24.94155</td>\n",
+       "      <td>121.50381</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>2012.667</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>90.45606</td>\n",
+       "      <td>9</td>\n",
+       "      <td>24.97433</td>\n",
+       "      <td>121.54310</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>2013.250</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>390.96960</td>\n",
+       "      <td>7</td>\n",
+       "      <td>24.97923</td>\n",
+       "      <td>121.53986</td>\n",
+       "      <td>40.6</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>2013.000</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>104.81010</td>\n",
+       "      <td>5</td>\n",
+       "      <td>24.96674</td>\n",
+       "      <td>121.54067</td>\n",
+       "      <td>52.5</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>2013.500</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>90.45606</td>\n",
+       "      <td>9</td>\n",
+       "      <td>24.97433</td>\n",
+       "      <td>121.54310</td>\n",
+       "      <td>63.9</td>\n",
+       "      <td>2013</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>408 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     transaction_date  house_age  transit_distance  local_convenience_stores  \\\n",
+       "0            2012.917       32.0          84.87882                        10   \n",
+       "1            2012.917       19.5         306.59470                         9   \n",
+       "2            2013.583       13.3         561.98450                         5   \n",
+       "3            2013.500       13.3         561.98450                         5   \n",
+       "4            2012.833        5.0         390.56840                         5   \n",
+       "..                ...        ...               ...                       ...   \n",
+       "409          2013.000       13.7        4082.01500                         0   \n",
+       "410          2012.667        5.6          90.45606                         9   \n",
+       "411          2013.250       18.8         390.96960                         7   \n",
+       "412          2013.000        8.1         104.81010                         5   \n",
+       "413          2013.500        6.5          90.45606                         9   \n",
+       "\n",
+       "     latitude  longitude  price_per_unit  Year  \n",
+       "0    24.98298  121.54024            37.9  2012  \n",
+       "1    24.98034  121.53951            42.2  2012  \n",
+       "2    24.98746  121.54391            47.3  2013  \n",
+       "3    24.98746  121.54391            54.8  2013  \n",
+       "4    24.97937  121.54245            43.1  2012  \n",
+       "..        ...        ...             ...   ...  \n",
+       "409  24.94155  121.50381            15.4  2013  \n",
+       "410  24.97433  121.54310            50.0  2012  \n",
+       "411  24.97923  121.53986            40.6  2013  \n",
+       "412  24.96674  121.54067            52.5  2013  \n",
+       "413  24.97433  121.54310            63.9  2013  \n",
+       "\n",
+       "[408 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code to explore data and train a regression model\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "data['Year'] = data['transaction_date'].astype(str).str[:4]\n",
+    "X,y = ['Year', 'house_age', 'transit_distance', 'local_convenience_stores', 'latitude', 'longitude'],['price_per_unit']\n",
+    "# data = data.drop('Year', axis=1)\n",
+    "data.Year = data.Year.astype(int)\n",
+    "data"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find Outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Baljo\\AppData\\Local\\Temp/ipykernel_3396/119626670.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "  fig.show()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 648x864 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "fig,ax = plt.subplots(2,1, figsize = (9,12))\n",
+    "ax[0].hist(data[y[0]], bins=100)\n",
+    "ax[0].set_ylabel('Frequency')\n",
+    "ax[0].axvline(data[y[0]].mean(), color='cyan', linestyle='dashed',linewidth=2)\n",
+    "ax[1].boxplot(data[y[0]], vert=False)\n",
+    "ax[1].set_xlabel(\"Unit Price\")\n",
+    "fig.show()\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Remove the Outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Baljo\\AppData\\Local\\Temp/ipykernel_3396/1064923321.py:10: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+      "  fig.show()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 648x864 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data = data[data.price_per_unit < 70]\n",
+    "\n",
+    "fig,ax = plt.subplots(2,1, figsize = (9,12))\n",
+    "ax[0].hist(data[y[0]], bins=100)\n",
+    "ax[0].set_ylabel('Frequency')\n",
+    "ax[0].axvline(data[y[0]].mean(), color='cyan', linestyle='dashed',linewidth=2)\n",
+    "ax[0].axvline(data[y[0]].median(), color='red', linestyle='dashed',linewidth=2)\n",
+    "ax[1].boxplot(data[y[0]], vert=False)\n",
+    "ax[1].set_xlabel(\"Unit Price\")\n",
+    "fig.show()\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Label vs Feature Correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "label = data[y[0]]\n",
+    "\n",
+    "for col in X:\n",
+    "    correlation = data[col].corr(label)\n",
+    "    fig = plt.figure(figsize=(10,9))\n",
+    "    plt.scatter(x=data[col], y=label)\n",
+    "    plt.ylabel('Frequency')\n",
+    "    plt.xlabel(col)\n",
+    "    plt.title(y[0] + \" VS \" + col + \": \" + str(correlation))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Label Cateogorical and Numerical Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#From the plots above, Year and local_convenience_stores are categorical\n",
+    "for col in data[['Year','local_convenience_stores']]:\n",
+    "    fig = plt.figure(figsize=(10,9))\n",
+    "    ax = fig.gca()\n",
+    "    data.boxplot(column = 'price_per_unit', by=col, ax=ax)\n",
+    "plt.show()    "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Split Data for Training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# Your code to explore data and train a regression model"
+    "from sklearn.model_selection import train_test_split\n",
+    "#Features and Label we will use\n",
+    "X, y = data[data.columns[1:-2]].values, data[data.columns[-2]].values\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=0)\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pipeline(steps=[('preprocessor',\n",
+      "                 ColumnTransformer(transformers=[('num',\n",
+      "                                                  Pipeline(steps=[('scaler',\n",
+      "                                                                   StandardScaler())]),\n",
+      "                                                  [0, 1, 3, 4])])),\n",
+      "                ('regressor', RandomForestRegressor())])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import StandardScaler, OrdinalEncoder\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "import numpy as np\n",
+    "\n",
+    "numeric_feat = [0,1,3,4]\n",
+    "numeric_transform = Pipeline(steps=[('scaler',StandardScaler())])\n",
+    "\n",
+    "preprocess = ColumnTransformer(transformers=[('num', numeric_transform, numeric_feat),])\n",
+    "\n",
+    "pipeline = Pipeline(steps=[('preprocessor', preprocess), ('regressor', RandomForestRegressor())])\n",
+    "\n",
+    "model = pipeline.fit(X_train, (y_train))\n",
+    "\n",
+    "print(model)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use and Predict Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE:  36.74076207519676\n",
+      "RMSE:  6.0614158474070035\n",
+      "R2:  0.6864894176615164\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "%matplotlib inline\n",
+    "\n",
+    "predictions = model.predict(X_test)\n",
+    "mse = mean_squared_error(y_test, predictions)\n",
+    "rmse = np.sqrt(mse)\n",
+    "r2 = r2_score(y_test, predictions)\n",
+    "print(\"MSE: \", mse)\n",
+    "print(\"RMSE: \", rmse)\n",
+    "print(\"R2: \", r2)\n",
+    "\n",
+    "plt.scatter(y_test, predictions)\n",
+    "plt.xlabel('Actual Labels')\n",
+    "plt.ylabel('Predicted Labels')\n",
+    "plt.title('Predictions vs Actuals')\n",
+    "z = np.polyfit(y_test, predictions, 1)\n",
+    "p = np.poly1d(z)\n",
+    "plt.plot(y_test,p(y_test), color='red')\n",
+    "plt.show()"
    ]
   },
   {
@@ -84,19 +761,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 105,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predictions:\n",
+      "49.2\n",
+      "16.85\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code to use the trained model"
+    "# Your code to use the trained model\n",
+    "import joblib\n",
+    "\n",
+    "filename = './real_estate_model.pkl'\n",
+    "joblib.dump(model, filename)\n",
+    "\n",
+    "loaded_model = joblib.load(filename)\n",
+    "\n",
+    "X_new = np.array([[16.2,289.3248,5,24.98203,121.54348],\n",
+    "                  [13.6,4082.015,0,24.94155,121.5038]])\n",
+    "\n",
+    "results = loaded_model.predict(X_new)\n",
+    "print('Predictions:')\n",
+    "for prediction in results:\n",
+    "    print(round(prediction,2))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.6 - AzureML",
+   "display_name": "base",
    "language": "python",
-   "name": "python3-azureml"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -108,7 +816,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "4bf75b2276798c27d66bd155d4cc4baf6547c7c6b4bb7d9d80b2113705f02767"
+   }
   }
  },
  "nbformat": 4,
diff --git a/challenges/real_estate_model.pkl b/challenges/real_estate_model.pkl
new file mode 100644
index 0000000..a857ab7
Binary files /dev/null and b/challenges/real_estate_model.pkl differ