Model/oversampled and pca.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">Classification of Arrhythmia</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H_THNkfZ1JbE"
   },
   "source": [
    "Dataset used in this project is available on the UCI machine learning Repository. \n",
    "\n",
    "* It can be found at the following address: https://archive.ics.uci.edu/ml/datasets/Arrhythmia. \n",
    "\n",
    "\n",
    "* It consists of 452 different examples spread over 16 classes. Of the 452 examples, 245 are of \"normal\" people. We also have 12 different types of arrhythmias. Among all these types of arrhythmias, the most representative are the \"coronary artery disease\" and \"Rjgbt boundle branch block\".\n",
    "\n",
    "\n",
    "* We have 279 features, which include age, sex, weight, height of patients and related information from the electrocardiogram. We explicitly observe that the number of features is relatively high compared to the number of examples we are available.\n",
    "\n",
    "\n",
    "* Our goal is to predict if a person is suffering from arrhythmia and if class, classify it in to one of 12 available groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zE10sTd_1JbG"
   },
   "source": [
    "### Importing Essential Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "EcD33ZuZ1JbH"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import math as mt\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "abXtJnLE1JbO"
   },
   "source": [
    "### Data Reading\n",
    "\n",
    "\n",
    "Starting with reading the data file and creating a dataframe for putting the output of all the models that we will be running. The purpose of doing this is it will be easy for us to compare the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "4sHaerCj1JbP"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"arrhythmia.csv\",header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "id": "wdOvvFNN1JbS",
    "outputId": "a5dd7386-bec5-4bae-db62-e4cd89e31887"
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     "execution_count": 3,
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   "source": [
    "df.head()"
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       "     0    1    2    3    4    5    6    7    8    9    ...  270   271   272  \\\n",
       "447   53    1  160   70   80  199  382  154  117  -37  ...  0.0   4.3  -5.0   \n",
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   "source": [
    "df.tail()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ce4X37cV1Jbd"
   },
   "source": [
    "**Basic Description of dataframe**"
   ]
  },
  {
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   "execution_count": 5,
   "metadata": {
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    "id": "uhurbWma1Jbe",
    "outputId": "127c6d0d-2785-4e68-cedc-d4ee94f33a9e"
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   "outputs": [
    {
     "data": {
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       "(452, 280)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Dimension of dataset.\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PagGspN01Jbh",
    "outputId": "0c6a1908-5953-4f4c-bdf2-9f0e6b632cad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 452 entries, 0 to 451\n",
      "Columns: 280 entries, 0 to 279\n",
      "dtypes: float64(120), int64(155), object(5)\n",
      "memory usage: 988.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#concise summary of the dataframe.\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    "id": "1DLGCi-21Jbl",
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       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>452.0</td>\n",
       "      <td>0.550885</td>\n",
       "      <td>0.497955</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>452.0</td>\n",
       "      <td>166.188053</td>\n",
       "      <td>37.170340</td>\n",
       "      <td>105.0</td>\n",
       "      <td>160.00</td>\n",
       "      <td>164.00</td>\n",
       "      <td>170.000</td>\n",
       "      <td>780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>452.0</td>\n",
       "      <td>68.170354</td>\n",
       "      <td>16.590803</td>\n",
       "      <td>6.0</td>\n",
       "      <td>59.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>79.000</td>\n",
       "      <td>176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>452.0</td>\n",
       "      <td>88.920354</td>\n",
       "      <td>15.364394</td>\n",
       "      <td>55.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>86.00</td>\n",
       "      <td>94.000</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>452.0</td>\n",
       "      <td>0.514823</td>\n",
       "      <td>0.347531</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.700</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>452.0</td>\n",
       "      <td>1.222345</td>\n",
       "      <td>1.426052</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.100</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>452.0</td>\n",
       "      <td>19.326106</td>\n",
       "      <td>13.503922</td>\n",
       "      <td>-44.2</td>\n",
       "      <td>11.45</td>\n",
       "      <td>18.10</td>\n",
       "      <td>25.825</td>\n",
       "      <td>88.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>452.0</td>\n",
       "      <td>29.473230</td>\n",
       "      <td>18.493927</td>\n",
       "      <td>-38.6</td>\n",
       "      <td>17.55</td>\n",
       "      <td>27.90</td>\n",
       "      <td>41.125</td>\n",
       "      <td>115.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>452.0</td>\n",
       "      <td>3.880531</td>\n",
       "      <td>4.407097</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.000</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>275 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     count        mean        std    min     25%     50%      75%    max\n",
       "0    452.0   46.471239  16.466631    0.0   36.00   47.00   58.000   83.0\n",
       "1    452.0    0.550885   0.497955    0.0    0.00    1.00    1.000    1.0\n",
       "2    452.0  166.188053  37.170340  105.0  160.00  164.00  170.000  780.0\n",
       "3    452.0   68.170354  16.590803    6.0   59.00   68.00   79.000  176.0\n",
       "4    452.0   88.920354  15.364394   55.0   80.00   86.00   94.000  188.0\n",
       "..     ...         ...        ...    ...     ...     ...      ...    ...\n",
       "275  452.0    0.514823   0.347531   -0.8    0.40    0.50    0.700    2.4\n",
       "276  452.0    1.222345   1.426052   -6.0    0.50    1.35    2.100    6.0\n",
       "277  452.0   19.326106  13.503922  -44.2   11.45   18.10   25.825   88.8\n",
       "278  452.0   29.473230  18.493927  -38.6   17.55   27.90   41.125  115.9\n",
       "279  452.0    3.880531   4.407097    1.0    1.00    1.00    6.000   16.0\n",
       "\n",
       "[275 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#descriptive statistics of dataframe.\n",
    "\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mUUqFfR71JbF"
   },
   "source": [
    "# 1. Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7x_lFwIZ1Jbo"
   },
   "source": [
    "### Handling Missing Values\n",
    "\n",
    "<br>\n",
    "While going through the dataset we observed that out of 279 attributes, 5 Attributes have missing value in the form \n",
    "of '?'. The approach which we will following is, first replacing '?' with numpy NAN and then imputing the mean using Simple Imputer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lkGjKgw31Jbp"
   },
   "source": [
    "**Checking for null values in dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gowC_0oL1Jbp",
    "outputId": "805c3f87-2a49-4beb-998c-e8c8dbf0ec01"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counting total Number of null values\n",
    "\n",
    "pd.isnull(df).sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Z5G4BcfO1Jbt"
   },
   "outputs": [],
   "source": [
    "#Replacing ? with np.nan value-\n",
    "\n",
    "df = df.replace('?', np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8SP3RHAI1Jbw",
    "outputId": "da8f30da-37f6-4b66-8d92-c1bd0c96776c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "408"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#final counting total number of null values in dataset\n",
    "\n",
    "nu=pd.isnull(df).sum().sum()\n",
    "nu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ddVfLwLw1Jbz"
   },
   "source": [
    "**Visualizing  the distribution of our missing data:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "id": "dYZmRnur1Jb0",
    "outputId": "4ead7bcf-619a-49e3-f736-c8646e83413d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total number of null value in each column')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.isnull(df).sum().plot()\n",
    "plt.xlabel('Columns')\n",
    "plt.ylabel('Total number of null value in each column')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 302
    },
    "id": "yv8oI-_J1Jb3",
    "outputId": "c5033547-c821-4ecb-8298-a600eeda5366"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total number of null value in each column')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Zooming in\n",
    "\n",
    "pd.isnull(df).sum()[7:17].plot(kind=\"bar\")\n",
    "plt.xlabel('Columns')\n",
    "plt.ylabel('Total number of null value in each column')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Column 13 contains more than 350 missing values out of total 452 instances. so we will drop column 13. other attributes have comparatively less null values. So instead of droping, we will replace the null value of other attributes with their mean values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "_N0LGsNs1Jb9"
   },
   "outputs": [],
   "source": [
    "#Dropping the column 13\n",
    "\n",
    "df.drop(columns = 13, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4f3jshNN1Jb_"
   },
   "source": [
    "**Using the mean strategy for imputation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "BS7xxLCr1JcA"
   },
   "outputs": [],
   "source": [
    "# make copy to avoid changing original data (when Imputing)\n",
    "\n",
    "new_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "iMolbQAe1JcC"
   },
   "outputs": [],
   "source": [
    "# make new columns indicating what will be imputed\n",
    "\n",
    "cols_with_missing = (col for col in new_df.columns if new_df[col].isnull().any())\n",
    "for col in cols_with_missing:\n",
    "    new_df[col] = new_df[col].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "2dsw6kII1JcF"
   },
   "outputs": [],
   "source": [
    "# Imputation\n",
    "# my_imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "my_imputer = SimpleImputer()\n",
    "new_df = pd.DataFrame(my_imputer.fit_transform(new_df))\n",
    "new_df.columns = df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "ColsJv0J1JcJ",
    "outputId": "edad166d-b9e4-491b-f3fa-4d41dab2c294"
   },
   "outputs": [
    {
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       "      <td>39.0</td>\n",
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       "      <td>-2.4</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>12.3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>94.0</td>\n",
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       "      <td>380.0</td>\n",
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       "      <td>143.0</td>\n",
       "      <td>28.0</td>\n",
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       "    <tr>\n",
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       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>177.0</td>\n",
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       "      <td>-16.0</td>\n",
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       "      <td>7.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 279 columns</p>\n",
       "</div>"
      ],
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       "    0    1      2     3      4      5      6      7      8     9    ...  270  \\\n",
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       "\n",
       "    271  272  273  274  275  276   277   278   279  \n",
       "0   9.0 -0.9  0.0  0.0  0.9  2.9  23.3  49.4   8.0  \n",
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       "2   9.5 -2.4  0.0  0.0  0.3  3.4  12.3  49.0  10.0  \n",
       "3  12.2 -2.2  0.0  0.0  0.4  2.6  34.6  61.6   1.0  \n",
       "4  13.1 -3.6  0.0  0.0 -0.1  3.9  25.4  62.8   7.0  \n",
       "\n",
       "[5 rows x 279 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# imputed dataframe\n",
    "\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fuqgoZT01JcM",
    "outputId": "98b78465-141a-4a17-f767-c5d54cd6bf73"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataSet with Zero null Values.\n",
    "\n",
    "pd.isnull(new_df).sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pqtcjgG_1JcP"
   },
   "source": [
    "**Generating final dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "f5FlHdB81JcQ"
   },
   "outputs": [],
   "source": [
    "#Creating column names\n",
    "\n",
    "final_df_columns=[\"Age\",\"Sex\",\"Height\",\"Weight\",\"QRS_Dur\",\n",
    "\"P-R_Int\",\"Q-T_Int\",\"T_Int\",\"P_Int\",\"QRS\",\"T\",\"P\",\"J\",\"Heart_Rate\",\n",
    "\"Q_Wave\",\"R_Wave\",\"S_Wave\",\"R'_Wave\",\"S'_Wave\",\"Int_Def\",\"Rag_R_Nom\",\n",
    "\"Diph_R_Nom\",\"Rag_P_Nom\",\"Diph_P_Nom\",\"Rag_T_Nom\",\"Diph_T_Nom\", \n",
    "\"DII00\", \"DII01\",\"DII02\", \"DII03\", \"DII04\",\"DII05\",\"DII06\",\"DII07\",\"DII08\",\"DII09\",\"DII10\",\"DII11\",\n",
    "\"DIII00\",\"DIII01\",\"DIII02\", \"DIII03\", \"DIII04\",\"DIII05\",\"DIII06\",\"DIII07\",\"DIII08\",\"DIII09\",\"DIII10\",\"DIII11\",\n",
    "\"AVR00\",\"AVR01\",\"AVR02\",\"AVR03\",\"AVR04\",\"AVR05\",\"AVR06\",\"AVR07\",\"AVR08\",\"AVR09\",\"AVR10\",\"AVR11\",\n",
    "\"AVL00\",\"AVL01\",\"AVL02\",\"AVL03\",\"AVL04\",\"AVL05\",\"AVL06\",\"AVL07\",\"AVL08\",\"AVL09\",\"AVL10\",\"AVL11\",\n",
    "\"AVF00\",\"AVF01\",\"AVF02\",\"AVF03\",\"AVF04\",\"AVF05\",\"AVF06\",\"AVF07\",\"AVF08\",\"AVF09\",\"AVF10\",\"AVF11\",\n",
    "\"V100\",\"V101\",\"V102\",\"V103\",\"V104\",\"V105\",\"V106\",\"V107\",\"V108\",\"V109\",\"V110\",\"V111\",\n",
    "\"V200\",\"V201\",\"V202\",\"V203\",\"V204\",\"V205\",\"V206\",\"V207\",\"V208\",\"V209\",\"V210\",\"V211\",\n",
    "\"V300\",\"V301\",\"V302\",\"V303\",\"V304\",\"V305\",\"V306\",\"V307\",\"V308\",\"V309\",\"V310\",\"V311\",\n",
    "\"V400\",\"V401\",\"V402\",\"V403\",\"V404\",\"V405\",\"V406\",\"V407\",\"V408\",\"V409\",\"V410\",\"V411\",\n",
    "\"V500\",\"V501\",\"V502\",\"V503\",\"V504\",\"V505\",\"V506\",\"V507\",\"V508\",\"V509\",\"V510\",\"V511\",\n",
    "\"V600\",\"V601\",\"V602\",\"V603\",\"V604\",\"V605\",\"V606\",\"V607\",\"V608\",\"V609\",\"V610\",\"V611\",\n",
    "\"JJ_Wave\",\"Amp_Q_Wave\",\"Amp_R_Wave\",\"Amp_S_Wave\",\"R_Prime_Wave\",\"S_Prime_Wave\",\"P_Wave\",\"T_Wave\",\n",
    "\"QRSA\",\"QRSTA\",\"DII170\",\"DII171\",\"DII172\",\"DII173\",\"DII174\",\"DII175\",\"DII176\",\"DII177\",\"DII178\",\"DII179\",\n",
    "\"DIII180\",\"DIII181\",\"DIII182\",\"DIII183\",\"DIII184\",\"DIII185\",\"DIII186\",\"DIII187\",\"DIII188\",\"DIII189\",\n",
    "\"AVR190\",\"AVR191\",\"AVR192\",\"AVR193\",\"AVR194\",\"AVR195\",\"AVR196\",\"AVR197\",\"AVR198\",\"AVR199\",\n",
    "\"AVL200\",\"AVL201\",\"AVL202\",\"AVL203\",\"AVL204\",\"AVL205\",\"AVL206\",\"AVL207\",\"AVL208\",\"AVL209\",\n",
    "\"AVF210\",\"AVF211\",\"AVF212\",\"AVF213\",\"AVF214\",\"AVF215\",\"AVF216\",\"AVF217\",\"AVF218\",\"AVF219\",\n",
    "\"V1220\",\"V1221\",\"V1222\",\"V1223\",\"V1224\",\"V1225\",\"V1226\",\"V1227\",\"V1228\",\"V1229\",\n",
    "\"V2230\",\"V2231\",\"V2232\",\"V2233\",\"V2234\",\"V2235\",\"V2236\",\"V2237\",\"V2238\",\"V2239\",\n",
    "\"V3240\",\"V3241\",\"V3242\",\"V3243\",\"V3244\",\"V3245\",\"V3246\",\"V3247\",\"V3248\",\"V3249\",\n",
    "\"V4250\",\"V4251\",\"V4252\",\"V4253\",\"V4254\",\"V4255\",\"V4256\",\"V4257\",\"V4258\",\"V4259\",\n",
    "\"V5260\",\"V5261\",\"V5262\",\"V5263\",\"V5264\",\"V5265\",\"V5266\",\"V5267\",\"V5268\",\"V5269\",\n",
    "\"V6270\",\"V6271\",\"V6272\",\"V6273\",\"V6274\",\"V6275\",\"V6276\",\"V6277\",\"V6278\",\"V6279\",\"class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "yGXCWE_N1JcS",
    "outputId": "54113ae3-713c-4f1b-b48d-879aad634626"
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       "      <td>185.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>96.0</td>\n",
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       "      <td>49.0</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>80.0</td>\n",
       "      <td>88.0</td>\n",
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       "      <td>7.0</td>\n",
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       "<p>5 rows × 279 columns</p>\n",
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      ],
      "text/plain": [
       "    Age  Sex  Height  Weight  QRS_Dur  P-R_Int  Q-T_Int  T_Int  P_Int   QRS  \\\n",
       "0  75.0  0.0   190.0    80.0     91.0    193.0    371.0  174.0  121.0 -16.0   \n",
       "1  56.0  1.0   165.0    64.0     81.0    174.0    401.0  149.0   39.0  25.0   \n",
       "2  54.0  0.0   172.0    95.0    138.0    163.0    386.0  185.0  102.0  96.0   \n",
       "3  55.0  0.0   175.0    94.0    100.0    202.0    380.0  179.0  143.0  28.0   \n",
       "4  75.0  0.0   190.0    80.0     88.0    181.0    360.0  177.0  103.0 -16.0   \n",
       "\n",
       "   ...  V6271  V6272  V6273  V6274  V6275  V6276  V6277  V6278  V6279  class  \n",
       "0  ...    0.0    9.0   -0.9    0.0    0.0    0.9    2.9   23.3   49.4    8.0  \n",
       "1  ...    0.0    8.5    0.0    0.0    0.0    0.2    2.1   20.4   38.8    6.0  \n",
       "2  ...    0.0    9.5   -2.4    0.0    0.0    0.3    3.4   12.3   49.0   10.0  \n",
       "3  ...    0.0   12.2   -2.2    0.0    0.0    0.4    2.6   34.6   61.6    1.0  \n",
       "4  ...    0.0   13.1   -3.6    0.0    0.0   -0.1    3.9   25.4   62.8    7.0  \n",
       "\n",
       "[5 rows x 279 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding Column names to dataset\n",
    "\n",
    "new_df.columns=final_df_columns\n",
    "new_df.to_csv(\"new data with target class.csv\")\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As our dataframe is completely cleaned and preprocessed. we will remove the target attribute and store our final dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=new_df[\"class\"]\n",
    "\n",
    "final_df = new_df.drop(columns =\"class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "N-PlnyUp1JcU",
    "outputId": "7204a9d2-8cbc-4ee4-9232-422d9c32d624"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(452, 278)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kl_IqSwy1Jca"
   },
   "source": [
    "# 2. Exploratory Data Analysis (EDA)\n",
    "\n",
    "Analyzing data sets to summarize their main characteristics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lNKH5Nd81Jcb"
   },
   "source": [
    "Making List of all the type of Arrythmia corresponsing to their class label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "ziL8KWK81Jcc"
   },
   "outputs": [],
   "source": [
    "#List with class names\n",
    "\n",
    "class_names = [\"Normal\", \n",
    "               \"Ischemic changes (CAD)\", \n",
    "               \"Old Anterior Myocardial Infraction\",\n",
    "               \"Old Inferior Myocardial Infraction\",\n",
    "               \"Sinus tachycardy\", \n",
    "               \"Sinus bradycardy\", \n",
    "               \"Ventricular Premature Contraction (PVC)\",\n",
    "               \"Supraventricular Premature Contraction\",\n",
    "               \"Left Boundle branch block\",\n",
    "               \"Right boundle branch block\",\n",
    "               \"1.Degree AtrioVentricular block\",\n",
    "               \"2.Degree AV block\",\n",
    "               \"3.Degree AV block\",\n",
    "               \"Left Ventricule hypertrophy\",\n",
    "               \"Atrial Fibrillation or Flutter\",\n",
    "               \"Others\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KSxfo2bv1Jce"
   },
   "source": [
    "### Analyzing the dataset and check how many examples we have for each class:\n",
    "we need to sort our dataset with respect to class attributes to count the number of instances available for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "P85a0KIG1Jci"
   },
   "outputs": [],
   "source": [
    "t=new_df.sort_values(by=[\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iPjqfB9P1Jcl",
    "outputId": "94ac7294-62ca-48b5-822a-63ef21506cd3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[245, 44, 15, 15, 13, 25, 3, 2, 9, 50, 4, 5, 22]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting the number of instances for each class\n",
    "\n",
    "la = t[\"class\"].value_counts(sort=False).tolist()\n",
    "la"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x ='class',data =new_df) \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets Count the total number of instances we have for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 656
    },
    "id": "tI_4FV7P1Jcn",
    "outputId": "d985bbca-bdc5-430f-ef48-7be025084b82"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[245, 44, 15, 15, 13, 25, 3, 2, 9, 50, 0, 0, 0, 4, 5, 22]\n"
     ]
    }
   ],
   "source": [
    "values = la[0:10]\n",
    "values.extend([0,0,0])\n",
    "values.extend(la[10:13])\n",
    "print(values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = class_names\n",
    "Log_Norm = []\n",
    "for i in values:\n",
    "    Log_Norm.append(mt.log10(i+1))\n",
    "fig1, ax1 = plt.subplots(figsize=(16,9))\n",
    "patches = plt.pie(Log_Norm, autopct='%1.1f%%', startangle=90)\n",
    "\n",
    "leg = plt.legend( loc = 'best', labels=['%s, %1.1f %%' % (l, s) for l, s in zip(labels, Log_Norm)])\n",
    "plt.axis('equal')\n",
    "\n",
    "for text in leg.get_texts():\n",
    "    plt.setp(text, color = 'Black')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RmfK89wRFShA"
   },
   "source": [
    "\n",
    "We found that Of the 452 examples, 245 are of class A which refers to \"normal\" people. We also have 12 different types of arrhythmias and 3 other type of arrthmias are not present in our dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IjqBY48R1Jc-"
   },
   "source": [
    "### Handling Outliers & Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 722
    },
    "id": "vs0EfHJd1JdA",
    "outputId": "3259e293-1e1f-4e72-ee6e-56165379c71a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 771.875x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#looking for pairwise relationships and outliers\n",
    "\n",
    "g = sns.PairGrid(final_df, vars=['Age', 'Sex', 'Height', 'Weight'],hue='Sex', palette='BrBG')\n",
    "g.map(plt.scatter, alpha=0.8)\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wsWpYkcw1JdC"
   },
   "source": [
    "According to scatter plots, there are few outliers in 'height' and 'weight' attributes.check the maximums of heights and weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HUKGpOCX1JdC",
    "outputId": "89f12eca-6f59-48a6-8cb0-b70df817195e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[780.0, 608.0, 190.0, 190.0, 190.0, 188.0, 186.0, 186.0, 186.0, 185.0]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(final_df['Height'], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NdnzTYov1JdE"
   },
   "source": [
    "The tallest person ever lived in the world was **272** cm (1940). His followers were **267** cm(1905) and **263.5** cm(1969). Replacing **780** and **608** with **180** and **108** cm respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "ELjee2jz1JdF"
   },
   "outputs": [],
   "source": [
    "final_df['Height']=final_df['Height'].replace(608,108)\n",
    "final_df['Height']=final_df['Height'].replace(780,180)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3OEvcVRc1JdH",
    "outputId": "b21be694-a1cd-48c1-9eb6-8314d2caa15a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[176.0, 124.0, 110.0, 106.0, 105.0, 105.0, 104.0, 104.0, 100.0, 98.0]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(final_df['Weight'], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jr6MJ5Z51JdJ"
   },
   "source": [
    "**176 kgs** is a possible weight. so we'll keep them in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 266
    },
    "id": "5xdaZN9P1JdM",
    "outputId": "69eb2473-da70-493c-f622-d670782f265e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=final_df[[\"QRS_Dur\",\"P-R_Int\",\"Q-T_Int\",\"T_Int\",\"P_Int\"]]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rxS-LUGN1JdO"
   },
   "source": [
    "PR interval is the period, measured in milliseconds, that extends from the beginning of the P wave until the beginning of the QRS complex; it is normally between 120 and 200ms in duration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_df['P-R_Int'].value_counts().sort_index().head().plot(kind='bar')\n",
    "plt.xlabel('P-R Interval Values')\n",
    "plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_df['P-R_Int'].value_counts().sort_index().tail().plot(kind='bar')\n",
    "plt.xlabel('P-R Interval Values')\n",
    "plt.ylabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PR Interval data is including outliers 0(x18). we'll keep them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=final_df[[\"QRS\",\"T\",\"P\",\"J\",\"Heart_Rate\"]]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=final_df[[\"R'_Wave\",\"S'_Wave\",\"Int_Def\",\"Rag_R_Nom\"]]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ivaH5gV21Jdh"
   },
   "source": [
    "*S*'Wave has 0's which is not a NaN. So, we can't assume it as including outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fy9ZTcx41Jdj",
    "outputId": "3bf28373-f994-4a6d-805e-24ceee2dce26"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    452\n",
       "Name: S'_Wave, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[\"S'_Wave\"].value_counts().sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EPTic09i1Jd_",
    "outputId": "187e30ce-558c-42d3-f8c9-10bc7b466e60"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216.0     1\n",
       "112.0     1\n",
       "84.0      1\n",
       "72.0      1\n",
       "68.0      1\n",
       "64.0      1\n",
       "48.0      6\n",
       "44.0      6\n",
       "40.0     13\n",
       "36.0     36\n",
       "32.0     63\n",
       "28.0     81\n",
       "24.0     88\n",
       "20.0     57\n",
       "16.0     13\n",
       "12.0      4\n",
       "0.0      79\n",
       "Name: V101, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[\"V101\"].value_counts().sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I0Itx4mM1JeC"
   },
   "source": [
    "**V101** has an outlier, but when we look at other sets (V201, V301, V501) we can see that there's an outlier similarly. Since our data is heavily biased, I can't say these outliers should be dropped. \n",
    "\n",
    "For example, when we look at our data, we can see that class # 8 (Supraventricular Premature Contraction) **has only 2 instances**. Or # 3 (Ventricular Premature Contraction (PVC)) has only 3. The outliers appearing with our plots might belong to these instances and needs to be kept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "id": "qlbDJN1w1JeC",
    "outputId": "8c5360cd-9402-4c50-cdc0-6ce8512dbaef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216.0     1\n",
       "136.0     1\n",
       "84.0      1\n",
       "72.0      1\n",
       "60.0      5\n",
       "56.0      4\n",
       "52.0     12\n",
       "48.0     19\n",
       "44.0     53\n",
       "40.0     68\n",
       "36.0     74\n",
       "32.0     69\n",
       "28.0     48\n",
       "24.0     39\n",
       "20.0     15\n",
       "16.0      6\n",
       "12.0      2\n",
       "0.0      34\n",
       "Name: V201, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[\"V201\"].value_counts().sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "id": "_abC3Bmk1JeD",
    "outputId": "7f28546e-448e-42a0-dec3-560c2c2288b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132.0     1\n",
       "88.0      2\n",
       "76.0      2\n",
       "72.0      1\n",
       "68.0      2\n",
       "64.0      3\n",
       "60.0     16\n",
       "56.0     26\n",
       "52.0     57\n",
       "48.0     68\n",
       "44.0     76\n",
       "40.0     74\n",
       "36.0     49\n",
       "32.0     32\n",
       "28.0     13\n",
       "24.0      4\n",
       "20.0      6\n",
       "16.0      1\n",
       "0.0      19\n",
       "Name: V301, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[\"V301\"].value_counts().sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "id": "YtmGtJa-1JeG",
    "outputId": "0c01f2f5-47af-4d25-b29a-507c4f98dbc7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136.0      1\n",
       "120.0      2\n",
       "116.0      1\n",
       "108.0      1\n",
       "96.0       1\n",
       "92.0       1\n",
       "84.0       1\n",
       "76.0       2\n",
       "72.0       3\n",
       "68.0       4\n",
       "64.0       5\n",
       "60.0       9\n",
       "56.0      22\n",
       "52.0      67\n",
       "48.0     105\n",
       "44.0     104\n",
       "40.0      73\n",
       "36.0      29\n",
       "32.0      12\n",
       "28.0       3\n",
       "24.0       2\n",
       "20.0       1\n",
       "0.0        3\n",
       "Name: V501, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[\"V501\"].value_counts().sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l8stuBYb1JeT"
   },
   "source": [
    "Now we can see outlier within the last two attributes of each series(DIII188, DIII189, AVR198, AVR199, AVL208, AVL209, AVF218, AVF219, V2238, V2239, V3248, V3249,V4258, V4259,V5268, V5269, V6278, V6279). Similiarly assuming that these outliers might belong to the classes with few instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "id": "Q4cJ8f4f1JeU",
    "outputId": "6b1cb28c-9d92-4518-c05a-f948f60f2089"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x379.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,5.27)})\n",
    "sns.boxplot(data=final_df[[\"AVR190\",\"AVR191\",\"AVR192\",\"AVR193\",\"AVR194\",\"AVR195\",\"AVR196\",\"AVR197\",\"AVR198\",\"AVR199\"]]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "id": "n9GSIdo11JeW",
    "outputId": "2401bc8d-0d75-4e3e-a338-cf7c17021f74"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x379.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,5.27)})\n",
    "sns.boxplot(data=final_df[[\"AVL200\",\"AVL201\",\"AVL202\",\"AVL203\",\"AVL204\",\"AVL205\",\"AVL206\",\"AVL207\",\"AVL208\",\"AVL209\"]]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "id": "N9d4I_9F1JeX",
    "outputId": "f3490c31-7bf8-4854-dc7e-0ce2c2342b6c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x379.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11.7,5.27)})\n",
    "sns.boxplot(data=final_df[[\"AVF210\",\"AVF211\",\"AVF212\",\"AVF213\",\"AVF214\",\"AVF215\",\"AVF216\",\"AVF217\",\"AVF218\",\"AVF219\"]]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6-cDAvG-1JfT",
    "outputId": "effd5687-919d-4dcd-a157-8c4611e099ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-9.23812218e-02, -1.78080037e-01, -3.46671442e-02, -9.01507390e-02,\n",
       "        3.23878728e-01, -9.99540161e-02,  2.83049762e-02,  9.76253981e-02,\n",
       "       -1.22002504e-01,  1.95854526e-02, -1.68648870e-04,  1.90644217e-01,\n",
       "       -3.08114850e-02,  3.33672764e-02,  3.96491896e-03,  4.26738126e-02,\n",
       "        1.95198200e-01,  6.39992472e-02,             nan,  4.27635327e-02,\n",
       "       -2.01150248e-02,  7.67517615e-03, -3.55694451e-02,  3.20969797e-02,\n",
       "        3.20969797e-02,  8.30559434e-02, -3.61168680e-03,  4.83674292e-02,\n",
       "        1.83082712e-01, -1.05049431e-02,  6.65428889e-02,  6.77662940e-02,\n",
       "        4.17574601e-02,  7.98435069e-02, -2.01150248e-02,  1.69531020e-02,\n",
       "       -3.60504699e-02, -6.66334979e-04, -2.36687153e-02,  1.16515578e-01,\n",
       "        8.16735582e-02,  1.44307782e-02, -3.20137575e-02,  8.34076694e-02,\n",
       "       -3.08114850e-02,  5.14341059e-02, -3.08114850e-02, -8.40208668e-02,\n",
       "       -1.15446555e-02, -5.76271452e-03,  2.10381327e-02,  1.73243101e-01,\n",
       "        2.54790200e-03,  1.09423863e-01, -3.08114850e-02,  1.41103081e-01,\n",
       "       -6.43991926e-02,  3.20969797e-02, -3.60504699e-02, -5.76271452e-03,\n",
       "       -5.76271452e-03,  4.01270853e-02, -9.51325536e-03,  8.62805101e-02,\n",
       "        1.27209954e-01,  6.16984103e-02,             nan,  1.52533990e-01,\n",
       "                   nan,  7.60606402e-02, -3.08114850e-02, -9.41856455e-03,\n",
       "        2.21830437e-03,  1.19743559e-02,  6.94395981e-02,  3.00259257e-02,\n",
       "        1.43283685e-01,  5.76478469e-03,  9.01134869e-02,  1.07405670e-01,\n",
       "        3.20969797e-02,  1.02461952e-03,             nan, -3.08114850e-02,\n",
       "       -3.08114850e-02,  5.47601969e-02,  6.15957389e-02,  1.20725739e-01,\n",
       "       -1.18167887e-01,  3.68875604e-01, -2.41468439e-02,  3.13982499e-01,\n",
       "        8.40764742e-03,  1.74345647e-01,  1.32965490e-02,  1.32965490e-02,\n",
       "       -2.59595293e-02,  3.82498115e-02,  7.54212108e-02,  4.02835784e-02,\n",
       "       -1.04802605e-02,  2.82523391e-01,  9.85924790e-02,  1.07254510e-01,\n",
       "       -2.80177282e-03,  1.04520372e-01,  1.21710657e-01,  1.15252587e-01,\n",
       "       -2.96323091e-02, -1.68648870e-04,  6.60135274e-02, -2.59005587e-02,\n",
       "        1.40502408e-01,  1.16503625e-01,  9.08650874e-02, -7.10618888e-04,\n",
       "       -3.15900299e-02,  4.72408574e-02,  1.32194508e-01,  9.37881579e-02,\n",
       "       -3.49177539e-02, -2.59595293e-02,  4.11421737e-02,  2.08403220e-04,\n",
       "        1.70669615e-01,  1.15369158e-02,  4.93696439e-02, -7.22179337e-03,\n",
       "       -1.33346534e-02,  1.04145773e-01,             nan,             nan,\n",
       "       -2.09065922e-02,  5.70033179e-02,  1.15436522e-01,  7.01651905e-02,\n",
       "        1.01877932e-01,  4.21390928e-02,             nan,  1.03193252e-01,\n",
       "                   nan,  9.50584501e-02,             nan, -3.08114850e-02,\n",
       "                   nan, -2.87284109e-02,  9.75462789e-02,  6.36924587e-02,\n",
       "        1.30055964e-01, -3.65958306e-02,             nan,  1.41505551e-01,\n",
       "       -3.08114850e-02,  1.27789569e-03, -1.33346534e-02,             nan,\n",
       "                   nan,  1.72850920e-02, -2.00524860e-02, -9.03327100e-03,\n",
       "       -1.35180130e-01, -1.97554501e-01,  1.07901579e-01,             nan,\n",
       "       -1.05921951e-01, -6.72553225e-02, -1.02058765e-01, -1.71762707e-01,\n",
       "       -2.54345726e-03, -3.78137539e-02,  4.89226443e-03, -1.58536214e-01,\n",
       "       -4.42099728e-03, -5.86568608e-02, -6.83466737e-02,  5.64189125e-03,\n",
       "       -6.37805443e-02, -3.51255618e-02, -3.59550977e-03,  1.43428758e-02,\n",
       "        1.30359938e-01, -4.20653173e-02,  3.90143500e-03,  2.50605654e-02,\n",
       "       -8.74656504e-03,  6.89710671e-02,  5.00846798e-02,  1.04976063e-01,\n",
       "        4.85126664e-02,  7.66276154e-02,  1.65693257e-01, -3.06138176e-03,\n",
       "        1.22928173e-01,  3.08114850e-02,  9.31930303e-02,  3.77147563e-02,\n",
       "        1.14137877e-01,  1.25873240e-01, -7.81911502e-03, -1.38023671e-02,\n",
       "       -5.68445025e-02, -1.42730678e-01,  6.00241993e-02,             nan,\n",
       "       -2.87341746e-02, -8.49412178e-02, -6.51453141e-02, -1.34656794e-01,\n",
       "       -4.77386153e-02, -4.89496873e-02,  7.06530907e-02, -9.93289932e-02,\n",
       "        1.35415997e-02, -9.35100948e-02, -4.59119818e-02,  3.37566701e-02,\n",
       "       -3.26766118e-02, -5.52098645e-04,  6.94466318e-02, -2.56278593e-02,\n",
       "        1.41273961e-01,  9.78392462e-03,  2.35488426e-01,  2.49754659e-02,\n",
       "       -4.22246326e-02,  2.29694987e-02,  6.83389267e-02,  1.28489868e-01,\n",
       "        1.19809260e-01, -9.27914558e-02,  2.66138922e-02, -4.23710548e-02,\n",
       "        2.18811416e-01, -9.39122715e-02, -3.79381660e-02, -2.13866537e-02,\n",
       "       -1.08902188e-01, -9.45432486e-02,  1.51781561e-01, -2.90002767e-02,\n",
       "        6.01905939e-02, -1.89458038e-01,  9.55972551e-02, -7.10324386e-02,\n",
       "        4.24733004e-02,  8.72420403e-02, -1.59611689e-01, -4.46742653e-02,\n",
       "        1.24822829e-01, -7.40099741e-03,  9.53309571e-02, -1.50609932e-01,\n",
       "        3.04286463e-02, -4.73402930e-02, -8.68730068e-02,  1.08922991e-01,\n",
       "       -8.33957097e-02,  3.41381321e-02,  2.04870406e-02, -1.62153153e-01,\n",
       "        7.96689001e-02, -6.84101110e-02,  5.38080797e-02,             nan,\n",
       "       -8.11663795e-02,  6.21744575e-02, -1.05802701e-02,  5.26170978e-02,\n",
       "       -6.21293028e-02, -1.64320758e-01,  3.61878403e-02, -7.15557917e-02,\n",
       "       -3.69920743e-02,             nan, -8.64268780e-02, -3.07975937e-02,\n",
       "        3.47646535e-03, -9.27527477e-03])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding correlation with target feature using pearson correlation\n",
    "\n",
    "target=new_df[\"class\"]\n",
    "pearsoncorr = final_df.corrwith(other = target,method='pearson')\n",
    "pearsoncorr.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t0SFO1AU_dvz",
    "outputId": "7f60452a-7103-4495-c24c-4350396f28b4"
   },
   "source": [
    "## Feature Scaling and Splitting dataset\n",
    "<br />\n",
    "We will be using 80% of our dataset for training purpose and 20% for testing purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(final_df, target ,test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation strategy\n",
    "\n",
    "\n",
    "As the dependent variable is a categorical variable we will be using classification models. The best evaluation strategy for classification models is comparing the precision and recall. Thinking about the classification evaluation metrics, the importance of our models' precitions (we can't accept a result having the probability of saying to a healthy person that you have Cardiac Arrhythmia (FN)).\n",
    "\n",
    "We definitely will focus on Sensitivity (the percentage of sick people who are correctly identified as having the condition) not Specificity (percentage of healthy people who are correctly identified as not having the condition)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing evaluation metrices.\n",
    "\n",
    "from sklearn.metrics import r2_score,mean_squared_error,accuracy_score,recall_score,precision_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **3. Modeling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# will store result of each model.\n",
    "\n",
    "result = pd.DataFrame(columns=['Model','Train Accuracy','Test Accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knnclassifier = KNeighborsClassifier()\n",
    "knnclassifier.fit(X_train, y_train)\n",
    "y_pred = knnclassifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model  Train Accuracy  Test Accuracy\n",
       "0  KNN Classifier        0.648199       0.648352"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_train_accuracy = accuracy_score(y_train, knnclassifier.predict(X_train))\n",
    "knn_test_accuracy = accuracy_score(y_test, knnclassifier.predict(X_test))\n",
    "\n",
    "result = result.append(pd.Series({'Model':'KNN Classifier','Train Accuracy':knn_train_accuracy,'Test Accuracy':knn_test_accuracy}),ignore_index=True)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lgclassifier = LogisticRegression(solver = 'saga',random_state = 0) \n",
    "lgclassifier.fit(X_train, y_train) \n",
    "y_pred = lgclassifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Train Accuracy  Test Accuracy\n",
       "0       KNN Classifier        0.648199       0.648352\n",
       "1  Logestic Regression        0.939058       0.780220"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lg_train_recall = recall_score(y_train, lgclassifier.predict(X_train),average='weighted')\n",
    "lg_test_recall = recall_score(y_test, lgclassifier.predict(X_test),average='weighted')\n",
    "lg_train_accuracy = accuracy_score(y_train, lgclassifier.predict(X_train))\n",
    "lg_test_accuracy = accuracy_score(y_test, lgclassifier.predict(X_test))\n",
    "result = result.append(pd.Series({'Model':'Logestic Regression','Train Accuracy':lg_train_accuracy,'Test Accuracy':lg_test_accuracy}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier \n",
    "dtclassifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0,max_depth=5)\n",
    "dtclassifier.fit(X_train, y_train) \n",
    "y_pred_test = dtclassifier.predict(X_test)\n",
    "y_pred_train = dtclassifier.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.681319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Train Accuracy  Test Accuracy\n",
       "0            KNN Classifier        0.648199       0.648352\n",
       "1       Logestic Regression        0.939058       0.780220\n",
       "2  Decision Tree Classifier        0.789474       0.681319"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_train_accuracy = accuracy_score(y_train,y_pred_train )\n",
    "dt_test_accuracy = accuracy_score(y_test, y_pred_test)\n",
    "result = result.append(pd.Series({'Model':'Decision Tree Classifier','Train Accuracy':dt_train_accuracy,'Test Accuracy':dt_test_accuracy}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC \n",
    "lsvclassifier = LinearSVC(C=0.01)\n",
    "lsvclassifier.fit(X_train, y_train) \n",
    "y_pred_test = lsvclassifier.predict(X_test)\n",
    "y_pred_train = lsvclassifier.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.681319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Train Accuracy  Test Accuracy\n",
       "0            KNN Classifier        0.648199       0.648352\n",
       "1       Logestic Regression        0.939058       0.780220\n",
       "2  Decision Tree Classifier        0.789474       0.681319\n",
       "3                Linear SVC        0.880886       0.780220"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsvc_train_accuracy_score = accuracy_score(y_train, y_pred_train)\n",
    "lsvc_test_accuracy_score = accuracy_score(y_test, y_pred_test)\n",
    "\n",
    "result = result.append(pd.Series({'Model':'Linear SVC','Train Accuracy':lsvc_train_accuracy_score,'Test Accuracy':lsvc_test_accuracy_score}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kernelized SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "KSVC_clf = svm.SVC(kernel='sigmoid',C=10,gamma=0.001)\n",
    "KSVC_clf.fit(X_train, y_train)\n",
    "y_pred_train = KSVC_clf.predict(X_train)\n",
    "y_pred_test = KSVC_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.681319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kernelized SVC</td>\n",
       "      <td>0.850416</td>\n",
       "      <td>0.791209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Train Accuracy  Test Accuracy\n",
       "0            KNN Classifier        0.648199       0.648352\n",
       "1       Logestic Regression        0.939058       0.780220\n",
       "2  Decision Tree Classifier        0.789474       0.681319\n",
       "3                Linear SVC        0.880886       0.780220\n",
       "4            Kernelized SVC        0.850416       0.791209"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ksvc_train_accuracy_score = accuracy_score(y_train, y_pred_train)\n",
    "ksvc_test_accuracy_score = accuracy_score(y_test, y_pred_test)\n",
    "\n",
    "result = result.append(pd.Series({'Model':'Kernelized SVC','Train Accuracy':ksvc_train_accuracy_score,'Test Accuracy':ksvc_test_accuracy_score}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=10, max_features=100, max_leaf_nodes=30,\n",
       "                       n_estimators=300)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_clf = RandomForestClassifier(n_estimators=300, criterion='gini',max_features=100,max_depth=10,max_leaf_nodes=30)\n",
    "rf_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.681319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kernelized SVC</td>\n",
       "      <td>0.850416</td>\n",
       "      <td>0.791209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.875346</td>\n",
       "      <td>0.758242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Train Accuracy  Test Accuracy\n",
       "0            KNN Classifier        0.648199       0.648352\n",
       "1       Logestic Regression        0.939058       0.780220\n",
       "2  Decision Tree Classifier        0.789474       0.681319\n",
       "3                Linear SVC        0.880886       0.780220\n",
       "4            Kernelized SVC        0.850416       0.791209\n",
       "5  Random Forest Classifier        0.875346       0.758242"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train = rf_clf.predict(X_train)\n",
    "y_pred_test = rf_clf.predict(X_test)\n",
    "\n",
    "rf_train_accuracy_score = accuracy_score(y_train, y_pred_train)\n",
    "rf_test_accuracy_score = accuracy_score(y_test, y_pred_test)\n",
    "\n",
    "result = result.append(pd.Series({'Model':'Random Forest Classifier','Train Accuracy':rf_train_accuracy_score,'Test Accuracy':rf_test_accuracy_score}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found that the best model in term of recall is kernelized SVM with accuracy percentage of **79.12** over other models. We also found that Logestic Regression has better accuracy score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(.98)\n",
    "pca.fit(X_train)\n",
    "\n",
    "pca.n_components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_pca = pca.transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "kFold = StratifiedKFold(n_splits=5)\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN with PCA @"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=KNeighborsClassifier(n_jobs=-1),\n",
       "             param_grid={'n_neighbors': [1, 2, 3, 4, 5, 7, 10]},\n",
       "             return_train_score=True, scoring='recall_weighted')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_clf = KNeighborsClassifier(n_jobs=-1)\n",
    "\n",
    "param_grid={'n_neighbors':[1,2,3,4,5,7,10]}\n",
    "\n",
    "grid_search = GridSearchCV(knn_clf, param_grid, scoring = 'recall_weighted',cv=kFold, return_train_score=True)\n",
    "grid_search.fit(X_train_pca,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 5}"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf = KNeighborsClassifier(n_neighbors=5)\n",
    "knn_clf.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Recall score: 0.6454293628808865\n",
      "Test Recall score: 0.6483516483516484\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[51,  0,  0,  0,  0,  1,  0,  0,  0,  0,  0],\n",
       "       [ 6,  2,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 2,  0,  2,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 3,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  1],\n",
       "       [ 8,  0,  0,  0,  0,  0,  0,  0,  2,  0,  0],\n",
       "       [ 2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 6,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0]], dtype=int64)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train = knn_clf.predict(X_train_pca)\n",
    "y_pred_test = knn_clf.predict(X_test_pca)\n",
    "\n",
    "lreg_train_recall_score = recall_score(y_train, y_pred_train, average='weighted')\n",
    "lreg_test_recall_score = recall_score(y_test, y_pred_test, average='weighted')\n",
    "print('Train Recall score: {}'\n",
    "      .format(lreg_train_recall_score))\n",
    "print('Test Recall score: {}'\n",
    "      .format(lreg_test_recall_score))\n",
    "\n",
    "confusion_matrix(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logestic with PCA @"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lgclassifier = LogisticRegression(solver = 'saga',random_state = 0) \n",
    "lgclassifier.fit(X_train_pca, y_train) \n",
    "y_pred = lgclassifier.predict(X_test_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Recall score: 0.9279778393351801\n",
      "Test Recall score: 0.7582417582417582\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[45,  2,  0,  1,  0,  2,  0,  0,  0,  0,  2],\n",
       "       [ 2,  6,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  4,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  1,  0,  0,  0,  1,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  2,  0,  0,  0],\n",
       "       [ 2,  1,  0,  0,  0,  0,  0,  0,  7,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  1],\n",
       "       [ 3,  1,  1,  0,  0,  0,  0,  0,  0,  0,  1]], dtype=int64)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train = lgclassifier.predict(X_train_pca)\n",
    "y_pred_test = lgclassifier.predict(X_test_pca)\n",
    "\n",
    "lreg_train_recall_score = recall_score(y_train, y_pred_train, average='weighted')\n",
    "lreg_test_recall_score = recall_score(y_test, y_pred_test, average='weighted')\n",
    "print('Train Recall score: {}'\n",
    "      .format(lreg_train_recall_score))\n",
    "print('Test Recall score: {}'\n",
    "      .format(lreg_test_recall_score))\n",
    "\n",
    "confusion_matrix(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear svm with PCA @"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=LinearSVC(),\n",
       "             param_grid={'C': [1e-05, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100]},\n",
       "             return_train_score=True, scoring='recall_weighted')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "LSVC_clf = LinearSVC()\n",
    "\n",
    "param_grid = {'C': [0.00001,0.0001,0.001,0.01,0.1,1,10,100]}\n",
    "\n",
    "grid_search = GridSearchCV(LSVC_clf, param_grid, scoring = 'recall_weighted',cv=kFold, return_train_score=True)\n",
    "grid_search.fit(X_train_pca,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.001}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=0.001)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LSVC_clf = LinearSVC(C=0.001)\n",
    "LSVC_clf.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Recall score: 0.8088642659279779\n",
      "Test Recall score: 0.8021978021978022\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[50,  2,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 2,  6,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  4,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0],\n",
       "       [ 2,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0, 10,  0,  0],\n",
       "       [ 1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1],\n",
       "       [ 2,  2,  1,  0,  0,  0,  0,  0,  1,  0,  0]], dtype=int64)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train = LSVC_clf.predict(X_train_pca)\n",
    "y_pred_test = LSVC_clf.predict(X_test_pca)\n",
    "\n",
    "lscvc_pca_train_recall_score = recall_score(y_train, y_pred_train, average='weighted')\n",
    "lscv_pca_test_recall_score = recall_score(y_test, y_pred_test, average='weighted')\n",
    "print('Train Recall score: {}'\n",
    "      .format(lscvc_pca_train_recall_score))\n",
    "print('Test Recall score: {}'\n",
    "      .format(lscv_pca_test_recall_score))\n",
    "\n",
    "confusion_matrix(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kernal svm with PCA @"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=SVC(kernel='sigmoid'),\n",
       "             param_grid={'C': [0.0001, 0.001, 0.01, 0.1, 1, 10],\n",
       "                         'gamma': [0.0001, 0.001, 0.01, 0.1, 1, 10]},\n",
       "             return_train_score=True, scoring='recall_weighted')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "KSVC_clf = svm.SVC(kernel='sigmoid')\n",
    "\n",
    "param_grid = {'C': [0.0001,0.001,0.01,0.1,1,10],\n",
    "          'gamma': [0.0001,0.001,0.01,0.1,1,10]}\n",
    "\n",
    "grid_search = GridSearchCV(KSVC_clf, param_grid, scoring = 'recall_weighted',cv=kFold, return_train_score=True)\n",
    "grid_search.fit(X_train_pca,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 0.001}"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, gamma=0.001, kernel='sigmoid')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "KSVC_clf = svm.SVC(kernel='sigmoid',C=10,gamma=0.001)\n",
    "KSVC_clf.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Recall score: 0.8393351800554016\n",
      "Test Recall score: 0.8021978021978022\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[52,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 2,  6,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  4,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0],\n",
       "       [ 3,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0],\n",
       "       [ 2,  0,  0,  0,  0,  0,  0,  0,  8,  0,  0],\n",
       "       [ 2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 5,  0,  1,  0,  0,  0,  0,  0,  0,  0,  0]], dtype=int64)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train = KSVC_clf.predict(X_train_pca)\n",
    "y_pred_test = KSVC_clf.predict(X_test_pca)\n",
    "\n",
    "kscv_pca_train_recall_score = recall_score(y_train, y_pred_train, average='weighted')\n",
    "kscv_pca_test_recall_score = recall_score(y_test, y_pred_test, average='weighted')\n",
    "print('Train Recall score: {}'\n",
    "      .format(kscv_pca_train_recall_score))\n",
    "print('Test Recall score: {}'\n",
    "      .format(kscv_pca_test_recall_score))\n",
    "\n",
    "confusion_matrix(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20]},\n",
       "             return_train_score=True, scoring='recall_weighted')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt_clf = DecisionTreeClassifier()\n",
    "param_grid = {'max_depth': [1,2, 3, 4, 5,6,7,8,9,10,15,20]}\n",
    "\n",
    "grid_search = GridSearchCV(dt_clf, param_grid, scoring = 'recall_weighted',cv=kFold, return_train_score=True)\n",
    "grid_search.fit(X_train_pca,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 4}"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=1)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_clf = DecisionTreeClassifier(max_depth=1)\n",
    "dt_clf.fit(X_train_pca, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Recall score: 0.5346260387811634\n",
      "Test Recall score: 0.5714285714285714\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[52,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 8,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 4,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 3,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [10,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 6,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0]], dtype=int64)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train = dt_clf.predict(X_train_pca)\n",
    "y_pred_test = dt_clf.predict(X_test_pca)\n",
    "\n",
    "dt_pca_train_recall_score = recall_score(y_train, y_pred_train, average='weighted')\n",
    "dt_pca_test_recall_score = recall_score(y_test, y_pred_test, average='weighted')\n",
    "\n",
    "print('Train Recall score: {}'\n",
    "      .format(dt_pca_train_recall_score))\n",
    "print('Test Recall score: {}'\n",
    "      .format(dt_pca_test_recall_score))\n",
    "\n",
    "confusion_matrix(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sampling and PCA\n",
    "\n",
    "\n",
    "Since our dataset is imbalance with number of instances of class 7 and class 8 are 2,3 respectively, whereas for class 1, having examples 245. So we will try to solve class imbalancy by randomly resample the training dataset using Oversampling.\n",
    "\n",
    "We will be using PCA(Principal Component Analysis) to reduce the dimension of our sampled dataset to get best feature to find better accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Over Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3185, 278)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#performing over sampling\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=0)\n",
    "X_resampled, y_resampled = ros.fit_resample(final_df, target)\n",
    "\n",
    "X_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({8.0: 245,\n",
       "         6.0: 245,\n",
       "         10.0: 245,\n",
       "         1.0: 245,\n",
       "         7.0: 245,\n",
       "         14.0: 245,\n",
       "         3.0: 245,\n",
       "         16.0: 245,\n",
       "         2.0: 245,\n",
       "         4.0: 245,\n",
       "         5.0: 245,\n",
       "         9.0: 245,\n",
       "         15.0: 245})"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding frequency of each class\n",
    "\n",
    "import collections\n",
    "counter = collections.Counter(y_resampled)\n",
    "counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x379.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y_resampled) \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(X_resampled, y_resampled , test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train1)\n",
    "X_train1 = scaler.transform(X_train1)\n",
    "X_test1 = scaler.transform(X_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(.98)\n",
    "pca.fit(X_train1)\n",
    "\n",
    "pca.n_components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train1 = pca.transform(X_train1)\n",
    "X_test1 = pca.transform(X_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = KNeighborsClassifier()\n",
    "classifier.fit(X_train1, y_train1)\n",
    "Y_pred = classifier.predict(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.681319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kernelized SVC</td>\n",
       "      <td>0.850416</td>\n",
       "      <td>0.791209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.883657</td>\n",
       "      <td>0.758242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Knn with PCA</td>\n",
       "      <td>0.968603</td>\n",
       "      <td>0.943485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Train Accuracy  Test Accuracy\n",
       "0            KNN Classifier        0.648199       0.648352\n",
       "1       Logestic Regression        0.939058       0.780220\n",
       "2  Decision Tree Classifier        0.789474       0.681319\n",
       "3                Linear SVC        0.880886       0.780220\n",
       "4            Kernelized SVC        0.850416       0.791209\n",
       "5  Random Forest Classifier        0.883657       0.758242\n",
       "6              Knn with PCA        0.968603       0.943485"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knnp_train_accuracy = accuracy_score(y_train1,classifier.predict(X_train1))\n",
    "knnp_test_accuracy = accuracy_score(y_test1,Y_pred)\n",
    "#print(knnp_train_accuracy,knnp_test_accuracy)\n",
    "result = result.append(pd.Series({'Model':'Knn with PCA','Train Accuracy':knnp_train_accuracy,'Test Accuracy':knnp_test_accuracy}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lgpclassifier = LogisticRegression(C=10,random_state = 0) \n",
    "lgpclassifier.fit(X_train1, y_train1) \n",
    "y_pred_train1 = lgpclassifier.predict(X_train1)\n",
    "y_pred_test1 = lgpclassifier.predict(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.681319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kernelized SVC</td>\n",
       "      <td>0.850416</td>\n",
       "      <td>0.791209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.883657</td>\n",
       "      <td>0.758242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Knn with PCA</td>\n",
       "      <td>0.968603</td>\n",
       "      <td>0.943485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Logestic Regression PCA</td>\n",
       "      <td>0.999608</td>\n",
       "      <td>0.970173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Train Accuracy  Test Accuracy\n",
       "0            KNN Classifier        0.648199       0.648352\n",
       "1       Logestic Regression        0.939058       0.780220\n",
       "2  Decision Tree Classifier        0.789474       0.681319\n",
       "3                Linear SVC        0.880886       0.780220\n",
       "4            Kernelized SVC        0.850416       0.791209\n",
       "5  Random Forest Classifier        0.883657       0.758242\n",
       "6              Knn with PCA        0.968603       0.943485\n",
       "7   Logestic Regression PCA        0.999608       0.970173"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgp_train_accuracy = accuracy_score(y_train1,y_pred_train1)\n",
    "lgp_test_accuracy = accuracy_score(y_test1,y_pred_test1)\n",
    "#print(lgp_train_accuracy,knnp_test_accuracy)\n",
    "result = result.append(pd.Series({'Model':'Logestic Regression PCA','Train Accuracy':lgp_train_accuracy,'Test Accuracy':lgp_test_accuracy}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier \n",
    "dtpclassifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
    "dtpclassifier.fit(X_train1, y_train1) \n",
    "y_pred_test = dtpclassifier.predict(X_test1)\n",
    "y_pred_train = dtpclassifier.predict(X_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtp_train_recall_score = recall_score(y_train1, y_pred_train, average='weighted')\n",
    "dtp_test_recall_score = recall_score(y_test1, y_pred_test, average='weighted')\n",
    "\n",
    "dtp_train_accuracy_score = accuracy_score(y_train1, y_pred_train)\n",
    "dtp_test_accuracy_score = accuracy_score(y_test1, y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.681319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kernelized SVC</td>\n",
       "      <td>0.850416</td>\n",
       "      <td>0.791209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.883657</td>\n",
       "      <td>0.758242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Knn with PCA</td>\n",
       "      <td>0.968603</td>\n",
       "      <td>0.943485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Logestic Regression PCA</td>\n",
       "      <td>0.999608</td>\n",
       "      <td>0.970173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree with PCA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Train Accuracy  Test Accuracy\n",
       "0            KNN Classifier        0.648199       0.648352\n",
       "1       Logestic Regression        0.939058       0.780220\n",
       "2  Decision Tree Classifier        0.789474       0.681319\n",
       "3                Linear SVC        0.880886       0.780220\n",
       "4            Kernelized SVC        0.850416       0.791209\n",
       "5  Random Forest Classifier        0.883657       0.758242\n",
       "6              Knn with PCA        0.968603       0.943485\n",
       "7   Logestic Regression PCA        0.999608       0.970173\n",
       "8    Decision Tree with PCA        1.000000       0.962323"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = result.append(pd.Series({'Model':'Decision Tree with PCA','Train Accuracy':dtp_train_accuracy_score,'Test Accuracy':dtp_test_accuracy_score}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear SVM with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC \n",
    "classifier = SVC(kernel = 'linear', random_state = 0,probability=True)\n",
    "classifier.fit(X_train1, y_train1) \n",
    "y_pred = classifier.predict(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.681319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kernelized SVC</td>\n",
       "      <td>0.850416</td>\n",
       "      <td>0.791209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.883657</td>\n",
       "      <td>0.758242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Knn with PCA</td>\n",
       "      <td>0.968603</td>\n",
       "      <td>0.943485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Logestic Regression PCA</td>\n",
       "      <td>0.999608</td>\n",
       "      <td>0.970173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree with PCA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Linear SVM with PCA</td>\n",
       "      <td>0.999608</td>\n",
       "      <td>0.971743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Model  Train Accuracy  Test Accuracy\n",
       "0            KNN Classifier        0.648199       0.648352\n",
       "1       Logestic Regression        0.939058       0.780220\n",
       "2  Decision Tree Classifier        0.789474       0.681319\n",
       "3                Linear SVC        0.880886       0.780220\n",
       "4            Kernelized SVC        0.850416       0.791209\n",
       "5  Random Forest Classifier        0.883657       0.758242\n",
       "6              Knn with PCA        0.968603       0.943485\n",
       "7   Logestic Regression PCA        0.999608       0.970173\n",
       "8    Decision Tree with PCA        1.000000       0.962323\n",
       "9       Linear SVM with PCA        0.999608       0.971743"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(\"Accuracy Score\",accuracy_score(y_test1,y_pred)*100)\n",
    "lsvcp_train_accuracy = accuracy_score(y_train1,classifier.predict(X_train1))\n",
    "lsvcp_test_accuracy = accuracy_score(y_test1,y_pred)\n",
    "result = result.append(pd.Series({'Model':'Linear SVM with PCA','Train Accuracy':lsvcp_train_accuracy,'Test Accuracy':lsvcp_test_accuracy}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kernelized SVM with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "KSVC_clf = svm.SVC(kernel='rbf',C=1,gamma=0.1)\n",
    "KSVC_clf.fit(X_train1, y_train1)\n",
    "y_pred_train1 = KSVC_clf.predict(X_train1)\n",
    "y_pred_test1 = KSVC_clf.predict(X_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.681319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kernelized SVC</td>\n",
       "      <td>0.850416</td>\n",
       "      <td>0.791209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.883657</td>\n",
       "      <td>0.758242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Knn with PCA</td>\n",
       "      <td>0.968603</td>\n",
       "      <td>0.943485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Logestic Regression PCA</td>\n",
       "      <td>0.999608</td>\n",
       "      <td>0.970173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree with PCA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Linear SVM with PCA</td>\n",
       "      <td>0.999608</td>\n",
       "      <td>0.971743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kernelized SVM with PCA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  Train Accuracy  Test Accuracy\n",
       "0             KNN Classifier        0.648199       0.648352\n",
       "1        Logestic Regression        0.939058       0.780220\n",
       "2   Decision Tree Classifier        0.789474       0.681319\n",
       "3                 Linear SVC        0.880886       0.780220\n",
       "4             Kernelized SVC        0.850416       0.791209\n",
       "5   Random Forest Classifier        0.883657       0.758242\n",
       "6               Knn with PCA        0.968603       0.943485\n",
       "7    Logestic Regression PCA        0.999608       0.970173\n",
       "8     Decision Tree with PCA        1.000000       0.962323\n",
       "9        Linear SVM with PCA        0.999608       0.971743\n",
       "10   Kernelized SVM with PCA        1.000000       0.995290"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ksvcp_train_accuracy_score = accuracy_score(y_train1, y_pred_train1)\n",
    "ksvcp_test_accuracy_score = accuracy_score(y_test1, y_pred_test1)\n",
    "result = result.append(pd.Series({'Model':'Kernelized SVM with PCA','Train Accuracy':ksvcp_train_accuracy_score,'Test Accuracy':ksvcp_test_accuracy_score}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfp_clf = RandomForestClassifier()\n",
    "rfp_clf.fit(X_train1, y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train1 = rfp_clf.predict(X_train1)\n",
    "y_pred_test1 = rfp_clf.predict(X_test1)\n",
    "rfp_train_accuracy_score = accuracy_score(y_train1, y_pred_train1)\n",
    "rfp_test_accuracy_score = accuracy_score(y_test1, y_pred_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNN Classifier</td>\n",
       "      <td>0.648199</td>\n",
       "      <td>0.648352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Logestic Regression</td>\n",
       "      <td>0.939058</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree Classifier</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.681319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>0.880886</td>\n",
       "      <td>0.780220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kernelized SVC</td>\n",
       "      <td>0.850416</td>\n",
       "      <td>0.791209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest Classifier</td>\n",
       "      <td>0.883657</td>\n",
       "      <td>0.758242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Knn with PCA</td>\n",
       "      <td>0.968603</td>\n",
       "      <td>0.943485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Logestic Regression PCA</td>\n",
       "      <td>0.999608</td>\n",
       "      <td>0.970173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree with PCA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Linear SVM with PCA</td>\n",
       "      <td>0.999608</td>\n",
       "      <td>0.971743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kernelized SVM with PCA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Random Forest with PCA</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  Train Accuracy  Test Accuracy\n",
       "0             KNN Classifier        0.648199       0.648352\n",
       "1        Logestic Regression        0.939058       0.780220\n",
       "2   Decision Tree Classifier        0.789474       0.681319\n",
       "3                 Linear SVC        0.880886       0.780220\n",
       "4             Kernelized SVC        0.850416       0.791209\n",
       "5   Random Forest Classifier        0.883657       0.758242\n",
       "6               Knn with PCA        0.968603       0.943485\n",
       "7    Logestic Regression PCA        0.999608       0.970173\n",
       "8     Decision Tree with PCA        1.000000       0.962323\n",
       "9        Linear SVM with PCA        0.999608       0.971743\n",
       "10   Kernelized SVM with PCA        1.000000       0.995290\n",
       "11    Random Forest with PCA        1.000000       0.995290"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = result.append(pd.Series({'Model':'Random Forest with PCA','Train Accuracy':rfp_train_accuracy_score,'Test Accuracy':rfp_test_accuracy_score}),ignore_index=True )\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "jqPHSNsABChs"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot(kind=\"bar\",figsize=(15,4))\n",
    "plt.title('Train&Test Scores of Classifiers')\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Scores')\n",
    "plt.legend(loc=4 , bbox_to_anchor=(1.2, 0))\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result\n",
    "\n",
    "The models started performing better after we applied PCA on the resampled data. The reason behind this is, PCA reduces the complexity of the data. It creates components based on giving importance to variables with large variance and also the components which it creates are non collinear in nature which means it takes care of collinearity in large data set. PCA also improves the overall execution time and quality of the models and it is very beneficial when we are working with huge amount of variables. \n",
    "\n",
    "The Best model recall score is Kernalized SVM with PCA having accuracy of **99.52%.**"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "EDA Final.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}