diff --git a/.ipynb_checkpoints/Solutions-checkpoint.ipynb b/.ipynb_checkpoints/Solutions-checkpoint.ipynb
new file mode 100644
index 0000000..fd4dfe2
--- /dev/null
+++ b/.ipynb_checkpoints/Solutions-checkpoint.ipynb
@@ -0,0 +1,2016 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e4f6e1b",
+   "metadata": {},
+   "source": [
+    "## Import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ca212e64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import datetime\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "879fa2fb",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b6c0b1ed",
+   "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>gender</th>\n",
+       "      <th>SeniorCitizen</th>\n",
+       "      <th>Partner</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>PhoneService</th>\n",
+       "      <th>OnlineSecurity</th>\n",
+       "      <th>OnlineBackup</th>\n",
+       "      <th>DeviceProtection</th>\n",
+       "      <th>TechSupport</th>\n",
+       "      <th>StreamingTV</th>\n",
+       "      <th>StreamingMovies</th>\n",
+       "      <th>Contract</th>\n",
+       "      <th>MonthlyCharges</th>\n",
+       "      <th>TotalCharges</th>\n",
+       "      <th>Churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.5</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>108.15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>45</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>70.70</td>\n",
+       "      <td>151.65</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7038</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>24</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>84.80</td>\n",
+       "      <td>1990.5</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7039</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>72</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>103.20</td>\n",
+       "      <td>7362.9</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7040</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>11</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>29.60</td>\n",
+       "      <td>346.45</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7041</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>74.40</td>\n",
+       "      <td>306.6</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7042</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>66</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>105.65</td>\n",
+       "      <td>6844.5</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7043 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
+       "0     Female              0     Yes         No       1           No   \n",
+       "1       Male              0      No         No      34          Yes   \n",
+       "2       Male              0      No         No       2          Yes   \n",
+       "3       Male              0      No         No      45           No   \n",
+       "4     Female              0      No         No       2          Yes   \n",
+       "...      ...            ...     ...        ...     ...          ...   \n",
+       "7038    Male              0     Yes        Yes      24          Yes   \n",
+       "7039  Female              0     Yes        Yes      72          Yes   \n",
+       "7040  Female              0     Yes        Yes      11           No   \n",
+       "7041    Male              1     Yes         No       4          Yes   \n",
+       "7042    Male              0      No         No      66          Yes   \n",
+       "\n",
+       "     OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV  \\\n",
+       "0                No          Yes               No          No          No   \n",
+       "1               Yes           No              Yes          No          No   \n",
+       "2               Yes          Yes               No          No          No   \n",
+       "3               Yes           No              Yes         Yes          No   \n",
+       "4                No           No               No          No          No   \n",
+       "...             ...          ...              ...         ...         ...   \n",
+       "7038            Yes           No              Yes         Yes         Yes   \n",
+       "7039             No          Yes              Yes          No         Yes   \n",
+       "7040            Yes           No               No          No          No   \n",
+       "7041             No           No               No          No          No   \n",
+       "7042            Yes           No              Yes         Yes         Yes   \n",
+       "\n",
+       "     StreamingMovies        Contract  MonthlyCharges TotalCharges Churn  \n",
+       "0                 No  Month-to-month           29.85        29.85    No  \n",
+       "1                 No        One year           56.95       1889.5    No  \n",
+       "2                 No  Month-to-month           53.85       108.15   Yes  \n",
+       "3                 No        One year           42.30      1840.75    No  \n",
+       "4                 No  Month-to-month           70.70       151.65   Yes  \n",
+       "...              ...             ...             ...          ...   ...  \n",
+       "7038             Yes        One year           84.80       1990.5    No  \n",
+       "7039             Yes        One year          103.20       7362.9    No  \n",
+       "7040              No  Month-to-month           29.60       346.45    No  \n",
+       "7041              No  Month-to-month           74.40        306.6   Yes  \n",
+       "7042             Yes        Two year          105.65       6844.5    No  \n",
+       "\n",
+       "[7043 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata = pd.read_csv('/Users/yuriawano/lab-handling-data-imbalance-classification/files_for_lab/Customer-Churn.csv')\n",
+    "churndata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3ad505a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7043, 16)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "569f28be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# standardize the header\n",
+    "churndata.columns = [columns.lower() for columns in churndata.columns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2fcd3922",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['gender', 'seniorcitizen', 'partner', 'dependents', 'tenure',\n",
+       "       'phoneservice', 'onlinesecurity', 'onlinebackup', 'deviceprotection',\n",
+       "       'techsupport', 'streamingtv', 'streamingmovies', 'contract',\n",
+       "       'monthlycharges', 'totalcharges', 'churn'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4bc0be4",
+   "metadata": {},
+   "source": [
+    "## Check the datatypes of all the columns in the data. \n",
+    "You would see that the column TotalCharges is object type. Convert this column into numeric type using pd.to_numeric function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "58628135",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender               object\n",
+       "seniorcitizen         int64\n",
+       "partner              object\n",
+       "dependents           object\n",
+       "tenure                int64\n",
+       "phoneservice         object\n",
+       "onlinesecurity       object\n",
+       "onlinebackup         object\n",
+       "deviceprotection     object\n",
+       "techsupport          object\n",
+       "streamingtv          object\n",
+       "streamingmovies      object\n",
+       "contract             object\n",
+       "monthlycharges      float64\n",
+       "totalcharges         object\n",
+       "churn                object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e512e97d",
+   "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>totalcharges</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1889.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>108.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1840.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>151.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7038</th>\n",
+       "      <td>1990.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7039</th>\n",
+       "      <td>7362.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7040</th>\n",
+       "      <td>346.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7041</th>\n",
+       "      <td>306.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7042</th>\n",
+       "      <td>6844.50</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7043 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      totalcharges\n",
+       "0            29.85\n",
+       "1          1889.50\n",
+       "2           108.15\n",
+       "3          1840.75\n",
+       "4           151.65\n",
+       "...            ...\n",
+       "7038       1990.50\n",
+       "7039       7362.90\n",
+       "7040        346.45\n",
+       "7041        306.60\n",
+       "7042       6844.50\n",
+       "\n",
+       "[7043 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata[['totalcharges']] = churndata[['totalcharges']].apply(pd.to_numeric, errors='coerce')\n",
+    "churndata[['totalcharges']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c7247811",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata['totalcharges'].dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d98f917",
+   "metadata": {},
+   "source": [
+    "## Deal with null values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fa36e5d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender               0\n",
+       "seniorcitizen        0\n",
+       "partner              0\n",
+       "dependents           0\n",
+       "tenure               0\n",
+       "phoneservice         0\n",
+       "onlinesecurity       0\n",
+       "onlinebackup         0\n",
+       "deviceprotection     0\n",
+       "techsupport          0\n",
+       "streamingtv          0\n",
+       "streamingmovies      0\n",
+       "contract             0\n",
+       "monthlycharges       0\n",
+       "totalcharges        11\n",
+       "churn                0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "71037922",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fill_mean(data, columns):\n",
+    "    \"\"\"\n",
+    "    data: dataframe\n",
+    "    columns: columns\n",
+    "    return: modified dataframe with mean filled \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    for col in columns:\n",
+    "        mean_value = data[columns].mean()\n",
+    "        data[columns] = data[columns].fillna(mean_value)\n",
+    "    \n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9eabb326",
+   "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>gender</th>\n",
+       "      <th>seniorcitizen</th>\n",
+       "      <th>partner</th>\n",
+       "      <th>dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>phoneservice</th>\n",
+       "      <th>onlinesecurity</th>\n",
+       "      <th>onlinebackup</th>\n",
+       "      <th>deviceprotection</th>\n",
+       "      <th>techsupport</th>\n",
+       "      <th>streamingtv</th>\n",
+       "      <th>streamingmovies</th>\n",
+       "      <th>contract</th>\n",
+       "      <th>monthlycharges</th>\n",
+       "      <th>totalcharges</th>\n",
+       "      <th>churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>108.15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>45</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>70.70</td>\n",
+       "      <td>151.65</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7038</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>24</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>84.80</td>\n",
+       "      <td>1990.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7039</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>72</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>103.20</td>\n",
+       "      <td>7362.90</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7040</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>11</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>29.60</td>\n",
+       "      <td>346.45</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7041</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>74.40</td>\n",
+       "      <td>306.60</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7042</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>66</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>105.65</td>\n",
+       "      <td>6844.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7043 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      gender  seniorcitizen partner dependents  tenure phoneservice  \\\n",
+       "0     Female              0     Yes         No       1           No   \n",
+       "1       Male              0      No         No      34          Yes   \n",
+       "2       Male              0      No         No       2          Yes   \n",
+       "3       Male              0      No         No      45           No   \n",
+       "4     Female              0      No         No       2          Yes   \n",
+       "...      ...            ...     ...        ...     ...          ...   \n",
+       "7038    Male              0     Yes        Yes      24          Yes   \n",
+       "7039  Female              0     Yes        Yes      72          Yes   \n",
+       "7040  Female              0     Yes        Yes      11           No   \n",
+       "7041    Male              1     Yes         No       4          Yes   \n",
+       "7042    Male              0      No         No      66          Yes   \n",
+       "\n",
+       "     onlinesecurity onlinebackup deviceprotection techsupport streamingtv  \\\n",
+       "0                No          Yes               No          No          No   \n",
+       "1               Yes           No              Yes          No          No   \n",
+       "2               Yes          Yes               No          No          No   \n",
+       "3               Yes           No              Yes         Yes          No   \n",
+       "4                No           No               No          No          No   \n",
+       "...             ...          ...              ...         ...         ...   \n",
+       "7038            Yes           No              Yes         Yes         Yes   \n",
+       "7039             No          Yes              Yes          No         Yes   \n",
+       "7040            Yes           No               No          No          No   \n",
+       "7041             No           No               No          No          No   \n",
+       "7042            Yes           No              Yes         Yes         Yes   \n",
+       "\n",
+       "     streamingmovies        contract  monthlycharges  totalcharges churn  \n",
+       "0                 No  Month-to-month           29.85         29.85    No  \n",
+       "1                 No        One year           56.95       1889.50    No  \n",
+       "2                 No  Month-to-month           53.85        108.15   Yes  \n",
+       "3                 No        One year           42.30       1840.75    No  \n",
+       "4                 No  Month-to-month           70.70        151.65   Yes  \n",
+       "...              ...             ...             ...           ...   ...  \n",
+       "7038             Yes        One year           84.80       1990.50    No  \n",
+       "7039             Yes        One year          103.20       7362.90    No  \n",
+       "7040              No  Month-to-month           29.60        346.45    No  \n",
+       "7041              No  Month-to-month           74.40        306.60   Yes  \n",
+       "7042             Yes        Two year          105.65       6844.50    No  \n",
+       "\n",
+       "[7043 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fill_mean(churndata, ['totalcharges'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7811892c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata['totalcharges'].isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef4677c5",
+   "metadata": {},
+   "source": [
+    "## Use the following features: tenure, SeniorCitizen, MonthlyCharges and TotalCharges\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3c235b3b",
+   "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>tenure</th>\n",
+       "      <th>seniorcitizen</th>\n",
+       "      <th>monthlycharges</th>\n",
+       "      <th>totalcharges</th>\n",
+       "      <th>churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>108.15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70.70</td>\n",
+       "      <td>151.65</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7038</th>\n",
+       "      <td>24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.80</td>\n",
+       "      <td>1990.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7039</th>\n",
+       "      <td>72</td>\n",
+       "      <td>0</td>\n",
+       "      <td>103.20</td>\n",
+       "      <td>7362.90</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7040</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.60</td>\n",
+       "      <td>346.45</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7041</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>74.40</td>\n",
+       "      <td>306.60</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7042</th>\n",
+       "      <td>66</td>\n",
+       "      <td>0</td>\n",
+       "      <td>105.65</td>\n",
+       "      <td>6844.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7043 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      tenure  seniorcitizen  monthlycharges  totalcharges churn\n",
+       "0          1              0           29.85         29.85    No\n",
+       "1         34              0           56.95       1889.50    No\n",
+       "2          2              0           53.85        108.15   Yes\n",
+       "3         45              0           42.30       1840.75    No\n",
+       "4          2              0           70.70        151.65   Yes\n",
+       "...      ...            ...             ...           ...   ...\n",
+       "7038      24              0           84.80       1990.50    No\n",
+       "7039      72              0          103.20       7362.90    No\n",
+       "7040      11              0           29.60        346.45    No\n",
+       "7041       4              1           74.40        306.60   Yes\n",
+       "7042      66              0          105.65       6844.50    No\n",
+       "\n",
+       "[7043 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = churndata[['tenure', 'seniorcitizen', 'monthlycharges', 'totalcharges', 'churn']]\n",
+    "\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae6f5abc",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5738d6d3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00118eee",
+   "metadata": {},
+   "source": [
+    "## Split the data into a training set and a test set.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdc871ee",
+   "metadata": {},
+   "source": [
+    "Here I will split first before normalizing / standardizing so it is similar to real time cases when we want to predict future customers "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2ed9856c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data.drop('churn', axis =1)\n",
+    "y = data['churn']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "af7dd0d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5033977b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_num = X_train.select_dtypes(np.number)\n",
+    "X_test_num = X_test.select_dtypes(np.number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "feba8302",
+   "metadata": {},
+   "source": [
+    "## Scale the features either by using normalizer or a standard scaler."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c45c86b",
+   "metadata": {},
+   "source": [
+    "### Scale the numericals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f70f1cb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformer = StandardScaler().fit(X_train_num)\n",
+    "x_train_stand = transformer.transform(X_train_num)\n",
+    "\n",
+    "\n",
+    "# saving in a pickle\n",
+    "with open('num_train_transformer.pickle', 'wb') as file:\n",
+    "    pickle.dump(transformer, file)\n",
+    "    \n",
+    "# loading from a pickle  \n",
+    "with open('num_train_transformer.pickle', 'rb') as file:\n",
+    "    loaded_transformer = pickle.load(file)\n",
+    "\n",
+    "X_train_n = loaded_transformer.transform(X_train_num)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2334aa7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformer = StandardScaler().fit(X_test_num)\n",
+    "X_test_n = transformer.transform(X_test_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d0abd101",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((5634, 4), (1409, 4))"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train_n.shape, X_test_n.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00d423f5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "526cc711",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3fe43db",
+   "metadata": {},
+   "source": [
+    "## Fit a logistic regression model on the training data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "2b0192a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(max_iter=10000, random_state=42)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "classification = LogisticRegression(random_state=42, max_iter=10000)\n",
+    "classification.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07fb139e",
+   "metadata": {},
+   "source": [
+    "## Check the accuracy on the test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "50ce28a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8069552874378992"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classification.score(X_test, y_test) # checking the score without the need for predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "587e2a45",
+   "metadata": {},
+   "source": [
+    "## Check for the imbalance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7a92c40c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "No     5174\n",
+       "Yes    1869\n",
+       "Name: churn, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['churn'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24f0ea7d",
+   "metadata": {},
+   "source": [
+    "## Use the resampling strategies used in class for upsampling and downsampling to create a balance between the two classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "818bebb1",
+   "metadata": {},
+   "source": [
+    "### Upsampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "876e424d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category_No = data[data['churn'] == 'No']\n",
+    "category_Yes = data[data['churn'] == 'Yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "33965b67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category_Yes = category_Yes.sample(len(category_No), replace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "965a8ee7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5174, 5) (5174, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(category_No.shape, category_Yes.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "310717f7",
+   "metadata": {},
+   "source": [
+    "### Downsampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e8500acd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category_No_down = data[data['churn'] == 'No']\n",
+    "category_Yes_down = data[data['churn'] == 'Yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c2cb832b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category_No_down = category_No_down.sample(len(category_Yes_down), replace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "edabb733",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1869, 5) (1869, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(category_No_down.shape, category_Yes_down.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2194aabe",
+   "metadata": {},
+   "source": [
+    "## Each time fit the model and see how the accuracy of the model is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fbb515c",
+   "metadata": {},
+   "source": [
+    "### Upsampling's accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "7a81ed43",
+   "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>tenure</th>\n",
+       "      <th>seniorcitizen</th>\n",
+       "      <th>monthlycharges</th>\n",
+       "      <th>totalcharges</th>\n",
+       "      <th>churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>89.10</td>\n",
+       "      <td>1949.40</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.75</td>\n",
+       "      <td>301.90</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1675</th>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>78.90</td>\n",
+       "      <td>2384.15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2526</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19.40</td>\n",
+       "      <td>19.40</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5023</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70.30</td>\n",
+       "      <td>70.30</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4901</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>74.70</td>\n",
+       "      <td>74.70</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2397</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>88.35</td>\n",
+       "      <td>88.35</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10348 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      tenure  seniorcitizen  monthlycharges  totalcharges churn\n",
+       "0          1              0           29.85         29.85    No\n",
+       "1         34              0           56.95       1889.50    No\n",
+       "3         45              0           42.30       1840.75    No\n",
+       "6         22              0           89.10       1949.40    No\n",
+       "7         10              0           29.75        301.90    No\n",
+       "...      ...            ...             ...           ...   ...\n",
+       "1675      29              0           78.90       2384.15   Yes\n",
+       "2526       1              0           19.40         19.40   Yes\n",
+       "5023       1              0           70.30         70.30   Yes\n",
+       "4901       1              0           74.70         74.70   Yes\n",
+       "2397       1              0           88.35         88.35   Yes\n",
+       "\n",
+       "[10348 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "upsample_df = pd.concat([category_No, category_Yes])\n",
+    "upsample_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "b936b3fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_up = upsample_df.drop('churn', axis = 1)\n",
+    "y_up = upsample_df['churn']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "042a47df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X_up, y_up, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "3e51b049",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(max_iter=10000, random_state=42)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "classification = LogisticRegression(random_state=42, max_iter=10000)\n",
+    "classification.fit(X_up, y_up)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "08361547",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7328502415458937"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classification.score(X_test, y_test) # checking the score without the need for predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f921f113",
+   "metadata": {},
+   "source": [
+    "### Downscaling accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "8dd7d388",
+   "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>tenure</th>\n",
+       "      <th>seniorcitizen</th>\n",
+       "      <th>monthlycharges</th>\n",
+       "      <th>totalcharges</th>\n",
+       "      <th>churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5704</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>86.85</td>\n",
+       "      <td>1564.40</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1312</th>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70.95</td>\n",
+       "      <td>3629.20</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2684</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>105.20</td>\n",
+       "      <td>7386.05</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4824</th>\n",
+       "      <td>72</td>\n",
+       "      <td>1</td>\n",
+       "      <td>97.25</td>\n",
+       "      <td>7133.10</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4042</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.35</td>\n",
+       "      <td>262.30</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7021</th>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>59.80</td>\n",
+       "      <td>727.80</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7026</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>44.20</td>\n",
+       "      <td>403.35</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7032</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>75.75</td>\n",
+       "      <td>75.75</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7034</th>\n",
+       "      <td>67</td>\n",
+       "      <td>0</td>\n",
+       "      <td>102.95</td>\n",
+       "      <td>6886.25</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7041</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>74.40</td>\n",
+       "      <td>306.60</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3738 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      tenure  seniorcitizen  monthlycharges  totalcharges churn\n",
+       "5704      19              0           86.85       1564.40    No\n",
+       "1312      48              0           70.95       3629.20    No\n",
+       "2684      69              0          105.20       7386.05    No\n",
+       "4824      72              1           97.25       7133.10    No\n",
+       "4042       5              0           51.35        262.30    No\n",
+       "...      ...            ...             ...           ...   ...\n",
+       "7021      12              0           59.80        727.80   Yes\n",
+       "7026       9              0           44.20        403.35   Yes\n",
+       "7032       1              1           75.75         75.75   Yes\n",
+       "7034      67              0          102.95       6886.25   Yes\n",
+       "7041       4              1           74.40        306.60   Yes\n",
+       "\n",
+       "[3738 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "downsample_df = pd.concat([category_No_down, category_Yes_down])\n",
+    "downsample_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6e2205be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_down = upsample_df.drop('churn', axis = 1)\n",
+    "y_down = upsample_df['churn']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "223bff54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X_down, y_down, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "62370cbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(max_iter=10000, random_state=42)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "classification = LogisticRegression(random_state=42, max_iter=10000)\n",
+    "classification.fit(X_down, y_down)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "507b16b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7328502415458937"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classification.score(X_test, y_test) # checking the score without the need for predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bad81c15",
+   "metadata": {},
+   "source": [
+    "### Result of upsampling and downsampling score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d85b2f7",
+   "metadata": {},
+   "source": [
+    "Same accuracy score is produced and I am not sure if that happens..... "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b88b869f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Solutions.ipynb b/Solutions.ipynb
new file mode 100644
index 0000000..fd4dfe2
--- /dev/null
+++ b/Solutions.ipynb
@@ -0,0 +1,2016 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2e4f6e1b",
+   "metadata": {},
+   "source": [
+    "## Import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ca212e64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import datetime\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "import pickle\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "879fa2fb",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b6c0b1ed",
+   "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>gender</th>\n",
+       "      <th>SeniorCitizen</th>\n",
+       "      <th>Partner</th>\n",
+       "      <th>Dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>PhoneService</th>\n",
+       "      <th>OnlineSecurity</th>\n",
+       "      <th>OnlineBackup</th>\n",
+       "      <th>DeviceProtection</th>\n",
+       "      <th>TechSupport</th>\n",
+       "      <th>StreamingTV</th>\n",
+       "      <th>StreamingMovies</th>\n",
+       "      <th>Contract</th>\n",
+       "      <th>MonthlyCharges</th>\n",
+       "      <th>TotalCharges</th>\n",
+       "      <th>Churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.5</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>108.15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>45</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>70.70</td>\n",
+       "      <td>151.65</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7038</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>24</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>84.80</td>\n",
+       "      <td>1990.5</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7039</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>72</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>103.20</td>\n",
+       "      <td>7362.9</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7040</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>11</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>29.60</td>\n",
+       "      <td>346.45</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7041</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>74.40</td>\n",
+       "      <td>306.6</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7042</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>66</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>105.65</td>\n",
+       "      <td>6844.5</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7043 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
+       "0     Female              0     Yes         No       1           No   \n",
+       "1       Male              0      No         No      34          Yes   \n",
+       "2       Male              0      No         No       2          Yes   \n",
+       "3       Male              0      No         No      45           No   \n",
+       "4     Female              0      No         No       2          Yes   \n",
+       "...      ...            ...     ...        ...     ...          ...   \n",
+       "7038    Male              0     Yes        Yes      24          Yes   \n",
+       "7039  Female              0     Yes        Yes      72          Yes   \n",
+       "7040  Female              0     Yes        Yes      11           No   \n",
+       "7041    Male              1     Yes         No       4          Yes   \n",
+       "7042    Male              0      No         No      66          Yes   \n",
+       "\n",
+       "     OnlineSecurity OnlineBackup DeviceProtection TechSupport StreamingTV  \\\n",
+       "0                No          Yes               No          No          No   \n",
+       "1               Yes           No              Yes          No          No   \n",
+       "2               Yes          Yes               No          No          No   \n",
+       "3               Yes           No              Yes         Yes          No   \n",
+       "4                No           No               No          No          No   \n",
+       "...             ...          ...              ...         ...         ...   \n",
+       "7038            Yes           No              Yes         Yes         Yes   \n",
+       "7039             No          Yes              Yes          No         Yes   \n",
+       "7040            Yes           No               No          No          No   \n",
+       "7041             No           No               No          No          No   \n",
+       "7042            Yes           No              Yes         Yes         Yes   \n",
+       "\n",
+       "     StreamingMovies        Contract  MonthlyCharges TotalCharges Churn  \n",
+       "0                 No  Month-to-month           29.85        29.85    No  \n",
+       "1                 No        One year           56.95       1889.5    No  \n",
+       "2                 No  Month-to-month           53.85       108.15   Yes  \n",
+       "3                 No        One year           42.30      1840.75    No  \n",
+       "4                 No  Month-to-month           70.70       151.65   Yes  \n",
+       "...              ...             ...             ...          ...   ...  \n",
+       "7038             Yes        One year           84.80       1990.5    No  \n",
+       "7039             Yes        One year          103.20       7362.9    No  \n",
+       "7040              No  Month-to-month           29.60       346.45    No  \n",
+       "7041              No  Month-to-month           74.40        306.6   Yes  \n",
+       "7042             Yes        Two year          105.65       6844.5    No  \n",
+       "\n",
+       "[7043 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata = pd.read_csv('/Users/yuriawano/lab-handling-data-imbalance-classification/files_for_lab/Customer-Churn.csv')\n",
+    "churndata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3ad505a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7043, 16)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "569f28be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# standardize the header\n",
+    "churndata.columns = [columns.lower() for columns in churndata.columns]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2fcd3922",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['gender', 'seniorcitizen', 'partner', 'dependents', 'tenure',\n",
+       "       'phoneservice', 'onlinesecurity', 'onlinebackup', 'deviceprotection',\n",
+       "       'techsupport', 'streamingtv', 'streamingmovies', 'contract',\n",
+       "       'monthlycharges', 'totalcharges', 'churn'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4bc0be4",
+   "metadata": {},
+   "source": [
+    "## Check the datatypes of all the columns in the data. \n",
+    "You would see that the column TotalCharges is object type. Convert this column into numeric type using pd.to_numeric function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "58628135",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender               object\n",
+       "seniorcitizen         int64\n",
+       "partner              object\n",
+       "dependents           object\n",
+       "tenure                int64\n",
+       "phoneservice         object\n",
+       "onlinesecurity       object\n",
+       "onlinebackup         object\n",
+       "deviceprotection     object\n",
+       "techsupport          object\n",
+       "streamingtv          object\n",
+       "streamingmovies      object\n",
+       "contract             object\n",
+       "monthlycharges      float64\n",
+       "totalcharges         object\n",
+       "churn                object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e512e97d",
+   "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>totalcharges</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1889.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>108.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1840.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>151.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7038</th>\n",
+       "      <td>1990.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7039</th>\n",
+       "      <td>7362.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7040</th>\n",
+       "      <td>346.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7041</th>\n",
+       "      <td>306.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7042</th>\n",
+       "      <td>6844.50</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7043 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      totalcharges\n",
+       "0            29.85\n",
+       "1          1889.50\n",
+       "2           108.15\n",
+       "3          1840.75\n",
+       "4           151.65\n",
+       "...            ...\n",
+       "7038       1990.50\n",
+       "7039       7362.90\n",
+       "7040        346.45\n",
+       "7041        306.60\n",
+       "7042       6844.50\n",
+       "\n",
+       "[7043 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata[['totalcharges']] = churndata[['totalcharges']].apply(pd.to_numeric, errors='coerce')\n",
+    "churndata[['totalcharges']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c7247811",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata['totalcharges'].dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d98f917",
+   "metadata": {},
+   "source": [
+    "## Deal with null values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fa36e5d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gender               0\n",
+       "seniorcitizen        0\n",
+       "partner              0\n",
+       "dependents           0\n",
+       "tenure               0\n",
+       "phoneservice         0\n",
+       "onlinesecurity       0\n",
+       "onlinebackup         0\n",
+       "deviceprotection     0\n",
+       "techsupport          0\n",
+       "streamingtv          0\n",
+       "streamingmovies      0\n",
+       "contract             0\n",
+       "monthlycharges       0\n",
+       "totalcharges        11\n",
+       "churn                0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "71037922",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fill_mean(data, columns):\n",
+    "    \"\"\"\n",
+    "    data: dataframe\n",
+    "    columns: columns\n",
+    "    return: modified dataframe with mean filled \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    for col in columns:\n",
+    "        mean_value = data[columns].mean()\n",
+    "        data[columns] = data[columns].fillna(mean_value)\n",
+    "    \n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9eabb326",
+   "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>gender</th>\n",
+       "      <th>seniorcitizen</th>\n",
+       "      <th>partner</th>\n",
+       "      <th>dependents</th>\n",
+       "      <th>tenure</th>\n",
+       "      <th>phoneservice</th>\n",
+       "      <th>onlinesecurity</th>\n",
+       "      <th>onlinebackup</th>\n",
+       "      <th>deviceprotection</th>\n",
+       "      <th>techsupport</th>\n",
+       "      <th>streamingtv</th>\n",
+       "      <th>streamingmovies</th>\n",
+       "      <th>contract</th>\n",
+       "      <th>monthlycharges</th>\n",
+       "      <th>totalcharges</th>\n",
+       "      <th>churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>34</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>108.15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>45</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>70.70</td>\n",
+       "      <td>151.65</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7038</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>24</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>84.80</td>\n",
+       "      <td>1990.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7039</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>72</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>One year</td>\n",
+       "      <td>103.20</td>\n",
+       "      <td>7362.90</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7040</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>11</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>29.60</td>\n",
+       "      <td>346.45</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7041</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Month-to-month</td>\n",
+       "      <td>74.40</td>\n",
+       "      <td>306.60</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7042</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>66</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Two year</td>\n",
+       "      <td>105.65</td>\n",
+       "      <td>6844.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7043 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      gender  seniorcitizen partner dependents  tenure phoneservice  \\\n",
+       "0     Female              0     Yes         No       1           No   \n",
+       "1       Male              0      No         No      34          Yes   \n",
+       "2       Male              0      No         No       2          Yes   \n",
+       "3       Male              0      No         No      45           No   \n",
+       "4     Female              0      No         No       2          Yes   \n",
+       "...      ...            ...     ...        ...     ...          ...   \n",
+       "7038    Male              0     Yes        Yes      24          Yes   \n",
+       "7039  Female              0     Yes        Yes      72          Yes   \n",
+       "7040  Female              0     Yes        Yes      11           No   \n",
+       "7041    Male              1     Yes         No       4          Yes   \n",
+       "7042    Male              0      No         No      66          Yes   \n",
+       "\n",
+       "     onlinesecurity onlinebackup deviceprotection techsupport streamingtv  \\\n",
+       "0                No          Yes               No          No          No   \n",
+       "1               Yes           No              Yes          No          No   \n",
+       "2               Yes          Yes               No          No          No   \n",
+       "3               Yes           No              Yes         Yes          No   \n",
+       "4                No           No               No          No          No   \n",
+       "...             ...          ...              ...         ...         ...   \n",
+       "7038            Yes           No              Yes         Yes         Yes   \n",
+       "7039             No          Yes              Yes          No         Yes   \n",
+       "7040            Yes           No               No          No          No   \n",
+       "7041             No           No               No          No          No   \n",
+       "7042            Yes           No              Yes         Yes         Yes   \n",
+       "\n",
+       "     streamingmovies        contract  monthlycharges  totalcharges churn  \n",
+       "0                 No  Month-to-month           29.85         29.85    No  \n",
+       "1                 No        One year           56.95       1889.50    No  \n",
+       "2                 No  Month-to-month           53.85        108.15   Yes  \n",
+       "3                 No        One year           42.30       1840.75    No  \n",
+       "4                 No  Month-to-month           70.70        151.65   Yes  \n",
+       "...              ...             ...             ...           ...   ...  \n",
+       "7038             Yes        One year           84.80       1990.50    No  \n",
+       "7039             Yes        One year          103.20       7362.90    No  \n",
+       "7040              No  Month-to-month           29.60        346.45    No  \n",
+       "7041              No  Month-to-month           74.40        306.60   Yes  \n",
+       "7042             Yes        Two year          105.65       6844.50    No  \n",
+       "\n",
+       "[7043 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fill_mean(churndata, ['totalcharges'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7811892c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churndata['totalcharges'].isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef4677c5",
+   "metadata": {},
+   "source": [
+    "## Use the following features: tenure, SeniorCitizen, MonthlyCharges and TotalCharges\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3c235b3b",
+   "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>tenure</th>\n",
+       "      <th>seniorcitizen</th>\n",
+       "      <th>monthlycharges</th>\n",
+       "      <th>totalcharges</th>\n",
+       "      <th>churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.85</td>\n",
+       "      <td>108.15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70.70</td>\n",
+       "      <td>151.65</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7038</th>\n",
+       "      <td>24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.80</td>\n",
+       "      <td>1990.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7039</th>\n",
+       "      <td>72</td>\n",
+       "      <td>0</td>\n",
+       "      <td>103.20</td>\n",
+       "      <td>7362.90</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7040</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.60</td>\n",
+       "      <td>346.45</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7041</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>74.40</td>\n",
+       "      <td>306.60</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7042</th>\n",
+       "      <td>66</td>\n",
+       "      <td>0</td>\n",
+       "      <td>105.65</td>\n",
+       "      <td>6844.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7043 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      tenure  seniorcitizen  monthlycharges  totalcharges churn\n",
+       "0          1              0           29.85         29.85    No\n",
+       "1         34              0           56.95       1889.50    No\n",
+       "2          2              0           53.85        108.15   Yes\n",
+       "3         45              0           42.30       1840.75    No\n",
+       "4          2              0           70.70        151.65   Yes\n",
+       "...      ...            ...             ...           ...   ...\n",
+       "7038      24              0           84.80       1990.50    No\n",
+       "7039      72              0          103.20       7362.90    No\n",
+       "7040      11              0           29.60        346.45    No\n",
+       "7041       4              1           74.40        306.60   Yes\n",
+       "7042      66              0          105.65       6844.50    No\n",
+       "\n",
+       "[7043 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = churndata[['tenure', 'seniorcitizen', 'monthlycharges', 'totalcharges', 'churn']]\n",
+    "\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae6f5abc",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5738d6d3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00118eee",
+   "metadata": {},
+   "source": [
+    "## Split the data into a training set and a test set.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fdc871ee",
+   "metadata": {},
+   "source": [
+    "Here I will split first before normalizing / standardizing so it is similar to real time cases when we want to predict future customers "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2ed9856c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data.drop('churn', axis =1)\n",
+    "y = data['churn']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "af7dd0d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5033977b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_num = X_train.select_dtypes(np.number)\n",
+    "X_test_num = X_test.select_dtypes(np.number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "feba8302",
+   "metadata": {},
+   "source": [
+    "## Scale the features either by using normalizer or a standard scaler."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c45c86b",
+   "metadata": {},
+   "source": [
+    "### Scale the numericals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f70f1cb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformer = StandardScaler().fit(X_train_num)\n",
+    "x_train_stand = transformer.transform(X_train_num)\n",
+    "\n",
+    "\n",
+    "# saving in a pickle\n",
+    "with open('num_train_transformer.pickle', 'wb') as file:\n",
+    "    pickle.dump(transformer, file)\n",
+    "    \n",
+    "# loading from a pickle  \n",
+    "with open('num_train_transformer.pickle', 'rb') as file:\n",
+    "    loaded_transformer = pickle.load(file)\n",
+    "\n",
+    "X_train_n = loaded_transformer.transform(X_train_num)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2334aa7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformer = StandardScaler().fit(X_test_num)\n",
+    "X_test_n = transformer.transform(X_test_num)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d0abd101",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((5634, 4), (1409, 4))"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train_n.shape, X_test_n.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00d423f5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "526cc711",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3fe43db",
+   "metadata": {},
+   "source": [
+    "## Fit a logistic regression model on the training data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "2b0192a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(max_iter=10000, random_state=42)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "classification = LogisticRegression(random_state=42, max_iter=10000)\n",
+    "classification.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07fb139e",
+   "metadata": {},
+   "source": [
+    "## Check the accuracy on the test data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "50ce28a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8069552874378992"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classification.score(X_test, y_test) # checking the score without the need for predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "587e2a45",
+   "metadata": {},
+   "source": [
+    "## Check for the imbalance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7a92c40c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "No     5174\n",
+       "Yes    1869\n",
+       "Name: churn, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['churn'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24f0ea7d",
+   "metadata": {},
+   "source": [
+    "## Use the resampling strategies used in class for upsampling and downsampling to create a balance between the two classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "818bebb1",
+   "metadata": {},
+   "source": [
+    "### Upsampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "876e424d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category_No = data[data['churn'] == 'No']\n",
+    "category_Yes = data[data['churn'] == 'Yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "33965b67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category_Yes = category_Yes.sample(len(category_No), replace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "965a8ee7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5174, 5) (5174, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(category_No.shape, category_Yes.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "310717f7",
+   "metadata": {},
+   "source": [
+    "### Downsampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e8500acd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category_No_down = data[data['churn'] == 'No']\n",
+    "category_Yes_down = data[data['churn'] == 'Yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c2cb832b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "category_No_down = category_No_down.sample(len(category_Yes_down), replace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "edabb733",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1869, 5) (1869, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(category_No_down.shape, category_Yes_down.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2194aabe",
+   "metadata": {},
+   "source": [
+    "## Each time fit the model and see how the accuracy of the model is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fbb515c",
+   "metadata": {},
+   "source": [
+    "### Upsampling's accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "7a81ed43",
+   "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>tenure</th>\n",
+       "      <th>seniorcitizen</th>\n",
+       "      <th>monthlycharges</th>\n",
+       "      <th>totalcharges</th>\n",
+       "      <th>churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>29.85</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56.95</td>\n",
+       "      <td>1889.50</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42.30</td>\n",
+       "      <td>1840.75</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>89.10</td>\n",
+       "      <td>1949.40</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.75</td>\n",
+       "      <td>301.90</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1675</th>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>78.90</td>\n",
+       "      <td>2384.15</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2526</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19.40</td>\n",
+       "      <td>19.40</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5023</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70.30</td>\n",
+       "      <td>70.30</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4901</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>74.70</td>\n",
+       "      <td>74.70</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2397</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>88.35</td>\n",
+       "      <td>88.35</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10348 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      tenure  seniorcitizen  monthlycharges  totalcharges churn\n",
+       "0          1              0           29.85         29.85    No\n",
+       "1         34              0           56.95       1889.50    No\n",
+       "3         45              0           42.30       1840.75    No\n",
+       "6         22              0           89.10       1949.40    No\n",
+       "7         10              0           29.75        301.90    No\n",
+       "...      ...            ...             ...           ...   ...\n",
+       "1675      29              0           78.90       2384.15   Yes\n",
+       "2526       1              0           19.40         19.40   Yes\n",
+       "5023       1              0           70.30         70.30   Yes\n",
+       "4901       1              0           74.70         74.70   Yes\n",
+       "2397       1              0           88.35         88.35   Yes\n",
+       "\n",
+       "[10348 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "upsample_df = pd.concat([category_No, category_Yes])\n",
+    "upsample_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "b936b3fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_up = upsample_df.drop('churn', axis = 1)\n",
+    "y_up = upsample_df['churn']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "042a47df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X_up, y_up, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "3e51b049",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(max_iter=10000, random_state=42)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "classification = LogisticRegression(random_state=42, max_iter=10000)\n",
+    "classification.fit(X_up, y_up)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "08361547",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7328502415458937"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classification.score(X_test, y_test) # checking the score without the need for predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f921f113",
+   "metadata": {},
+   "source": [
+    "### Downscaling accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "8dd7d388",
+   "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>tenure</th>\n",
+       "      <th>seniorcitizen</th>\n",
+       "      <th>monthlycharges</th>\n",
+       "      <th>totalcharges</th>\n",
+       "      <th>churn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5704</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0</td>\n",
+       "      <td>86.85</td>\n",
+       "      <td>1564.40</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1312</th>\n",
+       "      <td>48</td>\n",
+       "      <td>0</td>\n",
+       "      <td>70.95</td>\n",
+       "      <td>3629.20</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2684</th>\n",
+       "      <td>69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>105.20</td>\n",
+       "      <td>7386.05</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4824</th>\n",
+       "      <td>72</td>\n",
+       "      <td>1</td>\n",
+       "      <td>97.25</td>\n",
+       "      <td>7133.10</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4042</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51.35</td>\n",
+       "      <td>262.30</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7021</th>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>59.80</td>\n",
+       "      <td>727.80</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7026</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>44.20</td>\n",
+       "      <td>403.35</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7032</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>75.75</td>\n",
+       "      <td>75.75</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7034</th>\n",
+       "      <td>67</td>\n",
+       "      <td>0</td>\n",
+       "      <td>102.95</td>\n",
+       "      <td>6886.25</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7041</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>74.40</td>\n",
+       "      <td>306.60</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3738 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      tenure  seniorcitizen  monthlycharges  totalcharges churn\n",
+       "5704      19              0           86.85       1564.40    No\n",
+       "1312      48              0           70.95       3629.20    No\n",
+       "2684      69              0          105.20       7386.05    No\n",
+       "4824      72              1           97.25       7133.10    No\n",
+       "4042       5              0           51.35        262.30    No\n",
+       "...      ...            ...             ...           ...   ...\n",
+       "7021      12              0           59.80        727.80   Yes\n",
+       "7026       9              0           44.20        403.35   Yes\n",
+       "7032       1              1           75.75         75.75   Yes\n",
+       "7034      67              0          102.95       6886.25   Yes\n",
+       "7041       4              1           74.40        306.60   Yes\n",
+       "\n",
+       "[3738 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "downsample_df = pd.concat([category_No_down, category_Yes_down])\n",
+    "downsample_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6e2205be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_down = upsample_df.drop('churn', axis = 1)\n",
+    "y_down = upsample_df['churn']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "223bff54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X_down, y_down, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "62370cbf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(max_iter=10000, random_state=42)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "classification = LogisticRegression(random_state=42, max_iter=10000)\n",
+    "classification.fit(X_down, y_down)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "507b16b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7328502415458937"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classification.score(X_test, y_test) # checking the score without the need for predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bad81c15",
+   "metadata": {},
+   "source": [
+    "### Result of upsampling and downsampling score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d85b2f7",
+   "metadata": {},
+   "source": [
+    "Same accuracy score is produced and I am not sure if that happens..... "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b88b869f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.8"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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