notebooks/winequality-red.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>qualityBool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  qualityBool  \n",
       "0      9.4        5            0  \n",
       "1      9.8        5            0  \n",
       "2      9.8        5            0  \n",
       "3      9.8        6            1  \n",
       "4      9.4        5            0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#File path to the csv file\n",
    "csv_file = \"https://raw.githubusercontent.com/cassieview/wine-quality-azure-ml-visual-interface/master/dataset/winequality-red.csv\"\n",
    "\n",
    "# Read csv file into dataframe\n",
    "df = pd.read_csv(csv_file)\n",
    "\n",
    "# Print first 5 rows in the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1599 entries, 0 to 1598\n",
      "Data columns (total 13 columns):\n",
      "fixed acidity           1599 non-null float64\n",
      "volatile acidity        1599 non-null float64\n",
      "citric acid             1599 non-null float64\n",
      "residual sugar          1599 non-null float64\n",
      "chlorides               1599 non-null float64\n",
      "free sulfur dioxide     1599 non-null float64\n",
      "total sulfur dioxide    1599 non-null float64\n",
      "density                 1599 non-null float64\n",
      "pH                      1599 non-null float64\n",
      "sulphates               1599 non-null float64\n",
      "alcohol                 1599 non-null float64\n",
      "quality                 1599 non-null int64\n",
      "qualityBool             1599 non-null int64\n",
      "dtypes: float64(11), int64(2)\n",
      "memory usage: 162.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\casiljan\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2be299270b8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'volatile acidity', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2be29f55a58>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.barplot(x = 'quality', y = 'chlorides', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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.barplot(x=\"quality\", y=\"sulphates\", data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2be29d495f8>"
      ]
     },
     "execution_count": 26,
     "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": [
    "sns.barplot(x = 'quality', y = 'citric acid', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2be2a00da90>"
      ]
     },
     "execution_count": 27,
     "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": [
    "sns.barplot(x = 'quality', y = 'total sulfur dioxide', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   volatile acidity  citric acid  chlorides  sulphates  alcohol\n",
       "0              0.70         0.00      0.076       0.56      9.4\n",
       "1              0.88         0.00      0.098       0.68      9.8\n",
       "2              0.76         0.04      0.092       0.65      9.8\n",
       "3              0.28         0.56      0.075       0.58      9.8\n",
       "4              0.70         0.00      0.076       0.56      9.4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X= df[['fixed acidity','volatile acidity','citric acid',\n",
    "#       'residual sugar','chlorides','free sulfur dioxide',\n",
    "#       'total sulfur dioxide','density','pH','sulphates','alcohol']]\n",
    "X= df[['volatile acidity','citric acid','chlorides','sulphates','alcohol']]\n",
    "y = df['qualityBool']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.30, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>0.470</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.76</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>0.820</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.53</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>0.290</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.84</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>0.645</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.42</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>0.450</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.63</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      volatile acidity  citric acid  chlorides  sulphates  alcohol\n",
       "1109             0.470         0.43      0.171       0.76     10.8\n",
       "1032             0.820         0.00      0.095       0.53      9.6\n",
       "1002             0.290         0.33      0.063       0.84     11.7\n",
       "487              0.645         0.36      0.053       0.42     10.0\n",
       "979              0.450         0.49      0.075       0.63     10.4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1119,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(480,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.7583333333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\casiljan\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2053: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\casiljan\\AppData\\Local\\Continuum\\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",
      "C:\\Users\\casiljan\\AppData\\Local\\Continuum\\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",
      "C:\\Users\\casiljan\\AppData\\Local\\Continuum\\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": [
    "#run alg\n",
    "svm= SVC()\n",
    "model = CalibratedClassifierCV(svm).fit(X_train, Y_train)\n",
    "# evaluate the test set\n",
    "accuracy = model.score(X_test, Y_test)\n",
    "# log accuracy which is a single numerical value\n",
    "print (\"Accuracy is {}\".format(accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.75\n"
     ]
    }
   ],
   "source": [
    "lrmodel = LogisticRegression(random_state=0, solver='lbfgs').fit(X_train, Y_train)\n",
    "# evaluate the test set\n",
    "lraccuracy = lrmodel.score(X_test, Y_test)\n",
    "# log accuracy which is a single numerical value\n",
    "print (\"Accuracy is {}\".format(lraccuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.7604166666666666\n"
     ]
    }
   ],
   "source": [
    "dtmodel =DecisionTreeClassifier(random_state=0).fit(X_train, Y_train)\n",
    "# evaluate the test set\n",
    "dtaccuracy = dtmodel.score(X_test, Y_test)\n",
    "# log accuracy which is a single numerical value\n",
    "print (\"Accuracy is {}\".format(dtaccuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate and log precision, recall, and thresholds, which are list of numerical values\n",
    "y_scores = model.predict_proba(X_test)\n",
    "precision, recall, thresholds = precision_recall_curve(Y_test, y_scores[:,1],pos_label=1)\n",
    "\n",
    "# serialize the model on disk in the 'outputs' folder\n",
    "#print (\"Export the model to \" + model_name)\n",
    "#with open('cat_insert_procedure_model.pkl', 'wb') as fid:\n",
    "#    joblib.dump(model, fid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print('accuracy: '+ str(accuracy)+' precision: '+ str(precision)+' recall: '+ str(recall)+' thresholds: '+ str(thresholds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_item = np.array(X_test.iloc[0]).reshape(-1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Result should be: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Result should be: {}\".format(Y_test.iloc[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic model prediction is 1\n"
     ]
    }
   ],
   "source": [
    "#test lr individual result\n",
    "lrresult = lrmodel.predict(x_test_item)\n",
    "print(\"Logistic model prediction is {}\".format(lrresult[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree model prediction is 1\n"
     ]
    }
   ],
   "source": [
    "#test dt individual result\n",
    "dtresult = dtmodel.predict(x_test_item)\n",
    "print(\"Decision Tree model prediction is {}\".format(dtresult[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC model prediction is 1\n"
     ]
    }
   ],
   "source": [
    "#test SVC individual result\n",
    "result = model.predict(x_test_item)\n",
    "print(\"SVC model prediction is {}\".format(result[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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