ML_models.ipynb

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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install joblib"
      ],
      "metadata": {
        "id": "lDvppPsPZxxR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer"
      ],
      "metadata": {
        "id": "lBnY25tF4C9o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final=pd.read_csv('/content/drive/MyDrive/airline_sentiment_analysis.csv')"
      ],
      "metadata": {
        "id": "FGWpCosg3om1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final.head()"
      ],
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            "text/plain": [
              "   Unnamed: 0 airline_sentiment  \\\n",
              "0           1          positive   \n",
              "1           3          negative   \n",
              "2           4          negative   \n",
              "3           5          negative   \n",
              "4           6          positive   \n",
              "\n",
              "                                                text  \n",
              "0  @VirginAmerica plus you've added commercials t...  \n",
              "1  @VirginAmerica it's really aggressive to blast...  \n",
              "2  @VirginAmerica and it's a really big bad thing...  \n",
              "3  @VirginAmerica seriously would pay $30 a fligh...  \n",
              "4  @VirginAmerica yes, nearly every time I fly VX...  "
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              "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
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              "      <th>2</th>\n",
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              "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
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              "      <td>positive</td>\n",
              "      <td>@VirginAmerica yes, nearly every time I fly VX...</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "To check for NANs "
      ],
      "metadata": {
        "id": "Fmyu15xuZF2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final.isna().sum()"
      ],
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        },
        "id": "DjkIMQrG4Hn6",
        "outputId": "9403c0c4-e061-421d-e760-6cce3065e19f"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0           0\n",
              "airline_sentiment    0\n",
              "text                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final = final.drop('Unnamed: 0', axis = 1)"
      ],
      "metadata": {
        "id": "F-EkDcUZ4whP"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "final.head()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  airline_sentiment                                               text\n",
              "0          positive  @VirginAmerica plus you've added commercials t...\n",
              "1          negative  @VirginAmerica it's really aggressive to blast...\n",
              "2          negative  @VirginAmerica and it's a really big bad thing...\n",
              "3          negative  @VirginAmerica seriously would pay $30 a fligh...\n",
              "4          positive  @VirginAmerica yes, nearly every time I fly VX..."
            ],
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              "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
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              "      <td>negative</td>\n",
              "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>negative</td>\n",
              "      <td>@VirginAmerica seriously would pay $30 a fligh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>positive</td>\n",
              "      <td>@VirginAmerica yes, nearly every time I fly VX...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "X = final['text']\n",
        "y = final['airline_sentiment']"
      ],
      "metadata": {
        "id": "lbdc_k8v5OYp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = np.array(final['text'])\n",
        "text = text.flatten()"
      ],
      "metadata": {
        "id": "r5tKQW5HCfUd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(text)):\n",
        "  text[i] = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\\(\\),]|'\n",
        "                       '(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', text[i])\n",
        "  text[i] = re.sub(\"(@[A-Za-z0-9_]+)\", \"\", text[i])\n",
        "  text[i] = re.sub(\"(#[A-Za-z0-9_]+)\", \"\", text[i])\n"
      ],
      "metadata": {
        "id": "Dk1HFT1hAUSI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = np.array(text)\n",
        "X = pd.DataFrame(text, columns=['text'])"
      ],
      "metadata": {
        "id": "ubuiK85K6GdS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 21)"
      ],
      "metadata": {
        "id": "dsn44LIb8LOx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install nltk"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4LmKXfB9oHc",
        "outputId": "eeb38487-691c-4fdc-f755-27f57f8c943f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.7)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk) (4.64.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk) (1.1.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk) (2022.6.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "V-k5XMCrAS1a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "\n",
        "lr = Pipeline([('vectorize', CountVectorizer()),\n",
        "               ('tfidf', TfidfTransformer()),\n",
        "               ('clf', LogisticRegression()),\n",
        "              ])\n",
        "\n",
        "lr.fit(X_train.flatten(),y_train.flatten())\n",
        "y_pred1 = lr.predict(X_test.flatten())\n",
        "\n",
        "print(accuracy_score(y_pred1,y_test.flatten()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "peqo3e3B9j78",
        "outputId": "274cb11d-36e5-41c3-961d-b97a761e6882"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9094846253789519\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_pred1,y_test.flatten()))\n",
        "print(confusion_matrix(y_pred1,y_test.flatten()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7PlaeBmAbNm",
        "outputId": "0e88d7d1-ed17-4e6f-e6d9-381f88c20813"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.99      0.91      0.95      2008\n",
            "    positive       0.60      0.91      0.72       301\n",
            "\n",
            "    accuracy                           0.91      2309\n",
            "   macro avg       0.79      0.91      0.84      2309\n",
            "weighted avg       0.94      0.91      0.92      2309\n",
            "\n",
            "[[1825  183]\n",
            " [  26  275]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgboost = Pipeline([('vectorizer', CountVectorizer()),\n",
        "               ('tfidf', TfidfTransformer()),\n",
        "               ('clf', XGBClassifier()),\n",
        "              ])\n",
        "xgboost.fit(X_train.flatten(), y_train.flatten())\n",
        "\n",
        "y_pred = xgboost.predict(X_test.flatten())\n",
        "\n",
        "print(accuracy_score(y_pred,y_test.flatten()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OgqEviMY-pJ6",
        "outputId": "c72a814e-7830-4951-c9ad-483014390cc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8895625812039845\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_vectorizer = TfidfVectorizer() \n",
        "\n",
        "tfidf_train_vectors = tfidf_vectorizer.fit_transform(X_train.flatten())\n",
        "joblib.dump(tfidf_vectorizer.vocabulary_,open(\"feature.pkl\",\"wb\"))\n",
        "tfidf_test_vectors = tfidf_vectorizer.transform(X_test.flatten())"
      ],
      "metadata": {
        "id": "WEMGvv3f9xzv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier()\n",
        "\n",
        "classifier.fit(tfidf_train_vectors,y_train)\n",
        "y_pred = classifier.predict(tfidf_test_vectors)"
      ],
      "metadata": {
        "id": "ea4-X2gZA3yR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))\n",
        "print(confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4ZzYmd9A31p",
        "outputId": "0c835ab8-8465-4742-bf54-c75fedd3fed3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.89      0.99      0.93      1851\n",
            "    positive       0.90      0.50      0.64       458\n",
            "\n",
            "    accuracy                           0.89      2309\n",
            "   macro avg       0.89      0.74      0.79      2309\n",
            "weighted avg       0.89      0.89      0.88      2309\n",
            "\n",
            "[[1825   26]\n",
            " [ 228  230]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "import pickle\n",
        "clf = SVC()\n",
        "clf.fit(tfidf_train_vectors,y_train)\n",
        "import joblib\n",
        "filename = 'classifier.joblib.pkl'\n",
        "_ = joblib.dump(clf, filename, compress=9)\n",
        "\n",
        "y_pred = clf.predict(tfidf_test_vectors)\n",
        "print(classification_report(y_test,y_pred))\n",
        "print(confusion_matrix(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4_YB4QBB6nf",
        "outputId": "faac3986-79a3-4d74-faab-767c1ec67502"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.92      0.98      0.95      1851\n",
            "    positive       0.89      0.64      0.75       458\n",
            "\n",
            "    accuracy                           0.91      2309\n",
            "   macro avg       0.91      0.81      0.85      2309\n",
            "weighted avg       0.91      0.91      0.91      2309\n",
            "\n",
            "[[1816   35]\n",
            " [ 164  294]]\n"
          ]
        }
      ]
    }
  ]
}