supervised/k-nearest neighbors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-nearest neighbors project\n",
    "##### For the course 2022 Python Machine Learning & Data Science Masterclass\n",
    "\n",
    "For this project, we will use the [Sonar dataset](https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)) and create a KNN model capable of detecting the difference between a rock or a mine based on the response of the 60 separate sonar frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Freq_1</th>\n",
       "      <th>Freq_2</th>\n",
       "      <th>Freq_3</th>\n",
       "      <th>Freq_4</th>\n",
       "      <th>Freq_5</th>\n",
       "      <th>Freq_6</th>\n",
       "      <th>Freq_7</th>\n",
       "      <th>Freq_8</th>\n",
       "      <th>Freq_9</th>\n",
       "      <th>Freq_10</th>\n",
       "      <th>...</th>\n",
       "      <th>Freq_52</th>\n",
       "      <th>Freq_53</th>\n",
       "      <th>Freq_54</th>\n",
       "      <th>Freq_55</th>\n",
       "      <th>Freq_56</th>\n",
       "      <th>Freq_57</th>\n",
       "      <th>Freq_58</th>\n",
       "      <th>Freq_59</th>\n",
       "      <th>Freq_60</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>0.0428</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0986</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>0.2872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.2280</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.6194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Freq_1  Freq_2  Freq_3  Freq_4  Freq_5  Freq_6  Freq_7  Freq_8  Freq_9  \\\n",
       "0  0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n",
       "1  0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   \n",
       "2  0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   \n",
       "3  0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   \n",
       "4  0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n",
       "\n",
       "   Freq_10  ...  Freq_52  Freq_53  Freq_54  Freq_55  Freq_56  Freq_57  \\\n",
       "0   0.2111  ...   0.0027   0.0065   0.0159   0.0072   0.0167   0.0180   \n",
       "1   0.2872  ...   0.0084   0.0089   0.0048   0.0094   0.0191   0.0140   \n",
       "2   0.6194  ...   0.0232   0.0166   0.0095   0.0180   0.0244   0.0316   \n",
       "3   0.1264  ...   0.0121   0.0036   0.0150   0.0085   0.0073   0.0050   \n",
       "4   0.4459  ...   0.0031   0.0054   0.0105   0.0110   0.0015   0.0072   \n",
       "\n",
       "   Freq_58  Freq_59  Freq_60  Label  \n",
       "0   0.0084   0.0090   0.0032      R  \n",
       "1   0.0049   0.0052   0.0044      R  \n",
       "2   0.0164   0.0095   0.0078      R  \n",
       "3   0.0044   0.0040   0.0117      R  \n",
       "4   0.0048   0.0107   0.0094      R  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar_df = pd.read_csv('data/Sonar.csv')\n",
    "sonar_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2474e2dc0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a heatmap\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(sonar_df.corr(), cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    111\n",
       "R     97\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar_df[\"Label\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Freq_1</th>\n",
       "      <th>Freq_2</th>\n",
       "      <th>Freq_3</th>\n",
       "      <th>Freq_4</th>\n",
       "      <th>Freq_5</th>\n",
       "      <th>Freq_6</th>\n",
       "      <th>Freq_7</th>\n",
       "      <th>Freq_8</th>\n",
       "      <th>Freq_9</th>\n",
       "      <th>Freq_10</th>\n",
       "      <th>...</th>\n",
       "      <th>Freq_53</th>\n",
       "      <th>Freq_54</th>\n",
       "      <th>Freq_55</th>\n",
       "      <th>Freq_56</th>\n",
       "      <th>Freq_57</th>\n",
       "      <th>Freq_58</th>\n",
       "      <th>Freq_59</th>\n",
       "      <th>Freq_60</th>\n",
       "      <th>Label</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>0.0428</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0986</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>0.2872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.2280</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.6194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Freq_1  Freq_2  Freq_3  Freq_4  Freq_5  Freq_6  Freq_7  Freq_8  Freq_9  \\\n",
       "0  0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n",
       "1  0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   \n",
       "2  0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   \n",
       "3  0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   \n",
       "4  0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n",
       "\n",
       "   Freq_10  ...  Freq_53  Freq_54  Freq_55  Freq_56  Freq_57  Freq_58  \\\n",
       "0   0.2111  ...   0.0065   0.0159   0.0072   0.0167   0.0180   0.0084   \n",
       "1   0.2872  ...   0.0089   0.0048   0.0094   0.0191   0.0140   0.0049   \n",
       "2   0.6194  ...   0.0166   0.0095   0.0180   0.0244   0.0316   0.0164   \n",
       "3   0.1264  ...   0.0036   0.0150   0.0085   0.0073   0.0050   0.0044   \n",
       "4   0.4459  ...   0.0054   0.0105   0.0110   0.0015   0.0072   0.0048   \n",
       "\n",
       "   Freq_59  Freq_60  Label  Target  \n",
       "0   0.0090   0.0032      R       0  \n",
       "1   0.0052   0.0044      R       0  \n",
       "2   0.0095   0.0078      R       0  \n",
       "3   0.0040   0.0117      R       0  \n",
       "4   0.0107   0.0094      R       0  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# It would be easier to map the Label(target) values to 0 and 1\n",
    "sonar_df['Target'] = sonar_df['Label'].map({'R': 0, 'M': 1})\n",
    "sonar_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Target     1.000000\n",
       "Freq_11    0.432855\n",
       "Freq_12    0.392245\n",
       "Freq_49    0.351312\n",
       "Freq_10    0.341142\n",
       "Name: Target, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most correlated features with\n",
    "np.abs(sonar_df.corr()['Target']).sort_values(ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creation of train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we separate features and prediction column\n",
    "X = sonar_df.drop([\"Label\", \"Target\"], axis=1)\n",
    "y = sonar_df[\"Target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=52)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creation of Pipeline\n",
    "\n",
    "We will create a pipeline to scale the features and create the KNN model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "scaler = StandardScaler()\n",
    "knn_model = KNeighborsClassifier()\n",
    "\n",
    "pipe_operations = [('scaler', scaler), ('knn_model', knn_model)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline = Pipeline(pipe_operations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we create a GridSearchCV to tune the hyperparameter (k) of KNN. This GridSearch will use the pipeline\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "k_values = list(range(1, 30)) # we will try 30 different values for k\n",
    "param_grid = {'knn_model__n_neighbors': k_values} \n",
    "\n",
    "grid_model = GridSearchCV(pipeline, param_grid, cv=5, scoring=\"accuracy\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Pipeline(memory=None,\n",
       "                                steps=[('scaler',\n",
       "                                        StandardScaler(copy=True,\n",
       "                                                       with_mean=True,\n",
       "                                                       with_std=True)),\n",
       "                                       ('knn_model',\n",
       "                                        KNeighborsClassifier(algorithm='auto',\n",
       "                                                             leaf_size=30,\n",
       "                                                             metric='minkowski',\n",
       "                                                             metric_params=None,\n",
       "                                                             n_jobs=None,\n",
       "                                                             n_neighbors=5, p=2,\n",
       "                                                             weights='uniform'))],\n",
       "                                verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'knn_model__n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
       "                                                    10, 11, 12, 13, 14, 15, 16,\n",
       "                                                    17, 18, 19, 20, 21, 22, 23,\n",
       "                                                    24, 25, 26, 27, 28, 29]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'memory': None,\n",
       " 'steps': [('scaler',\n",
       "   StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "  ('knn_model',\n",
       "   KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                        metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                        weights='uniform'))],\n",
       " 'verbose': False,\n",
       " 'scaler': StandardScaler(copy=True, with_mean=True, with_std=True),\n",
       " 'knn_model': KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                      metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                      weights='uniform'),\n",
       " 'scaler__copy': True,\n",
       " 'scaler__with_mean': True,\n",
       " 'scaler__with_std': True,\n",
       " 'knn_model__algorithm': 'auto',\n",
       " 'knn_model__leaf_size': 30,\n",
       " 'knn_model__metric': 'minkowski',\n",
       " 'knn_model__metric_params': None,\n",
       " 'knn_model__n_jobs': None,\n",
       " 'knn_model__n_neighbors': 1,\n",
       " 'knn_model__p': 2,\n",
       " 'knn_model__weights': 'uniform'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the best parameters\n",
    "grid_model.best_estimator_.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.83448276, 0.8137931 , 0.77241379, 0.77931034, 0.75172414,\n",
       "       0.75172414, 0.74482759, 0.75172414, 0.72413793, 0.70344828,\n",
       "       0.73103448, 0.71034483, 0.71724138, 0.71034483, 0.71034483,\n",
       "       0.71034483, 0.71034483, 0.68965517, 0.67586207, 0.68275862,\n",
       "       0.68965517, 0.71034483, 0.67586207, 0.71724138, 0.71724138,\n",
       "       0.69655172, 0.68275862, 0.68275862, 0.70344828])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the mean test score for each k\n",
    "accuracy_k = grid_model.cv_results_['mean_test_score']\n",
    "accuracy_k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6), dpi=200)\n",
    "sns.lineplot(x=k_values, y=accuracy_k, marker=\"o\")\n",
    "plt.xlabel(\"K\")\n",
    "plt.ylabel(\"Accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, plot_confusion_matrix, accuracy_score\n",
    "\n",
    "y_pred = grid_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fe248889670>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "\n",
    "plot_confusion_matrix(grid_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.85      0.89        33\n",
      "           1       0.85      0.93      0.89        30\n",
      "\n",
      "    accuracy                           0.89        63\n",
      "   macro avg       0.89      0.89      0.89        63\n",
      "weighted avg       0.89      0.89      0.89        63\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888888888888888"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}