Covid_Knn_Lin R Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fever</th>\n",
       "      <th>bodyPain</th>\n",
       "      <th>age</th>\n",
       "      <th>runnyNose</th>\n",
       "      <th>diffBreath</th>\n",
       "      <th>infectionProb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>99.254765</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.954284</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101.647720</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101.318212</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.139178</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "        fever  bodyPain  age  runnyNose  diffBreath  infectionProb\n",
       "0   99.254765         1   85          1           1              1\n",
       "1   98.954284         0   55          0          -1              0\n",
       "2  101.647720         0   84          0           0              1\n",
       "3  101.318212         1   12          0           1              1\n",
       "4   99.139178         1   24          0           0              0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvs = pd.read_csv(r'C:\\Users\\arun kumar\\Desktop\\figma+HACKATHONS\\Coronavirus-Probability-Checker\\Coronavirus-Probability-Checker-master\\dataset.csv')\n",
    "cvs.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fever</th>\n",
       "      <th>bodyPain</th>\n",
       "      <th>age</th>\n",
       "      <th>runnyNose</th>\n",
       "      <th>diffBreath</th>\n",
       "      <th>infectionProb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>99.999273</td>\n",
       "      <td>0.510903</td>\n",
       "      <td>49.899965</td>\n",
       "      <td>0.491866</td>\n",
       "      <td>-0.027345</td>\n",
       "      <td>0.500519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.134316</td>\n",
       "      <td>0.499968</td>\n",
       "      <td>28.847724</td>\n",
       "      <td>0.500020</td>\n",
       "      <td>0.826508</td>\n",
       "      <td>0.500086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>98.000300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.036548</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>99.993046</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.962298</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.992313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             fever     bodyPain          age    runnyNose   diffBreath  \\\n",
       "count  2889.000000  2889.000000  2889.000000  2889.000000  2889.000000   \n",
       "mean     99.999273     0.510903    49.899965     0.491866    -0.027345   \n",
       "std       1.134316     0.499968    28.847724     0.500020     0.826508   \n",
       "min      98.000300     0.000000     1.000000     0.000000    -1.000000   \n",
       "25%      99.036548     0.000000    25.000000     0.000000    -1.000000   \n",
       "50%      99.993046     1.000000    49.000000     0.000000     0.000000   \n",
       "75%     100.962298     1.000000    74.000000     1.000000     1.000000   \n",
       "max     101.992313     1.000000   100.000000     1.000000     1.000000   \n",
       "\n",
       "       infectionProb  \n",
       "count    2889.000000  \n",
       "mean        0.500519  \n",
       "std         0.500086  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         1.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvs.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2889 entries, 0 to 2888\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   fever          2889 non-null   float64\n",
      " 1   bodyPain       2889 non-null   int64  \n",
      " 2   age            2889 non-null   int64  \n",
      " 3   runnyNose      2889 non-null   int64  \n",
      " 4   diffBreath     2889 non-null   int64  \n",
      " 5   infectionProb  2889 non-null   int64  \n",
      "dtypes: float64(1), int64(5)\n",
      "memory usage: 135.5 KB\n"
     ]
    }
   ],
   "source": [
    "cvs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fever</th>\n",
       "      <th>bodyPain</th>\n",
       "      <th>age</th>\n",
       "      <th>runnyNose</th>\n",
       "      <th>diffBreath</th>\n",
       "      <th>infectionProb</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>2</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2884</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2885</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2886</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2887</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2888</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2889 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fever  bodyPain    age  runnyNose  diffBreath  infectionProb\n",
       "0     False     False  False      False       False          False\n",
       "1     False     False  False      False       False          False\n",
       "2     False     False  False      False       False          False\n",
       "3     False     False  False      False       False          False\n",
       "4     False     False  False      False       False          False\n",
       "...     ...       ...    ...        ...         ...            ...\n",
       "2884  False     False  False      False       False          False\n",
       "2885  False     False  False      False       False          False\n",
       "2886  False     False  False      False       False          False\n",
       "2887  False     False  False      False       False          False\n",
       "2888  False     False  False      False       False          False\n",
       "\n",
       "[2889 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvs.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = cvs.drop('infectionProb', 1)\n",
    "y = cvs['infectionProb']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin = LinearRegression()\n",
    "\n",
    "lin.fit(X_train, y_train)\n",
    "\n",
    "prediction = lin.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean absolute error:  0.5005795530855176\n",
      "mean squared error:  0.2507596168529061\n",
      "root mean squared error:  0.5007590407101065\n",
      "Score:  -0.003230654891159368\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "abs_err = mean_absolute_error(y_test, prediction)\n",
    "squ_err = mean_squared_error(y_test, prediction)\n",
    "\n",
    "print(\"mean absolute error: \", (abs_err))\n",
    "print(\"mean squared error: \", (squ_err))\n",
    "print(\"root mean squared error: \", (np.sqrt(squ_err)))\n",
    "print(\"Score: \", (r2_score(y_test, prediction)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "predicting new points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.array([[104.344456, 1, 78, -1, 0],\n",
    "                  [91.6587345, 0, 45, 0, 0],\n",
    "                  [89.8738, 0, 45, 0, -1],\n",
    "                  [90.10218, 1, 85, 0, -1],\n",
    "                  [74.345, 0, 85, 0, 1],\n",
    "                  [94.647, 1, 35, 0, 0],\n",
    "                  [89.563, 1, 86, 1, 1],\n",
    "                  [90.85635, 1, 42, 0, -1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 5)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "regr = LinearRegression()\n",
    "\n",
    "regr.fit(X_train, y_train)\n",
    "\n",
    "new_pred = regr.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pred.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fever', 'bodyPain', 'age', 'runnyNose', 'diffBreath'], dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN Classifier appoach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "model = KNeighborsClassifier(n_neighbors=8)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "val = np.array([[97, 0, 45, 0, -1]])\n",
    "\n",
    "pred = lin.predict(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.49      0.62      0.55       293\n",
      "           1       0.46      0.34      0.39       285\n",
      "\n",
      "    accuracy                           0.48       578\n",
      "   macro avg       0.48      0.48      0.47       578\n",
      "weighted avg       0.48      0.48      0.47       578\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "report = classification_report(y_test, y_pred)\n",
    "\n",
    "print(\"\", (report))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CONFUSION MATRIX-           actual values-\n",
    "                             (1)      (0)\n",
    "                        (1)   TP      FP\n",
    "            pred values-(0)   TN      FN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [[181 112]\n",
      " [188  97]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"\", (matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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