diff --git a/notebooks/06_01b.ipynb b/notebooks/06_01b.ipynb
index 4db1212..269904f 100644
--- a/notebooks/06_01b.ipynb
+++ b/notebooks/06_01b.ipynb
@@ -23,24 +23,215 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "1bef47b2",
    "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>names</th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>steve</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>john</td>\n",
+       "      <td>22</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>richard</td>\n",
+       "      <td>20</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sarah</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>randy</td>\n",
+       "      <td>24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>micheal</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>julie</td>\n",
+       "      <td>22</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     names  age  gender  rank\n",
+       "0    steve   20    Male     2\n",
+       "1     john   22    Male     1\n",
+       "2  richard   20     NaN     4\n",
+       "3    sarah   21  Female     5\n",
+       "4    randy   24     NaN     3\n",
+       "5  micheal   23    Male     7\n",
+       "6    julie   22     NaN     6"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data = {'names':['steve', 'john', 'richard', 'sarah', 'randy', 'micheal', 'julie'],\n",
     "'age':[20, 22, 20, 21, 24, 23, 22],\n",
     "'gender':['Male', 'Male', np.nan, 'Female', np.nan, 'Male', np.nan],\n",
-    "'rank':[2, 1, 4, 5, 3, 7, 6]}"
+    "'rank':[2, 1, 4, 5, 3, 7, 6]}\n",
+    "df = DataFrame(data)\n",
+    "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "385b7880",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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>names</th>\n",
+       "      <th>age</th>\n",
+       "      <th>rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>steve</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>john</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>richard</td>\n",
+       "      <td>20</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sarah</td>\n",
+       "      <td>21</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>randy</td>\n",
+       "      <td>24</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>micheal</td>\n",
+       "      <td>23</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>julie</td>\n",
+       "      <td>22</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     names  age  rank\n",
+       "0    steve   20     2\n",
+       "1     john   22     1\n",
+       "2  richard   20     4\n",
+       "3    sarah   21     5\n",
+       "4    randy   24     3\n",
+       "5  micheal   23     7\n",
+       "6    julie   22     6"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.drop('gender',axis=1)\n",
+    "df"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -50,6 +241,456 @@
     "### Label Encoding"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8eb03450",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LabelEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.LabelEncoder.html\">?<span>Documentation for LabelEncoder</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LabelEncoder()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "LabelEncoder()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "label_encoder = LabelEncoder()\n",
+    "label_encoder.fit(df['names'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6, 0, 4, 5, 3, 2, 1])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "label_encoder_names = label_encoder.transform(df['names'])\n",
+    "label_encoder_names"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "46c8b54e-bc23-4ef7-95a3-8b3dac69b324",
@@ -57,6 +698,603 @@
    "source": [
     "### One Hot Encoder"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9b9515b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "onehot_encoder = OneHotEncoder(sparse_output=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a55d65d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder(sparse_output=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(sparse_output=False)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "OneHotEncoder(sparse_output=False)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "onehot_encoder.fit(df[['names']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "37de8628",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "onehot_encoder_names = onehot_encoder.transform(df[['names']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "92bd5136",
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>john</th>\n",
+       "      <th>julie</th>\n",
+       "      <th>micheal</th>\n",
+       "      <th>randy</th>\n",
+       "      <th>richard</th>\n",
+       "      <th>sarah</th>\n",
+       "      <th>steve</th>\n",
+       "      <th>names</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>steve</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>john</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>richard</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>sarah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>randy</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>micheal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>julie</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  john julie micheal randy richard sarah steve    names\n",
+       "0  0.0   0.0     0.0   0.0     0.0   0.0   1.0    steve\n",
+       "1  1.0   0.0     0.0   0.0     0.0   0.0   0.0     john\n",
+       "2  0.0   0.0     0.0   0.0     1.0   0.0   0.0  richard\n",
+       "3  0.0   0.0     0.0   0.0     0.0   1.0   0.0    sarah\n",
+       "4  0.0   0.0     0.0   1.0     0.0   0.0   0.0    randy\n",
+       "5  0.0   0.0     1.0   0.0     0.0   0.0   0.0  micheal\n",
+       "6  0.0   1.0     0.0   0.0     0.0   0.0   0.0    julie"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "onehot_encoded_df = DataFrame(onehot_encoder_names,columns=onehot_encoder.categories_)\n",
+    "onehot_encoded_df['names'] = df[['names']]\n",
+    "onehot_encoded_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d8a6247",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -75,7 +1313,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/06_02b.ipynb b/notebooks/06_02b.ipynb
index ade325d..84cb5ec 100644
--- a/notebooks/06_02b.ipynb
+++ b/notebooks/06_02b.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "868ec360-501a-4032-a6b7-c7fac40a78df",
    "metadata": {},
    "outputs": [],
@@ -24,10 +24,146 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a67f7690",
    "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>Unnamed: 0</th>\n",
+       "      <th>mpg</th>\n",
+       "      <th>cyl</th>\n",
+       "      <th>disp</th>\n",
+       "      <th>hp</th>\n",
+       "      <th>drat</th>\n",
+       "      <th>wt</th>\n",
+       "      <th>qsec</th>\n",
+       "      <th>vs</th>\n",
+       "      <th>am</th>\n",
+       "      <th>gear</th>\n",
+       "      <th>carb</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Mazda RX4</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>110</td>\n",
+       "      <td>3.90</td>\n",
+       "      <td>2.620</td>\n",
+       "      <td>16.46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Mazda RX4 Wag</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>110</td>\n",
+       "      <td>3.90</td>\n",
+       "      <td>2.875</td>\n",
+       "      <td>17.02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Datsun 710</td>\n",
+       "      <td>22.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>93</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>2.320</td>\n",
+       "      <td>18.61</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Hornet 4 Drive</td>\n",
+       "      <td>21.4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>258.0</td>\n",
+       "      <td>110</td>\n",
+       "      <td>3.08</td>\n",
+       "      <td>3.215</td>\n",
+       "      <td>19.44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Hornet Sportabout</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>175</td>\n",
+       "      <td>3.15</td>\n",
+       "      <td>3.440</td>\n",
+       "      <td>17.02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Unnamed: 0   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
+       "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
+       "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
+       "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
+       "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
+       "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
+       "\n",
+       "   carb  \n",
+       "0     4  \n",
+       "1     4  \n",
+       "2     1  \n",
+       "3     1  \n",
+       "4     2  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "address = '/workspaces/python-for-data-science-and-machine-learning-essential-training-part-1-3006708/data/mtcars.csv'\n",
     "\n",
@@ -35,6 +171,37 @@
     "dataset.head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3b99480a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x743050abbe90>]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(dataset['mpg'])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4c753f3f-ea35-4502-a099-168880862c3f",
@@ -43,6 +210,467 @@
     "### Normalization"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a36f47aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MinMaxScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "MinMaxScaler()"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "minmax_scaler = MinMaxScaler()\n",
+    "minmax_scaler.fit(dataset[['mpg']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4dfaa39f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x74304db2c050>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scaled_data = minmax_scaler.transform(dataset[['mpg']])\n",
+    "plt.plot(scaled_data)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "496a3803-34bb-4ddc-9dc5-5fca9b9b0fd1",
@@ -50,6 +678,38 @@
    "source": [
     "### Standardization"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "14edaffc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x74304db579e0>]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "standard_scaler = scale(dataset[['mpg']])\n",
+    "plt.plot(standard_scaler)"
+   ]
   }
  ],
  "metadata": {
@@ -68,7 +728,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,