Restaurant_Reviews.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Restaurant_Reviews.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shantanu9326/Data-Science-Portfolio/blob/master/Restaurant_Reviews.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqi_lFJM4K7B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xt5ga3Rk4er4",
        "colab_type": "code",
        "outputId": "b1d1415a-3aee-4b52-97a1-0aebfe4fd7e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Running or Importing .py Files with Google Colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BB4vkylz4pmI",
        "colab_type": "code",
        "outputId": "43f1914e-435d-4c08-fb88-a2fa70f31484",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        }
      },
      "source": [
        "# Importing the dataset\n",
        "dataset = pd.read_csv('/content/drive/My Drive/app/Restaurant_Reviews.tsv', delimiter = '\\t', quoting = 3)\n",
        "dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Review</th>\n",
              "      <th>Liked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Wow... Loved this place.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Crust is not good.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Not tasty and the texture was just nasty.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Stopped by during the late May bank holiday of...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The selection on the menu was great and so wer...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Now I am getting angry and I want my damn pho.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Honeslty it didn't taste THAT fresh.)</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>The potatoes were like rubber and you could te...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>The fries were great too.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>A great touch.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Service was very prompt.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Would not go back.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>The cashier had no care what so ever on what I...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>I tried the Cape Cod ravoli, chicken, with cra...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>I was disgusted because I was pretty sure that...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>I was shocked because no signs indicate cash o...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Highly recommended.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Waitress was a little slow in service.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>This place is not worth your time, let alone V...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>did not like at all.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>The Burrittos Blah!</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>The food, amazing.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Service is also cute.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>I could care less... The interior is just beau...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>So they performed.</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>That's right....the red velvet cake.....ohhh t...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>- They never brought a salad we asked for.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>This hole in the wall has great Mexican street...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Took an hour to get our food only 4 tables in ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>The worst was the salmon sashimi.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>970</th>\n",
              "      <td>I immediately said I wanted to talk to the man...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>971</th>\n",
              "      <td>The ambiance isn't much better.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>972</th>\n",
              "      <td>Unfortunately, it only set us up for disapppoi...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>973</th>\n",
              "      <td>The food wasn't good.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>974</th>\n",
              "      <td>Your servers suck, wait, correction, our serve...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>975</th>\n",
              "      <td>What happened next was pretty....off putting.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>976</th>\n",
              "      <td>too bad cause I know it's family owned, I real...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>977</th>\n",
              "      <td>Overpriced for what you are getting.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>978</th>\n",
              "      <td>I vomited in the bathroom mid lunch.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>979</th>\n",
              "      <td>I kept looking at the time and it had soon bec...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>980</th>\n",
              "      <td>I have been to very few places to eat that und...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>981</th>\n",
              "      <td>We started with the tuna sashimi which was bro...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>982</th>\n",
              "      <td>Food was below average.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>983</th>\n",
              "      <td>It sure does beat the nachos at the movies but...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>984</th>\n",
              "      <td>All in all, Ha Long Bay was a bit of a flop.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>985</th>\n",
              "      <td>The problem I have is that they charge $11.99 ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>986</th>\n",
              "      <td>Shrimp- When I unwrapped it (I live only 1/2 a...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>987</th>\n",
              "      <td>It lacked flavor, seemed undercooked, and dry.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>988</th>\n",
              "      <td>It really is impressive that the place hasn't ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>989</th>\n",
              "      <td>I would avoid this place if you are staying in...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>990</th>\n",
              "      <td>The refried beans that came with my meal were ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>991</th>\n",
              "      <td>Spend your money and time some place else.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>992</th>\n",
              "      <td>A lady at the table next to us found a live gr...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>993</th>\n",
              "      <td>the presentation of the food was awful.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994</th>\n",
              "      <td>I can't tell you how disappointed I was.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>I think food should have flavor and texture an...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>Appetite instantly gone.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>Overall I was not impressed and would not go b...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>The whole experience was underwhelming, and I ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>Then, as if I hadn't wasted enough of my life ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                Review  Liked\n",
              "0                             Wow... Loved this place.      1\n",
              "1                                   Crust is not good.      0\n",
              "2            Not tasty and the texture was just nasty.      0\n",
              "3    Stopped by during the late May bank holiday of...      1\n",
              "4    The selection on the menu was great and so wer...      1\n",
              "5       Now I am getting angry and I want my damn pho.      0\n",
              "6                Honeslty it didn't taste THAT fresh.)      0\n",
              "7    The potatoes were like rubber and you could te...      0\n",
              "8                            The fries were great too.      1\n",
              "9                                       A great touch.      1\n",
              "10                            Service was very prompt.      1\n",
              "11                                  Would not go back.      0\n",
              "12   The cashier had no care what so ever on what I...      0\n",
              "13   I tried the Cape Cod ravoli, chicken, with cra...      1\n",
              "14   I was disgusted because I was pretty sure that...      0\n",
              "15   I was shocked because no signs indicate cash o...      0\n",
              "16                                 Highly recommended.      1\n",
              "17              Waitress was a little slow in service.      0\n",
              "18   This place is not worth your time, let alone V...      0\n",
              "19                                did not like at all.      0\n",
              "20                                 The Burrittos Blah!      0\n",
              "21                                  The food, amazing.      1\n",
              "22                               Service is also cute.      1\n",
              "23   I could care less... The interior is just beau...      1\n",
              "24                                  So they performed.      1\n",
              "25   That's right....the red velvet cake.....ohhh t...      1\n",
              "26          - They never brought a salad we asked for.      0\n",
              "27   This hole in the wall has great Mexican street...      1\n",
              "28   Took an hour to get our food only 4 tables in ...      0\n",
              "29                   The worst was the salmon sashimi.      0\n",
              "..                                                 ...    ...\n",
              "970  I immediately said I wanted to talk to the man...      0\n",
              "971                    The ambiance isn't much better.      0\n",
              "972  Unfortunately, it only set us up for disapppoi...      0\n",
              "973                              The food wasn't good.      0\n",
              "974  Your servers suck, wait, correction, our serve...      0\n",
              "975      What happened next was pretty....off putting.      0\n",
              "976  too bad cause I know it's family owned, I real...      0\n",
              "977               Overpriced for what you are getting.      0\n",
              "978               I vomited in the bathroom mid lunch.      0\n",
              "979  I kept looking at the time and it had soon bec...      0\n",
              "980  I have been to very few places to eat that und...      0\n",
              "981  We started with the tuna sashimi which was bro...      0\n",
              "982                            Food was below average.      0\n",
              "983  It sure does beat the nachos at the movies but...      0\n",
              "984       All in all, Ha Long Bay was a bit of a flop.      0\n",
              "985  The problem I have is that they charge $11.99 ...      0\n",
              "986  Shrimp- When I unwrapped it (I live only 1/2 a...      0\n",
              "987     It lacked flavor, seemed undercooked, and dry.      0\n",
              "988  It really is impressive that the place hasn't ...      0\n",
              "989  I would avoid this place if you are staying in...      0\n",
              "990  The refried beans that came with my meal were ...      0\n",
              "991         Spend your money and time some place else.      0\n",
              "992  A lady at the table next to us found a live gr...      0\n",
              "993            the presentation of the food was awful.      0\n",
              "994           I can't tell you how disappointed I was.      0\n",
              "995  I think food should have flavor and texture an...      0\n",
              "996                           Appetite instantly gone.      0\n",
              "997  Overall I was not impressed and would not go b...      0\n",
              "998  The whole experience was underwhelming, and I ...      0\n",
              "999  Then, as if I hadn't wasted enough of my life ...      0\n",
              "\n",
              "[1000 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQO71GfA6aen",
        "colab_type": "code",
        "outputId": "b2690359-dced-47fd-89a9-edcbe49a97a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Cleaning the texts\n",
        "# Importing Libraries\n",
        "import re #Regular Expression\n",
        "import nltk #Natural Language Library\n",
        "nltk.download('stopwords') \n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer #Porter Stemmer\n",
        "corpus = []\n",
        "for i in range(0, 1000):\n",
        "    review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])\n",
        "    review = review.lower() #Lowercase\n",
        "    review = review.split()\n",
        "    ps = PorterStemmer() #Apply Porter Stemmer\n",
        "    review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]\n",
        "    review = ' '.join(review)\n",
        "    corpus.append(review)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oEPo5JMK6kVI",
        "colab_type": "code",
        "outputId": "f9407d18-c705-44eb-ab44-93a74d3b9bfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        }
      },
      "source": [
        "# Creating the Bag of Words model\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(max_features = 1500)\n",
        "X = cv.fit_transform(corpus).toarray()\n",
        "y = dataset.iloc[:, 1].values\n",
        "print(X)\n",
        "print('\\n')\n",
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
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            " [0 0 0 ... 0 0 0]]\n",
            "\n",
            "\n",
            "[1 0 0 1 1 0 0 0 1 1 1 0 0 1 0 0 1 0 0 0 0 1 1 1 1 1 0 1 0 0 1 0 1 0 1 1 1\n",
            " 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 1 0 1 1 0 0 1 0 0 1 1 1 1 1 1 1 0 1 1 1 0 0\n",
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            " 0 0 1 1 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 0 0 0 1\n",
            " 1 0 1 1 1 1 1 0 1 0 1 0 0 1 1 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 0 1 0 1 0 0\n",
            " 0 0 0 1 1 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 0 0 0 0 1 1 1 0 1 0 1 0 1 1 1 0 1\n",
            " 0 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 1 1\n",
            " 0 1 0 1 1 0 0 0 1 0 0 0 1 1 1 0 1 0 1 0 0 1 1 1 0 0 1 1 1 1 1 1 0 0 0 1 1\n",
            " 0 1 1 0 0 1 0 0 1 1 1 0 1 1 1 1 1 0 0 1 0 1 1 0 1 1 1 0 1 1 0 1 0 0 1 1 1\n",
            " 0 0 1 1 0 1 0 1 0 0 0 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 0 1\n",
            " 1 1 0 1 1 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 0 1 1 0\n",
            " 1 1 0 0 1 1 0 1 0 0 0 0 1 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 1 0\n",
            " 1 0 0 1 1 1 0 0 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 0 1\n",
            " 0 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 1 1 1 1 0 0\n",
            " 0 0 0 1 1 0 0 1 1 1 0 0 1 0 0 0 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1\n",
            " 1 0 0 1 0 1 1 0 1 0 1 1 1 1 1 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0\n",
            " 1 1 1 0 1 0 1 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 0 0 1 0 1 1 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 1\n",
            " 1 1 0 1 0 1 0 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1\n",
            " 1 0 0 0 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 0 1 0 0 0 1 0 1 1 1 1 0 1 0 0 1 0 1\n",
            " 0 0 0 1 1 1 0 0 1 0 1 1 1 1 0 0 1 0 1 1 1 0 1 0 1 0 1 1 0 1 0 1 1 0 0 0 0\n",
            " 1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0 1 0 0 1\n",
            " 0 1 1 0 0 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0\n",
            " 1 0 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 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 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 0 0 0 0 0 0 0 0\n",
            " 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hj1LYjIb7CRW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfEUQwFg7OeV",
        "colab_type": "code",
        "outputId": "20918284-db65-4cde-98e3-3c6e9689f687",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Fitting Naive Bayes to the Training set\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKN-BUrd7WiZ",
        "colab_type": "code",
        "outputId": "31a9075c-dd07-4e5b-cd96-3962247d207e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "y_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
              "       1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n",
              "       0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
              "       1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
              "       0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENJvMYhR7bX4",
        "colab_type": "code",
        "outputId": "2bcfe060-35b6-413c-b2e1-4340eb2d7706",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        }
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[55 42]\n",
            " [12 91]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f41b657e7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ARlje4BF4AAAzbJ/SJPAAAEbY/mgxzvAAAK5AhwcAMMPuBo8ODwDgDnR4AAAjbD/DI/AA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\nQOABAFyBwAMAuAKBBwBwhf8H4KamArUOFK0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f41b653c4a8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1LitItC2V8c",
        "colab_type": "code",
        "outputId": "4b8d3db4-0799-4949-e644-56edfcde0517",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(f'Accuracy:{accuracy_score(y_test, y_pred)}')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy:0.73\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RAxv_dL7fwR",
        "colab_type": "code",
        "outputId": "5cfb9cac-d67d-4487-8b07-d22917cfbf15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import classification_report\n",
        "cr = classification_report(y_test, y_pred,target_names=['Negative', 'Positive'])\n",
        "print(cr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             precision    recall  f1-score   support\n",
            "\n",
            "   Negative       0.65      0.90      0.76        97\n",
            "   Positive       0.85      0.55      0.67       103\n",
            "\n",
            "avg / total       0.76      0.72      0.71       200\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JDuwhNz7xTz",
        "colab_type": "code",
        "outputId": "e5a3ab60-e3d5-4418-811f-c129060f723a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "source": [
        "# Fitting Logistic Regression to the Training set\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "print(y_pred)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True)\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(f'Accuracy:{accuracy_score(y_test, y_pred)}')\n",
        "\n",
        "# Making the Classification Report\n",
        "from sklearn.metrics import classification_report\n",
        "cr = classification_report(y_test, y_pred,target_names=['Negative', 'Positive'])\n",
        "print(cr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 1 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 0 0 1 0 0\n",
            " 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 1 1 0 0\n",
            " 0 0 0 1 0 1 1 1 1 0 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 1 0 0 0\n",
            " 1 0 1 0 0 1 1 1 1 0 1 1 1 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0\n",
            " 0 0 0 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0\n",
            " 0 0 1 0 1 1 0 0 0 1 0 1 1 0 0]\n",
            "[[76 21]\n",
            " [37 66]]\n",
            "Accuracy:0.71\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "   Negative       0.67      0.78      0.72        97\n",
            "   Positive       0.76      0.64      0.69       103\n",
            "\n",
            "avg / total       0.72      0.71      0.71       200\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f41b553b588>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gipligLz8Wit",
        "colab_type": "code",
        "outputId": "72f690f8-82ff-4ce6-b890-c8e90aaf253f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "source": [
        "# Fitting K-NN to the Training set\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "print(y_pred)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True)\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(f'Accuracy:{accuracy_score(y_test, y_pred)}')\n",
        "\n",
        "# Making the Classification Report\n",
        "from sklearn.metrics import classification_report\n",
        "cr = classification_report(y_test, y_pred,target_names=['Negative', 'Positive'])\n",
        "print(cr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 1 0 1 1 1 0 0 0 0 1 1 0 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 1 0 0 1 0 1 0 0\n",
            " 0 0 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 1 1\n",
            " 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 1 0 0 0\n",
            " 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0\n",
            " 0 1 0 0 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0 0 1\n",
            " 0 0 0 0 1 0 1 0 0 1 0 1 1 0 0]\n",
            "[[74 23]\n",
            " [55 48]]\n",
            "Accuracy:0.61\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "   Negative       0.57      0.76      0.65        97\n",
            "   Positive       0.68      0.47      0.55       103\n",
            "\n",
            "avg / total       0.63      0.61      0.60       200\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f41b553b860>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BoB7C2f8noU",
        "colab_type": "code",
        "outputId": "1a7e0fd3-4564-40e7-8720-24bfbc6e2621",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "source": [
        "# Fitting Kernel SVM to the Training set\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'linear', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "print(y_pred)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True)\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(f'Accuracy:{accuracy_score(y_test, y_pred)}')\n",
        "\n",
        "# Making the Classification Report\n",
        "from sklearn.metrics import classification_report\n",
        "cr = classification_report(y_test, y_pred,target_names=['Negative', 'Positive'])\n",
        "print(cr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 0 0 1 0 0\n",
            " 0 0 1 1 1 1 0 0 0 1 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0\n",
            " 0 1 1 1 0 1 1 1 1 0 1 1 1 0 1 1 0 1 0 0 1 0 0 0 0 1 0 1 1 1 0 0 1 1 0 0 0\n",
            " 1 0 1 0 0 1 1 1 1 0 1 1 1 0 0 1 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0\n",
            " 0 1 0 0 1 1 1 0 1 0 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0\n",
            " 0 0 1 0 1 1 0 0 0 1 0 1 1 0 1]\n",
            "[[74 23]\n",
            " [33 70]]\n",
            "Accuracy:0.72\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "   Negative       0.69      0.76      0.73        97\n",
            "   Positive       0.75      0.68      0.71       103\n",
            "\n",
            "avg / total       0.72      0.72      0.72       200\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f41b51b36a0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ug0pvj3B8-4f",
        "colab_type": "code",
        "outputId": "edc1bc45-ce89-4d4e-8aef-5b20b302000a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "source": [
        "# Fitting Decision Tree Classification to the Training set\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "print(y_pred)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True)\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(f'Accuracy:{accuracy_score(y_test, y_pred)}')\n",
        "\n",
        "# Making the Classification Report\n",
        "from sklearn.metrics import classification_report\n",
        "cr = classification_report(y_test, y_pred,target_names=['Negative', 'Positive'])\n",
        "print(cr)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 1 0 1 0 1 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 1 1 0 1 1 0 0\n",
            " 0 0 1 1 1 1 0 0 0 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0\n",
            " 0 0 0 1 0 1 1 0 1 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0\n",
            " 1 0 0 0 1 1 1 1 1 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 1 0 1 1 0 0 1 1 0 0 0 1 1\n",
            " 0 1 0 0 1 1 0 0 1 0 1 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 1 0 1 1 1 0 0 0\n",
            " 0 1 1 0 1 1 1 0 0 1 0 1 1 0 0]\n",
            "[[74 23]\n",
            " [35 68]]\n",
            "Accuracy:0.71\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "   Negative       0.68      0.76      0.72        97\n",
            "   Positive       0.75      0.66      0.70       103\n",
            "\n",
            "avg / total       0.71      0.71      0.71       200\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f41b51c1828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIbK2TI09OkP",
        "colab_type": "code",
        "outputId": "e5637f7d-c368-41cc-c787-6deb05d195af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        }
      },
      "source": [
        "# Fitting Random Forest Classification to the Training set\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "print(y_pred)\n",
        "\n",
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "sns.heatmap(cm,annot=True)\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(f'Accuracy:{accuracy_score(y_test, y_pred)}')\n",
        "\n",
        "# Making the Classification Report\n",
        "from sklearn.metrics import classification_report\n",
        "cr = classification_report(y_test, y_pred,target_names=['Negative', 'Positive'])\n",
        "print(cr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 1 0 0 1 1 1 1 1 1 1 0 0 0 1 0 1 1 0 0 1 0 1 1 0 0 0 0 1 1 0 0\n",
            " 0 0 1 1 0 0 0 0 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0\n",
            " 0 0 0 1 0 1 0 1 1 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 0 0 0\n",
            " 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0\n",
            " 0 0 0 0 1 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0\n",
            " 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0]\n",
            "[[87 10]\n",
            " [46 57]]\n",
            "Accuracy:0.72\n",
            "             precision    recall  f1-score   support\n",
            "\n",
            "   Negative       0.65      0.90      0.76        97\n",
            "   Positive       0.85      0.55      0.67       103\n",
            "\n",
            "avg / total       0.76      0.72      0.71       200\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f41b5163748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ie1Y618Jn6k0",
        "colab_type": "text"
      },
      "source": [
        "#Conclusion\n",
        "\n",
        "Naive Bayes has the highest precision, while Decision Tree and Random Forest has the highest recall. F1 score for the Decision Tree & Random Forest are higher because of the high recall. However, in most cases having high precision is preferable to high recall, because the consequences of many false negatives is usually worse (in this case giving many restaurants a bad reputation by mistake).\n",
        "\n",
        "Here are all my results:\n",
        "\n",
        "####Logistic regression\n",
        "Accuracy: 0.71\n",
        "Precision: 0.67\n",
        "Recall: 0.78\n",
        "F1 score: 0.72\n",
        "\n",
        "####K-Nearest Neighbor\n",
        "Accuracy: 0.61\n",
        "Precision: 0.57\n",
        "Recall: 0.76\n",
        "F1 score: 0.65\n",
        "\n",
        "####SVM\n",
        "Accuracy: 0.72\n",
        "Precision: 0.69\n",
        "Recall: 0.76\n",
        "F1 score: 0.73\n",
        "\n",
        "####Kernel SVM \n",
        "Accuracy: 0.72\n",
        "Precision: 0.72\n",
        "Recall: 0.72\n",
        "F1 score: 0.65\n",
        "\n",
        "####Naive Bayes\n",
        "Accuracy: 0.73\n",
        "Precision: 0.82\n",
        "Recall: 0.57\n",
        "F1 score: 0.67\n",
        "\n",
        "####Decision Tree\n",
        "Accuracy: 0.71\n",
        "Precision: 0.68\n",
        "Recall: 0.76\n",
        "F1 score: 0.72\n",
        "\n",
        "####Random Forest\n",
        "Accuracy: 0.72\n",
        "Precision: 0.65\n",
        "Recall: 0.90\n",
        "F1 score: 0.76"
      ]
    }
  ]
}