notebooks/mod_04_topic_07_bayes_knn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Практика застосування kNN та Naive Bayes. EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Знайомство з даними"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import zscore\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import category_encoders as ce\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from sklearn.metrics import balanced_accuracy_score, confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital  ... euribor3m nr.employed   y\n",
       "0   56  housemaid  married  ...     4.857      5191.0  no\n",
       "1   57   services  married  ...     4.857      5191.0  no\n",
       "2   37   services  married  ...     4.857      5191.0  no\n",
       "3   40     admin.  married  ...     4.857      5191.0  no\n",
       "4   56   services  married  ...     4.857      5191.0  no\n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('../datasets/mod_04_topic_07_bank_data.csv', sep=';')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('duration', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Розподіл ознак"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41188.00000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.02406</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>5167.035911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.42125</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>72.251528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>4963.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>5099.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.961000</td>\n",
       "      <td>5228.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.00000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>94.767000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5228.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age      campaign  ...     euribor3m   nr.employed\n",
       "count  41188.00000  41188.000000  ...  41188.000000  41188.000000\n",
       "mean      40.02406      2.567593  ...      3.621291   5167.035911\n",
       "std       10.42125      2.770014  ...      1.734447     72.251528\n",
       "min       17.00000      1.000000  ...      0.634000   4963.600000\n",
       "25%       32.00000      1.000000  ...      1.344000   5099.100000\n",
       "50%       38.00000      2.000000  ...      4.857000   5191.000000\n",
       "75%       47.00000      3.000000  ...      4.961000   5228.100000\n",
       "max       98.00000     56.000000  ...      5.045000   5228.100000\n",
       "\n",
       "[8 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0.784697\n",
       "campaign          4.762507\n",
       "pdays            -4.922190\n",
       "previous          3.832042\n",
       "emp.var.rate     -0.724096\n",
       "cons.price.idx   -0.230888\n",
       "cons.conf.idx     0.303180\n",
       "euribor3m        -0.709188\n",
       "nr.employed      -1.044262\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.skew(numeric_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Практика застосування kNN та Naive Bayes. Підготовка й обробка даних"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Вимоги алгоритмів до обробки даних\n",
    "### Очистка від викидів"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[zscore(data['campaign']).abs().lt(2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Видалення ознак, тісно пов`язаних з іншими ознаками"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtx = data.drop('y', axis=1).corr(numeric_only=True).abs()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "sns.heatmap(mtx,\n",
    "            cmap='crest',\n",
    "            annot=True,\n",
    "            fmt=\".2f\",\n",
    "            linewidth=.5,\n",
    "            mask=np.triu(np.ones_like(mtx, dtype=bool)),\n",
    "            square=True,\n",
    "            cbar=False,\n",
    "            ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(\n",
    "    ['emp.var.rate',\n",
    "     'cons.price.idx',\n",
    "     'nr.employed'],\n",
    "    axis=1,\n",
    "    inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Огляд категоріальних ознак"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job            12\n",
       "marital         4\n",
       "education       8\n",
       "default         3\n",
       "housing         3\n",
       "loan            3\n",
       "contact         2\n",
       "month          10\n",
       "day_of_week     5\n",
       "poutcome        3\n",
       "y               2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.select_dtypes(include='object').nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter('ignore')\n",
    "\n",
    "    data['y'] = data['y'].replace({'no': 0, 'yes': 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Розбиття на тренувальну і тестову вибірки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = (\n",
    "    train_test_split(\n",
    "        data.drop('y', axis=1),\n",
    "        data['y'],\n",
    "        test_size=0.2,\n",
    "        random_state=42))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Кодування категоріальних змінних"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact',\n",
       "       'month', 'day_of_week', 'poutcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols = X_train.select_dtypes(include='object').columns\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder = ce.WOEEncoder(cols=cat_cols)\n",
    "\n",
    "X_train = encoder.fit_transform(X_train, y_train)\n",
    "X_test = encoder.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Нормалізація змінних. Зменшення асиметрії"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "power_transform = PowerTransformer().set_output(transform='pandas')\n",
    "\n",
    "X_train = power_transform.fit_transform(X_train)\n",
    "X_test = power_transform.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age              0.007984\n",
       "job              0.048212\n",
       "marital          0.826785\n",
       "education        0.056809\n",
       "default          2.514470\n",
       "housing         -0.106005\n",
       "loan            -1.703180\n",
       "contact         -0.586952\n",
       "month            0.104885\n",
       "day_of_week     -0.179013\n",
       "campaign         0.237176\n",
       "pdays           -4.813348\n",
       "previous         2.069751\n",
       "poutcome         2.070238\n",
       "cons.conf.idx    0.049700\n",
       "euribor3m       -0.615990\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Балансування класів"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "0    0.884514\n",
       "1    0.115486\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\install\\miniconda3\\envs\\env_mlf\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py:136: UserWarning: Could not find the number of physical cores for the following reason:\n",
      "found 0 physical cores < 1\n",
      "Returning the number of logical cores instead. You can silence this warning by setting LOKY_MAX_CPU_COUNT to the number of cores you want to use.\n",
      "  warnings.warn(\n",
      "  File \"d:\\install\\miniconda3\\envs\\env_mlf\\Lib\\site-packages\\joblib\\externals\\loky\\backend\\context.py\", line 282, in _count_physical_cores\n",
      "    raise ValueError(f\"found {cpu_count_physical} physical cores < 1\")\n"
     ]
    }
   ],
   "source": [
    "sm = SMOTE(random_state=42, k_neighbors=50)\n",
    "X_res, y_res = sm.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Практика застосування kNN та Naive Bayes. Побудова й оцінка моделей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kNN-класифікатор"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN model accuracy: 69.8%\n"
     ]
    }
   ],
   "source": [
    "knn_mod = KNeighborsClassifier(n_neighbors=7, n_jobs=-1).fit(X_res, y_res)\n",
    "\n",
    "knn_preds = knn_mod.predict(X_test)\n",
    "\n",
    "knn_score = balanced_accuracy_score(y_test, knn_preds)\n",
    "\n",
    "print(f'KNN model accuracy: {knn_score:.1%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes класифікатор"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GNB model accuracy: 70.9%\n"
     ]
    }
   ],
   "source": [
    "gnb_mod = GaussianNB().fit(X_res, y_res)\n",
    "\n",
    "gnb_preds = gnb_mod.predict(X_test)\n",
    "\n",
    "gnb_score = balanced_accuracy_score(y_test, gnb_preds)\n",
    "\n",
    "print(f'GNB model accuracy: {gnb_score:.1%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5934, 1123],\n",
       "       [ 384,  522]], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, gnb_preds)"
   ]
  }
 ],
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