covid_pred_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Covid data analysis using Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from yellowbrick.model_selection import FeatureImportances\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Simran\\anaconda3\\lib\\site-packages\\gensim\\similarities\\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S3 Datasource is not available by default. If you wish to use it, please install mindsdb_native[extra_data_sources]\n",
      "Microsoft SQL Server Datasource is not available by default. If you wish to use it, please install mindsdb_native[extra_data_sources]\n",
      "Athena Datasource is not available by default. If you wish to use it, please install mindsdb_native[extra_data_sources]\n",
      "Google Cloud Storage Datasource is not available by default. If you wish to use it, please install mindsdb_native[extra_data_sources]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING:mindsdb-logger-core-logger---:C:\\Users\\Simran\\anaconda3\\lib\\site-packages\\mindsdb_native\\libs\\helpers\\general_helpers.py:123 - There is a new version of MindsDB 2.43.0, please upgrade using:\n",
      "pip3 install mindsdb_native --upgrade\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from mindsdb_native import MySqlDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mysql data source\n",
    "mysql_ds = MySqlDS(query=\"SELECT * FROM covid_flu_data\", user=\"root\", password=\"pass\", database=\"world\", port=3306)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#separating data from target\n",
    "covid_data=mysql_ds[mysql_ds.columns[:20]]\n",
    "covid_target=mysql_ds[mysql_ds.columns[20]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COUGH</th>\n",
       "      <th>MUSCLE_ACHES</th>\n",
       "      <th>TIREDNESS</th>\n",
       "      <th>SORE_THROAT</th>\n",
       "      <th>RUNNY_NOSE</th>\n",
       "      <th>STUFFY_NOSE</th>\n",
       "      <th>FEVER</th>\n",
       "      <th>NAUSEA</th>\n",
       "      <th>VOMITING</th>\n",
       "      <th>DIARRHEA</th>\n",
       "      <th>SHORTNESS_OF_BREATH</th>\n",
       "      <th>DIFFICULTY_BREATHING</th>\n",
       "      <th>LOSS_OF_TASTE</th>\n",
       "      <th>LOSS_OF_SMELL</th>\n",
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       "      <th>ITCHY_EYES</th>\n",
       "      <th>ITCHY_MOUTH</th>\n",
       "      <th>ITCHY_INNER_EAR</th>\n",
       "      <th>SNEEZING</th>\n",
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      "text/plain": [
       "   COUGH  MUSCLE_ACHES  TIREDNESS  SORE_THROAT  RUNNY_NOSE  STUFFY_NOSE  \\\n",
       "0      0             0          1            0           1            0   \n",
       "1      0             0          1            0           0            0   \n",
       "2      0             1          1            1           0            0   \n",
       "3      0             0          0            1           1            0   \n",
       "4      0             0          1            0           1            0   \n",
       "\n",
       "   FEVER  NAUSEA  VOMITING  DIARRHEA  SHORTNESS_OF_BREATH  \\\n",
       "0      0       0         0         0                    0   \n",
       "1      0       0         0         0                    0   \n",
       "2      0       0         0         0                    0   \n",
       "3      0       0         0         0                    0   \n",
       "4      0       0         0         0                    0   \n",
       "\n",
       "   DIFFICULTY_BREATHING  LOSS_OF_TASTE  LOSS_OF_SMELL  ITCHY_NOSE  ITCHY_EYES  \\\n",
       "0                     0              1              0           1           0   \n",
       "1                     0              1              0           1           0   \n",
       "2                     0              1              0           1           0   \n",
       "3                     0              1              1           0           0   \n",
       "4                     0              1              1           0           1   \n",
       "\n",
       "   ITCHY_MOUTH  ITCHY_INNER_EAR  SNEEZING  PINK_EYE  \n",
       "0            0                1         0         1  \n",
       "1            1                1         1         1  \n",
       "2            0                0         0         1  \n",
       "3            1                0         1         1  \n",
       "4            0                1         1         1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displays the first 5 rows\n",
    "covid_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20482    FLU\n",
       "41460    FLU\n",
       "42984    FLU\n",
       "20258    FLU\n",
       "29117    FLU\n",
       "Name: DIAGNOSE, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displays n random rows\n",
    "covid_target.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the dataset\n",
    "x_train,x_test,y_train,y_test=train_test_split(covid_data,covid_target,test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model=LogisticRegression(max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting the model\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9295996401259559"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculates the prediction score of the model\n",
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COVID', 'FLU', 'FLU', ..., 'COVID', 'ALLERGY', 'FLU'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#storing the predicted result for future use\n",
    "y_pred=model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.73525502e-12, 4.09474164e-11, 5.06805135e-01, 4.93194865e-01],\n",
       "       [2.51045298e-14, 2.51187143e-13, 7.20864597e-06, 9.99992791e-01],\n",
       "       [3.08028808e-07, 6.52375811e-08, 2.10652699e-08, 9.99999606e-01],\n",
       "       ...,\n",
       "       [8.83514989e-08, 1.67049753e-08, 5.11492005e-01, 4.88507890e-01],\n",
       "       [9.99963199e-01, 1.60643782e-05, 1.25408912e-09, 2.07358016e-05],\n",
       "       [8.46538335e-05, 3.14292647e-05, 4.13438424e-03, 9.95749533e-01]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns Probability estimates.\n",
    "model.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FLU', 'ALLERGY', 'COVID', 'COLD'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The 4 different classes in this dataset\n",
    "classes=y_test.unique()\n",
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1622,   18,    0,    6],\n",
       "       [  31,   57,    2,    7],\n",
       "       [   2,    2,  121,   67],\n",
       "       [  10,   24,  144, 2333]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To evaluate the performance of a classification model\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualising confusion matrix\n",
    "sns.heatmap(cm, annot=True,fmt='g',cmap='Blues')\n",
    "width = np.arange(len(classes))\n",
    "plt.xticks(width, classes, rotation=45)\n",
    "plt.yticks(width, classes)\n",
    "plt.xlabel(\"Actual Values\")\n",
    "plt.ylabel(\"Predicted Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FeatureImportances(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x0000020E2A883670>,\n",
       "                   estimator=LogisticRegression(solver='liblinear'),\n",
       "                   relative=False, stack=True)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The interpretation of the importance of coeficients depends on the model\n",
    "viz = FeatureImportances(model, stack=True, relative=False)\n",
    "viz.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}