Liver Disease Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  <h1><center>Liver Disease Prediction</center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the WHO data published in 2017 Liver Disease Deaths in India reached 259,749 or 2.95% of total deaths. This project aims to classify the data and provide useful insights which may help doctors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset used for classification is obtained from kaggle. It contains data about chemical compounds obtained from tests like SGOT, SGPT which mentions whether a patient needs to be diagnosed or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially, the required libraries are loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import itertools \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total_Bilirubin</th>\n",
       "      <th>Direct_Bilirubin</th>\n",
       "      <th>Alkaline_Phosphotase</th>\n",
       "      <th>Alamine_Aminotransferase</th>\n",
       "      <th>Aspartate_Aminotransferase</th>\n",
       "      <th>Total_Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin_and_Globulin_Ratio</th>\n",
       "      <th>Dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
       "0   65  Female              0.7               0.1                   187   \n",
       "1   62    Male             10.9               5.5                   699   \n",
       "2   62    Male              7.3               4.1                   490   \n",
       "3   58    Male              1.0               0.4                   182   \n",
       "4   72    Male              3.9               2.0                   195   \n",
       "\n",
       "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
       "0                        16                          18             6.8   \n",
       "1                        64                         100             7.5   \n",
       "2                        60                          68             7.0   \n",
       "3                        14                          20             6.8   \n",
       "4                        27                          59             7.3   \n",
       "\n",
       "   Albumin  Albumin_and_Globulin_Ratio  Dataset  \n",
       "0      3.3                        0.90        1  \n",
       "1      3.2                        0.74        1  \n",
       "2      3.3                        0.89        1  \n",
       "3      3.4                        1.00        1  \n",
       "4      2.4                        0.40        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import dataset\n",
    "liver_data_train = pd.read_csv('DataSet/Indian_Liver_Patients_Dataset.csv')\n",
    "liver_data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(583, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_data_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a column based on dataset to see if a patient has disease or not\n",
    "def label_disease(liver_data_train):\n",
    "    if liver_data_train[\"Dataset\"] == 1:\n",
    "        return 'yes'\n",
    "    return 'no'\n",
    "\n",
    "liver_data_train['HasDisease'] = liver_data_train.apply(lambda liver_data_train: label_disease(liver_data_train), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization and Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes    416\n",
       "no     167\n",
       "Name: HasDisease, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_data_train['HasDisease'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting some columns to gain insights from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "!conda install -c anaconda seaborn -y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, the relationship between gender and age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "bins = np.linspace(liver_data_train.Age.min(), liver_data_train.Age.max(),10)\n",
    "grids = sns.FacetGrid(liver_data_train, col=\"Gender\", hue=\"HasDisease\", palette=\"Set1\", col_wrap=2)\n",
    "grids.map(plt.hist, 'Age', bins=bins, ec=\"k\")\n",
    "\n",
    "grids.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amount of bilirubin based on gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(liver_data_train.Total_Bilirubin.min(), liver_data_train.Total_Bilirubin.max(),10)\n",
    "grids = sns.FacetGrid(liver_data_train, col=\"Gender\", hue=\"HasDisease\", palette=\"Set1\", col_wrap=2)\n",
    "grids.map(plt.hist, 'Total_Bilirubin', bins=bins, ec=\"k\")\n",
    "\n",
    "grids.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total protiens play a major role in liver disease. So plotting it gender wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(liver_data_train.Total_Protiens.min(), liver_data_train.Total_Protiens.max(),10)\n",
    "grids = sns.FacetGrid(liver_data_train, col=\"Gender\", hue=\"HasDisease\", palette=\"Set1\", col_wrap=2)\n",
    "grids.map(plt.hist, 'Total_Protiens', bins=bins, ec=\"k\")\n",
    "\n",
    "grids.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### convert males and females to 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "liver_data_train['Gender'].replace(to_replace=['Male','Female'], value=[0,1],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check if any column has null values and replace with mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                           0\n",
       "Gender                        0\n",
       "Total_Bilirubin               0\n",
       "Direct_Bilirubin              0\n",
       "Alkaline_Phosphotase          0\n",
       "Alamine_Aminotransferase      0\n",
       "Aspartate_Aminotransferase    0\n",
       "Total_Protiens                0\n",
       "Albumin                       0\n",
       "Albumin_and_Globulin_Ratio    4\n",
       "Dataset                       0\n",
       "HasDisease                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liver_data_train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "liver_data_train['Albumin_and_Globulin_Ratio'].fillna((liver_data_train['Albumin_and_Globulin_Ratio'].mean()), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting the feature sets, X and the labels, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Feature = liver_data_train[['Age','Gender','Total_Bilirubin','Direct_Bilirubin',\n",
    "                      'Alkaline_Phosphotase','Alamine_Aminotransferase','Aspartate_Aminotransferase',\n",
    "                      'Total_Protiens','Albumin','Albumin_and_Globulin_Ratio']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['yes', 'yes', 'yes', 'yes', 'yes'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Feature\n",
    "y = liver_data_train['HasDisease'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Standardization give data zero mean and unit variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Padhma Sahithya\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\Padhma Sahithya\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.25209764,  1.76228085, -0.41887783, -0.49396398, -0.42671496,\n",
       "        -0.35466541, -0.31839333,  0.29211961,  0.19896867, -0.14789798],\n",
       "       [ 1.06663704, -0.56744644,  1.22517135,  1.43042334,  1.68262856,\n",
       "        -0.09159933, -0.03433257,  0.93756634,  0.07315659, -0.65069686],\n",
       "       [ 1.06663704, -0.56744644,  0.6449187 ,  0.93150811,  0.82158795,\n",
       "        -0.11352151, -0.14518555,  0.47653296,  0.19896867, -0.17932291],\n",
       "       [ 0.81935624, -0.56744644, -0.37052344, -0.38705357, -0.44731402,\n",
       "        -0.36562649, -0.31146502,  0.29211961,  0.32478075,  0.16635131],\n",
       "       [ 1.68483904, -0.56744644,  0.0969023 ,  0.18313526, -0.39375647,\n",
       "        -0.29437943, -0.17636295,  0.75315299, -0.93334003, -1.71914446]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=7,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.2, random_state=5)\n",
    "liverDiseaseTree = DecisionTreeClassifier(criterion='entropy',max_depth=7)\n",
    "liverDiseaseTree.fit(X_trainset,y_trainset)\n",
    "liverDiseaseTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_yhat = liverDiseaseTree.predict(X_testset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.630479735318445\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "dt_f1 = f1_score(y_testset, dt_yhat, average='weighted')\n",
    "print(dt_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6923076923076923\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "dt_ja = jaccard_similarity_score(y_testset, dt_yhat)\n",
    "print(dt_ja)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Set: (466, 10) (466,)\n",
      "Test Set: (117, 10) (117,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print('Train Set:',X_train.shape, y_train.shape)\n",
    "print('Test Set:',X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.65811966, 0.62393162, 0.62393162, 0.61538462, 0.65811966,\n",
       "       0.64102564, 0.66666667, 0.62393162, 0.66666667])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Knn=10\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "mean_acc = np.zeros((Knn-1))\n",
    "std_acc = np.zeros((Knn-1))\n",
    "confusionMx = [];\n",
    "for n in range(1,Knn):\n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    knn_hat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, knn_hat)\n",
    "    std_acc[n-1]=np.std(knn_hat==y_test)/np.sqrt(knn_hat.shape[0])\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.6666666666666666 with k = 7\n"
     ]
    }
   ],
   "source": [
    "print( \"The best accuracy was with\", mean_acc.max(),\"with k =\",mean_acc.argmax()+1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "k_nearest = KNeighborsClassifier(n_neighbors = 7).fit(X_train,y_train)\n",
    "k_nearest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "knn_f1 = f1_score(y_test, knn_hat, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "knn_ja = jaccard_similarity_score(y_test, knn_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "LogReg = LogisticRegression(C=0.01, solver='liblinear').fit(X_trainset,y_trainset)\n",
    "LogReg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['yes', 'yes', 'yes', 'yes', 'yes'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogReg_yhat = LogReg.predict(X_test)\n",
    "LogReg_yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.38550075, 0.61449925],\n",
       "       [0.38140643, 0.61859357],\n",
       "       [0.38581889, 0.61418111],\n",
       "       [0.41372373, 0.58627627],\n",
       "       [0.45929773, 0.54070227]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogReg_yhat_prob = LogReg.predict_proba(X_test)\n",
    "LogReg_yhat_prob[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7033393670265179\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "LR_f1 = f1_score(y_test, LogReg_yhat, average='weighted')\n",
    "print(LR_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7350427350427351\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "LR_ja = jaccard_similarity_score(y_test, LogReg_yhat)\n",
    "print(LR_ja)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5722557867737494\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import log_loss\n",
    "LR_log = log_loss(y_test, LogReg_yhat_prob)\n",
    "print(LR_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import jaccard_similarity_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Padhma Sahithya\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "Svm = svm.SVC(kernel='rbf')\n",
    "Svm.fit(X_trainset,y_trainset)\n",
    "svm_yhat = Svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5934692088538241\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "svm_f1 = f1_score(y_test, svm_yhat, average='weighted')\n",
    "print(svm_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7008547008547008\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "svm_ja = jaccard_similarity_score(y_test, svm_yhat)\n",
    "print(svm_ja)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy Report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A report on accuracy of each model using different evaluation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Algorithm</th>\n",
       "      <th>Jaccard</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>LogLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.619966</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.630480</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.700855</td>\n",
       "      <td>0.593469</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.735043</td>\n",
       "      <td>0.703339</td>\n",
       "      <td>0.572256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Algorithm            Jaccard  F1-score   LogLoss\n",
       "KNN                 0.666667  0.619966        NA\n",
       "Decision Tree       0.692308  0.630480        NA\n",
       "SVM                 0.700855  0.593469        NA\n",
       "LogisticRegression  0.735043  0.703339  0.572256"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ja_list = [knn_ja, dt_ja, svm_ja, LR_ja]\n",
    "f1_list = [knn_f1, dt_f1, svm_f1, LR_f1]\n",
    "log_list = ['NA','NA','NA',LR_log]\n",
    "\n",
    "accuracyReport = pd.DataFrame(ja_list, index=['KNN','Decision Tree','SVM','LogisticRegression'])\n",
    "accuracyReport.columns = ['Jaccard']\n",
    "accuracyReport.insert(loc=1, column='F1-score',value=f1_list)\n",
    "accuracyReport.insert(loc=2, column='LogLoss',value=log_list)\n",
    "accuracyReport.columns.name = 'Algorithm'\n",
    "accuracyReport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy report shows the performance of each algorithm on test data"
   ]
  }
 ],
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