diff --git a/your-code/challenge-1.ipynb b/your-code/challenge-1.ipynb
index 9df5e53..d28dd13 100644
--- a/your-code/challenge-1.ipynb
+++ b/your-code/challenge-1.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,11 +35,623 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "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>Date</th>\n",
+       "      <th>Calorie burned</th>\n",
+       "      <th>Steps</th>\n",
+       "      <th>Distance</th>\n",
+       "      <th>Floors</th>\n",
+       "      <th>Minutes Sedentary</th>\n",
+       "      <th>Minutes Lightly Active</th>\n",
+       "      <th>Minutes Fairly Active</th>\n",
+       "      <th>Minutes Very Active</th>\n",
+       "      <th>Activity Calories</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Distance_miles</th>\n",
+       "      <th>Days</th>\n",
+       "      <th>Days_encoded</th>\n",
+       "      <th>Work_or_Weekend</th>\n",
+       "      <th>Hours Sleep</th>\n",
+       "      <th>Sleep efficiency</th>\n",
+       "      <th>Yesterday_sleep</th>\n",
+       "      <th>Yesterday_sleep_efficiency</th>\n",
+       "      <th>Months</th>\n",
+       "      <th>Months_encoded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2015-05-08</td>\n",
+       "      <td>1934</td>\n",
+       "      <td>905</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.355</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.403891</td>\n",
+       "      <td>Friday</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>92.086331</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015-05-09</td>\n",
+       "      <td>3631</td>\n",
+       "      <td>18925</td>\n",
+       "      <td>14.11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>611.000</td>\n",
+       "      <td>316</td>\n",
+       "      <td>61</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2248</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.767545</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.566667</td>\n",
+       "      <td>92.464358</td>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>92.086331</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015-05-10</td>\n",
+       "      <td>3204</td>\n",
+       "      <td>14228</td>\n",
+       "      <td>10.57</td>\n",
+       "      <td>1</td>\n",
+       "      <td>602.000</td>\n",
+       "      <td>226</td>\n",
+       "      <td>14</td>\n",
+       "      <td>77</td>\n",
+       "      <td>1719</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.567891</td>\n",
+       "      <td>Sunday</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.450000</td>\n",
+       "      <td>88.761468</td>\n",
+       "      <td>7.566667</td>\n",
+       "      <td>92.464358</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015-05-11</td>\n",
+       "      <td>2673</td>\n",
+       "      <td>6756</td>\n",
+       "      <td>5.02</td>\n",
+       "      <td>8</td>\n",
+       "      <td>749.000</td>\n",
+       "      <td>190</td>\n",
+       "      <td>23</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9620</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.119282</td>\n",
+       "      <td>Monday</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.183333</td>\n",
+       "      <td>88.857143</td>\n",
+       "      <td>6.450000</td>\n",
+       "      <td>88.761468</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015-05-12</td>\n",
+       "      <td>2495</td>\n",
+       "      <td>502</td>\n",
+       "      <td>3.73</td>\n",
+       "      <td>1</td>\n",
+       "      <td>876.000</td>\n",
+       "      <td>171</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7360</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.317714</td>\n",
+       "      <td>Tuesday</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.783333</td>\n",
+       "      <td>82.892057</td>\n",
+       "      <td>5.183333</td>\n",
+       "      <td>88.857143</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</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",
+       "      <td>...</td>\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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>2016-05-03</td>\n",
+       "      <td>3796</td>\n",
+       "      <td>18588</td>\n",
+       "      <td>14.13</td>\n",
+       "      <td>16</td>\n",
+       "      <td>599.000</td>\n",
+       "      <td>275</td>\n",
+       "      <td>49</td>\n",
+       "      <td>79</td>\n",
+       "      <td>236</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.779972</td>\n",
+       "      <td>Tuesday</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.283333</td>\n",
+       "      <td>91.951220</td>\n",
+       "      <td>5.233333</td>\n",
+       "      <td>90.229885</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>2016-05-04</td>\n",
+       "      <td>3525</td>\n",
+       "      <td>16382</td>\n",
+       "      <td>12.39</td>\n",
+       "      <td>16</td>\n",
+       "      <td>684.000</td>\n",
+       "      <td>333</td>\n",
+       "      <td>10</td>\n",
+       "      <td>55</td>\n",
+       "      <td>2075</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.698787</td>\n",
+       "      <td>Wednesday</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.766667</td>\n",
+       "      <td>95.081967</td>\n",
+       "      <td>6.283333</td>\n",
+       "      <td>91.951220</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>2016-05-05</td>\n",
+       "      <td>3649</td>\n",
+       "      <td>21913</td>\n",
+       "      <td>16.40</td>\n",
+       "      <td>19</td>\n",
+       "      <td>701.000</td>\n",
+       "      <td>287</td>\n",
+       "      <td>29</td>\n",
+       "      <td>90</td>\n",
+       "      <td>2249</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.190484</td>\n",
+       "      <td>Thursday</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.666667</td>\n",
+       "      <td>88.888889</td>\n",
+       "      <td>6.766667</td>\n",
+       "      <td>95.081967</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>2016-05-06</td>\n",
+       "      <td>3539</td>\n",
+       "      <td>19023</td>\n",
+       "      <td>14.79</td>\n",
+       "      <td>15</td>\n",
+       "      <td>575.000</td>\n",
+       "      <td>298</td>\n",
+       "      <td>8</td>\n",
+       "      <td>85</td>\n",
+       "      <td>2112</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.190077</td>\n",
+       "      <td>Friday</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.166667</td>\n",
+       "      <td>89.805825</td>\n",
+       "      <td>4.666667</td>\n",
+       "      <td>88.888889</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366</th>\n",
+       "      <td>2016-05-07</td>\n",
+       "      <td>304</td>\n",
+       "      <td>9526</td>\n",
+       "      <td>7.08</td>\n",
+       "      <td>2</td>\n",
+       "      <td>564.000</td>\n",
+       "      <td>370</td>\n",
+       "      <td>13</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1604</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.399307</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.366667</td>\n",
+       "      <td>89.165187</td>\n",
+       "      <td>6.166667</td>\n",
+       "      <td>89.805825</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>367 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Date  Calorie burned  Steps  Distance  Floors  Minutes Sedentary  \\\n",
+       "0    2015-05-08            1934    905      0.65       0              1.355   \n",
+       "1    2015-05-09            3631  18925     14.11       4            611.000   \n",
+       "2    2015-05-10            3204  14228     10.57       1            602.000   \n",
+       "3    2015-05-11            2673   6756      5.02       8            749.000   \n",
+       "4    2015-05-12            2495    502      3.73       1            876.000   \n",
+       "..          ...             ...    ...       ...     ...                ...   \n",
+       "362  2016-05-03            3796  18588     14.13      16            599.000   \n",
+       "363  2016-05-04            3525  16382     12.39      16            684.000   \n",
+       "364  2016-05-05            3649  21913     16.40      19            701.000   \n",
+       "365  2016-05-06            3539  19023     14.79      15            575.000   \n",
+       "366  2016-05-07             304   9526      7.08       2            564.000   \n",
+       "\n",
+       "     Minutes Lightly Active  Minutes Fairly Active  Minutes Very Active  \\\n",
+       "0                        46                      0                    0   \n",
+       "1                       316                     61                   60   \n",
+       "2                       226                     14                   77   \n",
+       "3                       190                     23                    4   \n",
+       "4                       171                      0                    0   \n",
+       "..                      ...                    ...                  ...   \n",
+       "362                     275                     49                   79   \n",
+       "363                     333                     10                   55   \n",
+       "364                     287                     29                   90   \n",
+       "365                     298                      8                   85   \n",
+       "366                     370                     13                   10   \n",
+       "\n",
+       "     Activity Calories  ...  Distance_miles       Days  Days_encoded  \\\n",
+       "0                 1680  ...        0.403891     Friday           4.0   \n",
+       "1                 2248  ...        8.767545   Saturday           5.0   \n",
+       "2                 1719  ...        6.567891     Sunday           6.0   \n",
+       "3                 9620  ...        3.119282     Monday           0.0   \n",
+       "4                 7360  ...        2.317714    Tuesday           1.0   \n",
+       "..                 ...  ...             ...        ...           ...   \n",
+       "362                236  ...        8.779972    Tuesday           1.0   \n",
+       "363               2075  ...        7.698787  Wednesday           2.0   \n",
+       "364               2249  ...       10.190484   Thursday           3.0   \n",
+       "365               2112  ...        9.190077     Friday           4.0   \n",
+       "366               1604  ...        4.399307   Saturday           5.0   \n",
+       "\n",
+       "     Work_or_Weekend  Hours Sleep Sleep efficiency  Yesterday_sleep  \\\n",
+       "0                  1     6.400000        92.086331         0.000000   \n",
+       "1                  0     7.566667        92.464358         6.400000   \n",
+       "2                  0     6.450000        88.761468         7.566667   \n",
+       "3                  1     5.183333        88.857143         6.450000   \n",
+       "4                  1     6.783333        82.892057         5.183333   \n",
+       "..               ...          ...              ...              ...   \n",
+       "362                1     6.283333        91.951220         5.233333   \n",
+       "363                1     6.766667        95.081967         6.283333   \n",
+       "364                1     4.666667        88.888889         6.766667   \n",
+       "365                1     6.166667        89.805825         4.666667   \n",
+       "366                0     8.366667        89.165187         6.166667   \n",
+       "\n",
+       "     Yesterday_sleep_efficiency  Months  Months_encoded  \n",
+       "0                      0.000000     May               5  \n",
+       "1                     92.086331     May               5  \n",
+       "2                     92.464358     May               5  \n",
+       "3                     88.761468     May               5  \n",
+       "4                     88.857143     May               5  \n",
+       "..                          ...     ...             ...  \n",
+       "362                   90.229885     May               5  \n",
+       "363                   91.951220     May               5  \n",
+       "364                   95.081967     May               5  \n",
+       "365                   88.888889     May               5  \n",
+       "366                   89.805825     May               5  \n",
+       "\n",
+       "[367 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "fitbit = pd.read_csv(\"..\\data\\Fitbit2.csv\")\n",
+    "fitbit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>Date</th>\n",
+       "      <th>Calorie burned</th>\n",
+       "      <th>Steps</th>\n",
+       "      <th>Distance</th>\n",
+       "      <th>Floors</th>\n",
+       "      <th>Minutes Sedentary</th>\n",
+       "      <th>Minutes Lightly Active</th>\n",
+       "      <th>Minutes Fairly Active</th>\n",
+       "      <th>Minutes Very Active</th>\n",
+       "      <th>Activity Calories</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Distance_miles</th>\n",
+       "      <th>Days</th>\n",
+       "      <th>Days_encoded</th>\n",
+       "      <th>Work_or_Weekend</th>\n",
+       "      <th>Hours Sleep</th>\n",
+       "      <th>Sleep efficiency</th>\n",
+       "      <th>Yesterday_sleep</th>\n",
+       "      <th>Yesterday_sleep_efficiency</th>\n",
+       "      <th>Months</th>\n",
+       "      <th>Months_encoded</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2015-05-08</td>\n",
+       "      <td>1934</td>\n",
+       "      <td>905</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.355</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1680</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.403891</td>\n",
+       "      <td>Friday</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>92.086331</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015-05-09</td>\n",
+       "      <td>3631</td>\n",
+       "      <td>18925</td>\n",
+       "      <td>14.11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>611.000</td>\n",
+       "      <td>316</td>\n",
+       "      <td>61</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2248</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.767545</td>\n",
+       "      <td>Saturday</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.566667</td>\n",
+       "      <td>92.464358</td>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>92.086331</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015-05-10</td>\n",
+       "      <td>3204</td>\n",
+       "      <td>14228</td>\n",
+       "      <td>10.57</td>\n",
+       "      <td>1</td>\n",
+       "      <td>602.000</td>\n",
+       "      <td>226</td>\n",
+       "      <td>14</td>\n",
+       "      <td>77</td>\n",
+       "      <td>1719</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.567891</td>\n",
+       "      <td>Sunday</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.450000</td>\n",
+       "      <td>88.761468</td>\n",
+       "      <td>7.566667</td>\n",
+       "      <td>92.464358</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015-05-11</td>\n",
+       "      <td>2673</td>\n",
+       "      <td>6756</td>\n",
+       "      <td>5.02</td>\n",
+       "      <td>8</td>\n",
+       "      <td>749.000</td>\n",
+       "      <td>190</td>\n",
+       "      <td>23</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9620</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.119282</td>\n",
+       "      <td>Monday</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.183333</td>\n",
+       "      <td>88.857143</td>\n",
+       "      <td>6.450000</td>\n",
+       "      <td>88.761468</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015-05-12</td>\n",
+       "      <td>2495</td>\n",
+       "      <td>502</td>\n",
+       "      <td>3.73</td>\n",
+       "      <td>1</td>\n",
+       "      <td>876.000</td>\n",
+       "      <td>171</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7360</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.317714</td>\n",
+       "      <td>Tuesday</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.783333</td>\n",
+       "      <td>82.892057</td>\n",
+       "      <td>5.183333</td>\n",
+       "      <td>88.857143</td>\n",
+       "      <td>May</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date  Calorie burned  Steps  Distance  Floors  Minutes Sedentary  \\\n",
+       "0  2015-05-08            1934    905      0.65       0              1.355   \n",
+       "1  2015-05-09            3631  18925     14.11       4            611.000   \n",
+       "2  2015-05-10            3204  14228     10.57       1            602.000   \n",
+       "3  2015-05-11            2673   6756      5.02       8            749.000   \n",
+       "4  2015-05-12            2495    502      3.73       1            876.000   \n",
+       "\n",
+       "   Minutes Lightly Active  Minutes Fairly Active  Minutes Very Active  \\\n",
+       "0                      46                      0                    0   \n",
+       "1                     316                     61                   60   \n",
+       "2                     226                     14                   77   \n",
+       "3                     190                     23                    4   \n",
+       "4                     171                      0                    0   \n",
+       "\n",
+       "   Activity Calories  ...  Distance_miles      Days  Days_encoded  \\\n",
+       "0               1680  ...        0.403891    Friday           4.0   \n",
+       "1               2248  ...        8.767545  Saturday           5.0   \n",
+       "2               1719  ...        6.567891    Sunday           6.0   \n",
+       "3               9620  ...        3.119282    Monday           0.0   \n",
+       "4               7360  ...        2.317714   Tuesday           1.0   \n",
+       "\n",
+       "   Work_or_Weekend  Hours Sleep Sleep efficiency  Yesterday_sleep  \\\n",
+       "0                1     6.400000        92.086331         0.000000   \n",
+       "1                0     7.566667        92.464358         6.400000   \n",
+       "2                0     6.450000        88.761468         7.566667   \n",
+       "3                1     5.183333        88.857143         6.450000   \n",
+       "4                1     6.783333        82.892057         5.183333   \n",
+       "\n",
+       "   Yesterday_sleep_efficiency  Months  Months_encoded  \n",
+       "0                    0.000000     May               5  \n",
+       "1                   92.086331     May               5  \n",
+       "2                   92.464358     May               5  \n",
+       "3                   88.761468     May               5  \n",
+       "4                   88.857143     May               5  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitbit.head()"
    ]
   },
   {
@@ -59,20 +671,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Months_encoded', ylabel='Steps'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "sns.set(rc={\"figure.figsize\": (12, 4)})\n",
+    "barplot = sns.barplot(x=\"Months_encoded\", y=\"Steps\", data=fitbit)\n",
+    "barplot\n",
+    "barplot.figure.savefig('../barplot.png')\n",
+    "barplot"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1Q0GnT5/WsGHD7LoZ5+TkaM+ePUzqdhnTp0/Xxo0btW7dOts/SRo8eLAmTpzo2uDc2MGDB9WwYcMC4xl//vlnJqa8jFtuuUWlSpXSjz/+aLf+wIED3GQUws6dOxUVFcVkd4UQGxsrKW9Cz7/Lf9MHLu38+fPq1q2b9u3bp/DwcAUHB+uPP/7Qnj171Lx5c1eH5xHi4uL03XffKTc317Zu69atqlSpEvMsoEilpaWpR48eOnv2rFatWqW4uDhXh+QQhgYAl3Ho0CFNmjRJd9xxh/r27Ws302xAQIBCQkJcGJ37qlatmlq2bKkJEyZowoQJCgsL06JFi5Senq5HH33U1eG5pcs9oYiKiuLpxRVUrlxZN998s8aNG6exY8cqIiJCa9as0a5du3hf9GUEBASod+/emjdvnmJiYlS3bl1t2LBB33zzjZYtW+bq8Nzenj17VL16dVeH4RHq1q2rRo0aadSoURozZoxiY2O1bt06bdmyRa+//rqrw3NbwcHBslqtmjhxop5//nllZmbq2WefVZMmTRi6U0hdunRRYmKinnvuOfXu3Vu7d+/WsmXLPObd7vAckydP1rFjx5SYmKjIyEidOnXKti0yMlImk8mF0V0diQDgMjZt2qScnBx98skn+uSTT+y2derUiVe8XcGMGTP00ksvaejQofrzzz/VuHFjrVq1SjfeeKOrQ0MJYjQatXDhQr300ksaMmSI0tPTVatWLb366qsFur7jL/3791dgYKBmzpyp5ORkVa5cWXPmzNGtt97q6tDc3qlTp3hTQCEZjUYtWLBAs2bN0jPPPKO0tDRVq1ZNy5Yt4xXGVzFjxgyNHz9eDz30kPz8/NSuXTuNGDHC1WF5jKioKCUmJmrixInq1KmTSpcurZEjR9rmRQGKQm5urjZu3KicnBz16NGjwPZPP/1U5cqVc0FkhWewWq1WVwcBAAAAAACKB3MEAAAAAADgRUgEAAAAAADgRUgEAAAAAADgRUgEAAAAAADgRUgEAAAAAADgRUgEAAAAAADgRUgEAAAAAADgRUgEAACAy7Jara4OAQAAFDESAQAAuKnu3burevXqevDBBy+7z9ChQ1W9enU9/fTTRXrupKQk9enTR8ePH7etS0hIKPLzeLKnn35aCQkJRVJW9erVNWfOnCIpCwCAqyERAACAGzMajdq1a5eSkpIKbLt48aI+//zz63Leb7/9Vl988cV1KRsAALgWiQAAANxYrVq15O/vr48++qjAts8//1yBgYGKiYlxQWQAAMBTkQgAAMCNBQUFqVWrVpdMBGzcuFF33nmnfHx8bOuysrI0b948tW/fXnXq1FG7du20ePFiWSwW2z7du3fXc889p8WLF6t169aqU6eOHnzwQe3evVuS9M477+iZZ56RJLVp08ZuOEBOTo6mTp2q5s2bq379+nrsscd05MgR2/azZ8/qqaeeUvPmzVWnTh393//9n9atW+fwdWdlZWnq1Klq1aqVateurbvvvlsbN2602ychIUGzZ8/Wiy++qGbNmqlu3brq1auXDh8+bLffF198oQcffFD169fXbbfdpueff17p6em27YcPH9bgwYNt19S9e3d99913dmWkpaXpmWeeUXx8vOLi4jRt2jS7Os23efNmde7cWXXq1FHz5s01YcIEXbx40W6f7du364EHHlC9evV055136ttvv3W4fgAAuBYkAgAAcHMdOnQoMDzg/Pnz+vLLL9WxY0fbOqvVqn79+ikxMVH//ve/tXDhQrVv316zZs3SmDFj7MrctGmTPv30U40ePVozZszQ6dOnNWjQIOXm5qp169Z64oknJElz585V//79bcdt3LhRBw8e1JQpUzRmzBj9/PPPGjp0qG37iBEj9Ntvv2ns2LFasmSJatWqpVGjRmnr1q2Fvl6r1aoBAwbojTfeUM+ePbVgwQI1aNBAQ4cOLZBUWLFihX7//XdNnjxZEyZM0M8//6xRo0bZtn/++efq27evoqKiNGvWLA0fPlybN2+2xfzrr7+qc+fO+uOPPzR69GhNnz5dBoNBPXr00Pbt2yVJFotFvXv31hdffKFRo0ZpypQp+v777wskJt5//30NGDBAN998s+bNm6eBAwdq/fr16t+/v23SxV9++UWPPfaYQkJCNHv2bD3yyCMaNmxYoesGAICi4HP1XQAAgCu1bt1agYGB+uijj/Too49Kkj755BNFRUWpUaNGtv2+/PJLffvtt5oxY4buuusuSVLz5s0VEBCgl19+WY888oiqVq0qSTKbzVq6dKmCg4MlSRcuXNCoUaO0d+9e1a5dWxUqVJAk1axZU+XKlbOdIyYmRvPnz5evr68k6ciRI1qwYIHOnz+v4OBgbd++XQMGDFDbtm0lSfHx8QoPD5efn1+hr/fbb7/VV199pZkzZ6pDhw6SpBYtWigjI0PTp09Xx44dbb0gQkNDNX/+fJlMJknS0aNHNWfOHJ07d04RERGaM2eOatasqblz58pgMEiS/Pz89PLLL+v06dOaO3eu/Pz8tGLFCltdtG7dWh07dtTUqVP11ltv6csvv9Tu3bu1ZMkStWzZUpLUtGlTu4kCrVarpk+frhYtWmj69Om29RUrVtSjjz6qL774Qq1bt9aiRYsUFRWlBQsW2OowIiLCLpkCAMD1Ro8AAADcXEBAgBISEuyGB2zYsEH/+te/bDe3Ul6Xcx8fH7Vv397u+Hvuuce2PV+VKlVsN76SbPMMZGRkXDGWunXr2m5gJdmSBPld7W+99VbNmTNHgwcP1tq1a3X69GmNGjVKDRs2LPT1btmyRQaDQa1atZLZbLb9S0hI0KlTp3Tw4EHbvnXq1LElASQpNjbWdh2ZmZnas2eP2rZta1dPHTp00KZNm3TDDTdo+/btuv322+3qwsfHR3fddZd+/vlnXbhwQTt37pSvr69atGhh2yd/yEa+33//XUlJSUpISLCLOS4uTsHBwfrmm28kSd99951atGhhV4ft2rWzuwYAAK43egQAAOAB/vWvf2ngwIFKSkqSv7+/tmzZoiFDhtjtk5aWpoiIiAI3laVLl5Yk/fnnn7Z1gYGBdvsYjXnPBi417v3vgoKCrnjczJkztXDhQn344YfatGmTjEajmjVrpnHjxqls2bKFutbU1FRZrdbLJg9SUlJUs2bNq15HWlqarFaroqKiLnuutLQ03XDDDQXW33DDDbJarTp//rzS0tIUHh5ul0yQ/qrX/JglaezYsRo7duwlY84/X0REhN02Hx+fAusAALieSAQAAOABWrZsqVKlSumjjz5SUFCQypUrp9q1a9vtExYWpnPnzik3N9cuGZB/E1ocN5shISEaMWKERowYod9//12ffvqp5s+fr7Fjx2rx4sWFLiMoKEgrVqy45PabbrqpUOUEBwfLYDDo7NmzduuzsrK0detW1atXT2FhYTp9+nSBY0+dOiUpr84iIiIuWa/5N/9S3hAFSRo5cqTi4+MLlBcWFiZJCg8PL3A+q9WqtLS0Ql0TAABFgaEBAAB4AD8/P7Vt21abNm3Shx9+aJsD4O/i4+NlNpsLvGFg/fr1kmQ3n8DV5D9Zd8Tx48ft3nBw88036/HHH1ezZs104sSJQpcTHx+vixcvymq1qk6dOrZ/Bw4c0Lx582Q2mwtVTqlSpVSzZk19/vnnduu//PJL9enTRykpKYqLi9Pnn3+u8+fP27bn5uZqw4YNqlOnjvz8/NS0aVOZzWZt3rzZtk92dratu3/+tUZFRemPP/6wizkmJkYvvfSS9uzZIylvboEvv/zSbgjGV199pZycnELXDwAA14oeAQAAeIgOHTqob9++MhqNGj16dIHtLVu21K233qrRo0crOTlZNWrU0Pbt27VkyRJ16tRJVapUKfS58p9wf/LJJ2rZsqUqV6581WPKli2r2NhYTZgwQefPn1eFChX0888/64svvlDfvn0Lfe5WrVopLi5O/fv3V//+/VW5cmXt3r1bs2fPVosWLRQZGVnosgYPHqwnnnhCw4YN07333qvTp09rxowZatu2rapVq6aBAwfqyy+/1COPPKI+ffrI19dXK1eu1LFjx5SYmCgp7+b9tttu0+jRo3XmzBmVLVtWK1as0NmzZ23DDkwmk4YOHarnn39eJpNJt99+u9LT0zV//nwlJyfrlltukSQNGDBAmzdvVq9evdS7d2+dPXtWs2bNspszAACA641EAAAAHqJZs2YKDQ1VmTJlLnljbjAYtGjRIs2ePVvLli3T2bNnVa5cOQ0bNkw9e/Z06Fy33nqrmjVrppdeeklbtmwpdLf+uXPnasaMGXr55Zd17tw5lSlTRgMHDlSfPn0KfW6j0ajFixfr5Zdf1qJFi3TmzBnFxMSoZ8+eGjBggEPXcfvtt2vhwoWaO3euBgwYoMjISN19990aNGiQJKlq1apavXq1ZsyYoWeeeUYGg0F169bVihUr1LhxY7vrmj59umbPnq2srCx16NBB999/vz799FPbPv/+979VqlQpJSYm6s0331RQUJAaNmyo6dOnq3z58pLy3iKwcuVKTZkyRUOHDlVUVJTtlYQAABQXgzX/xbYAAAAAAKDEo0cAAAAoFrm5ubra8weDwcCr9AAAuM7oEQAAAIpF9+7dtX379ivuU7ZsWX322WfFFBEAAN6JRAAAACgWv//+uy5cuHDFffz8/FS9evViiggAAO9EIgAAAAAAAC/i+EuCAQAAAACAxyIRAAAAAACAFyERAAAAAACAFyERAAAAAACAFyERAAAAAACAFyERAAAAAACAFyERAAAAAACAFyERAAAAAACAF/l/8AKITV01itAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "sns.set(rc={\"figure.figsize\": (12, 4)})\n",
+    "barplot_1 = sns.barplot(y=\"Steps\", x=\"Months_encoded\", hue=\"Work_or_Weekend\", data=fitbit)\n",
+    "barplot_1.figure.savefig('../barplot1.png')\n",
+    "barplot_1"
    ]
   },
   {
@@ -88,10 +739,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot = [fitbit[\"Minutes Lightly Active\"], fitbit[\"Minutes Very Active\"], fitbit[\"Minutes Sedentary\"]]\n",
+    "\n",
+    "for i in plot:\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    scatter = sns.scatterplot (x = i, y = \"Steps\", data=fitbit)"
+   ]
   }
  ],
  "metadata": {
@@ -111,7 +800,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.5"
   },
   "nbTranslate": {
    "displayLangs": [
diff --git a/your-code/challenge-2.ipynb b/your-code/challenge-2.ipynb
index d7465f0..477f0ba 100644
--- a/your-code/challenge-2.ipynb
+++ b/your-code/challenge-2.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,13 +36,268 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>1305</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Spector, Mr. Woolf</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.513190</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A.5. 3236</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>1306</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17758</td>\n",
+       "      <td>108.9000</td>\n",
+       "      <td>C105</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>1307</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
+       "      <td>male</td>\n",
+       "      <td>38.500000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SOTON/O.Q. 3101262</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>1308</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Ware, Mr. Frederick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.513190</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>359309</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>1309</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Peter, Master. Michael J</td>\n",
+       "      <td>male</td>\n",
+       "      <td>25.315435</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2668</td>\n",
+       "      <td>22.3583</td>\n",
+       "      <td>U0</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      PassengerId  Survived  Pclass  \\\n",
+       "0               1       0.0       3   \n",
+       "1               2       1.0       1   \n",
+       "2               3       1.0       3   \n",
+       "3               4       1.0       1   \n",
+       "4               5       0.0       3   \n",
+       "...           ...       ...     ...   \n",
+       "1304         1305       NaN       3   \n",
+       "1305         1306       NaN       1   \n",
+       "1306         1307       NaN       3   \n",
+       "1307         1308       NaN       3   \n",
+       "1308         1309       NaN       3   \n",
+       "\n",
+       "                                                   Name  Gender        Age  \\\n",
+       "0                               Braund, Mr. Owen Harris    male  22.000000   \n",
+       "1     Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
+       "2                                Heikkinen, Miss. Laina  female  26.000000   \n",
+       "3          Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
+       "4                              Allen, Mr. William Henry    male  35.000000   \n",
+       "...                                                 ...     ...        ...   \n",
+       "1304                                 Spector, Mr. Woolf    male  29.513190   \n",
+       "1305                       Oliva y Ocana, Dona. Fermina  female  39.000000   \n",
+       "1306                       Saether, Mr. Simon Sivertsen    male  38.500000   \n",
+       "1307                                Ware, Mr. Frederick    male  29.513190   \n",
+       "1308                           Peter, Master. Michael J    male  25.315435   \n",
+       "\n",
+       "      SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
+       "0         1      0           A/5 21171    7.2500    U0        S  \n",
+       "1         1      0            PC 17599   71.2833   C85        C  \n",
+       "2         0      0    STON/O2. 3101282    7.9250    U0        S  \n",
+       "3         1      0              113803   53.1000  C123        S  \n",
+       "4         0      0              373450    8.0500    U0        S  \n",
+       "...     ...    ...                 ...       ...   ...      ...  \n",
+       "1304      0      0           A.5. 3236    8.0500    U0        S  \n",
+       "1305      0      0            PC 17758  108.9000  C105        C  \n",
+       "1306      0      0  SOTON/O.Q. 3101262    7.2500    U0        S  \n",
+       "1307      0      0              359309    8.0500    U0        S  \n",
+       "1308      1      1                2668   22.3583    U0        C  \n",
+       "\n",
+       "[1309 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "titanic = pd.read_csv(\"../data/titanic.csv\")\n",
+    "titanic\n"
    ]
   },
   {
@@ -54,10 +309,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      int64\n",
+       "Survived       float64\n",
+       "Pclass           int64\n",
+       "Name            object\n",
+       "Gender          object\n",
+       "Age            float64\n",
+       "SibSp            int64\n",
+       "Parch            int64\n",
+       "Ticket          object\n",
+       "Fare           float64\n",
+       "Cabin           object\n",
+       "Embarked        object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.dtypes"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -69,10 +349,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method DataFrame.select_dtypes of       PassengerId  Survived  Pclass  \\\n",
+       "0               1       0.0       3   \n",
+       "1               2       1.0       1   \n",
+       "2               3       1.0       3   \n",
+       "3               4       1.0       1   \n",
+       "4               5       0.0       3   \n",
+       "...           ...       ...     ...   \n",
+       "1304         1305       NaN       3   \n",
+       "1305         1306       NaN       1   \n",
+       "1306         1307       NaN       3   \n",
+       "1307         1308       NaN       3   \n",
+       "1308         1309       NaN       3   \n",
+       "\n",
+       "                                                   Name  Gender        Age  \\\n",
+       "0                               Braund, Mr. Owen Harris    male  22.000000   \n",
+       "1     Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
+       "2                                Heikkinen, Miss. Laina  female  26.000000   \n",
+       "3          Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
+       "4                              Allen, Mr. William Henry    male  35.000000   \n",
+       "...                                                 ...     ...        ...   \n",
+       "1304                                 Spector, Mr. Woolf    male  29.513190   \n",
+       "1305                       Oliva y Ocana, Dona. Fermina  female  39.000000   \n",
+       "1306                       Saether, Mr. Simon Sivertsen    male  38.500000   \n",
+       "1307                                Ware, Mr. Frederick    male  29.513190   \n",
+       "1308                           Peter, Master. Michael J    male  25.315435   \n",
+       "\n",
+       "      SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
+       "0         1      0           A/5 21171    7.2500    U0        S  \n",
+       "1         1      0            PC 17599   71.2833   C85        C  \n",
+       "2         0      0    STON/O2. 3101282    7.9250    U0        S  \n",
+       "3         1      0              113803   53.1000  C123        S  \n",
+       "4         0      0              373450    8.0500    U0        S  \n",
+       "...     ...    ...                 ...       ...   ...      ...  \n",
+       "1304      0      0           A.5. 3236    8.0500    U0        S  \n",
+       "1305      0      0            PC 17758  108.9000  C105        C  \n",
+       "1306      0      0  SOTON/O.Q. 3101262    7.2500    U0        S  \n",
+       "1307      0      0              359309    8.0500    U0        S  \n",
+       "1308      1      1                2668   22.3583    U0        C  \n",
+       "\n",
+       "[1309 rows x 12 columns]>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "titanic.select_dtypes\n",
+    "\n",
+    "#Numerical variables = Age, SibSp, Parch, Fare.\n",
+    "#Categorical Variables = PassengerId, Survived, Pclass, Name, Gender, Ticket, Cabin, Embarked."
+   ]
   },
   {
    "cell_type": "markdown",
@@ -82,13 +417,20 @@
     "**Hint**: To set the style you can use matplotlib or seaborn functions. Do some research on the matter."
    ]
   },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "sns.set_theme(style='classic')"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here\n"
+    "sns.set(rc={\"figure.figsize\": (12, 6)})"
    ]
   },
   {
@@ -100,11 +442,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Age', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here\n"
+    "sns.histplot(x=titanic.Age, bins=15)\n"
    ]
   },
   {
@@ -121,7 +484,8 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "As higher the bin, you get more bars, and thus you get more detailed information. \n",
+    "As lower the bin, you get less bars, and thus the information is less detailed. It is more general. \n",
     "\"\"\""
    ]
   },
@@ -135,22 +499,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Method 1 - matplotlib\n",
-    "# your code here"
+    "titanic.groupby([\"Gender\"])[\"Gender\"].count().plot(kind=\"bar\");"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Method 2 - seaborn\n",
-    "# your code here"
+    "sns.set(rc={\"figure.figsize\": (8, 4)})\n",
+    "\n",
+    "sns.histplot(x=titanic.Gender);"
    ]
   },
   {
@@ -162,11 +548,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "titanic.groupby([\"Pclass\"])[\"Pclass\"].count().plot(kind=\"bar\");"
    ]
   },
   {
@@ -318,7 +715,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.5"
   },
   "nbTranslate": {
    "displayLangs": [