EDA and Model Training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "720ab4d2",
   "metadata": {},
   "source": [
    "### Happiness Classifier EDA and Model Training\n",
    "[Kaggle Dataset - happydata.csv](https://www.kaggle.com/datasets/priyanshusethi/happiness-classification-dataset)\n",
    "\n",
    "infoavail = the availability of information about the city services  \n",
    "housecost = the cost of housing  \n",
    "schoolquality = the overall quality of public schools  \n",
    "policetrust = your trust in the local police  \n",
    "streetquality = the maintenance of streets and sidewalks  \n",
    "events = the availability of social community events  \n",
    "happy = decision attribute (D) with values 0 (unhappy) and 1 (happy)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "777e4df5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, mean_squared_error\n",
    "\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176a4080",
   "metadata": {},
   "source": [
    "#### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b627f30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>infoavail</th>\n",
       "      <th>housecost</th>\n",
       "      <th>schoolquality</th>\n",
       "      <th>policetrust</th>\n",
       "      <th>streetquality</th>\n",
       "      <th>ëvents</th>\n",
       "      <th>happy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   infoavail  housecost  schoolquality  policetrust  streetquality  ëvents  \\\n",
       "0          3          3              3            4              2       4   \n",
       "1          3          2              3            5              4       3   \n",
       "2          5          3              3            3              3       5   \n",
       "3          5          4              3            3              3       5   \n",
       "4          5          4              3            3              3       5   \n",
       "\n",
       "   happy  \n",
       "0      0  \n",
       "1      0  \n",
       "2      1  \n",
       "3      0  \n",
       "4      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ed7a1a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 143 entries, 0 to 142\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count  Dtype\n",
      "---  ------         --------------  -----\n",
      " 0   infoavail      143 non-null    int64\n",
      " 1   housecost      143 non-null    int64\n",
      " 2   schoolquality  143 non-null    int64\n",
      " 3   policetrust    143 non-null    int64\n",
      " 4   streetquality  143 non-null    int64\n",
      " 5   ëvents         143 non-null    int64\n",
      " 6   happy          143 non-null    int64\n",
      "dtypes: int64(7)\n",
      "memory usage: 7.9 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87b0239a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>infoavail</th>\n",
       "      <th>housecost</th>\n",
       "      <th>schoolquality</th>\n",
       "      <th>policetrust</th>\n",
       "      <th>streetquality</th>\n",
       "      <th>ëvents</th>\n",
       "      <th>happy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>143.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>143.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.314685</td>\n",
       "      <td>2.538462</td>\n",
       "      <td>3.265734</td>\n",
       "      <td>3.699301</td>\n",
       "      <td>3.615385</td>\n",
       "      <td>4.216783</td>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.799820</td>\n",
       "      <td>1.118155</td>\n",
       "      <td>0.992586</td>\n",
       "      <td>0.888383</td>\n",
       "      <td>1.131639</td>\n",
       "      <td>0.848693</td>\n",
       "      <td>0.500271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        infoavail   housecost  schoolquality  policetrust  streetquality  \\\n",
       "count  143.000000  143.000000     143.000000   143.000000     143.000000   \n",
       "mean     4.314685    2.538462       3.265734     3.699301       3.615385   \n",
       "std      0.799820    1.118155       0.992586     0.888383       1.131639   \n",
       "min      1.000000    1.000000       1.000000     1.000000       1.000000   \n",
       "25%      4.000000    2.000000       3.000000     3.000000       3.000000   \n",
       "50%      5.000000    3.000000       3.000000     4.000000       4.000000   \n",
       "75%      5.000000    3.000000       4.000000     4.000000       4.000000   \n",
       "max      5.000000    5.000000       5.000000     5.000000       5.000000   \n",
       "\n",
       "           ëvents       happy  \n",
       "count  143.000000  143.000000  \n",
       "mean     4.216783    0.538462  \n",
       "std      0.848693    0.500271  \n",
       "min      1.000000    0.000000  \n",
       "25%      4.000000    0.000000  \n",
       "50%      4.000000    1.000000  \n",
       "75%      5.000000    1.000000  \n",
       "max      5.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "133e988d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=3, nrows=2, figsize=(10, 5))\n",
    "index = 0\n",
    "axs = axs.flatten()\n",
    "\n",
    "for k,v in data.drop('happy', axis=1).items():\n",
    "    sns.histplot(x=k, data=data, ax=axs[index], bins=5, binwidth=0.85)\n",
    "    index += 1\n",
    "    \n",
    "plt.tight_layout(pad=0.4, w_pad=1, h_pad=1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5631876f",
   "metadata": {},
   "source": [
    "#### Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "623b9b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83594947",
   "metadata": {},
   "source": [
    "#### Sample and Lable Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f127519",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('happy', axis=1)\n",
    "y = data[['happy']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08a511c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 6) (43, 6) (100, 1) (43, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.3)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dd91a2f",
   "metadata": {},
   "source": [
    "#### Model Training and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "973953ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A:\\Applications\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_model = GaussianNB()\n",
    "nb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "385f4e76",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A:\\Applications\\Anaconda\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but GaussianNB was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_model.predict([[3, 3, 3, 4, 2, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0e345a39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy : 60.46511627906976 %\n",
      "Precision : 61.29032258064516 %\n",
      "Recall : 0.7916666666666666\n",
      "Mean Square Error : 0.3953488372093023\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = nb_model.predict(X_test)\n",
    "\n",
    "print(f'''\n",
    "Accuracy : {accuracy_score(y_test, y_pred)*100} %\n",
    "Precision : {precision_score(y_test, y_pred)*100} %\n",
    "Recall : {recall_score(y_test, y_pred)}\n",
    "Mean Square Error : {mean_squared_error(y_test, y_pred)}\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb2bc40c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A:\\Applications\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_model = LogisticRegression()\n",
    "log_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bc4721d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A:\\Applications\\Anaconda\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_model.predict([[3, 3, 3, 4, 2, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4bf39976",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy : 65.11627906976744 %\n",
      "Precision : 63.63636363636363 %\n",
      "Recall : 0.875\n",
      "Mean Square Error : 0.3488372093023256\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = log_model.predict(X_test)\n",
    "\n",
    "print(f'''\n",
    "Accuracy : {accuracy_score(y_test, y_pred)*100} %\n",
    "Precision : {precision_score(y_test, y_pred)*100} %\n",
    "Recall : {recall_score(y_test, y_pred)}\n",
    "Mean Square Error : {mean_squared_error(y_test, y_pred)}\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4070988c",
   "metadata": {},
   "source": [
    "`Accuracy is quite low, mainly because of small sample size`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5148d56",
   "metadata": {},
   "source": [
    "#### Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "909f9b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Logistic_Reg_Model_v1.2']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(value=log_model, filename='Logistic_Reg_Model_v1.2')"
   ]
  }
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