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 complere 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": 5,
   "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": 5,
     "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_data[['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']] = diabetes_data[['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']].replace(0,None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Plot histograms of each column. </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Outcome')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "\n",
    "plt.subplot(3,3,1)\n",
    "plt.hist(diabetes_data['Pregnancies'])\n",
    "plt.xlabel('Pregnancies')\n",
    "\n",
    "plt.subplot(3,3,2)\n",
    "plt.hist(diabetes_data['Glucose'])\n",
    "plt.xlabel('Glucose')\n",
    "\n",
    "plt.subplot(3,3,3)\n",
    "plt.hist(diabetes_data['BloodPressure'])\n",
    "plt.xlabel('Blood Pressure')\n",
    "\n",
    "plt.subplot(3,3,4)\n",
    "plt.hist(diabetes_data['SkinThickness'])\n",
    "plt.xlabel('Skin Thickness')\n",
    "\n",
    "plt.subplot(3,3,5)\n",
    "plt.hist(diabetes_data['Insulin'])\n",
    "plt.xlabel('Insulin')\n",
    "\n",
    "plt.subplot(3,3,6)\n",
    "plt.hist(diabetes_data['BMI'])\n",
    "plt.xlabel('BMI')\n",
    "\n",
    "plt.subplot(3,3,7)\n",
    "plt.hist(diabetes_data['DiabetesPedigreeFunction'])\n",
    "plt.xlabel('Diabetes Predigree Fucntion')\n",
    "\n",
    "plt.subplot(3,3,8)\n",
    "plt.hist(diabetes_data['Age'])\n",
    "plt.xlabel('Age')\n",
    "\n",
    "plt.subplot(3,3,9)\n",
    "plt.hist(diabetes_data['Outcome'])\n",
    "plt.xlabel('Outcome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Replace the zeros with mean and median values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Plot histograms of each column after replacing nan. </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Outcome')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "plt.hist(diabetes_data['Glucose'])\n",
    "plt.xlabel('Glucose')\n",
    "\n",
    "plt.subplot(2,3,2)\n",
    "plt.hist(diabetes_data['BloodPressure'])\n",
    "plt.xlabel('Blood Pressure')\n",
    "\n",
    "plt.subplot(2,3,3)\n",
    "plt.hist(diabetes_data['SkinThickness'])\n",
    "plt.xlabel('Skin Thickness')\n",
    "\n",
    "plt.subplot(2,3,4)\n",
    "plt.hist(diabetes_data['Insulin'])\n",
    "plt.xlabel('Insulin')\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "plt.hist(diabetes_data['BMI'])\n",
    "plt.xlabel('BMI')\n",
    "\n",
    "plt.subplot(2,3,6)\n",
    "plt.hist(diabetes_data['Outcome'])\n",
    "plt.xlabel('Outcome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Plot the correlation matrix heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = diabetes_data['Outcome']\n",
    "X = diabetes_data.drop(columns=['Outcome'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Create a 70/30 train and test split. </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test =train_test_split(X,y, test_size=0.30, random_state=100)"
   ]
  },
  {
   "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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_scaled = 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": 32,
   "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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For iteration with 1 clusters, the training score is 100.0%, and the test score is 67.97% \n",
      "For iteration with 2 clusters, the training score is 86.03%, and the test score is 70.13% \n",
      "For iteration with 3 clusters, the training score is 85.85%, and the test score is 71.0% \n",
      "For iteration with 4 clusters, the training score is 81.75%, and the test score is 70.56% \n",
      "For iteration with 5 clusters, the training score is 81.75%, and the test score is 71.86% \n",
      "For iteration with 6 clusters, the training score is 80.63%, and the test score is 72.29% \n",
      "For iteration with 7 clusters, the training score is 81.38%, and the test score is 69.26% \n",
      "For iteration with 8 clusters, the training score is 80.82%, and the test score is 70.56% \n",
      "For iteration with 9 clusters, the training score is 80.07%, and the test score is 70.13% \n"
     ]
    }
   ],
   "source": [
    "for i, x, y in zip(range(1,10), train_scores, test_scores):\n",
    "    print('For iteration with {} clusters, the training score is {}%, and the test score is {}% '\n",
    "          .format(i, round(100*x,2), round(100*y,2)))"
   ]
  },
  {
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best score for the training set is 100.0%, which used 1 cluster\n"
     ]
    }
   ],
   "source": [
    "print('The best score for the training set is {}%, which used {} cluster'.format(100*train_scores[0], 1))"
   ]
  },
  {
   "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": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best score on the test set is 72.2943722943723%, which used 6 clusters\n"
     ]
    }
   ],
   "source": [
    "print('The best score on the test set is {}%, which used {} clusters'.format(100*test_scores[5], 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Plot the train and test model performance by number of neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\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\\User\\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": 47,
   "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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(pl, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**<font color='teal'> Print the classification report </font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.79      0.77       150\n",
      "           1       0.58      0.54      0.56        81\n",
      "\n",
      "    accuracy                           0.70       231\n",
      "   macro avg       0.67      0.66      0.67       231\n",
      "weighted avg       0.70      0.70      0.70       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred, labels=[0,1]))"
   ]
  },
  {
   "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": 64,
   "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": 64,
     "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": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:0.7513878278584161\n",
      "Best Parameters: {'n_neighbors': 15}\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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={'n_estimators': 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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "param_grid_2 = {'n_estimators':np.arange(1,50)}\n",
    "RF = RandomForestClassifier()\n",
    "knn_rf = GridSearchCV(RF,param_grid_2, cv=5)\n",
    "knn_rf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:0.7708938120702827\n",
      "Best Parameters: {'n_estimators': 43}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Score:\" + str(knn_rf.best_score_))\n",
    "print(\"Best Parameters: \" + str(knn_rf.best_params_))"
   ]
  }
 ],
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