diff --git a/notebooks/modeling-logisticRegression.ipynb b/notebooks/modeling-logisticRegression.ipynb
new file mode 100644
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--- /dev/null
+++ b/notebooks/modeling-logisticRegression.ipynb
@@ -0,0 +1,866 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2a2f3924",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import sys\n",
+    "\n",
+    "import dvc.api\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import ElasticNet, LogisticRegression\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from fast_ml.model_development import train_valid_test_split\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from urllib.parse import urlparse\n",
+    "import mlflow\n",
+    "import mlflow.sklearn\n",
+    "import logging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2ff7fa2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "196c462d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sys.path.append(os.path.abspath(os.path.join('../scripts')))\n",
+    "from ml import Ml\n",
+    "from preprocess import Preprocess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9673a729",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ml = Ml()\n",
+    "preprocess = Preprocess()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ecfd9720",
+   "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>auction_id</th>\n",
+       "      <th>experiment</th>\n",
+       "      <th>date</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>device_make</th>\n",
+       "      <th>platform_os</th>\n",
+       "      <th>browser</th>\n",
+       "      <th>yes</th>\n",
+       "      <th>no</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0008ef63-77a7-448b-bd1e-075f42c55e39</td>\n",
+       "      <td>exposed</td>\n",
+       "      <td>2020-07-10</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>000eabc5-17ce-4137-8efe-44734d914446</td>\n",
+       "      <td>exposed</td>\n",
+       "      <td>2020-07-07</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0016d14a-ae18-4a02-a204-6ba53b52f2ed</td>\n",
+       "      <td>exposed</td>\n",
+       "      <td>2020-07-05</td>\n",
+       "      <td>2</td>\n",
+       "      <td>E5823</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile WebView</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>00187412-2932-4542-a8ef-3633901c98d9</td>\n",
+       "      <td>control</td>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Samsung SM-A705FN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>001a7785-d3fe-4e11-a344-c8735acacc2c</td>\n",
+       "      <td>control</td>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8072</th>\n",
+       "      <td>ffea24ec-cec1-43fb-b1d1-8f93828c2be2</td>\n",
+       "      <td>exposed</td>\n",
+       "      <td>2020-07-05</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8073</th>\n",
+       "      <td>ffea3210-2c3e-426f-a77d-0aa72e73b20f</td>\n",
+       "      <td>control</td>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8074</th>\n",
+       "      <td>ffeaa0f1-1d72-4ba9-afb4-314b3b00a7c7</td>\n",
+       "      <td>control</td>\n",
+       "      <td>2020-07-04</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8075</th>\n",
+       "      <td>ffeeed62-3f7c-4a6e-8ba7-95d303d40969</td>\n",
+       "      <td>exposed</td>\n",
+       "      <td>2020-07-05</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Samsung SM-A515F</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Samsung Internet</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8076</th>\n",
+       "      <td>fffbb9ff-568a-41a5-a0c3-6866592f80d8</td>\n",
+       "      <td>control</td>\n",
+       "      <td>2020-07-10</td>\n",
+       "      <td>14</td>\n",
+       "      <td>Samsung SM-G960F</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8077 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                auction_id experiment        date  hour  \\\n",
+       "0     0008ef63-77a7-448b-bd1e-075f42c55e39    exposed  2020-07-10     8   \n",
+       "1     000eabc5-17ce-4137-8efe-44734d914446    exposed  2020-07-07    10   \n",
+       "2     0016d14a-ae18-4a02-a204-6ba53b52f2ed    exposed  2020-07-05     2   \n",
+       "3     00187412-2932-4542-a8ef-3633901c98d9    control  2020-07-03    15   \n",
+       "4     001a7785-d3fe-4e11-a344-c8735acacc2c    control  2020-07-03    15   \n",
+       "...                                    ...        ...         ...   ...   \n",
+       "8072  ffea24ec-cec1-43fb-b1d1-8f93828c2be2    exposed  2020-07-05     7   \n",
+       "8073  ffea3210-2c3e-426f-a77d-0aa72e73b20f    control  2020-07-03    15   \n",
+       "8074  ffeaa0f1-1d72-4ba9-afb4-314b3b00a7c7    control  2020-07-04     9   \n",
+       "8075  ffeeed62-3f7c-4a6e-8ba7-95d303d40969    exposed  2020-07-05    15   \n",
+       "8076  fffbb9ff-568a-41a5-a0c3-6866592f80d8    control  2020-07-10    14   \n",
+       "\n",
+       "             device_make  platform_os                browser  yes  no  \n",
+       "0     Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "1     Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "2                  E5823            6  Chrome Mobile WebView    0   1  \n",
+       "3      Samsung SM-A705FN            6               Facebook    0   0  \n",
+       "4     Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "...                  ...          ...                    ...  ...  ..  \n",
+       "8072  Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "8073  Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "8074  Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "8075    Samsung SM-A515F            6       Samsung Internet    0   0  \n",
+       "8076    Samsung SM-G960F            6               Facebook    0   0  \n",
+       "\n",
+       "[8077 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('../data/AdSmartABdata.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaf248b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get URL from DVC\n",
+    "# path = 'data/AdSmartABdata.csv'\n",
+    "# repo = 'https://github.com/jedisam/abtest-mlops'\n",
+    "# version = '6db449393c9626c4fbca44946dfa103660685a27'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1152816f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data from dvc using the dvc.api.Dataset class\n",
+    "# data_url = dvc.api.get_url(\n",
+    "#     path=path,\n",
+    "#     repo=repo,\n",
+    "#     rev=version\n",
+    "# )\n",
+    "# data_url"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d15914fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read CSV file from remote repository\n",
+    "# data = pd.read_csv(data_url, sep=',')\n",
+    "# data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d08c96b9",
+   "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>auction_id</th>\n",
+       "      <th>experiment</th>\n",
+       "      <th>date</th>\n",
+       "      <th>hour</th>\n",
+       "      <th>device_make</th>\n",
+       "      <th>platform_os</th>\n",
+       "      <th>browser</th>\n",
+       "      <th>yes</th>\n",
+       "      <th>no</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0008ef63-77a7-448b-bd1e-075f42c55e39</td>\n",
+       "      <td>exposed</td>\n",
+       "      <td>2020-07-10</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>000eabc5-17ce-4137-8efe-44734d914446</td>\n",
+       "      <td>exposed</td>\n",
+       "      <td>2020-07-07</td>\n",
+       "      <td>10</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0016d14a-ae18-4a02-a204-6ba53b52f2ed</td>\n",
+       "      <td>exposed</td>\n",
+       "      <td>2020-07-05</td>\n",
+       "      <td>2</td>\n",
+       "      <td>E5823</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile WebView</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>00187412-2932-4542-a8ef-3633901c98d9</td>\n",
+       "      <td>control</td>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Samsung SM-A705FN</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>001a7785-d3fe-4e11-a344-c8735acacc2c</td>\n",
+       "      <td>control</td>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8072</th>\n",
+       "      <td>ffea24ec-cec1-43fb-b1d1-8f93828c2be2</td>\n",
+       "      <td>exposed</td>\n",
+       "      <td>2020-07-05</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8073</th>\n",
+       "      <td>ffea3210-2c3e-426f-a77d-0aa72e73b20f</td>\n",
+       "      <td>control</td>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8074</th>\n",
+       "      <td>ffeaa0f1-1d72-4ba9-afb4-314b3b00a7c7</td>\n",
+       "      <td>control</td>\n",
+       "      <td>2020-07-04</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Generic Smartphone</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Chrome Mobile</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8075</th>\n",
+       "      <td>ffeeed62-3f7c-4a6e-8ba7-95d303d40969</td>\n",
+       "      <td>exposed</td>\n",
+       "      <td>2020-07-05</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Samsung SM-A515F</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Samsung Internet</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8076</th>\n",
+       "      <td>fffbb9ff-568a-41a5-a0c3-6866592f80d8</td>\n",
+       "      <td>control</td>\n",
+       "      <td>2020-07-10</td>\n",
+       "      <td>14</td>\n",
+       "      <td>Samsung SM-G960F</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Facebook</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8077 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                auction_id experiment       date  hour  \\\n",
+       "0     0008ef63-77a7-448b-bd1e-075f42c55e39    exposed 2020-07-10     8   \n",
+       "1     000eabc5-17ce-4137-8efe-44734d914446    exposed 2020-07-07    10   \n",
+       "2     0016d14a-ae18-4a02-a204-6ba53b52f2ed    exposed 2020-07-05     2   \n",
+       "3     00187412-2932-4542-a8ef-3633901c98d9    control 2020-07-03    15   \n",
+       "4     001a7785-d3fe-4e11-a344-c8735acacc2c    control 2020-07-03    15   \n",
+       "...                                    ...        ...        ...   ...   \n",
+       "8072  ffea24ec-cec1-43fb-b1d1-8f93828c2be2    exposed 2020-07-05     7   \n",
+       "8073  ffea3210-2c3e-426f-a77d-0aa72e73b20f    control 2020-07-03    15   \n",
+       "8074  ffeaa0f1-1d72-4ba9-afb4-314b3b00a7c7    control 2020-07-04     9   \n",
+       "8075  ffeeed62-3f7c-4a6e-8ba7-95d303d40969    exposed 2020-07-05    15   \n",
+       "8076  fffbb9ff-568a-41a5-a0c3-6866592f80d8    control 2020-07-10    14   \n",
+       "\n",
+       "             device_make  platform_os                browser  yes  no  \n",
+       "0     Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "1     Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "2                  E5823            6  Chrome Mobile WebView    0   1  \n",
+       "3      Samsung SM-A705FN            6               Facebook    0   0  \n",
+       "4     Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "...                  ...          ...                    ...  ...  ..  \n",
+       "8072  Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "8073  Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "8074  Generic Smartphone            6          Chrome Mobile    0   0  \n",
+       "8075    Samsung SM-A515F            6       Samsung Internet    0   0  \n",
+       "8076    Samsung SM-G960F            6               Facebook    0   0  \n",
+       "\n",
+       "[8077 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# change the date column to datetime\n",
+    "# from preprocess import Preprocess\n",
+    "data = preprocess.convert_to_datetime(df, 'date')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1957a950",
+   "metadata": {},
+   "source": [
+    "# Exploring the categorical columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "da5d2e03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numerical_column = preprocess.get_numerical_columns(df)\n",
+    "categorical_column = preprocess.get_categorical_columns(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1c3f08b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop auction_id from categorical_column\n",
+    "categorical_column.remove('auction_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "629b90a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get column names have less than 10 more than 2 unique values\n",
+    "to_one_hot_encoding = [col for col in categorical_column if df[col].nunique() <= 10 and df[col].nunique() > 2]\n",
+    "\n",
+    "# Get Categorical Column names thoose are not in \"to_one_hot_encoding\"\n",
+    "to_label_encoding = [col for col in categorical_column if not col in to_one_hot_encoding]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d0879922",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Label encoding\n",
+    "label_encoded_columns = preprocess.label_encode(df, to_label_encoding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "53dbecea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copy our DataFrame to X variable\n",
+    "X = df.copy()\n",
+    "\n",
+    "# Droping Categorical Columns,\n",
+    "# \"inplace\" means replace our data with new one\n",
+    "# Don't forget to \"axis=1\"\n",
+    "X.drop(categorical_column, axis=1, inplace=True)\n",
+    "\n",
+    "# Merge DataFrames\n",
+    "X = pd.concat([X, label_encoded_columns], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "333fe00a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Select only rows with responses\n",
+    "X = X.query('yes == 1 | no == 1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "cd31d7a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop auction_id column\n",
+    "X.drop([\"auction_id\"], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "385095bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X['target'] = [1] * X.shape[0]\n",
+    "X.loc[X['no'] == 1, 'target'] = 0\n",
+    "y = X['target']\n",
+    "X.drop([\"target\"], axis=1, inplace=True)\n",
+    "X.drop(['yes', 'no'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a1f1f6d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the day of the week from the date column as a new column\n",
+    "X['day'] = X['date'].dt.dayofweek\n",
+    "X.drop([\"date\"], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fb870b8",
+   "metadata": {},
+   "source": [
+    "# Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "b51aee5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Training Accuracy scores': array([0.55533199, 0.5694165 , 0.54124748, 0.55577889, 0.55879397]),\n",
+       " 'Mean Training Accuracy': 55.61137680353478,\n",
+       " 'Training Precision scores': array([0.55333333, 0.5721393 , 0.5046729 , 0.54255319, 0.56291391]),\n",
+       " 'Mean Training Precision': 0.5471225265572623,\n",
+       " 'Training Recall scores': array([0.18122271, 0.25164114, 0.11816193, 0.22270742, 0.18558952]),\n",
+       " 'Mean Training Recall': 0.19186454282247042,\n",
+       " 'Training F1 scores': array([0.27302632, 0.34954407, 0.19148936, 0.31578947, 0.27914614]),\n",
+       " 'Mean Training F1 Score': 0.28179907306784935,\n",
+       " 'Validation Accuracy scores': array([0.5502008 , 0.47791165, 0.5502008 , 0.53225806, 0.55645161]),\n",
+       " 'Mean Validation Accuracy': 53.34045860862806,\n",
+       " 'Validation Precision scores': array([0.52380952, 0.33333333, 0.55172414, 0.48      , 0.55      ]),\n",
+       " 'Mean Validation Precision': 0.48777339901477834,\n",
+       " 'Validation Recall scores': array([0.19298246, 0.13043478, 0.13913043, 0.21052632, 0.19298246]),\n",
+       " 'Mean Validation Recall': 0.17321128909229594,\n",
+       " 'Validation F1 scores': array([0.28205128, 0.1875    , 0.22222222, 0.29268293, 0.28571429]),\n",
+       " 'Mean Validation F1 Score': 0.2540341433634117,\n",
+       " 'Coefficients': []}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logistic_regression_model = LogisticRegression(random_state=0)\n",
+    "logistic_regression_result = ml.cross_validation(logistic_regression_model, X, y, 5)\n",
+    "logistic_regression_result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "248e3c8f",
+   "metadata": {},
+   "source": [
+    "### Plot Accuracy Result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "76284cb6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Accuracy Result\n",
+    "model_name = \"Logistic Regression\"\n",
+    "ml.plot_result(model_name, \"Accuracy\", \"Accuracy scores in 5 Folds\", logistic_regression_result[\"Training Accuracy scores\"], logistic_regression_result[\"Validation Accuracy scores\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27d8de35",
+   "metadata": {},
+   "source": [
+    "### Plot Precision Result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "27134e4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Precision Result\n",
+    "ml.plot_result(model_name, \"Precision\", \"Precision scores in 5 Folds\", logistic_regression_result[\"Training Precision scores\"], logistic_regression_result[\"Validation Precision scores\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03e0f1a9",
+   "metadata": {},
+   "source": [
+    "### Recall Results plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9fabddb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot Recall Result\n",
+    "ml.plot_result(model_name, \"Recall\", \"Recall scores in 5 Folds\", logistic_regression_result[\"Training Recall scores\"], logistic_regression_result[\"Validation Recall scores\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb84d852",
+   "metadata": {},
+   "source": [
+    "### f1 score result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ef123525",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot F1-Score Result\n",
+    "ml.plot_result(model_name, \"F1\", \"F1 Scores in 5 Folds\", logistic_regression_result[\"Training F1 scores\"], logistic_regression_result[\"Validation F1 scores\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7eb38e7f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/modeling.ipynb b/notebooks/modeling.ipynb
index 028f9f3..1cc5dfb 100644
--- a/notebooks/modeling.ipynb
+++ b/notebooks/modeling.ipynb
@@ -2,18 +2,10 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 1,
    "id": "285e3e4a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ERROR! Session/line number was not unique in database. History logging moved to new session 68\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -42,7 +34,12 @@
     "# To evaluate end result we have\n",
     "from sklearn.metrics import accuracy_score, confusion_matrix\n",
     "from sklearn.model_selection import LeaveOneOut\n",
-    "from sklearn.model_selection import cross_val_score\n"
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "from urllib.parse import urlparse\n",
+    "import mlflow\n",
+    "import mlflow.sklearn\n",
+    "import logging\n"
    ]
   },
   {