diff --git a/test.ipynb b/test.ipynb
index 1653575..138c081 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -175,6 +175,179 @@
    ],
    "outputs": [],
    "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "# print movie table\r\n",
+    "movies.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   movieId                               title  \\\n",
+       "0        1                    Toy Story (1995)   \n",
+       "1        2                      Jumanji (1995)   \n",
+       "2        3             Grumpier Old Men (1995)   \n",
+       "3        4            Waiting to Exhale (1995)   \n",
+       "4        5  Father of the Bride Part II (1995)   \n",
+       "\n",
+       "                                        genres release_year  \n",
+       "0  Adventure|Animation|Children|Comedy|Fantasy         1995  \n",
+       "1                   Adventure|Children|Fantasy         1995  \n",
+       "2                               Comedy|Romance         1995  \n",
+       "3                         Comedy|Drama|Romance         1995  \n",
+       "4                                       Comedy         1995  "
+      ],
+      "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>movieId</th>\n",
+       "      <th>title</th>\n",
+       "      <th>genres</th>\n",
+       "      <th>release_year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Toy Story (1995)</td>\n",
+       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Jumanji (1995)</td>\n",
+       "      <td>Adventure|Children|Fantasy</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Grumpier Old Men (1995)</td>\n",
+       "      <td>Comedy|Romance</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Waiting to Exhale (1995)</td>\n",
+       "      <td>Comedy|Drama|Romance</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Father of the Bride Part II (1995)</td>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "# changed feature values based on README_users.txt\r\n",
+    "\r\n",
+    "ages_map = {1: 'Under 18',\r\n",
+    "            18: '18 - 24',\r\n",
+    "            25: '25 - 34',\r\n",
+    "            35: '35 - 44',\r\n",
+    "            45: '45 - 49',\r\n",
+    "            50: '50 - 55',\r\n",
+    "            56: '56+'}\r\n",
+    "\r\n",
+    "occupations_map = {0: 'Not specified',\r\n",
+    "                   1: 'Academic / Educator',\r\n",
+    "                   2: 'Artist',\r\n",
+    "                   3: 'Clerical / Admin',\r\n",
+    "                   4: 'College / Grad Student',\r\n",
+    "                   5: 'Customer Service',\r\n",
+    "                   6: 'Doctor / Health Care',\r\n",
+    "                   7: 'Executive / Managerial',\r\n",
+    "                   8: 'Farmer',\r\n",
+    "                   9: 'Homemaker',\r\n",
+    "                   10: 'K-12 student',\r\n",
+    "                   11: 'Lawyer',\r\n",
+    "                   12: 'Programmer',\r\n",
+    "                   13: 'Retired',\r\n",
+    "                   14: 'Sales / Marketing',\r\n",
+    "                   15: 'Scientist',\r\n",
+    "                   16: 'Self-Employed',\r\n",
+    "                   17: 'Technician / Engineer',\r\n",
+    "                   18: 'Tradesman / Craftsman',\r\n",
+    "                   19: 'Unemployed',\r\n",
+    "                   20: 'Writer'}\r\n",
+    "\r\n",
+    "gender_map = {'M': 'Male', 'F': 'Female'}\r\n",
+    "\r\n",
+    "users['age'] = users['age'].map(ages_map)\r\n",
+    "users['occupation'] = users['occupation'].map(occupations_map)\r\n",
+    "users['gender'] = users['gender'].map(gender_map)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "age_reindex = ['Under 18', '18 - 24', '25 - 34', '35 - 44', '45 - 49', '50 - 55', '56+']\r\n",
+    "\r\n",
+    "age_counts = users['age'].value_counts().reindex(age_reindex)\r\n",
+    "\r\n",
+    "sns.barplot(x=age_counts.values,\r\n",
+    "            y=age_counts.index,\r\n",
+    "            palette='magma').set_title(\r\n",
+    "                'Users age', fontsize=24)\r\n",
+    "\r\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 1296x576 with 1 Axes>"
+      ],
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"
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {}
   }
  ],
  "metadata": {