sentiment_analysis.ipynb

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      "execution_count": 9,
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        },
        "id": "fW4sQRwZ5qMy",
        "outputId": "b65641ac-2dfa-4ea2-8fbc-76d5672c4db7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.2.2)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.2)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n"
          ]
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      ],
      "source": [
        "!pip install kaggle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "pEltBI5z6oNF"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d kazanova/sentiment140"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_YL7h5K76eB",
        "outputId": "81834e34-f682-4499-c18c-3162355409b2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading sentiment140.zip to /content\n",
            " 98% 79.0M/80.9M [00:04<00:00, 25.4MB/s]\n",
            "100% 80.9M/80.9M [00:04<00:00, 20.2MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from zipfile import ZipFile\n",
        "dataset='/content/sentiment140.zip'\n",
        "with ZipFile(dataset,'r') as zip:\n",
        "  zip.extractall()\n",
        "  print(\"data extracted\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WisUziAh8CTA",
        "outputId": "91436e6d-5f60-4626-e16b-aa38b0f3a9f2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data extracted\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "ijidVJVa8qn-"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmBNA9tz-A5L",
        "outputId": "0ebc0aae-306c-4fb5-bfc7-c98bb0a0c81d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
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              "True"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(stopwords.words('english'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqlgLVWJ-LIr",
        "outputId": "53b9a3a4-6927-4a3b-9916-bdc374017b1d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data=pd.read_csv('/content/training.1600000.processed.noemoticon.csv',encoding='ISO-8859-1')"
      ],
      "metadata": {
        "id": "b9XdQ29k-qtF"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5ucaMtS_Qv-",
        "outputId": "37d0b193-5d04-4ab9-d8b9-4bc46f7c218e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1599999, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "2lmbF1vs_ZDh",
        "outputId": "428b2ed3-b2bf-43cf-e6a9-2220909ee4cc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY _TheSpecialOne_  \\\n",
              "0  0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   scotthamilton   \n",
              "1  0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY        mattycus   \n",
              "2  0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY         ElleCTF   \n",
              "3  0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY          Karoli   \n",
              "4  0  1467811372  Mon Apr 06 22:20:00 PDT 2009  NO_QUERY        joy_wolf   \n",
              "\n",
              "  @switchfoot http://twitpic.com/2y1zl - Awww, that's a bummer.  You shoulda got David Carr of Third Day to do it. ;D  \n",
              "0  is upset that he can't update his Facebook by ...                                                                   \n",
              "1  @Kenichan I dived many times for the ball. Man...                                                                   \n",
              "2    my whole body feels itchy and like its on fire                                                                    \n",
              "3  @nationwideclass no, it's not behaving at all....                                                                   \n",
              "4                      @Kwesidei not the whole crew                                                                    "
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              "      } 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-21b06746-cb25-4bb4-afff-abd9982b81b7 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",
              "variable_name": "twitter_data"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_names=['target','id','date','flag','user','text']\n",
        "twitter_data=pd.read_csv('/content/training.1600000.processed.noemoticon.csv',names=column_names, encoding='ISO-8859-1')"
      ],
      "metadata": {
        "id": "KYdjebFR_umY"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0e3J4-0iAPID",
        "outputId": "67c6cacb-6a10-450b-bdda-548cd36ea1f5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1600000, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PylMnMyiAVRc",
        "outputId": "e259864c-b5c8-4836-dd59-533041c14a9d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target          id                          date      flag  \\\n",
              "0       0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
              "1       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
              "2       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
              "3       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "4       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "\n",
              "              user                                               text  \n",
              "0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...  \n",
              "1    scotthamilton  is upset that he can't update his Facebook by ...  \n",
              "2         mattycus  @Kenichan I dived many times for the ball. Man...  \n",
              "3          ElleCTF    my whole body feels itchy and like its on fire   \n",
              "4           Karoli  @nationwideclass no, it's not behaving at all....  "
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              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>ElleCTF</td>\n",
              "      <td>my whole body feels itchy and like its on fire</td>\n",
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              "      <td>1467811193</td>\n",
              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>Karoli</td>\n",
              "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
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              "        document.querySelector('#df-eca562cb-1ae6-4af3-bcd3-4f96cb70a686 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-eca562cb-1ae6-4af3-bcd3-4f96cb70a686');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "      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-fc769384-f3fc-4c3d-80b4-ef6fd995befc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "twitter_data"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s7_TvX-JAzL4",
        "outputId": "f77db60d-8c87-4954-abf6-b3c289baf533"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target    0\n",
              "id        0\n",
              "date      0\n",
              "flag      0\n",
              "user      0\n",
              "text      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1flqmWPDNrf",
        "outputId": "79a8945f-8a85-4a71-ff13-ca765ead6eba"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    800000\n",
              "4    800000\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data.replace({'target':{4:1}},inplace=True)"
      ],
      "metadata": {
        "id": "KtGRpawbDnoS"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DyFWOOPRESMs",
        "outputId": "e285e33b-ee30-4f5e-ae88-1c78c705ebac"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    800000\n",
              "1    800000\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pot_stem=PorterStemmer()"
      ],
      "metadata": {
        "id": "W_KR7Yv-Es8S"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def stemming(content):\n",
        "  stemmed_content=re.sub('[^a-zA-Z]',' ',content)\n",
        "  stemmed_content=stemmed_content.lower()\n",
        "  stemmed_content=stemmed_content.split()\n",
        "  stemmed_content=[pot_stem.stem(word) for word in stemmed_content if not word in stopwords.words('english')]\n",
        "  stemmed_content=' '.join(stemmed_content)\n",
        "  return stemmed_content\n",
        ""
      ],
      "metadata": {
        "id": "KaZ_Zu0kcgWC"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data['stemmed_content']=twitter_data['text'].apply(stemming)"
      ],
      "metadata": {
        "id": "QqVvmwccdynG"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "twitter_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "0Fd2hLSOq8jY",
        "outputId": "e54bdf04-af2f-4aa7-a781-2d2c96a8cdde"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target          id                          date      flag  \\\n",
              "0       0  1467810369  Mon Apr 06 22:19:45 PDT 2009  NO_QUERY   \n",
              "1       0  1467810672  Mon Apr 06 22:19:49 PDT 2009  NO_QUERY   \n",
              "2       0  1467810917  Mon Apr 06 22:19:53 PDT 2009  NO_QUERY   \n",
              "3       0  1467811184  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "4       0  1467811193  Mon Apr 06 22:19:57 PDT 2009  NO_QUERY   \n",
              "\n",
              "              user                                               text  \\\n",
              "0  _TheSpecialOne_  @switchfoot http://twitpic.com/2y1zl - Awww, t...   \n",
              "1    scotthamilton  is upset that he can't update his Facebook by ...   \n",
              "2         mattycus  @Kenichan I dived many times for the ball. Man...   \n",
              "3          ElleCTF    my whole body feels itchy and like its on fire    \n",
              "4           Karoli  @nationwideclass no, it's not behaving at all....   \n",
              "\n",
              "                                     stemmed_content  \n",
              "0  switchfoot http twitpic com zl awww bummer sho...  \n",
              "1  upset updat facebook text might cri result sch...  \n",
              "2  kenichan dive mani time ball manag save rest g...  \n",
              "3                    whole bodi feel itchi like fire  \n",
              "4                      nationwideclass behav mad see  "
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>target</th>\n",
              "      <th>id</th>\n",
              "      <th>date</th>\n",
              "      <th>flag</th>\n",
              "      <th>user</th>\n",
              "      <th>text</th>\n",
              "      <th>stemmed_content</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1467810369</td>\n",
              "      <td>Mon Apr 06 22:19:45 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>_TheSpecialOne_</td>\n",
              "      <td>@switchfoot http://twitpic.com/2y1zl - Awww, t...</td>\n",
              "      <td>switchfoot http twitpic com zl awww bummer sho...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1467810672</td>\n",
              "      <td>Mon Apr 06 22:19:49 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>scotthamilton</td>\n",
              "      <td>is upset that he can't update his Facebook by ...</td>\n",
              "      <td>upset updat facebook text might cri result sch...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1467810917</td>\n",
              "      <td>Mon Apr 06 22:19:53 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>mattycus</td>\n",
              "      <td>@Kenichan I dived many times for the ball. Man...</td>\n",
              "      <td>kenichan dive mani time ball manag save rest g...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1467811184</td>\n",
              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>ElleCTF</td>\n",
              "      <td>my whole body feels itchy and like its on fire</td>\n",
              "      <td>whole bodi feel itchi like fire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1467811193</td>\n",
              "      <td>Mon Apr 06 22:19:57 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>Karoli</td>\n",
              "      <td>@nationwideclass no, it's not behaving at all....</td>\n",
              "      <td>nationwideclass behav mad see</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa0f8934-ac14-4df6-b488-835ae2f70a3d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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-aa0f8934-ac14-4df6-b488-835ae2f70a3d 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-aa0f8934-ac14-4df6-b488-835ae2f70a3d');\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-c36b2fd6-a146-43de-9ac9-73645bc7bfe0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c36b2fd6-a146-43de-9ac9-73645bc7bfe0')\"\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-c36b2fd6-a146-43de-9ac9-73645bc7bfe0 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",
              "variable_name": "twitter_data"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(twitter_data['stemmed_content'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPknIzMnrHuH",
        "outputId": "0a705810-f753-41b8-8355-57ae04e16d09"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0          switchfoot http twitpic com zl awww bummer sho...\n",
            "1          upset updat facebook text might cri result sch...\n",
            "2          kenichan dive mani time ball manag save rest g...\n",
            "3                            whole bodi feel itchi like fire\n",
            "4                              nationwideclass behav mad see\n",
            "                                 ...                        \n",
            "1599995                           woke school best feel ever\n",
            "1599996    thewdb com cool hear old walt interview http b...\n",
            "1599997                         readi mojo makeov ask detail\n",
            "1599998    happi th birthday boo alll time tupac amaru sh...\n",
            "1599999    happi charitytuesday thenspcc sparkschar speak...\n",
            "Name: stemmed_content, Length: 1600000, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=twitter_data['stemmed_content'].values\n",
        "y=twitter_data['target'].values"
      ],
      "metadata": {
        "id": "fyw33PuOrXM0"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkuugpEXrk8r",
        "outputId": "9a52d85b-92dc-4eec-a38b-26f7ec5ae798"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['switchfoot http twitpic com zl awww bummer shoulda got david carr third day'\n",
            " 'upset updat facebook text might cri result school today also blah'\n",
            " 'kenichan dive mani time ball manag save rest go bound' ...\n",
            " 'readi mojo makeov ask detail'\n",
            " 'happi th birthday boo alll time tupac amaru shakur'\n",
            " 'happi charitytuesday thenspcc sparkschar speakinguph h']\n",
            "[0 0 0 ... 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,stratify=y,random_state=2)"
      ],
      "metadata": {
        "id": "8G-OHp2Mr5JQ"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x.shape,x_train.shape,x_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ACUxPJksN6V",
        "outputId": "1f797843-d3a2-4980-80c8-a86d0fdf42aa"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1600000,) (1280000,) (320000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer=TfidfVectorizer()\n",
        "x_train=vectorizer.fit_transform(x_train)\n",
        "x_test=vectorizer.transform(x_test)"
      ],
      "metadata": {
        "id": "QwYsnybQsiyS"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train)\n",
        "print(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWTD_oTGtIwl",
        "outputId": "226477d2-90fa-46fe-eb65-a2f4da97a7c1"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 443066)\t0.4484755317023172\n",
            "  (0, 235045)\t0.41996827700291095\n",
            "  (0, 109306)\t0.3753708587402299\n",
            "  (0, 185193)\t0.5277679060576009\n",
            "  (0, 354543)\t0.3588091611460021\n",
            "  (0, 436713)\t0.27259876264838384\n",
            "  (1, 160636)\t1.0\n",
            "  (2, 288470)\t0.16786949597862733\n",
            "  (2, 132311)\t0.2028971570399794\n",
            "  (2, 150715)\t0.18803850583207948\n",
            "  (2, 178061)\t0.1619010109445149\n",
            "  (2, 409143)\t0.15169282335109835\n",
            "  (2, 266729)\t0.24123230668976975\n",
            "  (2, 443430)\t0.3348599670252845\n",
            "  (2, 77929)\t0.31284080750346344\n",
            "  (2, 433560)\t0.3296595898028565\n",
            "  (2, 406399)\t0.32105459490875526\n",
            "  (2, 129411)\t0.29074192727957143\n",
            "  (2, 407301)\t0.18709338684973031\n",
            "  (2, 124484)\t0.1892155960801415\n",
            "  (2, 109306)\t0.4591176413728317\n",
            "  (3, 172421)\t0.37464146922154384\n",
            "  (3, 411528)\t0.27089772444087873\n",
            "  (3, 388626)\t0.3940776331458846\n",
            "  (3, 56476)\t0.5200465453608686\n",
            "  :\t:\n",
            "  (1279996, 390130)\t0.22064742191076112\n",
            "  (1279996, 434014)\t0.2718945052332447\n",
            "  (1279996, 318303)\t0.21254698865277746\n",
            "  (1279996, 237899)\t0.2236567560099234\n",
            "  (1279996, 291078)\t0.17981734369155505\n",
            "  (1279996, 412553)\t0.18967045002348676\n",
            "  (1279997, 112591)\t0.7574829183045267\n",
            "  (1279997, 273084)\t0.4353549002982409\n",
            "  (1279997, 5685)\t0.48650358607431304\n",
            "  (1279998, 385313)\t0.4103285865588191\n",
            "  (1279998, 275288)\t0.38703346602729577\n",
            "  (1279998, 162047)\t0.34691726958159064\n",
            "  (1279998, 156297)\t0.3137096161546449\n",
            "  (1279998, 153281)\t0.28378968751027456\n",
            "  (1279998, 435463)\t0.2851807874350361\n",
            "  (1279998, 124765)\t0.32241752985927996\n",
            "  (1279998, 169461)\t0.2659980990397061\n",
            "  (1279998, 93795)\t0.21717768937055476\n",
            "  (1279998, 412553)\t0.2816582375021589\n",
            "  (1279999, 96224)\t0.5416162421321443\n",
            "  (1279999, 135384)\t0.6130934129868719\n",
            "  (1279999, 433612)\t0.3607341026233411\n",
            "  (1279999, 435572)\t0.31691096877786484\n",
            "  (1279999, 31410)\t0.248792678366695\n",
            "  (1279999, 242268)\t0.19572649660865402\n",
            "  (0, 420984)\t0.17915624523539803\n",
            "  (0, 409143)\t0.31430470598079707\n",
            "  (0, 398906)\t0.3491043873264267\n",
            "  (0, 388348)\t0.21985076072061738\n",
            "  (0, 279082)\t0.1782518010910344\n",
            "  (0, 271016)\t0.4535662391658828\n",
            "  (0, 171378)\t0.2805816206356073\n",
            "  (0, 138164)\t0.23688292264071403\n",
            "  (0, 132364)\t0.25525488955578596\n",
            "  (0, 106069)\t0.3655545001090455\n",
            "  (0, 67828)\t0.26800375270827315\n",
            "  (0, 31168)\t0.16247724180521766\n",
            "  (0, 15110)\t0.1719352837797837\n",
            "  (1, 366203)\t0.24595562404108307\n",
            "  (1, 348135)\t0.4739279595416274\n",
            "  (1, 256777)\t0.28751585696559306\n",
            "  (1, 217562)\t0.40288153995289894\n",
            "  (1, 145393)\t0.575262969264869\n",
            "  (1, 15110)\t0.211037449588008\n",
            "  (1, 6463)\t0.30733520460524466\n",
            "  (2, 400621)\t0.4317732461913093\n",
            "  (2, 256834)\t0.2564939661498776\n",
            "  (2, 183312)\t0.5892069252021465\n",
            "  (2, 89448)\t0.36340369428387626\n",
            "  (2, 34401)\t0.37916255084357414\n",
            "  :\t:\n",
            "  (319994, 123278)\t0.4530341382559843\n",
            "  (319995, 444934)\t0.3211092817599261\n",
            "  (319995, 420984)\t0.22631428606830145\n",
            "  (319995, 416257)\t0.23816465111736276\n",
            "  (319995, 324496)\t0.3613167933647574\n",
            "  (319995, 315813)\t0.28482299145634127\n",
            "  (319995, 296662)\t0.39924856793840147\n",
            "  (319995, 232891)\t0.25741278545890767\n",
            "  (319995, 213324)\t0.2683969144317078\n",
            "  (319995, 155493)\t0.2770682832971668\n",
            "  (319995, 109379)\t0.30208964848908326\n",
            "  (319995, 107868)\t0.3339934973754696\n",
            "  (319996, 438709)\t0.4143006291901984\n",
            "  (319996, 397506)\t0.9101400928717545\n",
            "  (319997, 444770)\t0.2668297951055569\n",
            "  (319997, 416695)\t0.29458327588067873\n",
            "  (319997, 349904)\t0.32484594100566083\n",
            "  (319997, 288421)\t0.48498483387153407\n",
            "  (319997, 261286)\t0.37323893626855326\n",
            "  (319997, 169411)\t0.403381646999604\n",
            "  (319997, 98792)\t0.4463892055808332\n",
            "  (319998, 438748)\t0.719789181620468\n",
            "  (319998, 130192)\t0.6941927210956169\n",
            "  (319999, 400636)\t0.2874420848216212\n",
            "  (319999, 389755)\t0.9577980203954275\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=LogisticRegression(max_iter=1000)"
      ],
      "metadata": {
        "id": "iK8x9EWuteUQ"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "8jALVP7QtNJT",
        "outputId": "d788f18d-b4de-400c-8320-f035b0a96e4c"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "text/html": [
              "<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>LogisticRegression(max_iter=1000)</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_training_predicition=model.predict(x_train)\n",
        "training_data_accuracy=accuracy_score(y_train,x_training_predicition )\n",
        "print(training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8whYCf4tbTQ",
        "outputId": "9318c731-2f0a-49d3-a2fe-4f048e4cedbf"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.81018984375\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "filename='trained_model.sav'\n",
        "pickle.dump(model,open(filename,'wb'))"
      ],
      "metadata": {
        "id": "el2_eIeEwdfJ"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "load_model=pickle.load(open('/content/trained_model.sav','rb'))"
      ],
      "metadata": {
        "id": "kuSn8Y3iwsl7"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_new=x_test[3]\n",
        "print(y_test[3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pGA7pa0w5J4",
        "outputId": "a747036e-39a3-4fe1-afa3-ee8312960596"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction=model.predict(x_new)\n",
        "print(prediction)\n",
        "if prediction==0:\n",
        "  print('negative tweet')\n",
        "else:\n",
        "  print('positive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRxJW5YXxAac",
        "outputId": "a85d32f2-19ed-4d94-ff81-205246050679"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "negative tweet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fklsl8s9xUtU"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}