Suicidal_text_EDA.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn"
      ],
      "metadata": {
        "id": "P4Ry4WLCSJiR"
      },
      "execution_count": null,
      "outputs": []
    },
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        {
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          "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": [
        "import pandas as pd\n",
        "dataset=pd.read_csv('/content/drive/MyDrive/Suicide_Detection.csv')"
      ],
      "metadata": {
        "id": "OcUoB4I8RoVg"
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      "execution_count": null,
      "outputs": []
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        "dataset.head()"
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            "text/plain": [
              "   Unnamed: 0                                               text        class\n",
              "0           2  Ex Wife Threatening SuicideRecently I left my ...      suicide\n",
              "1           3  Am I weird I don't get affected by compliments...  non-suicide\n",
              "2           4  Finally 2020 is almost over... So I can never ...  non-suicide\n",
              "3           8          i need helpjust help me im crying so hard      suicide\n",
              "4           9  I’m so lostHello, my name is Adam (16) and I’v...      suicide"
            ],
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      "cell_type": "markdown",
      "source": [
        "Data Preprocessing"
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        "id": "0bGnpyuuUc1r"
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    {
      "cell_type": "code",
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        "dataset.shape"
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            "text/plain": [
              "(232074, 3)"
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          "metadata": {},
          "execution_count": 5
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      "source": [
        "# reducing the dataset to decrease complexity\n",
        "df = dataset.sample(n=30000, random_state=42)"
      ],
      "metadata": {
        "id": "2srC45K2Ujex"
      },
      "execution_count": null,
      "outputs": []
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 30000 entries, 74414 to 120183\n",
            "Data columns (total 3 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Unnamed: 0  30000 non-null  int64 \n",
            " 1   text        30000 non-null  object\n",
            " 2   class       30000 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 937.5+ KB\n"
          ]
        }
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              "          Unnamed: 0\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ab00a23d-b9d4-4969-bbff-477db648547f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-509e3fce-3243-48ca-9f06-7d285030a984\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-509e3fce-3243-48ca-9f06-7d285030a984')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\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-509e3fce-3243-48ca-9f06-7d285030a984 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 117171.94257587027,\n        \"min\": 12.0,\n        \"max\": 348108.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          174846.61456666666,\n          175180.5,\n          30000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Unnamed: 0'].is_unique"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oSSKj8ygU04B",
        "outputId": "aa6bf942-5c2e-44c5-ceb1-2f01fbf2d822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns = 'Unnamed: 0',inplace=True)"
      ],
      "metadata": {
        "id": "jGb1OZ7CU_lq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uw-89C4SVCap",
        "outputId": "b0a0e607-e565-40d2-a1bf-47fd1eb75371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "text     0\n",
              "class    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6sJAopvVE99",
        "outputId": "92b88f04-5243-41a7-b79e-728095ee0199"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "df['class'].value_counts().plot(kind='pie',x='text',y='class')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "aTaZzxb6VG2k",
        "outputId": "f10fdb7a-e2b3-4bb7-8158-a3fe019b98b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"class\"].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaQPiHwHVi3Z",
        "outputId": "b16544da-f184-4e51-a051-c39b86b308cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "non-suicide    15043\n",
              "suicide        14957\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "df['class'].value_counts().plot(kind='bar',x='text',y='label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "0BBv9kSdVksC",
        "outputId": "056528c1-1f84-4477-fa06-3230100ed5c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Text Preprocessing"
      ],
      "metadata": {
        "id": "ihVG5Yr8WEDw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# lowercase\n",
        "df['text']= df['text'].str.lower()"
      ],
      "metadata": {
        "id": "RHvWkEAhWIeF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# remove punctuations\n",
        "df['text'] = df['text'].str.replace(r'[^\\w\\s]+', '',regex = True)"
      ],
      "metadata": {
        "id": "f8DIcjv9WMTd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.stem import PorterStemmer"
      ],
      "metadata": {
        "id": "5j2PfN8rW60C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3PM4RkzXkCo",
        "outputId": "4cf8fca8-b841-4350-ee20-2693765ab211"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# stop word removal\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "stop_words = stopwords.words('english')\n",
        "df['text'] = df['text'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop_words)]))"
      ],
      "metadata": {
        "id": "KBKJ4QOGWWsr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tokenize import sent_tokenize, word_tokenize"
      ],
      "metadata": {
        "id": "gDXwLq4BX86R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3GN3qoTHYq0k",
        "outputId": "1b25383a-b5ef-4ef6-edbf-01f8371bd3ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tokenization\n",
        "df['text'] = df['text'].apply(lambda x:nltk.word_tokenize(x))"
      ],
      "metadata": {
        "id": "rEApgGw_Wdi9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# stemming\n",
        "ps = PorterStemmer()\n",
        "df['text'] = df['text'].apply(lambda x : [ps.stem(i) for i in x])"
      ],
      "metadata": {
        "id": "EsUbjg06XvNH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text']=df['text'].apply(lambda x : ' '.join(x))"
      ],
      "metadata": {
        "id": "cF9o1dIuYyjB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
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        "id": "HulDTQC8Y3nC",
        "outputId": "9ab6c9c9-bae9-4855-9d74-bc1d58248a23"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                     text        class\n",
              "74414   dont know7 month self harm free urg get strong...      suicide\n",
              "149516  start becom rich start compani becom 16 afford...  non-suicide\n",
              "12484   poem haiku umegamedev hi hello hello stop fuck...  non-suicide\n",
              "14043   ive honestli got idea anymoreit feel everyon f...      suicide\n",
              "30673   ever cri like think unfair life cri cant cri e...  non-suicide"
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\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",
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              "</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",
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              "        document.querySelector('#df-4c9721db-abb0-4cb7-b46a-9fe6b38df19c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 30000,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 29990,\n        \"samples\": [\n          \"imagin phone better phone releas futur well dont imagin galaxi note 20 ultra alreadi better iphon 12 even announc\",\n          \"tri everyth suggest noth fuck workedi tri therapi tri talk peopl go shit tri tell teacher principl adult ive tri everyth im still get fuck bulli harass 247 im still shit everyth life liter way life hate highschool absolut choic unless someon know magic idea lost close end\",\n          \"need helpi realli struggl depress ptsd anxieti life im 29 though medic feel like thing gotten steadili wors sinc 2013 dad die im increasingli unabl see reason go thing keep go fianc im start feel selfish like burden everyon love want sleep im afraid pain id im also afraid would happen fail much wors could potenti make life like lost job couldnt get new one famili fianc got angri reject feel alon ever\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"non-suicide\",\n          \"suicide\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ind = df[df['text'].isnull()].index"
      ],
      "metadata": {
        "id": "5XU8ceTRZAYD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[ind]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "V3yNyL6GZQp1",
        "outputId": "55f83ad6-2df0-4872-e29e-d5839eb8be2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [text, class]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ce251a34-5e32-4a49-91d6-4843902f0e03\" class=\"colab-df-container\">\n",
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              "<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>text</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ce251a34-5e32-4a49-91d6-4843902f0e03 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ce251a34-5e32-4a49-91d6-4843902f0e03');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 'text' column contains None values as it may contain puctuations or emojies, So drop that rows\n",
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "XmrByq8RZS3-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "models"
      ],
      "metadata": {
        "id": "Z9-MrbqoZoYd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x,y = df['text'],df['class']"
      ],
      "metadata": {
        "id": "CD5jWEMkZn2b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "metadata": {
        "id": "ZRNYVXIFZ92g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# TF-IDF Vectorizer\n",
        "vectorizer = TfidfVectorizer(min_df=50,max_features=5000)\n",
        "x =  vectorizer.fit_transform(x).toarray()"
      ],
      "metadata": {
        "id": "x63CIEB9ZW2J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "dobFktkUaK6e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(x,y,test_size=0.30,random_state=5)"
      ],
      "metadata": {
        "id": "F90yCPc9Z2kK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape,X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsA5Du68aJEk",
        "outputId": "8bcfa2e7-22e0-4d1e-b61c-5d737307f7f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((21000, 2541), (9000, 2541))"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report , confusion_matrix"
      ],
      "metadata": {
        "id": "RP-z3L1hb6jP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "Pb1KDkw5uK09"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "Gz5UqagQwBzk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialzing empty lists to append all model's name and corresponding name\n",
        "acc = []\n",
        "model = []"
      ],
      "metadata": {
        "id": "LODF_hCl1B1j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Logistic Regression\n",
        "\n",
        "LRclassifier = LogisticRegression(C = 2, max_iter = 1000, n_jobs=-1)\n",
        "LRclassifier=LRclassifier.fit(X_train, y_train)\n",
        "y_pred = LRclassifier.predict(X_test)\n",
        "y_act=y_test\n",
        "print(\"Accuracy: \"+str(accuracy_score(y_act,y_pred)))\n",
        "print(classification_report(y_act, y_pred))\n",
        "sns.heatmap(confusion_matrix(y_act,y_pred),annot=True,cmap='Spectral')\n",
        "\n",
        "x = accuracy_score(y_act,y_pred)\n",
        "acc.append(x)\n",
        "model.append('Logistic Regression')"
      ],
      "metadata": {
        "id": "ip372yJUdTDq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "outputId": "48fa2255-3d14-4517-fdcd-6cc3418b8c99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9202222222222223\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " non-suicide       0.91      0.93      0.92      4503\n",
            "     suicide       0.93      0.91      0.92      4497\n",
            "\n",
            "    accuracy                           0.92      9000\n",
            "   macro avg       0.92      0.92      0.92      9000\n",
            "weighted avg       0.92      0.92      0.92      9000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pickle\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IlUXDoJm8Bx",
        "outputId": "ecdae019-8f33-48d3-87af-6824029af146"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: Could not find a version that satisfies the requirement pickle (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for pickle\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "o2Qnfgspm_yo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(LRclassifier,open('LogReg_model.pkl','wb'))"
      ],
      "metadata": {
        "id": "rKJKKGOrnCzs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(vectorizer,open('vectorizer.pkl','wb'))"
      ],
      "metadata": {
        "id": "QxjcMvccnk9R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier,VotingClassifier,AdaBoostClassifier,GradientBoostingClassifier,BaggingClassifier"
      ],
      "metadata": {
        "id": "1UzJD1sGasYk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV,RandomizedSearchCV"
      ],
      "metadata": {
        "id": "_tdudWzmcA6l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest\n",
        "\n",
        "RFclassifiers = RandomizedSearchCV(RandomForestClassifier(),{'n_estimators':[4,5],'criterion':['entropy'],\n",
        "                                                      'max_depth':range(1,4),'min_samples_split':range(2,5)},random_state=12)\n",
        "RFclassifiers.fit(X_train, y_train)\n",
        "\n",
        "y_act=y_test\n",
        "y_pred=RFclassifiers.predict(X_test)\n",
        "print(\"Accuracy: \"+str(accuracy_score(y_act,y_pred)))\n",
        "sns.heatmap(confusion_matrix(y_act,y_pred),annot=True,cmap='Spectral')\n",
        "print(classification_report(y_act,y_pred))\n",
        "\n",
        "print(RFclassifiers.best_estimator_)\n",
        "\n",
        "x = accuracy_score(y_act,y_pred)\n",
        "acc.append(x)\n",
        "model.append('Random Forest')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "PusmUlhqadBQ",
        "outputId": "72a7e414-2431-4a26-a5b4-947eab557b45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7863333333333333\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " non-suicide       0.74      0.89      0.81      4503\n",
            "     suicide       0.86      0.68      0.76      4497\n",
            "\n",
            "    accuracy                           0.79      9000\n",
            "   macro avg       0.80      0.79      0.78      9000\n",
            "weighted avg       0.80      0.79      0.78      9000\n",
            "\n",
            "RandomForestClassifier(criterion='entropy', max_depth=3, min_samples_split=4,\n",
            "                       n_estimators=5)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "rznj75eQa7Gb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Decision Tree\n",
        "\n",
        "Decisionclf = DecisionTreeClassifier(criterion='gini',splitter='random',min_samples_leaf=70,max_depth=4,random_state=0)\n",
        "Decisionclf.fit(X_train, y_train)\n",
        "\n",
        "y_act=y_test\n",
        "y_pred=Decisionclf.predict(X_test)\n",
        "print(\"Accuracy: \"+str(accuracy_score(y_act,y_pred)))\n",
        "sns.heatmap(confusion_matrix(y_act,y_pred),annot=True,cmap='PiYG')\n",
        "print(classification_report(y_act,y_pred))\n",
        "\n",
        "x = accuracy_score(y_act,y_pred)\n",
        "acc.append(x)\n",
        "model.append('Decision Tree')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "id": "-0wdF0Kkaw99",
        "outputId": "4c61f552-5e47-43c0-b626-6f194de25fb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7467777777777778\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " non-suicide       0.78      0.69      0.73      4503\n",
            "     suicide       0.72      0.80      0.76      4497\n",
            "\n",
            "    accuracy                           0.75      9000\n",
            "   macro avg       0.75      0.75      0.75      9000\n",
            "weighted avg       0.75      0.75      0.75      9000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB"
      ],
      "metadata": {
        "id": "6h4AiI8ebSJ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Naive Bayes\n",
        "nb = GaussianNB()\n",
        "nb2 = BernoulliNB()\n",
        "nb3 = MultinomialNB()\n",
        "VotingClassifiers = VotingClassifier(estimators=[('GaussianNB', nb),('BernoulliNB',nb2), ('MultinomialNB', nb3)], voting = 'soft')\n",
        "VotingClassifiers.fit(X_train, y_train)\n",
        "\n",
        "y_act=y_test\n",
        "print(\"Accuracy: \"+str(accuracy_score(y_act,y_pred)))\n",
        "y_pred=VotingClassifiers.predict(X_test)\n",
        "sns.heatmap(confusion_matrix(y_act,y_pred),annot=True,cmap='summer')\n",
        "print(classification_report(y_act,y_pred))\n",
        "\n",
        "x = accuracy_score(y_act,y_pred)\n",
        "acc.append(x)\n",
        "model.append('Naive Bayes')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "T9y2An2XbR8h",
        "outputId": "09b74d48-80fd-40e9-fc38-2a73a16e644d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7467777777777778\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " non-suicide       0.89      0.89      0.89      4503\n",
            "     suicide       0.89      0.89      0.89      4497\n",
            "\n",
            "    accuracy                           0.89      9000\n",
            "   macro avg       0.89      0.89      0.89      9000\n",
            "weighted avg       0.89      0.89      0.89      9000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier"
      ],
      "metadata": {
        "id": "6oCHaIMya_yL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# XG boost\n",
        "clf= XGBClassifier( eval_metric='map',max_depth=200,n_estimators=70,learning_rate=1.99)\n",
        "clf.fit(X_train,y_train.replace({\"non-suicide\":0,'suicide':1}))\n",
        "print('Accuracy:',clf.score(X_test,y_test.replace({\"non-suicide\":0,'suicide':1})))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VeMBLCK7bAQW",
        "outputId": "b874453a-f0a3-4622-a434-bc6d4c9dbcb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8643333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix and classification report\n",
        "y_act = y_test.replace({\"non-suicide\":0,'suicide':1})\n",
        "y_pred = clf.predict(X_test)\n",
        "print(\"Accuracy:\",accuracy_score(y_act,y_pred) )\n",
        "sns.heatmap(confusion_matrix(y_act,y_pred),annot=True,cmap='Spectral')\n",
        "print(classification_report(y_act,y_pred))\n",
        "\n",
        "\n",
        "x = accuracy_score(y_act,y_pred)\n",
        "acc.append(x)\n",
        "model.append('XG boost')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 604
        },
        "id": "-yfMKZixbC4k",
        "outputId": "ed2c38b8-b589-43d2-db06-d977999ed1e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8643333333333333\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.86      0.86      4503\n",
            "           1       0.86      0.87      0.86      4497\n",
            "\n",
            "    accuracy                           0.86      9000\n",
            "   macro avg       0.86      0.86      0.86      9000\n",
            "weighted avg       0.86      0.86      0.86      9000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=[10,5],dpi = 100)\n",
        "plt.title('Accuracy Comparison')\n",
        "plt.xlabel('Accuracy')\n",
        "plt.ylabel('Algorithm')\n",
        "sns.barplot(x = acc,y = model)"
      ],
      "metadata": {
        "id": "R16ExFF4bfVk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "2640cb5f-b74f-4ec2-f6b3-905f40d97e35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Accuracy Comparison'}, xlabel='Accuracy', ylabel='Algorithm'>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess(inp):\n",
        "    inp = inp.lower() #convert to lower case\n",
        "    inp = inp.replace(r'[^\\w\\s]+', '') #remove punctuations\n",
        "    inp = [word for word in inp.split() if word not in (stop_words)] #tokenize the sentence\n",
        "    inp = ' '.join([ps.stem(i) for i in inp]) #stemming\n",
        "    inputToModel = vectorizer.transform([inp]).toarray() #transform to vector form\n",
        "    return inputToModel"
      ],
      "metadata": {
        "id": "hZfxt2c77zNL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input=preprocess('life is not so good')\n",
        "predict = VotingClassifiers.predict(input) #Model prediction\n",
        "print('Output : ', predict[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HlaxYYKK8yLC",
        "outputId": "e53afce9-f571-467c-c62c-a666a4bfd2e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output :  non-suicide\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input=preprocess('I want to die')\n",
        "predict = VotingClassifiers.predict(input)\n",
        "print('Output : ', predict[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NhAj5s7P9RuV",
        "outputId": "6b9ff253-f3be-43e4-c268-5731e0cf68a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output :  suicide\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input=preprocess('I am happy')\n",
        "predict =  LRclassifier.predict(input)\n",
        "print('Output : ', predict[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-LDKJ1l2ycv",
        "outputId": "72e9224c-b06f-46c8-faa6-f46c9c2205c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output :  non-suicide\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LItTLrS225rT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}