Machine Leaning Algorithm.ipynb

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  "metadata": {
    "colab": {
      "name": "MLAlgo(Colab3).ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEeEv1wIzBFg"
      },
      "source": [
        "## **Import csv file (Output from vader)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pmGKCr9_xW1D"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "import nltk\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "8CD9i2Ye9i6k",
        "outputId": "ed362047-c7f0-4eb3-b7b2-255bd5927bd5"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1ae03f7d-2635-40a6-b79f-03a8d4f258b4\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1ae03f7d-2635-40a6-b79f-03a8d4f258b4\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving dataset2.0.csv to dataset2.0.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tq_nUo59Iguq"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from scipy import stats\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import mean_squared_error,mean_absolute_error, make_scorer,classification_report,confusion_matrix,accuracy_score,roc_auc_score,roc_curve\n",
        "from sklearn.model_selection import train_test_split,cross_val_score,KFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn import svm\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import warnings \n",
        "warnings.filterwarnings(\"ignore\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WtB61uwxOt9"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('dataset2.0.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Z0sboIBtwc2D",
        "outputId": "39c9eee2-6e7f-4a32-cdf5-28913aaaf5ac"
      },
      "source": [
        "#imported dataset\n",
        "df"
      ],
      "execution_count": null,
      "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",
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              "\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>Unnamed: 0</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Analysis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Tata Posts 38% Growth In Nov 2021 – Nexon, Al...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Tata Posts 38% Growth In Nov 2021 – Nexon, Alt...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Hi friends I am a Giveway hunter and i will op...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Look for other dealer Tata Safari is avail...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>11,000 km in 5 months with my Tata Safari: Own...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1817</th>\n",
              "      <td>1817</td>\n",
              "      <td>At Pothens Mahindra, Kollam, we delivered the...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1818</th>\n",
              "      <td>1818</td>\n",
              "      <td>Alexa in XUV700 says MG Hector is better😳😛 #bu...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1819</th>\n",
              "      <td>1819</td>\n",
              "      <td>So even XUV700 agrees MG Hector is a better ca...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1820</th>\n",
              "      <td>1820</td>\n",
              "      <td>The celebrations begin early as we deliver 70...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1821</th>\n",
              "      <td>1821</td>\n",
              "      <td>The celebrations begin early as we deliver 70...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1822 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0                                              tweet  Analysis\n",
              "0              0   Tata Posts 38% Growth In Nov 2021 – Nexon, Al...         1\n",
              "1              1  Tata Posts 38% Growth In Nov 2021 – Nexon, Alt...         1\n",
              "2              2  Hi friends I am a Giveway hunter and i will op...         1\n",
              "3              3      Look for other dealer Tata Safari is avail...         1\n",
              "4              4  11,000 km in 5 months with my Tata Safari: Own...         0\n",
              "...          ...                                                ...       ...\n",
              "1817        1817   At Pothens Mahindra, Kollam, we delivered the...         1\n",
              "1818        1818  Alexa in XUV700 says MG Hector is better😳😛 #bu...         1\n",
              "1819        1819  So even XUV700 agrees MG Hector is a better ca...         1\n",
              "1820        1820   The celebrations begin early as we deliver 70...         0\n",
              "1821        1821   The celebrations begin early as we deliver 70...         0\n",
              "\n",
              "[1822 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69f6NsNusJVk"
      },
      "source": [
        "del df['Unnamed: 0']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "SeggKvP5sSDp",
        "outputId": "d48e8d17-11f5-4dc1-ba48-f374dad4bf04"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "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>tweet</th>\n",
              "      <th>Analysis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tata Posts 38% Growth In Nov 2021 – Nexon, Al...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Tata Posts 38% Growth In Nov 2021 – Nexon, Alt...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hi friends I am a Giveway hunter and i will op...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Look for other dealer Tata Safari is avail...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11,000 km in 5 months with my Tata Safari: Own...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1817</th>\n",
              "      <td>At Pothens Mahindra, Kollam, we delivered the...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1818</th>\n",
              "      <td>Alexa in XUV700 says MG Hector is better😳😛 #bu...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1819</th>\n",
              "      <td>So even XUV700 agrees MG Hector is a better ca...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1820</th>\n",
              "      <td>The celebrations begin early as we deliver 70...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1821</th>\n",
              "      <td>The celebrations begin early as we deliver 70...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1822 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  tweet  Analysis\n",
              "0      Tata Posts 38% Growth In Nov 2021 – Nexon, Al...         1\n",
              "1     Tata Posts 38% Growth In Nov 2021 – Nexon, Alt...         1\n",
              "2     Hi friends I am a Giveway hunter and i will op...         1\n",
              "3         Look for other dealer Tata Safari is avail...         1\n",
              "4     11,000 km in 5 months with my Tata Safari: Own...         0\n",
              "...                                                 ...       ...\n",
              "1817   At Pothens Mahindra, Kollam, we delivered the...         1\n",
              "1818  Alexa in XUV700 says MG Hector is better😳😛 #bu...         1\n",
              "1819  So even XUV700 agrees MG Hector is a better ca...         1\n",
              "1820   The celebrations begin early as we deliver 70...         0\n",
              "1821   The celebrations begin early as we deliver 70...         0\n",
              "\n",
              "[1822 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fRn_oTY8Ik43"
      },
      "source": [
        " # **Data Splitting into Training and Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFpc-b-1wxwj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9987b036-e1cf-4099-e48b-42278bee2296"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train,valid = train_test_split(df,test_size = 0.2,random_state=0,stratify = df.Analysis) #stratification means that the train_test_split method returns training and test subsets that have the same proportions of class labels as the input dataset.\n",
        "print(\"train shape : \", train.shape)\n",
        "print(\"valid shape : \", valid.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train shape :  (1457, 2)\n",
            "valid shape :  (365, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01taws0WJwia"
      },
      "source": [
        "# **Counter Vectorizer**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_g0q4SYFxec",
        "outputId": "a3431509-288f-4f89-e40a-3a922ab2d42d"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from nltk.corpus import stopwords\n",
        "stop = list(stopwords.words('english'))\n",
        "vectorizer = CountVectorizer(decode_error = 'replace',stop_words = stop)\n",
        "\n",
        "X_train = vectorizer.fit_transform(train.tweet)\n",
        "X_valid = vectorizer.transform(valid.tweet)\n",
        "\n",
        "y_train = train.Analysis\n",
        "y_valid = valid.Analysis\n",
        "\n",
        "print(\"X_train.shape : \", X_train.shape)\n",
        "print(\"X_train.shape : \", X_valid.shape)\n",
        "print(\"y_train.shape : \", y_train.shape)\n",
        "print(\"y_valid.shape : \", y_valid.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train.shape :  (1457, 1966)\n",
            "X_train.shape :  (365, 1966)\n",
            "y_train.shape :  (1457,)\n",
            "y_valid.shape :  (365,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "XXbTcSoONYGW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35q1aTcZJ-ST"
      },
      "source": [
        "# **Naive Bayes Classifier** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHzGee17GpwI",
        "outputId": "caba2b82-6e14-4648-ea13-d9b772cf6853"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "naiveByes_clf = MultinomialNB()\n",
        "\n",
        "naiveByes_clf.fit(X_train,y_train)\n",
        "\n",
        "NB_prediction = naiveByes_clf.predict(X_valid)\n",
        "NB_accuracy = accuracy_score(y_valid,NB_prediction)\n",
        "print(\"training accuracy Score    : \",naiveByes_clf.score(X_train,y_train))\n",
        "print(\"Validation accuracy Score : \",NB_accuracy )\n",
        "print(classification_report(NB_prediction,y_valid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training accuracy Score    :  0.9361702127659575\n",
            "Validation accuracy Score :  0.821917808219178\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.86      0.66      0.75        85\n",
            "           0       0.91      0.78      0.84       138\n",
            "           1       0.75      0.96      0.84       142\n",
            "\n",
            "    accuracy                           0.82       365\n",
            "   macro avg       0.84      0.80      0.81       365\n",
            "weighted avg       0.84      0.82      0.82       365\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oUNYJsa_KSoa"
      },
      "source": [
        "# **Random Forest Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XL2eHGktwwLm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65792ca1-d88c-4c02-8e74-898d0ff74106"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf_clf = RandomForestClassifier()\n",
        "\n",
        "rf_clf.fit(X_train,y_train)\n",
        "\n",
        "rf_prediction = rf_clf.predict(X_valid)\n",
        "rf_accuracy = accuracy_score(y_valid,rf_prediction)\n",
        "print(\"Training accuracy Score    : \",rf_clf.score(X_train,y_train))\n",
        "print(\"Validation accuracy Score : \",rf_accuracy )\n",
        "print(classification_report(rf_prediction,y_valid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy Score    :  1.0\n",
            "Validation accuracy Score :  0.8712328767123287\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.83      0.90      0.86        60\n",
            "           0       0.94      0.78      0.85       144\n",
            "           1       0.84      0.94      0.89       161\n",
            "\n",
            "    accuracy                           0.87       365\n",
            "   macro avg       0.87      0.87      0.87       365\n",
            "weighted avg       0.88      0.87      0.87       365\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YJ9wkBXTKZ7f"
      },
      "source": [
        "# **Suport Vector Machine(SVM)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2b64FtLawvZJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8084166-0a58-41ae-9a3d-dbd40f86b13b"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svc = SVC()\n",
        "\n",
        "svc.fit(X_train, y_train)\n",
        "\n",
        "svc_prediction = svc.predict(X_valid)\n",
        "svc_accuracy = accuracy_score(y_valid,svc_prediction)\n",
        "print(\"Training accuracy Score    : \",svc.score(X_train,y_train))\n",
        "print(\"Validation accuracy Score : \",svc_accuracy )\n",
        "print(classification_report(svc_prediction,y_valid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy Score    :  0.9869595058339052\n",
            "Validation accuracy Score :  0.8794520547945206\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.77      0.96      0.85        52\n",
            "           0       0.95      0.80      0.87       141\n",
            "           1       0.87      0.92      0.90       172\n",
            "\n",
            "    accuracy                           0.88       365\n",
            "   macro avg       0.86      0.89      0.87       365\n",
            "weighted avg       0.89      0.88      0.88       365\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ircCZ1LKKlow"
      },
      "source": [
        "# **Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PO2VHKyTChMS",
        "outputId": "9ab92dab-f66d-40a5-c0e2-aedb214deb1d"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logreg = LogisticRegression()\n",
        "\n",
        "logreg.fit(X_train, y_train)\n",
        "\n",
        "logreg_prediction = logreg.predict(X_valid)\n",
        "logreg_accuracy = accuracy_score(y_valid,logreg_prediction)\n",
        "print(\"Training accuracy Score    : \",logreg.score(X_train,y_train))\n",
        "print(\"Validation accuracy Score : \",logreg_accuracy )\n",
        "print(classification_report(logreg_prediction,y_valid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy Score    :  0.9986273164035689\n",
            "Validation accuracy Score :  0.8931506849315068\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "          -1       0.88      0.90      0.89        63\n",
            "           0       0.90      0.84      0.87       128\n",
            "           1       0.90      0.93      0.91       174\n",
            "\n",
            "    accuracy                           0.89       365\n",
            "   macro avg       0.89      0.89      0.89       365\n",
            "weighted avg       0.89      0.89      0.89       365\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFhkwkhbr2e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "outputId": "76a1c075-8c37-4c6b-c499-360fa9a69bc0"
      },
      "source": [
        "models = pd.DataFrame({\n",
        "    'Model': ['Support Vector Machines', 'Logistic Regression', \n",
        "              'Random Forest', 'Naive Bayes'],\n",
        "    'Test accuracy': [svc_accuracy, logreg_accuracy, \n",
        "              rf_accuracy, NB_accuracy] })\n",
        "models"
      ],
      "execution_count": null,
      "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>Model</th>\n",
              "      <th>Test accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Support Vector Machines</td>\n",
              "      <td>0.879452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.893151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.871233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.821918</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Model  Test accuracy\n",
              "0  Support Vector Machines       0.879452\n",
              "1      Logistic Regression       0.893151\n",
              "2            Random Forest       0.871233\n",
              "3              Naive Bayes       0.821918"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "4zFhWASwJb4x",
        "outputId": "b5df6a20-a32b-4b04-89be-805e4257933a"
      },
      "source": [
        "models.plot('Model', 'Test accuracy', kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb869bd7410>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBLhQDczS4QD3cwsEQ50M7NEONDNzBJRU6BLOlzSC5LmSLqwidc3lXRn/vofJfWtd6FmZrZuzQa6pI7AtcCngAHA8ZIGVF12KvB2RHwcuBL4Tr0LNTOzdaulhb43MCci5kbEh8B44Miqa44Ebs2f3w0cIkn1K9PMzJrTqYZrdgBerTieD+yztmsiYoWkxUAP4M3KiySdBpyWH74r6YXWFL2B9aTqz9FW2rh/fvHnWT/+LOurLJ/nTmt7oZZAr5uIuAG4YUN+zbaSNC0ihhddRyr8edaPP8v6SuHzrKXLZQGwY8Vx7/xck9dI6gRsBSyqR4FmZlabWgJ9KrCLpH6SOgPHAROrrpkInJQ/Pwb4bURE/co0M7PmNNvlkveJnwU8BHQEboqIWZIuA6ZFxETgx8BtkuYAb5GFfipK1UVUAv4868efZX2V/vOUG9JmZmnwTFEzs0Q40M3MEuFANysJSefUcs42Xg70KpLOkbSlMj+W9LSkfyy6LjPWjCSrdPKGLiIVkraQ1CF/vquk0ZI2KbqutnCgN3ZKRCwB/hHYGvg8cHmxJZWXpM9KelHSYklLJC2VtKTouspE0vGSfgn0kzSx4jGJbFSZtc7jQBdJOwAPk/1fv6XQitpog84ULYmGNWj+CbgtH6LpdWla7wrgiIh4vuhCSuxJ4DWyqenfrzi/FJhZSEVpUES8L+lU4LqIuELSjKKLagsHemPTJT0M9AMuktQNWFVwTWX2vw7ztomIV4BXJI0C/hYRqyTtCuwOPFdsdaUmSfsBY8lWjIVsrk1peRx6lbxPbQgwNyLekdQD2CEi3BJqBUlXAdsB9wIfNJyPiF8UVlRJSZoO/ANZV+AfyGZxfxgRYwstrKQkHQicB/whIr4jaWfg3Ig4u+DSWs2BXiXvXhkL7BwRl0nqA2wXEVMKLq2UJN3cxOmIiFM2eDElJ+npiBgmaRywWUMXQUQMKbq2MpO0eUS8X3Qd9eAul8auI+ti+SRwGVk/5T3AiCKLKquI+ELRNSQkuS6CIuWf5Y+BrkAfSYOB/xsRZxRbWet5lEtj+0TEmcAygIh4G+hcbEnlJam3pAmS3sgf90jqXXRdJXUucBEwIb9ZvzPwWME1ldl/AYeRrwwbEc8Cnyi0ojZyoDe2PN92LwAk9cI3RdviZrLVOP8uf/wyP2ctFBG/i4jRwDX58dwy9/e2BxHxatWplYUUUicO9MauBiYAH5P0LeD3wLeLLanUekXEzRGxIn/cAvQquqgykrSfpNnA/+THgyVdV3BZZfaqpJFASNpE0vlAqUdkuQ+9SkT8LB9NcAjZmPQxHnbXJosknQDckR8fjzc/aa2GLoKJkHURSCp1F0HBTgeuIttCcwHZ5KIzC62ojRzoTXsRWEL++UjqExF/Kbak0jqFrIvgSrJurCcB3yhtpYh4tWqeW6m7CAoWqQ35dKBXyYeEfR34X7L/LCILokFF1lVW+aSY0UXXkYiPdBEA51DyLoKCPZXPDL0J+HUKu6x5HHqVfNelfSLC3QJtIOmCfJz0NeQ3mCv5Zl7LSepJ1kUwiqyh8TBwjv+ttk4+52QU2U+RI4C7gFsi4s+FFtYGbqE39iqwuOgiEtDQcpxWaBWJyEdeXZVaF0GR8hb5b4DfSDoY+ClwhqRngQsjYnKhBbaCW+hVJP0Y2A14gI9OVf9BYUUlIl9WoWu+mqW1kKTfA5+MiA+LriUF+bIeJ5Ctsvi/ZJOMJpIt/fHziOhXYHmt4hZ6Y3/JH53xhKI2k3Q72WiClWRrj2wp6aqI+G6xlZXSXOAPkiYC7zWcdGOj1SYDt5GNZJtfcX6apB8VVFObuIVu61XDWiOSxgLDgAuB6RHhm8wtJOnrTZ2PiEs3dC0pkKQUboRWcgs9J+m/IuLcfCOBpm7ieaRG62ySj8gYA/wwIpZLSuo/0YbSENySuubH7xZbUen1lHQBsAfQpeFkRHyyuJLaxoG+xm35r98rtIr0XA/MA54FHpe0E9kYf2shSQPJ/p1ukx+/CZwYEbMKLay8fgbcCXyGrFvwJGBhoRW1kbtcbIOT1CkiVhRdR9lIehL4akQ8lh8fBHw7IkYWWlhJSZoeEXtJmtnQBShpakSUdmVVr+VSRdL+kn4j6c+S5kp6WdLcousqq6Y23SZbmthabouGMAeIiEnAFsWVU3rL819fk/RpSUPJf/opK3e5NPZj4F+B6XhadT2cEhFXSTqMNZtu30Y2KcZaZq6kr7Gme/AEspEv1jrflLQV2a5F1wBbkv3fLy0HemOLI+JXRReREG+6XT+nAJcCvyC7cf9Efs5aISLuz58uBg4uspZ6cR96TtKw/OnnyHaB+QUfnVj0dBF1lV2+Bd0OZJtuDyb7bCdFxF6FFlYikj7bsAerpK3zTVeslSR1AY4F3iZbn/8Csr1aXwK+ERFvFlhemzjQc5LWtfNLlHkoU5G86XbbNewlWv3cWkfSXWT951uQdQP+iSzYDwCGRMRnCiyvTdzlkouIJH7kaocCGEA2NOwysv9EXdb5O6ya1vLcWmdARAyU1AmYHxEH5ud/na/jUloe5VJF0rclda843lrSN4usqeSuA/Yj29gCsk23ry2unFLaTNJQSXsBXfLnwxoeRRdXQh8C5ENn/1r1WqkHQrjLpYqkZyJiaNU5/5jbSg2fXeXnKunZiBhcdG1l4e7A+pL0BjCe7KedY/Pn5Mefi4hti6qtrdzl0lhHSZtGxAcAkjYDNi24pjLzpttt5O7Auvv3iufVyzuXerlnB3pjPwMezUdnQLZd2q0F1lN21ZtuHwP8R7El2cYsIpL9/+wulyZI+hTZJtEAv4mIh4qsp6zyES77Am+xZtPtR73pttn64UC39aqpexJmtn64y6WKpH3JpgH3J9vgoiPwXkRsWWhh5fWopKOBX6S29nQRJA0C+lLxf7dh0pGZW+hVJE0DjgN+DgwHTgR2jYiLCi2spCQtJRt7vgJYRtbtEv4G2XKSbgIGAbNYc2M5IsLT/1tB0q7AfwPb5uPSBwGjI6K0w5Qd6FUkTYuI4VVLarrbwAonaXZEDCi6jlRI+h3ZiJfrK4bU/ikiBhZbWeu5y6Wx9yV1BmZIugJ4DU/AarW1THxZDLziNdFbbLKkARExu+hCErF5REypWiuu1P8mHeiNfZ6s3/wssqU0dwSOLrSicruObC/R5/LjPcnWzthK0r9EhJfRrd1PyEL9dbKF4xq6r7w/a+u8KenvWTNH4hiyBlxpucvF1itJvwC+1rBNmqQBZGu6XEB2o3RIkfWViaQ5wL+RfXNcPTkrIl4prKgSk7QzcAMwkmzlxZeBsWX+PN1Cz0la5+p/bgW12q6Ve15GxGxJu0fEXC+L3mILI2Ji0UUk5JWIGCVpC6BDRCwtuqC2cqCvsYrsR6/byZbS/Fux5SRjlqT/Zs16GccCsyVtypotwKw2z0hq+PdZuVa/hy22zsuSfk22UfRviy6mHtzlUkHS7mSrAh4BzCYL94d986718rVwziBbaxrgD2T96svIbkq9W1RtZVOxHEUlD1tsJUmbky3rfBzZfZ77gfER8ftCC2sDB/paSDqWbJnX70TEd4uup8zyUO8TES8UXYtZUyRtDVxF1ofeseh6WsvD8SpI2kHSeZJ+T7YB77+STTywVpI0GpgB/Do/HiLJ/cCtIKm3pAmS3sgf90jqXXRdZSbpQEnXkW0K34VsC8rScgs9l08y6AbcBdwDLKp8PSLeKqKuspM0Hfgk2T6iDZM3nouIPYutrHwk/YasG/C2/NQJZC3KQ4urqrwkzQOeIfs/PzEi3iu2orZzoOfyv9yGD6PyQ2kY67vzBi8qAZKeioh9qza4mOlRQy0naUb1MM+mzlltJG0ZEUuKrqOePMolFxF9i64hUbMk/TPZxiG7AGcDTxZcU1ktknQCcEd+fDxVP0la8yRdEBFXAN9sauhsRJy94auqDwe6rW/jgK+SDbO7g6wv/RuFVlRep5CtBHol2U+RT5JtwGIt07Ae//RCq1gP3OViG5Sk3YDzI+JLRddilhq30G29yJci/R7wd8C9ZENAfwjsA3y/wNJKR9I1fPS+zkeUuYugSPn+tl8GBpCNcAGgzJtue9hiBUkdJf1P0XUk4kayERlHA2+SDV18Cfh4RFxZZGElNI01w+qGAS/mjyFkm7BY6/yMrPulH3ApMA+YWmRBbeUulyqS7gPGRcRfiq6lzKpHX0ia65FCbSPpKeCAhpnLkjYBnoiIfYutrJwkTY+Ivar2PpgaESOKrq213OXS2NZkIzOmAKvHpUbE6OJKKqUukoaSDfsE+KDyOCKeLqyy8toa2JJs022Arvk5a52GtYRek/Rp4K/ANgXW02ZuoVeRdGBT5yPidxu6ljKT9Ng6Xo4y91MWRdIXgEuAx8i+MX4CuCQibi2yrrKS9BngCbI9D64h+2Z5aZlXtHSgN0HStkDDj11TIuKNIusxayBpO7IbywB/jIjXi6zH2hcHehVJnwO+C0wiawX9A/DvEXF3kXWZQbbeELATFd2lEfF4cRWVj6SL1/FyRERp50k40KtIehY4tKFVng9teiQiBhdbmW3sJH2HbD35WazZsSh8f6dlJJ3XxOktgFOBHhHRdQOXVDe+KdpYh6oulkV4eKe1D2OA3SLig2avtLWKiNXzICR1A84hm3E7npLPkXBQNfZrSQ9JOlnSycADwK8Krqm0JB0laauK4+6SxhRZU4nNBTYpuogUSNpG0jeBmWQN22ER8eWy3y9zl0sTJH2WNTvsPBERE4qsp8zWskLg6pUXrXaS7gEGA4/y0S3oPFO0BSR9F/gs2QbR16a0a5YDvYqk70TEl5s7Z7Vpaqlcr4feOpJOauq8hy22jKRVZN8QV9D0UtlbFlJYHTjQq0h6OiKGVZ3z+t2tJOkm4B2ytVwAzgS2iYiTCyvKLFHuQ89J+hdJzwG7S5pZ8XgZeK7o+kpsHPAh2c7qd5K1jM4stKKSkrSLpLslzZY0t+FRdF3WfriFnstv3G0N/CdwYcVLS739nLUH+V63XydbD/0IspEZHSJiXeOqbSPiQK8iaV9gVkQszY+3BPpHxB+LraxcJP1XRJwr6Zc0sfSrx063XMViUqvvQTScK7o2ax88Dr2x/yZborTBu02cs+Y1bGT8vUKrSMsHkjoAL0o6C1hAtkCXGeA+9KYoKn5siYhV+Btfi0VEw/ZeQyLid5UPsnW8reXOATYn25d1L+DzwImFVmTtigO9sbmSzpa0Sf44h2xCh7VOU0PtTt7QRaQgIqZGxLsRMT8ivgD8H+DjRddl7Yf70KtI+hhwNfBJsr7fR4Fzyz6DbEOTdDzwz2QTtJ6oeGlLYGVEHFJIYSWU38c5E9gBmAj8Jj8+D5gZEUcWWJ61Iw50Wy8k7US2tVejUUNkIbSikMJKKN9F621gMnAI8DGySTDnRMSMImuz9sWBXkXSrmQ3QbeNiIH5ZsejI+KbBZdWSpK2AP4WEavyz3Z34FcRsbyZ32q5qlEtHYHXgD4RsazYyqy9cR96YzcCF5FvTxURM4HjCq2o3B4n245uB+Bhsht5txRaUfms/uYXESuB+Q5za4pHbzS2eURMkVR5zt0DraeIeF/SqcB1EXGFJHcTtMxgSUvy5wI2y49Lv/aI1ZcDvbE3Jf09+WQYSceQ/YhrrSNJ+wFjyTYQAOhYYD2lExH+vKwmDvTGziRbVnN3SQuAl8nCyFrnXLIurAkRMUvSzmSbHJtZnfmmaE7SbOB24I6IeCm/mdehYQkAM7P2zoGekzSY7Obn58i2nbsDuDMi/lpoYSXltVzMNjwHehPyBbqOBY4GXgJuj4gbi62qXCTtFRHTJR3Y1Ov5EgBmVkcO9HWQdBDZUqUDImLTgssxM1sn3xStImkEcDxZ6/xl4Hrg54UWVWL5piHVrYbFwDTgmxGxaMNXZZYmB3pO0rfJulneAsYD+0fE/GKrSpVo/ZkAAAVySURBVMKvgJVkN5whu0+xOfA62QSjI4opyyw9DvQ1lgGHR8SLRReSmFFVe7Q+17Bvq6QTCqvKLEGe+p+LiMsc5utFR0l7NxzkXVoNE2U8A9esjtxCt/Xti8BNkrqSTVVfApyaj/P/z0IrM0uMR7nYBpFvwk1ELC66FrNUuculiqRHazlntZG0laQfkG0U8qik7zeEu5nVlwM9J6mLpG2AnpK2lrRN/uhLtlOMtc5NZJtafC5/LAFuLrQis0S5yyWX7x16LvB3ZLupN6yfuwS4MSJ+WFRtZSZpRkQMae6cmbWdb4rmIuIqST8EvhIR3yi6noT8TdIBEfF7AEn7A38ruCazJLmFXkXSMxExtOg6UpEvevYToKHf/G3gpHwnKDOrI/ehN/aopKNVtWWRtU5EPBsRg4FBwKD8m+UnCy7LLEluoVeRtBTYgmy6+t/wNl91J+kvEdGn6DrMUuM+9CoR0a3oGjYC/unHbD1woDdB0mjgE/nhpIi4v8h6EuQfC83WA3e5VJF0OTAC+Fl+6nhgWkRcVFxV5ZN3XTX1j0vAZhHhxoRZnTnQq0iaCQyJiFX5cUfgmYgYVGxlZmbr5lEuTete8dzT1M2sFPxjb2P/CTwj6TGy7oFPABcWW5KZWfPc5dIESduT9aMHMDUiXi+4JDOzZrmF3rT9gAPIAr0TMKHYcszMmucWehVJ1wEfB+7ITx0LvBQRZxZXlZlZ8xzoVST9D9A/8g9GUgdgVkT0L7YyM7N18yiXxuYAldPSd8zPmZm1a26hV5H0O7IbolPyUyOAacBigIgYXVBpZmbr5JuijV1cdAFmZq3hFnoTJG0H7I2HLZpZibgPvYqkL5J1t3wWOAZ4StIpxVZlZtY8t9CrSHoBGBkRi/LjHsCTEbFbsZWZma2bW+iNLSLbpb7B0vycmVm75hZ6FUk/AfYE7iPrQz8SmJk/iIgfFFedmdnaeZRLYy/ljwb35b96JyMza9fcQjczS4Rb6FXyZXMbfZeLCO9Ub2btmgO9sfMrnncBjgZWFFSLmVnN3OVSA0lTImLvouswM1sXt9CrSNqm4rADsBfehs7MSsCB3th0sj50kXW1vAycWmhFZmY1cJeLmVkiPFM0J2lEvihXw/GJku6TdHVVN4yZWbvkQF/jeuBDAEmfAC4HfkK2DvoNBdZlZlYT96Gv0TEi3sqfHwvcEBH3APdImlFgXWZmNXELfY2Okhq+wR0C/LbiNX/jM7N2z0G1xh3A7yS9CfwNeAJA0sfJt58zM2vPPMqlgqR9ge2BhyPivfzcrkDXiHi60OLMzJrhQDczS4T70M3MEuFANzNLhAPdkiUpJP204riTpIWS7m/h+8yT1LOt15itbw50S9l7wEBJm+XHhwILCqzHbL1yoFvqHgQ+nT8/nmx4KpCtrCnpXkkzJT0laVB+voekhyXNkvT/yBZqa/g9J0iaImmGpOslddyQfxizdXGgW+rGA8dJ6gIMAv5Y8dqlwDMRMQj4CtlSDwBfB34fEXsAE4A+AJL6k80i3j8ihgArgbEb5E9hVgNPLLKkRcRMSX3JWucPVr18ANmOVETEb/OW+ZbAJ4DP5ucfkPR2fv0hZOvjT5UEsBnwxvr+M5jVyoFuG4OJwPeAg4AebXgfAbdGxEX1KMqs3tzlYhuDm4BLI+K5qvNPkHeZSDoIeDMilgCPA/+cn/8UsHV+/aPAMZI+lr+2jaSd1n/5ZrVxC92SFxHzgaubeOkS4CZJM4H3gZPy85cCd0iaBTwJ/CV/n9mS/gN4WFIHYDlwJvDK+v0TmNXGU//NzBLhLhczs0Q40M3MEuFANzNLhAPdzCwRDnQzs0Q40M3MEuFANzNLhAPdzCwR/x8mTLhVxnkRUAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}