DataPreProcessing1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "dataset = []\n",
    "dataset1 = pd.read_excel(\"dataset/DATASET_1.xlsx\")\n",
    "dataset2 = pd.read_excel(\"dataset/DATASET_2.xlsx\")\n",
    "dataset3 = pd.read_excel(\"dataset/DATASET_3.xlsx\")\n",
    "dataset4 = pd.read_excel(\"dataset/DATASET_4.xlsx\")\n",
    "dataset5 = pd.read_excel(\"dataset/DATASET_5.xlsx\")\n",
    "dataset6 = pd.read_excel(\"dataset/DATASET_6.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140, 11)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset3.rename(columns={'Place of Interest':'Place of interest', 'Leisure time Activity':'Leisure time activity',\n",
    "                     'Future Plan':'Future plan'}, inplace=True)\n",
    "dataset1.columns = ['ID','Current Place','Latitude cu_pl','Longitude cu_pl','Latitude pl_in','Longitude pl_in',\n",
    "                    'Place of interest','Food you like most','Leisure time activity',\n",
    "                    'Future plan','Typically your days are started at:']\n",
    "dataset2.columns = ['ID','Current Place','Latitude cu_pl','Longitude cu_pl','Latitude pl_in','Longitude pl_in',\n",
    "                    'Place of interest','Food you like most','Leisure time activity',\n",
    "                    'Future plan','Typically your days are started at:']\n",
    "dataset3.columns = ['ID','Current Place','Latitude cu_pl','Longitude cu_pl','Latitude pl_in','Longitude pl_in',\n",
    "                    'Place of interest','Food you like most','Leisure time activity',\n",
    "                    'Future plan','Typically your days are started at:']\n",
    "dataset4.columns = ['ID','Current Place','Latitude cu_pl','Longitude cu_pl','Latitude pl_in','Longitude pl_in',\n",
    "                    'Place of interest','Food you like most','Leisure time activity',\n",
    "                    'Future plan','Typically your days are started at:']\n",
    "dataset5.columns = ['ID','Current Place','Latitude cu_pl','Longitude cu_pl','Latitude pl_in','Longitude pl_in',\n",
    "                    'Place of interest','Food you like most','Leisure time activity',\n",
    "                    'Future plan','Typically your days are started at:']\n",
    "dataset6.columns = ['ID','Current Place','Latitude cu_pl','Longitude cu_pl','Latitude pl_in','Longitude pl_in',\n",
    "                    'Place of interest','Food you like most','Leisure time activity',\n",
    "                    'Future plan','Typically your days are started at:']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 81)"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_extend = pd.read_excel(\"dataset/EXTENDED_1.xlsx\")\n",
    "dataset_extend\n",
    "dataset_extend.columns=['Timestamp', 'Score', 'ID', 'Gender', 'Height', 'Weight', 'CGPA',\n",
    "       'Daily spent time in social media (hour)', 'Hometown',\n",
    "       'How many days in a week you feel more energetic',\n",
    "       'Favorite transport mode', 'Favorite food names',\n",
    "       'Expected food price of that favorite food',\n",
    "       'Month cost for that particular food',\n",
    "       'What do you like home delivery or eat in the restaurant',\n",
    "       'University travel cost monthly', 'Hobby', 'Travel package you prefer',\n",
    "       'Sickness-frequency in a month',\n",
    "       'Favorite food in summer/winter/rainy season', 'Food flavor',\n",
    "       'Hangout location(Spend most of the time)', 'Typically you go out with',\n",
    "       'Travel budget', 'favourite restaurant', 'Unnamed: 25',\n",
    "       'Restaurant location', 'Live with family(y/n)',\n",
    "       'Are you happy with current situation?',\n",
    "       'Number of programming hours spent per day', 'Sleep duration',\n",
    "       'Do you start your day by planning(y/n)',\n",
    "       'Do you have online shopping experience (y/n)',\n",
    "       'Do you use products for grooming(y/n)',\n",
    "       'Favourite food in rainy season', 'Prefers(food_quality/environment)',\n",
    "       'No of time home delivery over a month(Number)',\n",
    "       'No of time goes to restaurant over a month( n umber)',\n",
    "       'Pickup location of yours for bus', 'Pickup time of yours for bus',\n",
    "       'Your bank name', 'Preferred shopping area',\n",
    "       'Monthly frequencies of shopping',\n",
    "       'Do you prefer to play in Gaming Zone ?', 'Are you fashion active',\n",
    "       'Which colour do you prefer(red/yellow)?',\n",
    "       'Which cloths do you choose to wear(urban/linen shirt and trousers/jeans&shirt)?',\n",
    "       \"Do you feel low seeing other people's achievement on social media\",\n",
    "       'Recently how many of your family members/neighbours affected by dengue',\n",
    "       'do you find bargaining pleasing?',\n",
    "       'Actual motive behind your future goal?',\n",
    "       'Would you still pursue your goal if the motive behind it is fulfilled in another way?',\n",
    "       'Do you think it is a good idea to go to a well known shop just for the sake of social status?why?',\n",
    "       'How much do you earn monthly?', 'Wake up time?',\n",
    "       'When is your class starting time', 'When is your class ending time',\n",
    "       'What do you love most?', 'Are you satisfied with your country’s law?',\n",
    "       'Which law do you prefer more?',\n",
    "       'What’s your view about law enforcement groups?',\n",
    "       'Are you eager to harm yourself to defend your beliefs?',\n",
    "       'How frequently you visit a doctor within a year\"',\n",
    "       'How many people do you follow on social media?',\n",
    "       'How much time you spend in university without academic curriculum(in hour)?',\n",
    "       'Are you rich in vocabulary?',\n",
    "       'Class note taking tendency(High,Low,Medium)',\n",
    "       'Theory class attendance(High,Low,Medium)', 'Theory class interest(h,l,m)',\n",
    "       'Lab interest(h,l,m)',\n",
    "       'Preference(class lectures cl or online tutorials)'\n",
    "]\n",
    "dataset_merge = pd.merge(data_concate,dataset_extend)\n",
    "dataset_merge.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_merge.to_excel(\"mergedata1.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Recently how many of your family members/neighbours affected by dengue</th>\n",
       "      <th>Current Place</th>\n",
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      "text/plain": [
       "            ID  \\\n",
       "0  151340976.0   \n",
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       "\n",
       "  Recently how many of your family members/neighbours affected by dengue  \\\n",
       "0                                               none                       \n",
       "1                                                  1                       \n",
       "2                                               none                       \n",
       "3                                               none                       \n",
       "4                                                  0                       \n",
       "\n",
       "  Current Place  \n",
       "0         Goran  \n",
       "1      Banasree  \n",
       "2       Niketon  \n",
       "3      Shaymoli  \n",
       "4      Bangshal  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coulum_name_for_dataset = ['ID','Recently how many of your family members/neighbours affected by dengue','Current Place' ]\n",
    "new_dataset = dataset_merge[coulum_name_for_dataset]\n",
    "new_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Recently how many of your family members/neighbours affected by dengue</th>\n",
       "      <th>Current Place</th>\n",
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       "      <td>0</td>\n",
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       "            ID  \\\n",
       "0  151340976.0   \n",
       "1  151340977.0   \n",
       "2  151340980.0   \n",
       "3  151340981.0   \n",
       "4  151340982.0   \n",
       "\n",
       "  Recently how many of your family members/neighbours affected by dengue  \\\n",
       "0                                                  0                       \n",
       "1                                                  1                       \n",
       "2                                                  0                       \n",
       "3                                                  0                       \n",
       "4                                                  0                       \n",
       "\n",
       "  Current Place  \n",
       "0         Goran  \n",
       "1      Banasree  \n",
       "2       Niketon  \n",
       "3      Shaymoli  \n",
       "4      Bangshal  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "new_dataset_update['Recently how many of your family members/neighbours affected by dengue']=new_dataset_update['Recently how many of your family members/neighbours affected by dengue'].replace({'none': '0' \n",
    "                        \n",
    " \n",
    "                                                 })\n",
    "new_dataset_update.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dataset_update.to_excel(\"Dataset2.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import operator\n",
    "from collections import Counter\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score  \n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import metrics\n",
    "from sklearn.svm import SVC,SVR\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Recently how many of your family members/neighbours affected by dengue</th>\n",
       "      <th>Current Place</th>\n",
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       "      <td>Niketon</td>\n",
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      "text/plain": [
       "          ID  \\\n",
       "0  151340976   \n",
       "1  151340977   \n",
       "2  151340980   \n",
       "3  151340981   \n",
       "4  151340982   \n",
       "\n",
       "  Recently how many of your family members/neighbours affected by dengue  \\\n",
       "0                                                  0                       \n",
       "1                                                  1                       \n",
       "2                                                  0                       \n",
       "3                                                  0                       \n",
       "4                                                  0                       \n",
       "\n",
       "  Current Place  \n",
       "0         Goran  \n",
       "1      Banasree  \n",
       "2       Niketon  \n",
       "3      Shaymoli  \n",
       "4      Bangshal  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_new = pd.read_excel(\"Dataset2.xlsx\")\n",
    "columns = ['ID','Recently how many of your family members/neighbours affected by dengue','Current Place']\n",
    "dataset_new_update = dataset_new[columns]\n",
    "dataset_new_update.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
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      "text/plain": [
       "          ID  \\\n",
       "0  151340976   \n",
       "1  151340977   \n",
       "2  151340980   \n",
       "3  151340981   \n",
       "4  151340982   \n",
       "\n",
       "   Recently how many of your family members/neighbours affected by dengue  \\\n",
       "0                                                  1                        \n",
       "1                                                  2                        \n",
       "2                                                  1                        \n",
       "3                                                  1                        \n",
       "4                                                  3                        \n",
       "\n",
       "   Current Place  \n",
       "0              1  \n",
       "1              2  \n",
       "2              3  \n",
       "3              4  \n",
       "4              5  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_new_update[\"Current Place\"] = dataset_new_update[\"Current Place\"].astype('category')\n",
    "dataset_new_update[\"Current Place\"] = pd.factorize(dataset_new_update[\"Current Place\"])[0] + 1\n",
    "dataset_new_update[\"Recently how many of your family members/neighbours affected by dengue\"] = pd.factorize(dataset_new_update[\"Recently how many of your family members/neighbours affected by dengue\"])[0] + 1\n",
    "dataset_new_update.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a846683dd8>"
      ]
     },
     "execution_count": 14,
     "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": [
    "dataset_new_update.boxplot(by=['Current Place'], column=['Recently how many of your family members/neighbours affected by dengue'], grid= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "ax = sns.countplot(x=\"Current Place\", data=dataset_new_update)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with 3 neighbours:  0.08695652173913043\n"
     ]
    }
   ],
   "source": [
    "D=dataset_new_update.values\n",
    "X = D[:,:-1]\n",
    "y = D[:,-1]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.22)\n",
    "clf = KNeighborsClassifier(n_neighbors=3)\n",
    "clf.fit(X_train.astype(int), Y_train)\n",
    "yPred=clf.predict(X_test.astype(int))\n",
    "acc=accuracy_score(Y_test.astype(int), yPred.astype(int))\n",
    "print('Accuracy with 3 neighbours: ',acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_conf_mat(lTrue, lPred, title):\n",
    "    \"\"\" A function for plotting the confusion matrix given true and predicted labels.\"\"\"\n",
    "    cm = confusion_matrix(lTrue.astype(int), lPred.astype(int))\n",
    "    print(cm)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conf_mat(Y_test, yPred, 'K=3')"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}