GridSearchKNN_Case_Study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search Hyperparameter optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This case study is all about using grid searches to identify the optimal parameters for a machine learning algorithm. To complete this case study, you'll use the Pima Indian diabetes dataset from Kaggle and KNN. Follow along with the preprocessing steps of this case study."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Load the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "# set random seed to try make this exercise and solutions reproducible (NB: this is just for teaching purpose and not something you would do in real life)\n",
    "random_seed_number = 42\n",
    "np.random.seed(random_seed_number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Load the diabetes data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_data = pd.read_csv('data/diabetes.csv')\n",
    "diabetes_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Start by reviewing the data info.</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "diabetes_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Apply the describe function to the data.</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Currently, the missing values in the dataset are represented as zeros. Replace the zero values in the following columns ['Glucose','BloodPressure','SkinThickness','Insulin','BMI'] with nan .</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_data[['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']] = diabetes_data[['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']].replace(0,np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Plot histograms of each column. </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diabetes_data.hist(figsize=(12,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Replace the zeros with mean and median values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_data['Glucose'].fillna(diabetes_data['Glucose'].mean(), inplace = True)\n",
    "diabetes_data['BloodPressure'].fillna(diabetes_data['BloodPressure'].mean(), inplace = True)\n",
    "diabetes_data['SkinThickness'].fillna(diabetes_data['SkinThickness'].median(), inplace = True)\n",
    "diabetes_data['Insulin'].fillna(diabetes_data['Insulin'].median(), inplace = True)\n",
    "diabetes_data['BMI'].fillna(diabetes_data['BMI'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Plot histograms of each column after replacing nan. </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diabetes_data.hist(figsize=(12,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Plot the correlation matrix heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between various features\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "print('Correlation between various features')\n",
    "p=sns.heatmap(diabetes_data.corr(), annot=True,cmap ='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Define the `y` variable as the `Outcome` column.</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cols = diabetes_data.columns[diabetes_data.columns != 'Outcome']\n",
    "X = diabetes_data[X_cols]\n",
    "\n",
    "y = diabetes_data['Outcome']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Create a 70/30 train and test split. </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Using Sklearn, standarize the magnitude of the features by scaling the values. </font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Don't forget to fit() your scaler on X_train and then use that fitted scaler to transform() X_test. This is to avoid data leakage while you standardize your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Using a range of neighbor values of 1-10, apply the KNearestNeighbor classifier to classify the the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "\n",
    "test_scores = []\n",
    "train_scores = []\n",
    "\n",
    "for i in range(1,10):\n",
    "\n",
    "    knn = KNeighborsClassifier(i)\n",
    "    knn.fit(X_train,y_train)\n",
    "    \n",
    "    train_scores.append(knn.score(X_train,y_train))\n",
    "    test_scores.append(knn.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Print the train and test scores for each iteration.</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Scores:  [(1, 1.0), (2, 0.851024208566108), (3, 0.8547486033519553), (4, 0.8417132216014898), (5, 0.839851024208566), (6, 0.8230912476722533), (7, 0.8342644320297952), (8, 0.8212290502793296), (9, 0.8268156424581006)] \n",
      "\n",
      "Test Scores:  [(1, 0.6666666666666666), (2, 0.670995670995671), (3, 0.670995670995671), (4, 0.6926406926406926), (5, 0.7012987012987013), (6, 0.683982683982684), (7, 0.7316017316017316), (8, 0.7359307359307359), (9, 0.7316017316017316)]\n"
     ]
    }
   ],
   "source": [
    "print('Train Scores: ', list(zip(range(1,10),train_scores)), '\\n')\n",
    "print('Test Scores: ', list(zip(range(1,10),test_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Identify the number of neighbors that resulted in the max score in the training dataset. </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_train = train_scores.index(max(train_scores)) + 1\n",
    "best_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Identify the number of neighbors that resulted in the max score in the testing dataset. </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_test = test_scores.index(max(test_scores)) + 1\n",
    "best_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Plot the train and test model performance by number of neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmlam\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\jmlam\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "p = sns.lineplot(range(1,10),train_scores,marker='*',label='Train Score')\n",
    "p = sns.lineplot(range(1,10),test_scores,marker='o',label='Test Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Fit and score the best number of neighbors based on the plot. </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=7)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# K = [7,8,9] looks best \n",
    "\n",
    "best_k = 7\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=best_k)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = knn.predict(X_test)\n",
    "pl = confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Plot the confusion matrix for the model fit above. </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pl, marker='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Print the classification report </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.77      0.79       151\n",
      "           1       0.60      0.66      0.63        80\n",
      "\n",
      "    accuracy                           0.73       231\n",
      "   macro avg       0.71      0.72      0.71       231\n",
      "weighted avg       0.74      0.73      0.73       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### In the case of the K nearest neighbors algorithm, the K parameter is one of the most important parameters affecting the model performance.  The model performance isn't horrible, but what if we didn't consider a wide enough range of values in our neighbors for the KNN? An alternative to fitting a loop of models is to use a grid search to identify the proper number. It is common practice to use a grid search method for all adjustable parameters in any type of machine learning algorithm. First, you define the grid — aka the range of values — to test in the parameter being optimized, and then compare the model outcome performance based on the different values in the grid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Run the code in the next cell to see how to implement the grid search method for identifying the best parameter value for the n_neighbors parameter. Notice the param_grid is the range value to test and we apply cross validation with five folds to score each possible value of n_neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])})"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_grid = {'n_neighbors':np.arange(1,50)}\n",
    "knn = KNeighborsClassifier()\n",
    "knn_cv= GridSearchCV(knn,param_grid,cv=5)\n",
    "knn_cv.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Print the best score and best parameter for n_neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:0.7721840251252015\n",
      "Best Parameters: {'n_neighbors': 25}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Score:\" + str(knn_cv.best_score_))\n",
    "print(\"Best Parameters: \" + str(knn_cv.best_params_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you can see that the ideal number of n_neighbors for this model is 14 based on the grid search performed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Now, following the KNN example, apply this grid search method to find the optimal number of estimators in a Randon Forest model.\n",
    "</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={'n_estimators': [50, 100, 200]})"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "grid = {\n",
    "    'n_estimators': [50,100,200]\n",
    "}\n",
    "rf_cv = GridSearchCV(rf, grid, cv=5)\n",
    "rf_cv.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:0.7683218742042272\n",
      "Best Parameters: {'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Score:\" + str(rf_cv.best_score_))\n",
    "print(\"Best Parameters: \" + str(rf_cv.best_params_))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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