XgBoost.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Yuvika-14/Jwoc-Brics-Sentiment-Analysis/blob/main/XgBoost.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "**Importing the files**"
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      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        " # Import XGBoost classifier\n",
        "from xgboost import XGBClassifier\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the dataset**"
      ],
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      "source": [
        "df=pd.read_csv('dataset.csv')\n",
        "df.columns"
      ],
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        "outputId": "7d9de570-3e67-4151-9970-105ab04eed16"
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      "execution_count": 6,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'textDisplay', 'likeCount', 'label'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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      "source": [
        "df.shape\n",
        "df.head(4)"
      ],
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          "height": 175
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      "execution_count": 7,
      "outputs": [
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              "   Unnamed: 0                                        textDisplay  likeCount  \\\n",
              "0           0  hurray for Brics nation, I hope this changes t...          0   \n",
              "1           1                      BRICS are modern Axis Powers😂          0   \n",
              "2           2  As France is asked to remove Troops from Niger...          0   \n",
              "3           3                                          BE WARNED          0   \n",
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          },
          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Take care of missing data if any**"
      ],
      "metadata": {
        "id": "Ch_1zQzEsy4p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9hwEQerBs3sA",
        "outputId": "849cb9ec-bf6a-4115-fb21-7a6b4a9e57b1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0     0\n",
              "textDisplay    0\n",
              "likeCount      0\n",
              "label          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Drop unnecessary columns**"
      ],
      "metadata": {
        "id": "pcP_VmLZs6hi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "f = df[['textDisplay', 'label']]\n",
        "\n",
        "# Handle missing values if any\n",
        "df = df.dropna()\n"
      ],
      "metadata": {
        "id": "uGSMKfW1tDs2"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting the dataset into test and training set**"
      ],
      "metadata": {
        "id": "ZlnvN8d3tGAZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df['textDisplay'], df['label'],test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "xklesnbWtSyX"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the text data into numerical features using TF-IDF\n",
        "tfidf_vectorizer = TfidfVectorizer(max_features=5000)\n",
        "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
        "X_test_tfidf = tfidf_vectorizer.transform(X_test)\n",
        "\n"
      ],
      "metadata": {
        "id": "61U2Qw54tnEl"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training the dataset on XGboost model**"
      ],
      "metadata": {
        "id": "fXB7Gse9ts6m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_model = XGBClassifier()\n",
        "xgb_model.fit(X_train_tfidf, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "IAzR6YyLtxxo",
        "outputId": "d0a4c28b-1a22-4b12-bc12-a79dd894a15d"
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      "execution_count": 12,
      "outputs": [
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          "output_type": "execute_result",
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              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
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              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting the test results**"
      ],
      "metadata": {
        "id": "w4QU5_JFt3iO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_xgb = xgb_model.predict(X_test_tfidf)"
      ],
      "metadata": {
        "id": "BtRXt_p_t8x2"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluating the XGboost model**"
      ],
      "metadata": {
        "id": "vRn3pjKiuBsr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_xgb = accuracy_score(y_test, y_pred_xgb)\n",
        "conf_matrix_xgb = confusion_matrix(y_test, y_pred_xgb)\n",
        "classification_rep_xgb = classification_report(y_test, y_pred_xgb)\n",
        "\n",
        "# Print the evaluation metrics\n",
        "print(\"XGBoost Model Metrics:\")\n",
        "print(\"Accuracy:\", accuracy_xgb)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix_xgb)\n",
        "print(\"Classification Report:\\n\", classification_rep_xgb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHvxiO1KuHOV",
        "outputId": "ec18df06-e184-4d0f-d100-2cecacba6445"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Model Metrics:\n",
            "Accuracy: 0.5652173913043478\n",
            "Confusion Matrix:\n",
            " [[30 20]\n",
            " [30 35]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      0.60      0.55        50\n",
            "           1       0.64      0.54      0.58        65\n",
            "\n",
            "    accuracy                           0.57       115\n",
            "   macro avg       0.57      0.57      0.56       115\n",
            "weighted avg       0.58      0.57      0.57       115\n",
            "\n"
          ]
        }
      ]
    }
  ]
}