twitter.ipynb

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          "text": [
            "Saving train.csv to train (1).csv\n",
            "Saving test.csv to test (1).csv\n"
          ]
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        "from google.colab import files\n",
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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the provided training and test datasets\n",
        "train_filepath = 'train.csv'\n",
        "test_filepath = 'test.csv'\n",
        "\n",
        "train_data = pd.read_csv(train_filepath)\n",
        "test_data = pd.read_csv(test_filepath)\n",
        "\n",
        "\n",
        "# Display the first few rows of the training and test datasets\n",
        "train_data.head(), test_data.head()"
      ],
      "metadata": {
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        "outputId": "5d3e73a6-0320-47d0-add3-62253f2b762e"
      },
      "execution_count": 18,
      "outputs": [
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          "data": {
            "text/plain": [
              "(   id keyword location                                               text  \\\n",
              " 0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
              " 1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
              " 2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
              " 3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
              " 4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
              " \n",
              "    target  \n",
              " 0       1  \n",
              " 1       1  \n",
              " 2       1  \n",
              " 3       1  \n",
              " 4       1  ,\n",
              "    id keyword location                                               text\n",
              " 0   0     NaN      NaN                 Just happened a terrible car crash\n",
              " 1   2     NaN      NaN  Heard about #earthquake is different cities, s...\n",
              " 2   3     NaN      NaN  there is a forest fire at spot pond, geese are...\n",
              " 3   9     NaN      NaN           Apocalypse lighting. #Spokane #wildfires\n",
              " 4  11     NaN      NaN      Typhoon Soudelor kills 28 in China and Taiwan)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "# Preprocessing and Model Training\n",
        "# Since the focus is on the text of the tweets, we'll use only the 'text' column and 'target' for training\n",
        "\n",
        "# Creating a pipeline for preprocessing and model training\n",
        "pipeline = Pipeline([\n",
        "    ('tfidf', TfidfVectorizer(stop_words='english')),\n",
        "    ('classifier', MultinomialNB())\n",
        "])\n",
        "\n",
        "# Splitting the training data into train and validation sets\n",
        "X_train, X_val, y_train, y_val = train_test_split(train_data['text'], train_data['target'], test_size=0.2, random_state=42)\n",
        "\n",
        "# Training the model\n",
        "pipeline.fit(X_train, y_train)\n",
        "\n",
        "# Validating the model\n",
        "y_pred = pipeline.predict(X_val)\n",
        "accuracy = accuracy_score(y_val, y_pred)\n",
        "report = classification_report(y_val, y_pred)\n",
        "\n",
        "accuracy, report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJtw6yQfUVf7",
        "outputId": "03b061ba-67d5-4143-877a-86ef3cf0d975"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.7912015758371634,\n",
              " '              precision    recall  f1-score   support\\n\\n           0       0.78      0.89      0.83       874\\n           1       0.82      0.66      0.73       649\\n\\n    accuracy                           0.79      1523\\n   macro avg       0.80      0.77      0.78      1523\\nweighted avg       0.79      0.79      0.79      1523\\n')"
            ]
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      "source": [
        "# Making predictions on the test dataset\n",
        "test_predictions = pipeline.predict(test_data['text'])\n",
        "\n",
        "# Creating a DataFrame to display predictions alongside tweet IDs\n",
        "predictions_df = pd.DataFrame({\n",
        "    'id': test_data['id'],\n",
        "    'text': test_data['text'],\n",
        "    'predicted_target': test_predictions\n",
        "})\n",
        "\n",
        "predictions_df.head()\n"
      ],
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        "id": "fG7X7oGIUYmB",
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              "   id                                               text  predicted_target\n",
              "0   0                 Just happened a terrible car crash                 1\n",
              "1   2  Heard about #earthquake is different cities, s...                 1\n",
              "2   3  there is a forest fire at spot pond, geese are...                 1\n",
              "3   9           Apocalypse lighting. #Spokane #wildfires                 1\n",
              "4  11      Typhoon Soudelor kills 28 in China and Taiwan                 1"
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a5e9b0c1-4b88-4e41-84f5-b19cf684f58e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Generating confusion matrix for the validation set\n",
        "cm = confusion_matrix(y_val, y_pred)\n",
        "\n",
        "# Plotting the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Non-Disaster\", \"Disaster\"], yticklabels=[\"Non-Disaster\", \"Disaster\"])\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Label\")\n",
        "plt.ylabel(\"True Label\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "0TlElYeZVCpn",
        "outputId": "856f60c3-85dc-4eda-f113-5c3e2804ddda"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Corrected approach to plot the precision-recall curve using predict_proba method\n",
        "y_prob = pipeline.predict_proba(X_val)[:, 1]  # Probability of the positive class\n",
        "precision, recall, thresholds = precision_recall_curve(y_val, y_prob)\n",
        "\n",
        "# Calculating the area under the precision-recall curve\n",
        "auc_precision_recall = auc(recall, precision)\n",
        "\n",
        "# Plotting the precision-recall curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(recall, precision, label=f'Precision-Recall Curve (AUC = {auc_precision_recall:.2f})')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "304EGWRPWYIi",
        "outputId": "c9f0840c-3977-43eb-fc74-53cc1ca93ce7"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "\n",
        "# Computing ROC curve and ROC area\n",
        "fpr, tpr, _ = roc_curve(y_val, y_prob)\n",
        "roc_auc = roc_auc_score(y_val, y_prob)\n",
        "\n",
        "# Plotting ROC Curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='blue', label=f'ROC Curve (area = {roc_auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "rfULBnWWWayk",
        "outputId": "ee7a26b9-c898-40d5-9b52-951417a3844d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a histogram of the predicted probabilities\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(y_prob, bins=30, color='skyblue', edgecolor='black')\n",
        "plt.title('Histogram of Predicted Probabilities')\n",
        "plt.xlabel('Predicted Probability of Disaster')\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid(axis='y', alpha=0.75)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "silZWE7RWuo1",
        "outputId": "03adad2a-6962-45b5-8847-640992dbcb0e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}