SML_forged_banknotes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b6c301e",
   "metadata": {},
   "source": [
    "# Wavelet analysis and forged banknotes: Supervised ML  - Classification\n",
    "\n",
    "The objectives of the project:\n",
    "\n",
    "**A.** Train numerous classifier models belonging to four categories (Logistic Regression, K-Nearest Neighbor, Support Vector Machines and Decision Trees) that can predict if a banknote is genuine or not based on the dataset from OpenML containing wavelet analysis results for genuine and forged banknotes.\n",
    "\n",
    "**B.** Comparing the models' performance metrics to enable selecting the best model for the task. \n",
    "\n",
    "## 1. The data\n",
    "\n",
    "### 1.1. Description & exploration\n",
    "\n",
    "The dataset originates from OpenML and contains wavelet analysis results for genuine and forged banknotes. The authors describe it as follows: \n",
    "\n",
    "*Dataset about distinguishing genuine and forged banknotes. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. A Wavelet Transform tool was used to extract features from these images* [https://www.openml.org/search?type=data&sort=runs&id=1462&status=active]. \n",
    "\n",
    "Data reference: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
    "\n",
    "The data table contains 4 columns with parameters from wavelet analysis (V1 – variance of Wavelet Transformed image, V2 – skewness of Wavelet Transformed image, V3 – curtosis of Wavelet Transformed image, V4 – entropy of image) and Class column describing if the banknote is genuine (1) or forged (2). There are 762 rows for genuine banknotes and 610 rows for forged ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44109767",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           V1        V2       V3       V4  Class\n",
      "0     3.62160   8.66610  -2.8073 -0.44699      1\n",
      "1     4.54590   8.16740  -2.4586 -1.46210      1\n",
      "2     3.86600  -2.63830   1.9242  0.10645      1\n",
      "3     3.45660   9.52280  -4.0112 -3.59440      1\n",
      "4     0.32924  -4.45520   4.5718 -0.98880      1\n",
      "...       ...       ...      ...      ...    ...\n",
      "1367  0.40614   1.34920  -1.4501 -0.55949      2\n",
      "1368 -1.38870  -4.87730   6.4774  0.34179      2\n",
      "1369 -3.75030 -13.45860  17.5932 -2.77710      2\n",
      "1370 -3.56370  -8.38270  12.3930 -1.28230      2\n",
      "1371 -2.54190  -0.65804   2.6842  1.19520      2\n",
      "\n",
      "[1372 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# Data import\n",
    "import pandas as pd\n",
    "data = pd.read_csv('Banknote-authentication-dataset.csv')\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "867ae4e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    762\n",
       "2    610\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Genuine and forged count\n",
    "data.Class.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8a7b932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1372.000000</td>\n",
       "      <td>1372.000000</td>\n",
       "      <td>1372.000000</td>\n",
       "      <td>1372.000000</td>\n",
       "      <td>1372.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.433735</td>\n",
       "      <td>1.922353</td>\n",
       "      <td>1.397627</td>\n",
       "      <td>-1.191657</td>\n",
       "      <td>1.444606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.842763</td>\n",
       "      <td>5.869047</td>\n",
       "      <td>4.310030</td>\n",
       "      <td>2.101013</td>\n",
       "      <td>0.497103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-7.042100</td>\n",
       "      <td>-13.773100</td>\n",
       "      <td>-5.286100</td>\n",
       "      <td>-8.548200</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.773000</td>\n",
       "      <td>-1.708200</td>\n",
       "      <td>-1.574975</td>\n",
       "      <td>-2.413450</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.496180</td>\n",
       "      <td>2.319650</td>\n",
       "      <td>0.616630</td>\n",
       "      <td>-0.586650</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.821475</td>\n",
       "      <td>6.814625</td>\n",
       "      <td>3.179250</td>\n",
       "      <td>0.394810</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.824800</td>\n",
       "      <td>12.951600</td>\n",
       "      <td>17.927400</td>\n",
       "      <td>2.449500</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                V1           V2           V3           V4        Class\n",
       "count  1372.000000  1372.000000  1372.000000  1372.000000  1372.000000\n",
       "mean      0.433735     1.922353     1.397627    -1.191657     1.444606\n",
       "std       2.842763     5.869047     4.310030     2.101013     0.497103\n",
       "min      -7.042100   -13.773100    -5.286100    -8.548200     1.000000\n",
       "25%      -1.773000    -1.708200    -1.574975    -2.413450     1.000000\n",
       "50%       0.496180     2.319650     0.616630    -0.586650     1.000000\n",
       "75%       2.821475     6.814625     3.179250     0.394810     2.000000\n",
       "max       6.824800    12.951600    17.927400     2.449500     2.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f370226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V1       False\n",
       "V2       False\n",
       "V3       False\n",
       "V4       False\n",
       "Class    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3061cf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot for outliers inspection\n",
    "data.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "916471d0",
   "metadata": {},
   "source": [
    "The data is good quality - there are no gaps or Not-a-Numbers and the calculated summary statistics look reasonable. There are no actual outliers (single data points far away from the rest of the dataset), but V3 and V4 data have some \"tails\".\n",
    "\n",
    "### 1.2. Data preparation\n",
    "\n",
    "The data was normalized to the range between 0 and 1 and absolute value correlation matrix was calculated (see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "505eb7de",
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.769004</td>\n",
       "      <td>0.839643</td>\n",
       "      <td>0.106783</td>\n",
       "      <td>0.736628</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.835659</td>\n",
       "      <td>0.820982</td>\n",
       "      <td>0.121804</td>\n",
       "      <td>0.644326</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.786629</td>\n",
       "      <td>0.416648</td>\n",
       "      <td>0.310608</td>\n",
       "      <td>0.786951</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.757105</td>\n",
       "      <td>0.871699</td>\n",
       "      <td>0.054921</td>\n",
       "      <td>0.450440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.531578</td>\n",
       "      <td>0.348662</td>\n",
       "      <td>0.424662</td>\n",
       "      <td>0.687362</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>0.537124</td>\n",
       "      <td>0.565855</td>\n",
       "      <td>0.165249</td>\n",
       "      <td>0.726398</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>0.407690</td>\n",
       "      <td>0.332868</td>\n",
       "      <td>0.506753</td>\n",
       "      <td>0.808350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>0.237385</td>\n",
       "      <td>0.011768</td>\n",
       "      <td>0.985603</td>\n",
       "      <td>0.524755</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>0.250842</td>\n",
       "      <td>0.201701</td>\n",
       "      <td>0.761587</td>\n",
       "      <td>0.660675</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>0.324528</td>\n",
       "      <td>0.490747</td>\n",
       "      <td>0.343348</td>\n",
       "      <td>0.885949</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1372 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            V1        V2        V3        V4  Class\n",
       "0     0.769004  0.839643  0.106783  0.736628      0\n",
       "1     0.835659  0.820982  0.121804  0.644326      0\n",
       "2     0.786629  0.416648  0.310608  0.786951      0\n",
       "3     0.757105  0.871699  0.054921  0.450440      0\n",
       "4     0.531578  0.348662  0.424662  0.687362      0\n",
       "...        ...       ...       ...       ...    ...\n",
       "1367  0.537124  0.565855  0.165249  0.726398      1\n",
       "1368  0.407690  0.332868  0.506753  0.808350      1\n",
       "1369  0.237385  0.011768  0.985603  0.524755      1\n",
       "1370  0.250842  0.201701  0.761587  0.660675      1\n",
       "1371  0.324528  0.490747  0.343348  0.885949      1\n",
       "\n",
       "[1372 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalization (0 - 1)\n",
    "col_names = data.columns\n",
    "from sklearn import preprocessing\n",
    "data = pd.DataFrame(preprocessing.minmax_scale(data, feature_range=(0, 1), axis=0, copy=True))\n",
    "data.columns = col_names\n",
    "data.Class = data.Class.astype(int)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "92e5e46a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bewer\\AppData\\Local\\Temp/ipykernel_15884/1874121638.py:3: FutureWarning: this method is deprecated in favour of `Styler.format(precision=..)`\n",
      "  corr.style.background_gradient(cmap='coolwarm').set_precision(2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4c2da_row0_col0, #T_4c2da_row1_col1, #T_4c2da_row2_col2, #T_4c2da_row3_col3, #T_4c2da_row4_col4 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4c2da_row0_col1, #T_4c2da_row1_col0, #T_4c2da_row3_col4, #T_4c2da_row4_col2, #T_4c2da_row4_col3 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4c2da_row0_col2 {\n",
       "  background-color: #93b5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4c2da_row0_col3 {\n",
       "  background-color: #90b2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4c2da_row0_col4 {\n",
       "  background-color: #f7a688;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4c2da_row1_col2 {\n",
       "  background-color: #f49a7b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4c2da_row1_col3 {\n",
       "  background-color: #e0dbd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4c2da_row1_col4 {\n",
       "  background-color: #cad8ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4c2da_row2_col0 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4c2da_row2_col1 {\n",
       "  background-color: #f7a98b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4c2da_row2_col3 {\n",
       "  background-color: #9fbfff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4c2da_row2_col4 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4c2da_row3_col0 {\n",
       "  background-color: #3f53c6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4c2da_row3_col1 {\n",
       "  background-color: #b2ccfb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4c2da_row3_col2 {\n",
       "  background-color: #799cf8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4c2da_row4_col0 {\n",
       "  background-color: #f5c4ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_4c2da_row4_col1 {\n",
       "  background-color: #8badfd;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4c2da_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >V1</th>\n",
       "      <th class=\"col_heading level0 col1\" >V2</th>\n",
       "      <th class=\"col_heading level0 col2\" >V3</th>\n",
       "      <th class=\"col_heading level0 col3\" >V4</th>\n",
       "      <th class=\"col_heading level0 col4\" >Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4c2da_level0_row0\" class=\"row_heading level0 row0\" >V1</th>\n",
       "      <td id=\"T_4c2da_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
       "      <td id=\"T_4c2da_row0_col1\" class=\"data row0 col1\" >0.26</td>\n",
       "      <td id=\"T_4c2da_row0_col2\" class=\"data row0 col2\" >0.38</td>\n",
       "      <td id=\"T_4c2da_row0_col3\" class=\"data row0 col3\" >0.28</td>\n",
       "      <td id=\"T_4c2da_row0_col4\" class=\"data row0 col4\" >0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c2da_level0_row1\" class=\"row_heading level0 row1\" >V2</th>\n",
       "      <td id=\"T_4c2da_row1_col0\" class=\"data row1 col0\" >0.26</td>\n",
       "      <td id=\"T_4c2da_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
       "      <td id=\"T_4c2da_row1_col2\" class=\"data row1 col2\" >0.79</td>\n",
       "      <td id=\"T_4c2da_row1_col3\" class=\"data row1 col3\" >0.53</td>\n",
       "      <td id=\"T_4c2da_row1_col4\" class=\"data row1 col4\" >0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c2da_level0_row2\" class=\"row_heading level0 row2\" >V3</th>\n",
       "      <td id=\"T_4c2da_row2_col0\" class=\"data row2 col0\" >0.38</td>\n",
       "      <td id=\"T_4c2da_row2_col1\" class=\"data row2 col1\" >0.79</td>\n",
       "      <td id=\"T_4c2da_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
       "      <td id=\"T_4c2da_row2_col3\" class=\"data row2 col3\" >0.32</td>\n",
       "      <td id=\"T_4c2da_row2_col4\" class=\"data row2 col4\" >0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c2da_level0_row3\" class=\"row_heading level0 row3\" >V4</th>\n",
       "      <td id=\"T_4c2da_row3_col0\" class=\"data row3 col0\" >0.28</td>\n",
       "      <td id=\"T_4c2da_row3_col1\" class=\"data row3 col1\" >0.53</td>\n",
       "      <td id=\"T_4c2da_row3_col2\" class=\"data row3 col2\" >0.32</td>\n",
       "      <td id=\"T_4c2da_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
       "      <td id=\"T_4c2da_row3_col4\" class=\"data row3 col4\" >0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4c2da_level0_row4\" class=\"row_heading level0 row4\" >Class</th>\n",
       "      <td id=\"T_4c2da_row4_col0\" class=\"data row4 col0\" >0.72</td>\n",
       "      <td id=\"T_4c2da_row4_col1\" class=\"data row4 col1\" >0.44</td>\n",
       "      <td id=\"T_4c2da_row4_col2\" class=\"data row4 col2\" >0.16</td>\n",
       "      <td id=\"T_4c2da_row4_col3\" class=\"data row4 col3\" >0.02</td>\n",
       "      <td id=\"T_4c2da_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x18cef990820>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation matrix - absolute values\n",
    "corr = abs(data.corr())\n",
    "corr.style.background_gradient(cmap='coolwarm').set_precision(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b63a687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x18cf1b53f10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 762.375x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visual check of the correlations between the variables - pairplot\n",
    "import seaborn as sns\n",
    "sns.pairplot(data, hue='Class', palette={0:'green',1:'red'}, plot_kws={\"s\": 3})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef350a57",
   "metadata": {},
   "source": [
    "There is strong correlation (0.72) between V1 and Class, so V1 will be an important variable for the predictions. Correlation between the remaining parameters and Class is weaker: 0.44 for V2, 0.16 for V3 and only 0.02 for V4. This excludes V4 from useful parameters. There is also strong correlation (0.79) between V2 and V3. The correlation values are calculated considering linear correlation, but the correlations are actually non-linear. V3 will stay in the dataset for the calculations even though others could think of removing V3 as it is more linearly correlated with V2 than with Class (linear correlation is not everything). Classes are not easily separable in any of 2D plots. V2, V3 and V4 seem to be non-linearly correlated with each other, so it may be beneficial to get rid of one of them and stay with just three variables. The datapoints for different classes are least separable in V3-V4 plots, so there is no reason to use these variables together. Distribution of genuine and forged banknotes data overlaps the most on the V4 plot. To summarize, it can be deduced from the above, that V4 could be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "435884a6",
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.769004</td>\n",
       "      <td>0.839643</td>\n",
       "      <td>0.106783</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.835659</td>\n",
       "      <td>0.820982</td>\n",
       "      <td>0.121804</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.786629</td>\n",
       "      <td>0.416648</td>\n",
       "      <td>0.310608</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.757105</td>\n",
       "      <td>0.871699</td>\n",
       "      <td>0.054921</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.531578</td>\n",
       "      <td>0.348662</td>\n",
       "      <td>0.424662</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>0.537124</td>\n",
       "      <td>0.565855</td>\n",
       "      <td>0.165249</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>0.407690</td>\n",
       "      <td>0.332868</td>\n",
       "      <td>0.506753</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>0.237385</td>\n",
       "      <td>0.011768</td>\n",
       "      <td>0.985603</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1370</th>\n",
       "      <td>0.250842</td>\n",
       "      <td>0.201701</td>\n",
       "      <td>0.761587</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>0.324528</td>\n",
       "      <td>0.490747</td>\n",
       "      <td>0.343348</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1372 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            V1        V2        V3  Class\n",
       "0     0.769004  0.839643  0.106783      0\n",
       "1     0.835659  0.820982  0.121804      0\n",
       "2     0.786629  0.416648  0.310608      0\n",
       "3     0.757105  0.871699  0.054921      0\n",
       "4     0.531578  0.348662  0.424662      0\n",
       "...        ...       ...       ...    ...\n",
       "1367  0.537124  0.565855  0.165249      1\n",
       "1368  0.407690  0.332868  0.506753      1\n",
       "1369  0.237385  0.011768  0.985603      1\n",
       "1370  0.250842  0.201701  0.761587      1\n",
       "1371  0.324528  0.490747  0.343348      1\n",
       "\n",
       "[1372 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deleting V4\n",
    "data.drop(['V4'], axis=1, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3cee12f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# V1 vs. V2: genuine and forged plot\n",
    "plt.figure(dpi=150)\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "plt.scatter(data[data['Class'] == 0]['V1'], data[data['Class'] == 0]['V2'], s=3, color='green', alpha=0.5)\n",
    "plt.scatter(data[data['Class'] == 1]['V1'], data[data['Class'] == 1]['V2'], s=3, color='red', alpha=0.5)\n",
    "plt.show()\n",
    "\n",
    "# 3D plot of genuine and forged\n",
    "fig = plt.figure(figsize=(15, 15))\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "ax.scatter(data[data['Class'] == 0]['V1'], data[data['Class'] == 0]['V2'], data[data['Class'] == 0]['V3'],\n",
    "          s=3, color='green')\n",
    "ax.scatter(data[data['Class'] == 1]['V1'], data[data['Class'] == 1]['V2'], data[data['Class'] == 1]['V3'],\n",
    "          s=3, color='red')\n",
    "plt.xlabel('V1')\n",
    "plt.ylabel('V2')\n",
    "ax.set_zlabel('V3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef5b37b",
   "metadata": {},
   "source": [
    "The plots above shows genuine (green) and forged (red) banknotes data with respect to V1, V2 and V3 values.It is clearly visible, that there is an area in 2D plot where red and green points overlap – this part of the data would be very challenging for classifier models not utilizing V3 parameter.\n",
    "\n",
    "The task was to train a model that can predict if a banknote is genuine or not, so training data and test data were needed. Train-test split was performed using Scikit-learn's StratifiedShuffleSplit (to maintain the same ratio of predictor classes) with 10 folds to ensure that the split result does not determine model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b862a9a1",
   "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>train_idx</th>\n",
       "      <th>test_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[905, 3, 256, 46, 282, 900, 1058, 107, 1207, 1...</td>\n",
       "      <td>[388, 47, 916, 277, 1354, 878, 1222, 423, 510,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1229, 352, 682, 1213, 83, 876, 930, 983, 606,...</td>\n",
       "      <td>[272, 738, 102, 1234, 1135, 127, 95, 317, 279,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[558, 1301, 1327, 1337, 44, 848, 1291, 983, 13...</td>\n",
       "      <td>[920, 636, 946, 248, 481, 750, 306, 326, 510, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1318, 80, 1197, 303, 247, 970, 1342, 218, 545...</td>\n",
       "      <td>[1215, 863, 998, 600, 455, 1164, 809, 329, 98,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1249, 1067, 534, 545, 998, 617, 877, 84, 1224...</td>\n",
       "      <td>[881, 912, 381, 1337, 347, 474, 855, 20, 48, 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[36, 1244, 66, 435, 637, 606, 977, 80, 318, 71...</td>\n",
       "      <td>[991, 723, 1361, 1306, 759, 1286, 1126, 691, 2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[898, 248, 1228, 655, 1319, 393, 522, 331, 253...</td>\n",
       "      <td>[1028, 953, 1087, 206, 25, 225, 102, 724, 82, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[1214, 174, 357, 238, 1310, 997, 71, 226, 904,...</td>\n",
       "      <td>[1136, 855, 917, 384, 399, 452, 1230, 421, 102...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[717, 989, 196, 1310, 408, 436, 1166, 853, 933...</td>\n",
       "      <td>[1262, 164, 1305, 1338, 92, 784, 309, 623, 130...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[525, 409, 314, 1120, 1136, 920, 205, 596, 301...</td>\n",
       "      <td>[1019, 92, 272, 918, 513, 820, 1239, 671, 126,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           train_idx  \\\n",
       "0  [905, 3, 256, 46, 282, 900, 1058, 107, 1207, 1...   \n",
       "1  [1229, 352, 682, 1213, 83, 876, 930, 983, 606,...   \n",
       "2  [558, 1301, 1327, 1337, 44, 848, 1291, 983, 13...   \n",
       "3  [1318, 80, 1197, 303, 247, 970, 1342, 218, 545...   \n",
       "4  [1249, 1067, 534, 545, 998, 617, 877, 84, 1224...   \n",
       "5  [36, 1244, 66, 435, 637, 606, 977, 80, 318, 71...   \n",
       "6  [898, 248, 1228, 655, 1319, 393, 522, 331, 253...   \n",
       "7  [1214, 174, 357, 238, 1310, 997, 71, 226, 904,...   \n",
       "8  [717, 989, 196, 1310, 408, 436, 1166, 853, 933...   \n",
       "9  [525, 409, 314, 1120, 1136, 920, 205, 596, 301...   \n",
       "\n",
       "                                            test_idx  \n",
       "0  [388, 47, 916, 277, 1354, 878, 1222, 423, 510,...  \n",
       "1  [272, 738, 102, 1234, 1135, 127, 95, 317, 279,...  \n",
       "2  [920, 636, 946, 248, 481, 750, 306, 326, 510, ...  \n",
       "3  [1215, 863, 998, 600, 455, 1164, 809, 329, 98,...  \n",
       "4  [881, 912, 381, 1337, 347, 474, 855, 20, 48, 3...  \n",
       "5  [991, 723, 1361, 1306, 759, 1286, 1126, 691, 2...  \n",
       "6  [1028, 953, 1087, 206, 25, 225, 102, 724, 82, ...  \n",
       "7  [1136, 855, 917, 384, 399, 452, 1230, 421, 102...  \n",
       "8  [1262, 164, 1305, 1338, 92, 784, 309, 623, 130...  \n",
       "9  [1019, 92, 272, 918, 513, 820, 1239, 671, 126,...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train-test split\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "n_splits = 10\n",
    "\n",
    "strat_shuf_split = StratifiedShuffleSplit(n_splits=n_splits, test_size=0.1, random_state=0)\n",
    "indexes = pd.DataFrame(strat_shuf_split.split(data[data.columns[:-1]], data.Class))\n",
    "indexes.rename(columns = {0:'train_idx', 1:'test_idx'}, inplace = True)\n",
    "\n",
    "indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4b2172b",
   "metadata": {},
   "source": [
    "## 2. Classifier models\n",
    "\n",
    "### 2.1. Logistic regression\n",
    "\n",
    "Three logistic regression models (Standard logistic regression model as well as L1 and L2 regularized logistic regression models with Cs=10 and cv=4) were trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8cbce397",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lr</th>\n",
       "      <th>lr_l1</th>\n",
       "      <th>lr_l2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean accuracy</th>\n",
       "      <td>0.970290</td>\n",
       "      <td>0.992754</td>\n",
       "      <td>0.992754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean precision</th>\n",
       "      <td>0.972363</td>\n",
       "      <td>0.988838</td>\n",
       "      <td>0.988838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean recall</th>\n",
       "      <td>0.960656</td>\n",
       "      <td>0.995082</td>\n",
       "      <td>0.995082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean f1</th>\n",
       "      <td>0.966069</td>\n",
       "      <td>0.991882</td>\n",
       "      <td>0.991882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy SD</th>\n",
       "      <td>0.012738</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.008574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision SD</th>\n",
       "      <td>0.019217</td>\n",
       "      <td>0.015968</td>\n",
       "      <td>0.015968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall SD</th>\n",
       "      <td>0.028583</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>0.007512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 SD</th>\n",
       "      <td>0.015014</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>0.009542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      lr     lr_l1     lr_l2\n",
       "mean accuracy   0.970290  0.992754  0.992754\n",
       "mean precision  0.972363  0.988838  0.988838\n",
       "mean recall     0.960656  0.995082  0.995082\n",
       "mean f1         0.966069  0.991882  0.991882\n",
       "accuracy SD     0.012738  0.008574  0.008574\n",
       "precision SD    0.019217  0.015968  0.015968\n",
       "recall SD       0.028583  0.007512  0.007512\n",
       "f1 SD           0.015014  0.009542  0.009542"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import precision_score, accuracy_score, recall_score, f1_score\n",
    "#import statistics as st\n",
    "import numpy as np\n",
    "\n",
    "scores_columns = [\"acccuracy_lr\", \"precision_lr\", \"recall_lr\", \"f1_lr\", \"acccuracy_lr_l1\", \"precision_lr_l1\", \n",
    "                  \"recall_lr_l1\", \"f1_lr_l1\", \"acccuracy_lr_l2\", \"precision_lr_l2\", \"recall_lr_l2\", \"f1_lr_l2\"]\n",
    "scores = pd.DataFrame(index=range(0,n_splits),columns=scores_columns, dtype='float')\n",
    "\n",
    "for i in range(0, n_splits):\n",
    "    X_train, y_train = data[data.columns[:-1]].iloc[indexes.train_idx[i]], data.Class.iloc[indexes.train_idx[i]]\n",
    "    X_test, y_test = data[data.columns[:-1]].iloc[indexes.test_idx[i]], data.Class.iloc[indexes.test_idx[i]]\n",
    "\n",
    "    # Standard logistic regression\n",
    "    lr = LogisticRegression(solver='liblinear', max_iter=1000000).fit(X_train, y_train)\n",
    "    y_pred = lr.predict(X_test)\n",
    "    scores.acccuracy_lr[i] = accuracy_score(y_test, y_pred)\n",
    "    scores.precision_lr[i] = precision_score(y_test, y_pred)\n",
    "    scores.recall_lr[i] = recall_score(y_test, y_pred)\n",
    "    scores.f1_lr[i] = f1_score(y_test, y_pred)\n",
    "    \n",
    "    # L1 regularized logistic regression\n",
    "    lr_l1 = LogisticRegressionCV(Cs=10, cv=4, penalty='l1', solver='liblinear', max_iter=1000000).fit(X_train, y_train)\n",
    "    y_pred = lr_l1.predict(X_test)\n",
    "    scores.acccuracy_lr_l1[i] = accuracy_score(y_test, y_pred)\n",
    "    scores.precision_lr_l1[i] = precision_score(y_test, y_pred)\n",
    "    scores.recall_lr_l1[i] = recall_score(y_test, y_pred)\n",
    "    scores.f1_lr_l1[i] = f1_score(y_test, y_pred)\n",
    "    \n",
    "    # L2 regularized logistic regression\n",
    "    lr_l2 = LogisticRegressionCV(Cs=10, cv=4, penalty='l2', solver='liblinear', max_iter=1000000).fit(X_train, y_train)\n",
    "    y_pred = lr_l2.predict(X_test)\n",
    "    scores.acccuracy_lr_l2[i] = accuracy_score(y_test, y_pred)\n",
    "    scores.precision_lr_l2[i] = precision_score(y_test, y_pred)\n",
    "    scores.recall_lr_l2[i] = recall_score(y_test, y_pred)\n",
    "    scores.f1_lr_l2[i] = f1_score(y_test, y_pred)\n",
    "    \n",
    "scores_summary = pd.DataFrame(data=np.transpose(np.array(scores.mean()).reshape(3,4)), \n",
    "                              index=('mean accuracy', 'mean precision', 'mean recall', 'mean f1'),\n",
    "                              columns=('lr', 'lr_l1', 'lr_l2'), dtype='float')\n",
    "\n",
    "scores_SD = pd.DataFrame(data=np.transpose(np.array(np.std(scores)).reshape(3,4)), \n",
    "                              index=('accuracy SD', 'precision SD', 'recall SD', 'f1 SD'),\n",
    "                              columns=('lr', 'lr_l1', 'lr_l2'), dtype='float')\n",
    "del scores_columns, scores\n",
    "\n",
    "scores_SD_summary = pd.concat([scores_summary, scores_SD])\n",
    "\n",
    "del scores_summary, scores_SD\n",
    "\n",
    "scores_SD_summary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5713f88e",
   "metadata": {},
   "source": [
    "### 2.2. K-Nearest Neighbor\n",
    "\n",
    "Ten K-Nearest Neighbor (KNN) models were trained with k values from 1 to 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "732045e8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lr</th>\n",
       "      <th>lr_l1</th>\n",
       "      <th>lr_l2</th>\n",
       "      <th>KNN k=1</th>\n",
       "      <th>KNN k=2</th>\n",
       "      <th>KNN k=3</th>\n",
       "      <th>KNN k=4</th>\n",
       "      <th>KNN k=5</th>\n",
       "      <th>KNN k=6</th>\n",
       "      <th>KNN k=7</th>\n",
       "      <th>KNN k=8</th>\n",
       "      <th>KNN k=9</th>\n",
       "      <th>KNN k=10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean accuracy</th>\n",
       "      <td>0.970290</td>\n",
       "      <td>0.992754</td>\n",
       "      <td>0.992754</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.994203</td>\n",
       "      <td>0.995652</td>\n",
       "      <td>0.996377</td>\n",
       "      <td>0.995652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean precision</th>\n",
       "      <td>0.972363</td>\n",
       "      <td>0.988838</td>\n",
       "      <td>0.988838</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.988812</td>\n",
       "      <td>0.991987</td>\n",
       "      <td>0.991987</td>\n",
       "      <td>0.991987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean recall</th>\n",
       "      <td>0.960656</td>\n",
       "      <td>0.995082</td>\n",
       "      <td>0.995082</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>0.998361</td>\n",
       "      <td>0.998361</td>\n",
       "      <td>0.998361</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean f1</th>\n",
       "      <td>0.966069</td>\n",
       "      <td>0.991882</td>\n",
       "      <td>0.991882</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.996748</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.996748</td>\n",
       "      <td>0.996735</td>\n",
       "      <td>0.993509</td>\n",
       "      <td>0.995122</td>\n",
       "      <td>0.995948</td>\n",
       "      <td>0.995122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy SD</th>\n",
       "      <td>0.012738</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.005423</td>\n",
       "      <td>0.004807</td>\n",
       "      <td>0.004861</td>\n",
       "      <td>0.004807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision SD</th>\n",
       "      <td>0.019217</td>\n",
       "      <td>0.015968</td>\n",
       "      <td>0.015968</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007902</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007902</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.012427</td>\n",
       "      <td>0.010706</td>\n",
       "      <td>0.010706</td>\n",
       "      <td>0.010706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall SD</th>\n",
       "      <td>0.028583</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 SD</th>\n",
       "      <td>0.015014</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.006046</td>\n",
       "      <td>0.005374</td>\n",
       "      <td>0.005425</td>\n",
       "      <td>0.005374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      lr     lr_l1     lr_l2   KNN k=1   KNN k=2   KNN k=3  \\\n",
       "mean accuracy   0.970290  0.992754  0.992754  0.997826  0.997826  0.997101   \n",
       "mean precision  0.972363  0.988838  0.988838  0.995161  0.995161  0.993548   \n",
       "mean recall     0.960656  0.995082  0.995082  1.000000  1.000000  1.000000   \n",
       "mean f1         0.966069  0.991882  0.991882  0.997561  0.997561  0.996748   \n",
       "accuracy SD     0.012738  0.008574  0.008574  0.003321  0.003321  0.003550   \n",
       "precision SD    0.019217  0.015968  0.015968  0.007391  0.007391  0.007902   \n",
       "recall SD       0.028583  0.007512  0.007512  0.000000  0.000000  0.000000   \n",
       "f1 SD           0.015014  0.009542  0.009542  0.003726  0.003726  0.003983   \n",
       "\n",
       "                 KNN k=4   KNN k=5   KNN k=6   KNN k=7   KNN k=8   KNN k=9  \\\n",
       "mean accuracy   0.997826  0.997101  0.997101  0.994203  0.995652  0.996377   \n",
       "mean precision  0.995161  0.993548  0.995161  0.988812  0.991987  0.991987   \n",
       "mean recall     1.000000  1.000000  0.998361  0.998361  0.998361  1.000000   \n",
       "mean f1         0.997561  0.996748  0.996735  0.993509  0.995122  0.995948   \n",
       "accuracy SD     0.003321  0.003550  0.003550  0.005423  0.004807  0.004861   \n",
       "precision SD    0.007391  0.007902  0.007391  0.012427  0.010706  0.010706   \n",
       "recall SD       0.000000  0.000000  0.004918  0.004918  0.004918  0.000000   \n",
       "f1 SD           0.003726  0.003983  0.004000  0.006046  0.005374  0.005425   \n",
       "\n",
       "                KNN k=10  \n",
       "mean accuracy   0.995652  \n",
       "mean precision  0.991987  \n",
       "mean recall     0.998361  \n",
       "mean f1         0.995122  \n",
       "accuracy SD     0.004807  \n",
       "precision SD    0.010706  \n",
       "recall SD       0.004918  \n",
       "f1 SD           0.005374  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "k_values = range(1,11)\n",
    "\n",
    "for k in k_values:\n",
    "    \n",
    "    scores_columns = [\"acccuracy\", \"precision\", \"recall\", \"f1\"]\n",
    "    scores = pd.DataFrame(index=range(0,n_splits),columns=scores_columns, dtype='float')\n",
    "\n",
    "    for i in range(0, n_splits):\n",
    "        X_train, y_train = data[data.columns[:-1]].iloc[indexes.train_idx[i]], data.Class.iloc[indexes.train_idx[i]]\n",
    "        X_test, y_test = data[data.columns[:-1]].iloc[indexes.test_idx[i]], data.Class.iloc[indexes.test_idx[i]]\n",
    "    \n",
    "        # KNN model\n",
    "        knn = KNeighborsClassifier(n_neighbors=k)\n",
    "        knn = knn.fit(X_train, y_train)\n",
    "        y_pred= knn.predict(X_test)\n",
    "    \n",
    "        scores.acccuracy[i] = accuracy_score(y_test, y_pred)\n",
    "        scores.precision[i] = precision_score(y_test, y_pred)\n",
    "        scores.recall[i] = recall_score(y_test, y_pred)\n",
    "        scores.f1[i] = f1_score(y_test, y_pred)\n",
    "    \n",
    "    scores_summary = pd.DataFrame(data=np.transpose(np.array(scores.mean()).reshape(1,4)), \n",
    "                                  index=('mean accuracy', 'mean precision', 'mean recall', 'mean f1'), dtype='float')\n",
    "    \n",
    "    scores_SD = pd.DataFrame(data=np.transpose(np.array(np.std(scores)).reshape(1,4)), \n",
    "                             index=('accuracy SD', 'precision SD', 'recall SD', 'f1 SD'), dtype='float')\n",
    "\n",
    "    scores_SD_summary[f'KNN k={k}'] = pd.concat([scores_summary, scores_SD])\n",
    "\n",
    "del scores, scores_summary, scores_SD\n",
    "\n",
    "scores_SD_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e06dac67",
   "metadata": {},
   "source": [
    "### 2.3. Support Vector Machines\n",
    "\n",
    "Thirteen Support Vector Machines (SVM) models were trained. One with linear decision boundary and twelve with Gaussian kernel and combinations of the parameters from: gammas = [0.5, 1, 2, 10], Cs = [0.1, 1, 10]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "682c3114",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# SVM: Linear Decision Boundary\n",
    "from sklearn.svm import LinearSVC\n",
    "LSVC = LinearSVC()\n",
    "\n",
    "scores = pd.DataFrame(index=range(0,n_splits),columns=scores_columns, dtype='float')\n",
    "\n",
    "for i in range(0, n_splits):\n",
    "    X_train, y_train = data[data.columns[:-1]].iloc[indexes.train_idx[i]], data.Class.iloc[indexes.train_idx[i]]\n",
    "    X_test, y_test = data[data.columns[:-1]].iloc[indexes.test_idx[i]], data.Class.iloc[indexes.test_idx[i]]\n",
    "\n",
    "    LSVC = LSVC.fit(X_train, y_train)\n",
    "    y_pred = LSVC.predict(X_test)\n",
    "    \n",
    "    scores.acccuracy[i] = accuracy_score(y_test, y_pred)\n",
    "    scores.precision[i] = precision_score(y_test, y_pred)\n",
    "    scores.recall[i] = recall_score(y_test, y_pred)\n",
    "    scores.f1[i] = f1_score(y_test, y_pred)\n",
    "\n",
    "scores_summary = pd.DataFrame(data=np.transpose(np.array(scores.mean()).reshape(1,4)), \n",
    "                              index=('mean accuracy', 'mean precision', 'mean recall', 'mean f1'), dtype='float')\n",
    "\n",
    "scores_SD = pd.DataFrame(data=np.transpose(np.array(np.std(scores)).reshape(1,4)), \n",
    "                              index=('accuracy SD', 'precision SD', 'recall SD', 'f1 SD'), dtype='float')\n",
    "\n",
    "scores_SD_summary['SVM:LSVC'] = pd.concat([scores_summary, scores_SD])\n",
    "\n",
    "del scores, scores_summary, scores_SD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "af60b5d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>lr</th>\n",
       "      <th>lr_l1</th>\n",
       "      <th>lr_l2</th>\n",
       "      <th>KNN k=1</th>\n",
       "      <th>KNN k=2</th>\n",
       "      <th>KNN k=3</th>\n",
       "      <th>KNN k=4</th>\n",
       "      <th>KNN k=5</th>\n",
       "      <th>KNN k=6</th>\n",
       "      <th>KNN k=7</th>\n",
       "      <th>...</th>\n",
       "      <th>SVM: gamma=0.5, C=10</th>\n",
       "      <th>SVM: gamma=1, C=0.1</th>\n",
       "      <th>SVM: gamma=1, C=1</th>\n",
       "      <th>SVM: gamma=1, C=10</th>\n",
       "      <th>SVM: gamma=2, C=0.1</th>\n",
       "      <th>SVM: gamma=2, C=1</th>\n",
       "      <th>SVM: gamma=2, C=10</th>\n",
       "      <th>SVM: gamma=10, C=0.1</th>\n",
       "      <th>SVM: gamma=10, C=1</th>\n",
       "      <th>SVM: gamma=10, C=10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean accuracy</th>\n",
       "      <td>0.970290</td>\n",
       "      <td>0.992754</td>\n",
       "      <td>0.992754</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.994203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.992029</td>\n",
       "      <td>0.984058</td>\n",
       "      <td>0.989130</td>\n",
       "      <td>0.994928</td>\n",
       "      <td>0.985507</td>\n",
       "      <td>0.989130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.985507</td>\n",
       "      <td>0.994928</td>\n",
       "      <td>0.997826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean precision</th>\n",
       "      <td>0.972363</td>\n",
       "      <td>0.988838</td>\n",
       "      <td>0.988838</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.988812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.982460</td>\n",
       "      <td>0.965572</td>\n",
       "      <td>0.976262</td>\n",
       "      <td>0.988761</td>\n",
       "      <td>0.968600</td>\n",
       "      <td>0.976262</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.968600</td>\n",
       "      <td>0.988812</td>\n",
       "      <td>0.995161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean recall</th>\n",
       "      <td>0.960656</td>\n",
       "      <td>0.995082</td>\n",
       "      <td>0.995082</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>0.998361</td>\n",
       "      <td>0.998361</td>\n",
       "      <td>...</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>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean f1</th>\n",
       "      <td>0.966069</td>\n",
       "      <td>0.991882</td>\n",
       "      <td>0.991882</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.996748</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.996748</td>\n",
       "      <td>0.996735</td>\n",
       "      <td>0.993509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.991109</td>\n",
       "      <td>0.982387</td>\n",
       "      <td>0.987922</td>\n",
       "      <td>0.994322</td>\n",
       "      <td>0.983961</td>\n",
       "      <td>0.987922</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.983961</td>\n",
       "      <td>0.994335</td>\n",
       "      <td>0.997561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy SD</th>\n",
       "      <td>0.012738</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.005423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006019</td>\n",
       "      <td>0.009051</td>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.004640</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.005660</td>\n",
       "      <td>0.003321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision SD</th>\n",
       "      <td>0.019217</td>\n",
       "      <td>0.015968</td>\n",
       "      <td>0.015968</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007902</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007902</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.012427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012998</td>\n",
       "      <td>0.019357</td>\n",
       "      <td>0.016012</td>\n",
       "      <td>0.010224</td>\n",
       "      <td>0.018447</td>\n",
       "      <td>0.016012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018447</td>\n",
       "      <td>0.012427</td>\n",
       "      <td>0.007391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall SD</th>\n",
       "      <td>0.028583</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 SD</th>\n",
       "      <td>0.015014</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.006046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006650</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.009455</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009455</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>0.003726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      lr     lr_l1     lr_l2   KNN k=1   KNN k=2   KNN k=3  \\\n",
       "mean accuracy   0.970290  0.992754  0.992754  0.997826  0.997826  0.997101   \n",
       "mean precision  0.972363  0.988838  0.988838  0.995161  0.995161  0.993548   \n",
       "mean recall     0.960656  0.995082  0.995082  1.000000  1.000000  1.000000   \n",
       "mean f1         0.966069  0.991882  0.991882  0.997561  0.997561  0.996748   \n",
       "accuracy SD     0.012738  0.008574  0.008574  0.003321  0.003321  0.003550   \n",
       "precision SD    0.019217  0.015968  0.015968  0.007391  0.007391  0.007902   \n",
       "recall SD       0.028583  0.007512  0.007512  0.000000  0.000000  0.000000   \n",
       "f1 SD           0.015014  0.009542  0.009542  0.003726  0.003726  0.003983   \n",
       "\n",
       "                 KNN k=4   KNN k=5   KNN k=6   KNN k=7  ...  \\\n",
       "mean accuracy   0.997826  0.997101  0.997101  0.994203  ...   \n",
       "mean precision  0.995161  0.993548  0.995161  0.988812  ...   \n",
       "mean recall     1.000000  1.000000  0.998361  0.998361  ...   \n",
       "mean f1         0.997561  0.996748  0.996735  0.993509  ...   \n",
       "accuracy SD     0.003321  0.003550  0.003550  0.005423  ...   \n",
       "precision SD    0.007391  0.007902  0.007391  0.012427  ...   \n",
       "recall SD       0.000000  0.000000  0.004918  0.004918  ...   \n",
       "f1 SD           0.003726  0.003983  0.004000  0.006046  ...   \n",
       "\n",
       "                SVM: gamma=0.5, C=10  SVM: gamma=1, C=0.1  SVM: gamma=1, C=1  \\\n",
       "mean accuracy               0.992029             0.984058           0.989130   \n",
       "mean precision              0.982460             0.965572           0.976262   \n",
       "mean recall                 1.000000             1.000000           1.000000   \n",
       "mean f1                     0.991109             0.982387           0.987922   \n",
       "accuracy SD                 0.006019             0.009051           0.007425   \n",
       "precision SD                0.012998             0.019357           0.016012   \n",
       "recall SD                   0.000000             0.000000           0.000000   \n",
       "f1 SD                       0.006650             0.009950           0.008198   \n",
       "\n",
       "                SVM: gamma=1, C=10  SVM: gamma=2, C=0.1  SVM: gamma=2, C=1  \\\n",
       "mean accuracy             0.994928             0.985507           0.989130   \n",
       "mean precision            0.988761             0.968600           0.976262   \n",
       "mean recall               1.000000             1.000000           1.000000   \n",
       "mean f1                   0.994322             0.983961           0.987922   \n",
       "accuracy SD               0.004640             0.008574           0.007425   \n",
       "precision SD              0.010224             0.018447           0.016012   \n",
       "recall SD                 0.000000             0.000000           0.000000   \n",
       "f1 SD                     0.005179             0.009455           0.008198   \n",
       "\n",
       "                SVM: gamma=2, C=10  SVM: gamma=10, C=0.1  SVM: gamma=10, C=1  \\\n",
       "mean accuracy                  1.0              0.985507            0.994928   \n",
       "mean precision                 1.0              0.968600            0.988812   \n",
       "mean recall                    1.0              1.000000            1.000000   \n",
       "mean f1                        1.0              0.983961            0.994335   \n",
       "accuracy SD                    0.0              0.008574            0.005660   \n",
       "precision SD                   0.0              0.018447            0.012427   \n",
       "recall SD                      0.0              0.000000            0.000000   \n",
       "f1 SD                          0.0              0.009455            0.006306   \n",
       "\n",
       "                SVM: gamma=10, C=10  \n",
       "mean accuracy              0.997826  \n",
       "mean precision             0.995161  \n",
       "mean recall                1.000000  \n",
       "mean f1                    0.997561  \n",
       "accuracy SD                0.003321  \n",
       "precision SD               0.007391  \n",
       "recall SD                  0.000000  \n",
       "f1 SD                      0.003726  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVM: Gaussian kernel\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "gammas = [0.5, 1, 2, 10] \n",
    "Cs = [0.1, 1, 10]\n",
    "\n",
    "\n",
    "for gamma in gammas:\n",
    "    for C in Cs:\n",
    "        \n",
    "        svc = SVC(kernel='rbf', gamma=gamma, C=C)\n",
    "        scores = pd.DataFrame(index=range(0,n_splits),columns=scores_columns, dtype='float')\n",
    "        \n",
    "        for i in range(0, n_splits):\n",
    "            X_train, y_train = data[data.columns[:-1]].iloc[indexes.train_idx[i]], data.Class.iloc[indexes.train_idx[i]]\n",
    "            X_test, y_test = data[data.columns[:-1]].iloc[indexes.test_idx[i]], data.Class.iloc[indexes.test_idx[i]]\n",
    "\n",
    "            svc.fit(X_train, y_train)\n",
    "            y_pred= svc.predict(X_test)\n",
    "    \n",
    "            scores.acccuracy[i] = accuracy_score(y_test, y_pred)\n",
    "            scores.precision[i] = precision_score(y_test, y_pred)\n",
    "            scores.recall[i] = recall_score(y_test, y_pred)\n",
    "            scores.f1[i] = f1_score(y_test, y_pred)\n",
    "\n",
    "        scores_summary = pd.DataFrame(data=np.transpose(np.array(scores.mean()).reshape(1,4)), \n",
    "                                  index=('mean accuracy', 'mean precision', 'mean recall', 'mean f1'), dtype='float')\n",
    "\n",
    "        scores_SD = pd.DataFrame(data=np.transpose(np.array(np.std(scores)).reshape(1,4)), \n",
    "                                  index=('accuracy SD', 'precision SD', 'recall SD', 'f1 SD'), dtype='float')\n",
    "\n",
    "        scores_SD_summary[f'SVM: gamma={gamma}, C={C}'] = pd.concat([scores_summary, scores_SD])\n",
    "\n",
    "        del scores, scores_summary, scores_SD\n",
    "        \n",
    "scores_SD_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d8e97b",
   "metadata": {},
   "source": [
    "### 2.4. Decision Trees\n",
    "\n",
    "Two decision three models were trained: one with and one without use of grid search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f3185c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nodes:55, depth:9\n",
      "nodes:47, depth:8\n",
      "nodes:47, depth:8\n",
      "nodes:53, depth:8\n",
      "nodes:45, depth:8\n",
      "nodes:53, depth:8\n",
      "nodes:41, depth:8\n",
      "nodes:49, depth:8\n",
      "nodes:45, depth:8\n",
      "nodes:55, depth:8\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lr</th>\n",
       "      <th>lr_l1</th>\n",
       "      <th>lr_l2</th>\n",
       "      <th>KNN k=1</th>\n",
       "      <th>KNN k=2</th>\n",
       "      <th>KNN k=3</th>\n",
       "      <th>KNN k=4</th>\n",
       "      <th>KNN k=5</th>\n",
       "      <th>KNN k=6</th>\n",
       "      <th>KNN k=7</th>\n",
       "      <th>...</th>\n",
       "      <th>SVM: gamma=1, C=0.1</th>\n",
       "      <th>SVM: gamma=1, C=1</th>\n",
       "      <th>SVM: gamma=1, C=10</th>\n",
       "      <th>SVM: gamma=2, C=0.1</th>\n",
       "      <th>SVM: gamma=2, C=1</th>\n",
       "      <th>SVM: gamma=2, C=10</th>\n",
       "      <th>SVM: gamma=10, C=0.1</th>\n",
       "      <th>SVM: gamma=10, C=1</th>\n",
       "      <th>SVM: gamma=10, C=10</th>\n",
       "      <th>Decision Tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean accuracy</th>\n",
       "      <td>0.970290</td>\n",
       "      <td>0.992754</td>\n",
       "      <td>0.992754</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.994203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.984058</td>\n",
       "      <td>0.989130</td>\n",
       "      <td>0.994928</td>\n",
       "      <td>0.985507</td>\n",
       "      <td>0.989130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.985507</td>\n",
       "      <td>0.994928</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.986957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean precision</th>\n",
       "      <td>0.972363</td>\n",
       "      <td>0.988838</td>\n",
       "      <td>0.988838</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.988812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.965572</td>\n",
       "      <td>0.976262</td>\n",
       "      <td>0.988761</td>\n",
       "      <td>0.968600</td>\n",
       "      <td>0.976262</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.968600</td>\n",
       "      <td>0.988812</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.979306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean recall</th>\n",
       "      <td>0.960656</td>\n",
       "      <td>0.995082</td>\n",
       "      <td>0.995082</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>0.998361</td>\n",
       "      <td>0.998361</td>\n",
       "      <td>...</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>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean f1</th>\n",
       "      <td>0.966069</td>\n",
       "      <td>0.991882</td>\n",
       "      <td>0.991882</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.996748</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.996748</td>\n",
       "      <td>0.996735</td>\n",
       "      <td>0.993509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.982387</td>\n",
       "      <td>0.987922</td>\n",
       "      <td>0.994322</td>\n",
       "      <td>0.983961</td>\n",
       "      <td>0.987922</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.983961</td>\n",
       "      <td>0.994335</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.985389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy SD</th>\n",
       "      <td>0.012738</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.005423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009051</td>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.004640</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.005660</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.012467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision SD</th>\n",
       "      <td>0.019217</td>\n",
       "      <td>0.015968</td>\n",
       "      <td>0.015968</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007902</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007902</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.012427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019357</td>\n",
       "      <td>0.016012</td>\n",
       "      <td>0.010224</td>\n",
       "      <td>0.018447</td>\n",
       "      <td>0.016012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018447</td>\n",
       "      <td>0.012427</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.020013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall SD</th>\n",
       "      <td>0.028583</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 SD</th>\n",
       "      <td>0.015014</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.006046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.009455</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009455</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.013999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      lr     lr_l1     lr_l2   KNN k=1   KNN k=2   KNN k=3  \\\n",
       "mean accuracy   0.970290  0.992754  0.992754  0.997826  0.997826  0.997101   \n",
       "mean precision  0.972363  0.988838  0.988838  0.995161  0.995161  0.993548   \n",
       "mean recall     0.960656  0.995082  0.995082  1.000000  1.000000  1.000000   \n",
       "mean f1         0.966069  0.991882  0.991882  0.997561  0.997561  0.996748   \n",
       "accuracy SD     0.012738  0.008574  0.008574  0.003321  0.003321  0.003550   \n",
       "precision SD    0.019217  0.015968  0.015968  0.007391  0.007391  0.007902   \n",
       "recall SD       0.028583  0.007512  0.007512  0.000000  0.000000  0.000000   \n",
       "f1 SD           0.015014  0.009542  0.009542  0.003726  0.003726  0.003983   \n",
       "\n",
       "                 KNN k=4   KNN k=5   KNN k=6   KNN k=7  ...  \\\n",
       "mean accuracy   0.997826  0.997101  0.997101  0.994203  ...   \n",
       "mean precision  0.995161  0.993548  0.995161  0.988812  ...   \n",
       "mean recall     1.000000  1.000000  0.998361  0.998361  ...   \n",
       "mean f1         0.997561  0.996748  0.996735  0.993509  ...   \n",
       "accuracy SD     0.003321  0.003550  0.003550  0.005423  ...   \n",
       "precision SD    0.007391  0.007902  0.007391  0.012427  ...   \n",
       "recall SD       0.000000  0.000000  0.004918  0.004918  ...   \n",
       "f1 SD           0.003726  0.003983  0.004000  0.006046  ...   \n",
       "\n",
       "                SVM: gamma=1, C=0.1  SVM: gamma=1, C=1  SVM: gamma=1, C=10  \\\n",
       "mean accuracy              0.984058           0.989130            0.994928   \n",
       "mean precision             0.965572           0.976262            0.988761   \n",
       "mean recall                1.000000           1.000000            1.000000   \n",
       "mean f1                    0.982387           0.987922            0.994322   \n",
       "accuracy SD                0.009051           0.007425            0.004640   \n",
       "precision SD               0.019357           0.016012            0.010224   \n",
       "recall SD                  0.000000           0.000000            0.000000   \n",
       "f1 SD                      0.009950           0.008198            0.005179   \n",
       "\n",
       "                SVM: gamma=2, C=0.1  SVM: gamma=2, C=1  SVM: gamma=2, C=10  \\\n",
       "mean accuracy              0.985507           0.989130                 1.0   \n",
       "mean precision             0.968600           0.976262                 1.0   \n",
       "mean recall                1.000000           1.000000                 1.0   \n",
       "mean f1                    0.983961           0.987922                 1.0   \n",
       "accuracy SD                0.008574           0.007425                 0.0   \n",
       "precision SD               0.018447           0.016012                 0.0   \n",
       "recall SD                  0.000000           0.000000                 0.0   \n",
       "f1 SD                      0.009455           0.008198                 0.0   \n",
       "\n",
       "                SVM: gamma=10, C=0.1  SVM: gamma=10, C=1  SVM: gamma=10, C=10  \\\n",
       "mean accuracy               0.985507            0.994928             0.997826   \n",
       "mean precision              0.968600            0.988812             0.995161   \n",
       "mean recall                 1.000000            1.000000             1.000000   \n",
       "mean f1                     0.983961            0.994335             0.997561   \n",
       "accuracy SD                 0.008574            0.005660             0.003321   \n",
       "precision SD                0.018447            0.012427             0.007391   \n",
       "recall SD                   0.000000            0.000000             0.000000   \n",
       "f1 SD                       0.009455            0.006306             0.003726   \n",
       "\n",
       "                Decision Tree  \n",
       "mean accuracy        0.986957  \n",
       "mean precision       0.979306  \n",
       "mean recall          0.991803  \n",
       "mean f1              0.985389  \n",
       "accuracy SD          0.012467  \n",
       "precision SD         0.020013  \n",
       "recall SD            0.015114  \n",
       "f1 SD                0.013999  \n",
       "\n",
       "[8 rows x 27 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "\n",
    "scores = pd.DataFrame(index=range(0,n_splits),columns=scores_columns, dtype='float')\n",
    "\n",
    "for i in range(0, n_splits):\n",
    "    X_train, y_train = data[data.columns[:-1]].iloc[indexes.train_idx[i]], data.Class.iloc[indexes.train_idx[i]]\n",
    "    X_test, y_test = data[data.columns[:-1]].iloc[indexes.test_idx[i]], data.Class.iloc[indexes.test_idx[i]]\n",
    "\n",
    "    dt = dt.fit(X_train, y_train)\n",
    "    y_pred = dt.predict(X_test)\n",
    "    \n",
    "    scores.acccuracy[i] = accuracy_score(y_test, y_pred)\n",
    "    scores.precision[i] = precision_score(y_test, y_pred)\n",
    "    scores.recall[i] = recall_score(y_test, y_pred)\n",
    "    scores.f1[i] = f1_score(y_test, y_pred)\n",
    "    print(f'nodes:{dt.tree_.node_count}, depth:{dt.tree_.max_depth}')\n",
    "\n",
    "scores_summary = pd.DataFrame(data=np.transpose(np.array(scores.mean()).reshape(1,4)), \n",
    "                              index=('mean accuracy', 'mean precision', 'mean recall', 'mean f1'), dtype='float')\n",
    "\n",
    "scores_SD = pd.DataFrame(data=np.transpose(np.array(np.std(scores)).reshape(1,4)), \n",
    "                              index=('accuracy SD', 'precision SD', 'recall SD', 'f1 SD'), dtype='float')\n",
    "\n",
    "scores_SD_summary['Decision Tree'] = pd.concat([scores_summary, scores_SD])\n",
    "\n",
    "del scores, scores_summary, scores_SD\n",
    "\n",
    "scores_SD_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fdd3eefe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nodes:63, depth:8\n",
      "nodes:33, depth:5\n",
      "nodes:47, depth:8\n",
      "nodes:53, depth:8\n",
      "nodes:51, depth:7\n",
      "nodes:53, depth:8\n",
      "nodes:41, depth:8\n",
      "nodes:49, depth:8\n",
      "nodes:45, depth:8\n",
      "nodes:63, depth:7\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lr</th>\n",
       "      <th>lr_l1</th>\n",
       "      <th>lr_l2</th>\n",
       "      <th>KNN k=1</th>\n",
       "      <th>KNN k=2</th>\n",
       "      <th>KNN k=3</th>\n",
       "      <th>KNN k=4</th>\n",
       "      <th>KNN k=5</th>\n",
       "      <th>KNN k=6</th>\n",
       "      <th>KNN k=7</th>\n",
       "      <th>...</th>\n",
       "      <th>SVM: gamma=1, C=1</th>\n",
       "      <th>SVM: gamma=1, C=10</th>\n",
       "      <th>SVM: gamma=2, C=0.1</th>\n",
       "      <th>SVM: gamma=2, C=1</th>\n",
       "      <th>SVM: gamma=2, C=10</th>\n",
       "      <th>SVM: gamma=10, C=0.1</th>\n",
       "      <th>SVM: gamma=10, C=1</th>\n",
       "      <th>SVM: gamma=10, C=10</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <th>Decision Tree + Grid Search</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean accuracy</th>\n",
       "      <td>0.970290</td>\n",
       "      <td>0.992754</td>\n",
       "      <td>0.992754</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.997101</td>\n",
       "      <td>0.994203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.989130</td>\n",
       "      <td>0.994928</td>\n",
       "      <td>0.985507</td>\n",
       "      <td>0.989130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.985507</td>\n",
       "      <td>0.994928</td>\n",
       "      <td>0.997826</td>\n",
       "      <td>0.986957</td>\n",
       "      <td>0.982609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean precision</th>\n",
       "      <td>0.972363</td>\n",
       "      <td>0.988838</td>\n",
       "      <td>0.988838</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.988812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.976262</td>\n",
       "      <td>0.988761</td>\n",
       "      <td>0.968600</td>\n",
       "      <td>0.976262</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.968600</td>\n",
       "      <td>0.988812</td>\n",
       "      <td>0.995161</td>\n",
       "      <td>0.979306</td>\n",
       "      <td>0.978994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean recall</th>\n",
       "      <td>0.960656</td>\n",
       "      <td>0.995082</td>\n",
       "      <td>0.995082</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>0.998361</td>\n",
       "      <td>0.998361</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991803</td>\n",
       "      <td>0.981967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean f1</th>\n",
       "      <td>0.966069</td>\n",
       "      <td>0.991882</td>\n",
       "      <td>0.991882</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.996748</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.996748</td>\n",
       "      <td>0.996735</td>\n",
       "      <td>0.993509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.987922</td>\n",
       "      <td>0.994322</td>\n",
       "      <td>0.983961</td>\n",
       "      <td>0.987922</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.983961</td>\n",
       "      <td>0.994335</td>\n",
       "      <td>0.997561</td>\n",
       "      <td>0.985389</td>\n",
       "      <td>0.980351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy SD</th>\n",
       "      <td>0.012738</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.005423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.004640</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008574</td>\n",
       "      <td>0.005660</td>\n",
       "      <td>0.003321</td>\n",
       "      <td>0.012467</td>\n",
       "      <td>0.012634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision SD</th>\n",
       "      <td>0.019217</td>\n",
       "      <td>0.015968</td>\n",
       "      <td>0.015968</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007902</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.007902</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.012427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016012</td>\n",
       "      <td>0.010224</td>\n",
       "      <td>0.018447</td>\n",
       "      <td>0.016012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.018447</td>\n",
       "      <td>0.012427</td>\n",
       "      <td>0.007391</td>\n",
       "      <td>0.020013</td>\n",
       "      <td>0.017460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall SD</th>\n",
       "      <td>0.028583</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>0.007512</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>0.004918</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015114</td>\n",
       "      <td>0.018619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f1 SD</th>\n",
       "      <td>0.015014</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>0.009542</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.006046</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>0.005179</td>\n",
       "      <td>0.009455</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009455</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.013999</td>\n",
       "      <td>0.014305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      lr     lr_l1     lr_l2   KNN k=1   KNN k=2   KNN k=3  \\\n",
       "mean accuracy   0.970290  0.992754  0.992754  0.997826  0.997826  0.997101   \n",
       "mean precision  0.972363  0.988838  0.988838  0.995161  0.995161  0.993548   \n",
       "mean recall     0.960656  0.995082  0.995082  1.000000  1.000000  1.000000   \n",
       "mean f1         0.966069  0.991882  0.991882  0.997561  0.997561  0.996748   \n",
       "accuracy SD     0.012738  0.008574  0.008574  0.003321  0.003321  0.003550   \n",
       "precision SD    0.019217  0.015968  0.015968  0.007391  0.007391  0.007902   \n",
       "recall SD       0.028583  0.007512  0.007512  0.000000  0.000000  0.000000   \n",
       "f1 SD           0.015014  0.009542  0.009542  0.003726  0.003726  0.003983   \n",
       "\n",
       "                 KNN k=4   KNN k=5   KNN k=6   KNN k=7  ...  \\\n",
       "mean accuracy   0.997826  0.997101  0.997101  0.994203  ...   \n",
       "mean precision  0.995161  0.993548  0.995161  0.988812  ...   \n",
       "mean recall     1.000000  1.000000  0.998361  0.998361  ...   \n",
       "mean f1         0.997561  0.996748  0.996735  0.993509  ...   \n",
       "accuracy SD     0.003321  0.003550  0.003550  0.005423  ...   \n",
       "precision SD    0.007391  0.007902  0.007391  0.012427  ...   \n",
       "recall SD       0.000000  0.000000  0.004918  0.004918  ...   \n",
       "f1 SD           0.003726  0.003983  0.004000  0.006046  ...   \n",
       "\n",
       "                SVM: gamma=1, C=1  SVM: gamma=1, C=10  SVM: gamma=2, C=0.1  \\\n",
       "mean accuracy            0.989130            0.994928             0.985507   \n",
       "mean precision           0.976262            0.988761             0.968600   \n",
       "mean recall              1.000000            1.000000             1.000000   \n",
       "mean f1                  0.987922            0.994322             0.983961   \n",
       "accuracy SD              0.007425            0.004640             0.008574   \n",
       "precision SD             0.016012            0.010224             0.018447   \n",
       "recall SD                0.000000            0.000000             0.000000   \n",
       "f1 SD                    0.008198            0.005179             0.009455   \n",
       "\n",
       "                SVM: gamma=2, C=1  SVM: gamma=2, C=10  SVM: gamma=10, C=0.1  \\\n",
       "mean accuracy            0.989130                 1.0              0.985507   \n",
       "mean precision           0.976262                 1.0              0.968600   \n",
       "mean recall              1.000000                 1.0              1.000000   \n",
       "mean f1                  0.987922                 1.0              0.983961   \n",
       "accuracy SD              0.007425                 0.0              0.008574   \n",
       "precision SD             0.016012                 0.0              0.018447   \n",
       "recall SD                0.000000                 0.0              0.000000   \n",
       "f1 SD                    0.008198                 0.0              0.009455   \n",
       "\n",
       "                SVM: gamma=10, C=1  SVM: gamma=10, C=10  Decision Tree  \\\n",
       "mean accuracy             0.994928             0.997826       0.986957   \n",
       "mean precision            0.988812             0.995161       0.979306   \n",
       "mean recall               1.000000             1.000000       0.991803   \n",
       "mean f1                   0.994335             0.997561       0.985389   \n",
       "accuracy SD               0.005660             0.003321       0.012467   \n",
       "precision SD              0.012427             0.007391       0.020013   \n",
       "recall SD                 0.000000             0.000000       0.015114   \n",
       "f1 SD                     0.006306             0.003726       0.013999   \n",
       "\n",
       "                Decision Tree + Grid Search  \n",
       "mean accuracy                      0.982609  \n",
       "mean precision                     0.978994  \n",
       "mean recall                        0.981967  \n",
       "mean f1                            0.980351  \n",
       "accuracy SD                        0.012634  \n",
       "precision SD                       0.017460  \n",
       "recall SD                          0.018619  \n",
       "f1 SD                              0.014305  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid search for decision tree\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {'max_depth':range(1, dt.tree_.max_depth+1, 1),\n",
    "              'max_features': range(1, len(dt.feature_importances_)+1)}\n",
    "\n",
    "GR = GridSearchCV(DecisionTreeClassifier(),\n",
    "                  param_grid=param_grid,\n",
    "                  scoring='accuracy',                             #zastanowić się nad tym, czy accuracy\n",
    "                  n_jobs=-1)\n",
    "\n",
    "scores = pd.DataFrame(index=range(0,n_splits),columns=scores_columns, dtype='float')\n",
    "\n",
    "for i in range(0, n_splits):\n",
    "    X_train, y_train = data[data.columns[:-1]].iloc[indexes.train_idx[i]], data.Class.iloc[indexes.train_idx[i]]\n",
    "    X_test, y_test = data[data.columns[:-1]].iloc[indexes.test_idx[i]], data.Class.iloc[indexes.test_idx[i]]\n",
    "\n",
    "    GR = GR.fit(X_train, y_train)\n",
    "    y_pred = GR.predict(X_test)\n",
    "    \n",
    "    scores.acccuracy[i] = accuracy_score(y_test, y_pred)\n",
    "    scores.precision[i] = precision_score(y_test, y_pred)\n",
    "    scores.recall[i] = recall_score(y_test, y_pred)\n",
    "    scores.f1[i] = f1_score(y_test, y_pred)\n",
    "    print(f'nodes:{GR.best_estimator_.tree_.node_count}, depth:{GR.best_estimator_.tree_.max_depth}')\n",
    "\n",
    "scores_summary = pd.DataFrame(data=np.transpose(np.array(scores.mean()).reshape(1,4)), \n",
    "                              index=('mean accuracy', 'mean precision', 'mean recall', 'mean f1'), dtype='float')\n",
    "\n",
    "scores_SD = pd.DataFrame(data=np.transpose(np.array(np.std(scores)).reshape(1,4)), \n",
    "                              index=('accuracy SD', 'precision SD', 'recall SD', 'f1 SD'), dtype='float')\n",
    "\n",
    "scores_SD_summary['Decision Tree + Grid Search'] = pd.concat([scores_summary, scores_SD])\n",
    "\n",
    "del scores, scores_summary, scores_SD\n",
    "\n",
    "scores_SD_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a43d10e9",
   "metadata": {},
   "source": [
    "## 3. Performance and stability summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9e6f1357",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy: SVM: gamma=2, C=10 \n",
      "Best precision: SVM: gamma=2, C=10 \n",
      "Best recall: KNN k=1 \n",
      "Best f1: SVM: gamma=2, C=10\n",
      "\n",
      "Best stability regarding accuracy: SVM: gamma=2, C=10 \n",
      "Best stability regarding precision: SVM: gamma=2, C=10 \n",
      "Best stability regarding recall: KNN k=1 \n",
      "Best stability regarding f1: SVM: gamma=2, C=10\n"
     ]
    }
   ],
   "source": [
    "# Best performances\n",
    "print('Best accuracy:',scores_SD_summary.loc['mean accuracy'].idxmax(), \n",
    "      '\\n'+'Best precision:',scores_SD_summary.loc['mean precision'].idxmax(),\n",
    "      '\\n'+'Best recall:', scores_SD_summary.loc['mean recall'].idxmax(),\n",
    "      '\\n'+'Best f1:', scores_SD_summary.loc['mean f1'].idxmax())\n",
    "\n",
    "# Best stability\n",
    "print('\\n'+'Best stability regarding accuracy:',scores_SD_summary.loc['accuracy SD'].idxmin(), \n",
    "      '\\n'+'Best stability regarding precision:',scores_SD_summary.loc['precision SD'].idxmin(),\n",
    "      '\\n'+'Best stability regarding recall:', scores_SD_summary.loc['recall SD'].idxmin(),\n",
    "      '\\n'+'Best stability regarding f1:', scores_SD_summary.loc['f1 SD'].idxmin())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49f276a9",
   "metadata": {},
   "source": [
    "In the above summary Support Vector Machine with Gaussian kernel, gamma=2 and C=10 stands out. The mean performance metrics values of the model are presented below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2782f7dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM: gamma=2, C=10 accuracy: 1.0 \n",
      "SVM: gamma=2, C=10 precision: 1.0 \n",
      "SVM: gamma=2, C=10 recall: 1.0 \n",
      "SVM: gamma=2, C=10 f1: 1.0\n"
     ]
    }
   ],
   "source": [
    "print('SVM: gamma=2, C=10 accuracy:',scores_SD_summary['SVM: gamma=2, C=10'].loc['mean accuracy'], \n",
    "      '\\n'+'SVM: gamma=2, C=10 precision:',scores_SD_summary['SVM: gamma=2, C=10'].loc['mean precision'],\n",
    "      '\\n'+'SVM: gamma=2, C=10 recall:', scores_SD_summary['SVM: gamma=2, C=10'].loc['mean recall'],\n",
    "      '\\n'+'SVM: gamma=2, C=10 f1:', scores_SD_summary['SVM: gamma=2, C=10'].loc['mean f1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a7d1e3b",
   "metadata": {},
   "source": [
    "This results are for train test split 90%-10%. For 80%-20% performance metrics for the Support Vector Machine with Gaussian kernel, gamma=2 and C=10 (the winner) stay the same - you can see for yourself, this is a Jupyter Notebook after all. For 70%-30% the winner model stays the same with mean accuracy of 0.9985436893203883, mean precision of 0.9967969472710454, mean recall of 1.0 and mean f1 score of 0.9983798115410887.\n",
    "\n",
    "If the performance metrics were slightly lower, further gamma and C adjustment could be of interest, but this is not the case."
   ]
  }
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