heart-disease-prediction.ipynb

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   "source": [
    "# Heart Disease Prediction"
   ]
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   "source": [
    "**Heart disease** describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease, heart rhythm problems (arrhythmia) and heart defects you’re born with (congenital heart defects), among others.\n",
    "\n",
    "In this Data Science Project I will be applying Machine Learning techniques to classify whether a person is suffering from Heart Disease or not."
   ]
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   },
   "source": [
    "**Importing the required modules**"
   ]
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
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   "source": [
    "**Data Processing**"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
       "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
       "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
       "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
       "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   2     3       0  \n",
       "1   0     3       0  \n",
       "2   0     3       0  \n",
       "3   1     3       0  \n",
       "4   3     2       0  "
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   "source": [
    "dataset = pd.read_csv('../input/heart-disease-dataset/heart.csv')\n",
    "dataset.head()"
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   "source": [
    "**Attribute Information:**\n",
    "\n",
    "1. age: age in years\n",
    "2. sex: (1 = male; 0 = female)\n",
    "3. cp: chest pain type (4 values)\n",
    "4. trestbps: resting blood pressure\n",
    "5. chol: serum cholestoral in mg/dl\n",
    "6. fbs: fasting blood sugar > 120 mg/dl\n",
    "7. restecg: resting electrocardiographic results (values 0,1,2)\n",
    "8. thalach: maximum heart rate achieved\n",
    "9. exang: exercise induced angina (1 = yes; 0 = no)\n",
    "10. oldpeak: ST depression induced by exercise relative to rest\n",
    "11. slope: the slope of the peak exercise ST segment\n",
    "12. ca: number of major vessels (0-3) colored by flourosopy\n",
    "13. thal: 0 = normal; 1 = fixed defect; 2 = reversable defect\n",
    "14. target: heart disease detected(1 = yes; 0 = no)"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1025 entries, 0 to 1024\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       1025 non-null   int64  \n",
      " 1   sex       1025 non-null   int64  \n",
      " 2   cp        1025 non-null   int64  \n",
      " 3   trestbps  1025 non-null   int64  \n",
      " 4   chol      1025 non-null   int64  \n",
      " 5   fbs       1025 non-null   int64  \n",
      " 6   restecg   1025 non-null   int64  \n",
      " 7   thalach   1025 non-null   int64  \n",
      " 8   exang     1025 non-null   int64  \n",
      " 9   oldpeak   1025 non-null   float64\n",
      " 10  slope     1025 non-null   int64  \n",
      " 11  ca        1025 non-null   int64  \n",
      " 12  thal      1025 non-null   int64  \n",
      " 13  target    1025 non-null   int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 112.2 KB\n"
     ]
    }
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   "source": [
    "dataset.info()"
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       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
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    "dataset.isnull().sum()"
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
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       "      <th>count</th>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.00000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>1025.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>54.434146</td>\n",
       "      <td>0.695610</td>\n",
       "      <td>0.942439</td>\n",
       "      <td>131.611707</td>\n",
       "      <td>246.00000</td>\n",
       "      <td>0.149268</td>\n",
       "      <td>0.529756</td>\n",
       "      <td>149.114146</td>\n",
       "      <td>0.336585</td>\n",
       "      <td>1.071512</td>\n",
       "      <td>1.385366</td>\n",
       "      <td>0.754146</td>\n",
       "      <td>2.323902</td>\n",
       "      <td>0.513171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.072290</td>\n",
       "      <td>0.460373</td>\n",
       "      <td>1.029641</td>\n",
       "      <td>17.516718</td>\n",
       "      <td>51.59251</td>\n",
       "      <td>0.356527</td>\n",
       "      <td>0.527878</td>\n",
       "      <td>23.005724</td>\n",
       "      <td>0.472772</td>\n",
       "      <td>1.175053</td>\n",
       "      <td>0.617755</td>\n",
       "      <td>1.030798</td>\n",
       "      <td>0.620660</td>\n",
       "      <td>0.500070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>275.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age          sex           cp     trestbps        chol  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
       "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
       "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
       "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
       "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
       "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
       "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
       "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
       "\n",
       "               fbs      restecg      thalach        exang      oldpeak  \\\n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
       "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
       "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
       "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
       "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
       "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
       "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
       "\n",
       "             slope           ca         thal       target  \n",
       "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
       "mean      1.385366     0.754146     2.323902     0.513171  \n",
       "std       0.617755     1.030798     0.620660     0.500070  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       1.000000     0.000000     2.000000     0.000000  \n",
       "50%       1.000000     0.000000     2.000000     1.000000  \n",
       "75%       2.000000     1.000000     3.000000     1.000000  \n",
       "max       2.000000     4.000000     3.000000     1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cubic-highland",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:29.276161Z",
     "iopub.status.busy": "2021-05-06T05:56:29.275556Z",
     "iopub.status.idle": "2021-05-06T05:56:29.278177Z",
     "shell.execute_reply": "2021-05-06T05:56:29.277667Z"
    },
    "papermill": {
     "duration": 0.03208,
     "end_time": "2021-05-06T05:56:29.278299",
     "exception": false,
     "start_time": "2021-05-06T05:56:29.246219",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = dataset.drop(['target'],axis=1)\n",
    "y = dataset.iloc[:, -1].values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "permanent-chorus",
   "metadata": {
    "papermill": {
     "duration": 0.028673,
     "end_time": "2021-05-06T05:56:29.331829",
     "exception": false,
     "start_time": "2021-05-06T05:56:29.303156",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Feature Selection**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "competent-indian",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:29.388572Z",
     "iopub.status.busy": "2021-05-06T05:56:29.387980Z",
     "iopub.status.idle": "2021-05-06T05:56:29.724218Z",
     "shell.execute_reply": "2021-05-06T05:56:29.723737Z"
    },
    "papermill": {
     "duration": 0.366755,
     "end_time": "2021-05-06T05:56:29.724355",
     "exception": false,
     "start_time": "2021-05-06T05:56:29.357600",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "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": [
    "correlation = dataset.corr()\n",
    "sns.heatmap(dataset.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "careful-trademark",
   "metadata": {
    "papermill": {
     "duration": 0.025694,
     "end_time": "2021-05-06T05:56:29.776061",
     "exception": false,
     "start_time": "2021-05-06T05:56:29.750367",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Data preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "interim-lindsay",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:29.833571Z",
     "iopub.status.busy": "2021-05-06T05:56:29.832295Z",
     "iopub.status.idle": "2021-05-06T05:56:29.836505Z",
     "shell.execute_reply": "2021-05-06T05:56:29.836923Z"
    },
    "papermill": {
     "duration": 0.035444,
     "end_time": "2021-05-06T05:56:29.837083",
     "exception": false,
     "start_time": "2021-05-06T05:56:29.801639",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "operating-rendering",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:29.896108Z",
     "iopub.status.busy": "2021-05-06T05:56:29.895211Z",
     "iopub.status.idle": "2021-05-06T05:56:29.902515Z",
     "shell.execute_reply": "2021-05-06T05:56:29.902933Z"
    },
    "papermill": {
     "duration": 0.03969,
     "end_time": "2021-05-06T05:56:29.903092",
     "exception": false,
     "start_time": "2021-05-06T05:56:29.863402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "material-joint",
   "metadata": {
    "papermill": {
     "duration": 0.026868,
     "end_time": "2021-05-06T05:56:29.956301",
     "exception": false,
     "start_time": "2021-05-06T05:56:29.929433",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**In this project, I'm gonna apply 7 types of algorithms in machine learning. these are Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree, and Random Forest model. now I'm super excited to start step by step**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suspended-beach",
   "metadata": {
    "papermill": {
     "duration": 0.026582,
     "end_time": "2021-05-06T05:56:30.009313",
     "exception": false,
     "start_time": "2021-05-06T05:56:29.982731",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#  Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "unauthorized-difference",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.067226Z",
     "iopub.status.busy": "2021-05-06T05:56:30.066313Z",
     "iopub.status.idle": "2021-05-06T05:56:30.083269Z",
     "shell.execute_reply": "2021-05-06T05:56:30.082495Z"
    },
    "papermill": {
     "duration": 0.047846,
     "end_time": "2021-05-06T05:56:30.083462",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.035616",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "tired-apparel",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.197328Z",
     "iopub.status.busy": "2021-05-06T05:56:30.196505Z",
     "iopub.status.idle": "2021-05-06T05:56:30.199293Z",
     "shell.execute_reply": "2021-05-06T05:56:30.197830Z"
    },
    "papermill": {
     "duration": 0.064125,
     "end_time": "2021-05-06T05:56:30.199416",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.135291",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "neural-sauce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.256494Z",
     "iopub.status.busy": "2021-05-06T05:56:30.255948Z",
     "iopub.status.idle": "2021-05-06T05:56:30.262430Z",
     "shell.execute_reply": "2021-05-06T05:56:30.262835Z"
    },
    "papermill": {
     "duration": 0.037387,
     "end_time": "2021-05-06T05:56:30.263037",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.225650",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 98,  25],\n",
       "       [ 10, 124]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "checked-fifteen",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.321372Z",
     "iopub.status.busy": "2021-05-06T05:56:30.320505Z",
     "iopub.status.idle": "2021-05-06T05:56:30.325785Z",
     "shell.execute_reply": "2021-05-06T05:56:30.326300Z"
    },
    "papermill": {
     "duration": 0.03697,
     "end_time": "2021-05-06T05:56:30.326449",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.289479",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8638132295719845"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy=accuracy_score(y_test,y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "august-legend",
   "metadata": {
    "papermill": {
     "duration": 0.027214,
     "end_time": "2021-05-06T05:56:30.380969",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.353755",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# K-Nearest Neighbors (K-NN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "joined-parade",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.439005Z",
     "iopub.status.busy": "2021-05-06T05:56:30.438161Z",
     "iopub.status.idle": "2021-05-06T05:56:30.445001Z",
     "shell.execute_reply": "2021-05-06T05:56:30.445417Z"
    },
    "papermill": {
     "duration": 0.037263,
     "end_time": "2021-05-06T05:56:30.445564",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.408301",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "developed-myanmar",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.504232Z",
     "iopub.status.busy": "2021-05-06T05:56:30.503387Z",
     "iopub.status.idle": "2021-05-06T05:56:30.520859Z",
     "shell.execute_reply": "2021-05-06T05:56:30.521323Z"
    },
    "papermill": {
     "duration": 0.048259,
     "end_time": "2021-05-06T05:56:30.521476",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.473217",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "regional-huntington",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.580200Z",
     "iopub.status.busy": "2021-05-06T05:56:30.579348Z",
     "iopub.status.idle": "2021-05-06T05:56:30.585116Z",
     "shell.execute_reply": "2021-05-06T05:56:30.585595Z"
    },
    "papermill": {
     "duration": 0.036723,
     "end_time": "2021-05-06T05:56:30.585755",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.549032",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[106,  17],\n",
       "       [ 14, 120]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "appropriate-livestock",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.645114Z",
     "iopub.status.busy": "2021-05-06T05:56:30.644243Z",
     "iopub.status.idle": "2021-05-06T05:56:30.649341Z",
     "shell.execute_reply": "2021-05-06T05:56:30.649747Z"
    },
    "papermill": {
     "duration": 0.036398,
     "end_time": "2021-05-06T05:56:30.649919",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.613521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8793774319066148"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "driving-watson",
   "metadata": {
    "papermill": {
     "duration": 0.028504,
     "end_time": "2021-05-06T05:56:30.706577",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.678073",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cutting-opposition",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.767211Z",
     "iopub.status.busy": "2021-05-06T05:56:30.766327Z",
     "iopub.status.idle": "2021-05-06T05:56:30.795296Z",
     "shell.execute_reply": "2021-05-06T05:56:30.795705Z"
    },
    "papermill": {
     "duration": 0.060608,
     "end_time": "2021-05-06T05:56:30.795854",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.735246",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear', random_state=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = SVC(kernel = 'linear', random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "attempted-perspective",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.857307Z",
     "iopub.status.busy": "2021-05-06T05:56:30.856426Z",
     "iopub.status.idle": "2021-05-06T05:56:30.864251Z",
     "shell.execute_reply": "2021-05-06T05:56:30.864622Z"
    },
    "papermill": {
     "duration": 0.039985,
     "end_time": "2021-05-06T05:56:30.864772",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.824787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred= classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "accredited-jordan",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.926582Z",
     "iopub.status.busy": "2021-05-06T05:56:30.925984Z",
     "iopub.status.idle": "2021-05-06T05:56:30.931005Z",
     "shell.execute_reply": "2021-05-06T05:56:30.931438Z"
    },
    "papermill": {
     "duration": 0.037868,
     "end_time": "2021-05-06T05:56:30.931590",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.893722",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 99,  24],\n",
       "       [ 12, 122]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "stainless-southeast",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:30.993670Z",
     "iopub.status.busy": "2021-05-06T05:56:30.993062Z",
     "iopub.status.idle": "2021-05-06T05:56:30.997649Z",
     "shell.execute_reply": "2021-05-06T05:56:30.998085Z"
    },
    "papermill": {
     "duration": 0.036889,
     "end_time": "2021-05-06T05:56:30.998232",
     "exception": false,
     "start_time": "2021-05-06T05:56:30.961343",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8599221789883269"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acute-rubber",
   "metadata": {
    "papermill": {
     "duration": 0.030616,
     "end_time": "2021-05-06T05:56:31.058720",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.028104",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Kernel SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "threaded-chamber",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:31.122959Z",
     "iopub.status.busy": "2021-05-06T05:56:31.122385Z",
     "iopub.status.idle": "2021-05-06T05:56:31.145816Z",
     "shell.execute_reply": "2021-05-06T05:56:31.146274Z"
    },
    "papermill": {
     "duration": 0.05726,
     "end_time": "2021-05-06T05:56:31.146429",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.089169",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(random_state=0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = SVC(kernel = \"rbf\", random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "necessary-correlation",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:31.209438Z",
     "iopub.status.busy": "2021-05-06T05:56:31.208865Z",
     "iopub.status.idle": "2021-05-06T05:56:31.219623Z",
     "shell.execute_reply": "2021-05-06T05:56:31.220073Z"
    },
    "papermill": {
     "duration": 0.043496,
     "end_time": "2021-05-06T05:56:31.220222",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.176726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "optional-joseph",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:31.283943Z",
     "iopub.status.busy": "2021-05-06T05:56:31.283372Z",
     "iopub.status.idle": "2021-05-06T05:56:31.288373Z",
     "shell.execute_reply": "2021-05-06T05:56:31.288842Z"
    },
    "papermill": {
     "duration": 0.038075,
     "end_time": "2021-05-06T05:56:31.289021",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.250946",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[116,   7],\n",
       "       [  2, 132]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "grave-adolescent",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:31.362096Z",
     "iopub.status.busy": "2021-05-06T05:56:31.361515Z",
     "iopub.status.idle": "2021-05-06T05:56:31.367203Z",
     "shell.execute_reply": "2021-05-06T05:56:31.366690Z"
    },
    "papermill": {
     "duration": 0.046014,
     "end_time": "2021-05-06T05:56:31.367334",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.321320",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9649805447470817"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "happy-eclipse",
   "metadata": {
    "papermill": {
     "duration": 0.036224,
     "end_time": "2021-05-06T05:56:31.444983",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.408759",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "choice-omega",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:31.513434Z",
     "iopub.status.busy": "2021-05-06T05:56:31.512554Z",
     "iopub.status.idle": "2021-05-06T05:56:31.518564Z",
     "shell.execute_reply": "2021-05-06T05:56:31.518074Z"
    },
    "papermill": {
     "duration": 0.041953,
     "end_time": "2021-05-06T05:56:31.518720",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.476767",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = GaussianNB()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "robust-coast",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:31.590694Z",
     "iopub.status.busy": "2021-05-06T05:56:31.589036Z",
     "iopub.status.idle": "2021-05-06T05:56:31.593736Z",
     "shell.execute_reply": "2021-05-06T05:56:31.594265Z"
    },
    "papermill": {
     "duration": 0.041995,
     "end_time": "2021-05-06T05:56:31.594433",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.552438",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "vanilla-cologne",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:31.664069Z",
     "iopub.status.busy": "2021-05-06T05:56:31.663429Z",
     "iopub.status.idle": "2021-05-06T05:56:31.667636Z",
     "shell.execute_reply": "2021-05-06T05:56:31.668125Z"
    },
    "papermill": {
     "duration": 0.041153,
     "end_time": "2021-05-06T05:56:31.668283",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.627130",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 98,  25],\n",
       "       [ 13, 121]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "figured-desert",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:31.738129Z",
     "iopub.status.busy": "2021-05-06T05:56:31.737229Z",
     "iopub.status.idle": "2021-05-06T05:56:31.742532Z",
     "shell.execute_reply": "2021-05-06T05:56:31.743077Z"
    },
    "papermill": {
     "duration": 0.042006,
     "end_time": "2021-05-06T05:56:31.743231",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.701225",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8521400778210116"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frequent-rugby",
   "metadata": {
    "papermill": {
     "duration": 0.032846,
     "end_time": "2021-05-06T05:56:31.809147",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.776301",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Decision Tree Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "hourly-diagram",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:31.879047Z",
     "iopub.status.busy": "2021-05-06T05:56:31.878090Z",
     "iopub.status.idle": "2021-05-06T05:56:31.886403Z",
     "shell.execute_reply": "2021-05-06T05:56:31.886789Z"
    },
    "papermill": {
     "duration": 0.044686,
     "end_time": "2021-05-06T05:56:31.886963",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.842277",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=6, random_state=0)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0, max_depth = 6)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "clinical-coaching",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:31.957774Z",
     "iopub.status.busy": "2021-05-06T05:56:31.956880Z",
     "iopub.status.idle": "2021-05-06T05:56:31.962533Z",
     "shell.execute_reply": "2021-05-06T05:56:31.962996Z"
    },
    "papermill": {
     "duration": 0.042532,
     "end_time": "2021-05-06T05:56:31.963153",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.920621",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ideal-denver",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:32.033635Z",
     "iopub.status.busy": "2021-05-06T05:56:32.032763Z",
     "iopub.status.idle": "2021-05-06T05:56:32.038455Z",
     "shell.execute_reply": "2021-05-06T05:56:32.038828Z"
    },
    "papermill": {
     "duration": 0.041975,
     "end_time": "2021-05-06T05:56:32.038999",
     "exception": false,
     "start_time": "2021-05-06T05:56:31.997024",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[117,   6],\n",
       "       [ 14, 120]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "alien-bobby",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:32.109599Z",
     "iopub.status.busy": "2021-05-06T05:56:32.108744Z",
     "iopub.status.idle": "2021-05-06T05:56:32.113863Z",
     "shell.execute_reply": "2021-05-06T05:56:32.114264Z"
    },
    "papermill": {
     "duration": 0.041746,
     "end_time": "2021-05-06T05:56:32.114426",
     "exception": false,
     "start_time": "2021-05-06T05:56:32.072680",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9221789883268483"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moral-jerusalem",
   "metadata": {
    "papermill": {
     "duration": 0.033544,
     "end_time": "2021-05-06T05:56:32.182005",
     "exception": false,
     "start_time": "2021-05-06T05:56:32.148461",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "racial-technical",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:32.253351Z",
     "iopub.status.busy": "2021-05-06T05:56:32.252497Z",
     "iopub.status.idle": "2021-05-06T05:56:32.277109Z",
     "shell.execute_reply": "2021-05-06T05:56:32.277543Z"
    },
    "papermill": {
     "duration": 0.061637,
     "end_time": "2021-05-06T05:56:32.277697",
     "exception": false,
     "start_time": "2021-05-06T05:56:32.216060",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=6, n_estimators=10,\n",
       "                       random_state=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0, max_depth = 6)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "hourly-martin",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:32.351732Z",
     "iopub.status.busy": "2021-05-06T05:56:32.351160Z",
     "iopub.status.idle": "2021-05-06T05:56:32.355755Z",
     "shell.execute_reply": "2021-05-06T05:56:32.356261Z"
    },
    "papermill": {
     "duration": 0.044196,
     "end_time": "2021-05-06T05:56:32.356445",
     "exception": false,
     "start_time": "2021-05-06T05:56:32.312249",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dedicated-chapter",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:32.429016Z",
     "iopub.status.busy": "2021-05-06T05:56:32.428429Z",
     "iopub.status.idle": "2021-05-06T05:56:32.433445Z",
     "shell.execute_reply": "2021-05-06T05:56:32.433872Z"
    },
    "papermill": {
     "duration": 0.042808,
     "end_time": "2021-05-06T05:56:32.434055",
     "exception": false,
     "start_time": "2021-05-06T05:56:32.391247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[115,   8],\n",
       "       [  5, 129]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "controlled-trouble",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-06T05:56:32.506832Z",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9494163424124513"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  },
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   "id": "disciplinary-scroll",
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   },
   "source": [
    "**In the conclusion, I was attempting to generalize better accuracy and none of the regression was able to do better than 96% (Kernel-SVM). But all of the algorithms given super good predictions in this project. these are, logistic regression predicts 86%, K-NN 87%, SVM 85%, Naive Bayes 85%, Decision Tree Classifier 92%, and the last one, Random Forest Classifier 94%. \n",
    "According to the above predictions, the Kernel SVM algorithm is best for prediction amongst the other 6 Regression.**"
   ]
  }
 ],
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