Assignment 5/New_Headline_Sarcasm_Detection.ipynb

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      "name": "New Headline Sarcasm Detection.ipynb",
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      "name": "python3",
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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/18520918/CS114.L11.KHCL/blob/master/Assignment%205/New_Headline_Sarcasm_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IPWeVOfFvBnY"
      },
      "source": [
        "# **News Headlines Sarcasm Detection**\n",
        "> Dữ liệu bài toán được lấy từ https://www.kaggle.com/rmisra/news-headlines-dataset-for-sarcasm-detection\n"
      ]
    },
    {
      "cell_type": "code",
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        "id": "4p4LVsQVMZkd",
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np \n",
        "from google.colab import files\n",
        "import io\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ab00b448-f3cd-42f4-8a60-0c431a12de80\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ab00b448-f3cd-42f4-8a60-0c431a12de80\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Sarcasm_Headlines_Dataset.json to Sarcasm_Headlines_Dataset.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7DAM9Lq5PMFF"
      },
      "source": [
        "data = pd.read_json(io.BytesIO(uploaded['Sarcasm_Headlines_Dataset.json']),lines=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22axaHWjPPhC",
        "outputId": "5c6e7166-7292-42fe-e59f-66cff93c228b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<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>article_link</th>\n",
              "      <th>headline</th>\n",
              "      <th>is_sarcastic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/versace-b...</td>\n",
              "      <td>former versace store clerk sues over secret 'b...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/roseanne-...</td>\n",
              "      <td>the 'roseanne' revival catches up to our thorn...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>https://local.theonion.com/mom-starting-to-fea...</td>\n",
              "      <td>mom starting to fear son's web series closest ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>https://politics.theonion.com/boehner-just-wan...</td>\n",
              "      <td>boehner just wants wife to listen, not come up...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/jk-rowlin...</td>\n",
              "      <td>j.k. rowling wishes snape happy birthday in th...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26704</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/american-...</td>\n",
              "      <td>american politics in moral free-fall</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26705</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/americas-...</td>\n",
              "      <td>america's best 20 hikes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26706</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/reparatio...</td>\n",
              "      <td>reparations and obama</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26707</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/israeli-b...</td>\n",
              "      <td>israeli ban targeting boycott supporters raise...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26708</th>\n",
              "      <td>https://www.huffingtonpost.com/entry/gourmet-g...</td>\n",
              "      <td>gourmet gifts for the foodie 2014</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26709 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            article_link  ... is_sarcastic\n",
              "0      https://www.huffingtonpost.com/entry/versace-b...  ...            0\n",
              "1      https://www.huffingtonpost.com/entry/roseanne-...  ...            0\n",
              "2      https://local.theonion.com/mom-starting-to-fea...  ...            1\n",
              "3      https://politics.theonion.com/boehner-just-wan...  ...            1\n",
              "4      https://www.huffingtonpost.com/entry/jk-rowlin...  ...            0\n",
              "...                                                  ...  ...          ...\n",
              "26704  https://www.huffingtonpost.com/entry/american-...  ...            0\n",
              "26705  https://www.huffingtonpost.com/entry/americas-...  ...            0\n",
              "26706  https://www.huffingtonpost.com/entry/reparatio...  ...            0\n",
              "26707  https://www.huffingtonpost.com/entry/israeli-b...  ...            0\n",
              "26708  https://www.huffingtonpost.com/entry/gourmet-g...  ...            0\n",
              "\n",
              "[26709 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5k2YPtebQIK"
      },
      "source": [
        "data = data.drop('article_link',axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3t0Fix5fnY8",
        "outputId": "d7e83ca0-63c5-4f40-8d99-d9de5b635b70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<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>headline</th>\n",
              "      <th>is_sarcastic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>former versace store clerk sues over secret 'b...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the 'roseanne' revival catches up to our thorn...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>mom starting to fear son's web series closest ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>boehner just wants wife to listen, not come up...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>j.k. rowling wishes snape happy birthday in th...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26704</th>\n",
              "      <td>american politics in moral free-fall</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26705</th>\n",
              "      <td>america's best 20 hikes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26706</th>\n",
              "      <td>reparations and obama</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26707</th>\n",
              "      <td>israeli ban targeting boycott supporters raise...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26708</th>\n",
              "      <td>gourmet gifts for the foodie 2014</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26709 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                headline  is_sarcastic\n",
              "0      former versace store clerk sues over secret 'b...             0\n",
              "1      the 'roseanne' revival catches up to our thorn...             0\n",
              "2      mom starting to fear son's web series closest ...             1\n",
              "3      boehner just wants wife to listen, not come up...             1\n",
              "4      j.k. rowling wishes snape happy birthday in th...             0\n",
              "...                                                  ...           ...\n",
              "26704               american politics in moral free-fall             0\n",
              "26705                            america's best 20 hikes             0\n",
              "26706                              reparations and obama             0\n",
              "26707  israeli ban targeting boycott supporters raise...             0\n",
              "26708                  gourmet gifts for the foodie 2014             0\n",
              "\n",
              "[26709 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WOX_WrNfvFAw"
      },
      "source": [
        "### **Tiền xử lí dữ liệu với thư viện nltk:**\n",
        "1. Loại bỏ article_link trong DataFrame.\n",
        "2. Duyệt qua tất cả các headline trong dataset hiện có, ta thực hiện loại bỏ tất cả các kí tự không phải là chữ cái.\n",
        "3. Chuyển chuỗi bình luận thành các từ riêng biệt với hàm split().\n",
        "4. Loại bỏ các từ không có nhiều ý nghĩa trong câu với thư viện PorterStemmer và chuyển các từ ngữ trong câu về dạng nguyên mẫu.\n",
        "5. Ghép các từ trong một headline lại thành một câu thông thường và thêm vào trong một List."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PrdNEpVUfn9s",
        "outputId": "f4541f82-a3bc-4f88-9074-e7b9ae5ec7b4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "nltk.download('stopwords')\n",
        "\n",
        "corpus =[]\n",
        "\n",
        "for headline in data.values[:,0]:\n",
        "  headline = re.sub('[^a-zA-Z]', ' ', headline)\n",
        "  headline = headline.split()\n",
        "  ps=PorterStemmer()\n",
        "  headline = [ps.stem(word) for word in headline if word not in stopwords.words(\"english\")]\n",
        "  headline = \" \".join(headline)\n",
        "  corpus.append(headline)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XCF9sCiLjEtW"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer()\n",
        "X = cv.fit_transform(corpus).toarray()\n",
        "Y = data.iloc[:,1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2VC-enPvIlH"
      },
      "source": [
        "### **Tiền xử lí với thư viện CountVectorizer().**\n",
        "1. Với mỗi một headline trong corpus sẽ được đưa về dạng một vector.\n",
        "2. Mỗi vector được đưa về dạng array."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-ixPvnYm29d",
        "outputId": "5ff59f14-454f-421d-8463-1ddc4e618c37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,Y,train_size=0.8,random_state=0)\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "model = LogisticRegression().fit(X_train,y_train)\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test,model.predict(X_test))\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(cm, annot=True,fmt='.0f', linewidths = 0.5, square = True, cmap='Blues')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.ylabel('Actual label')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AsmPlwsnjIN",
        "outputId": "4ebeee65-38d6-470c-94ad-f63e18bee2e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(model.score(X_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7909022837888431\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADXBgDHcrtn3",
        "outputId": "89b91899-6bf9-4e1c-ed9e-3117987e346b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(X.shape[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "16899\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzTZcXfPpLwo"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(max_features=16000)\n",
        "X = cv.fit_transform(corpus).toarray()\n",
        "Y = data.iloc[:,1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73DcO-0QrpnR",
        "outputId": "9c141d02-2227-4af6-f5bc-7a07cb00a3ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,Y,train_size=0.8,random_state=0)\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "model = LogisticRegression().fit(X_train,y_train)\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test,model.predict(X_test))\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(cm, annot=True,fmt='.0f', linewidths = 0.5, square = True, cmap='Blues')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.ylabel('Actual label')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/vS9HfPAd/O3ZFwE44YxxXHPzvQBsN2ojzvjPQ1hrzTVYvDh5579+h/kvL+Sas49hw/WGMG/+AgD2/7czlnxeUvcWLVrEIQf9C+tvsAFn/OjHS9q//a2T+dXll3HLxDsBOP/cn3LFZb+g/4D+DB06jK+f/C022qjbJxOoID1ZDLEEFkPqkYWLFvOV0y5n0n3TGPy61fnTz7/MdbfeB8APLrie7//suqWO79+/H2NPPoxP/Nf53P3AdIatvSYLFi5asv/wr57HHff+tanfQXqtuPBn57PFFm/gxTmv/E/E5HvuZvbs55c6bqutt+bnl17GoEGDuPTin/O9757CKd/9frO7K7U9h8nUI08+M5tJ900D4MW587nvkSfZaPg6nR7/nrdvxT0PTufuB6pV0Gc9P4fFi/vkkTJSUZ568kluuvEGDvyXDy1pW7RoEaed+h2OPe6LSx2709t2ZtCgQQD845u35+knn2xqX9X+Ipq3tbM+S4YiYivgAF55Wux0YFxmTumra6o5Nh0xjO3fOJIJ9zzK27ffgiMP3pWP7LcTd9z7V75y2uU898I8Rm26Ppkw7odHsd7Qwfzymts57bzfLTnHj0/8VxYtXsyvrpvEt8++uoXfRlq1fOfb3+LY477InDlzlrRd/PML2G33PRg+fP1OP3fFZb9kl3ft2owuSqucPkmGIuLLwMVUq0neVm8BXBQRX+nic6MjYmJETBwzpssH26pF1hw0kItO/SRfPPUyXpjzEmf/4ia22f9E3nbwt3nymdl8+/MfBGBA//68Y4ctOPyr57LHEafx/ne/md122hKAw//jXP7poG/xniO+xy47vIGP7LdTK7+StMr4ww3XM2zYMLbZdrslbU8//RTXXnM1h3z0Xzv93G9+fSX3Tr6Hjx/xyWZ0U6uQdnpQayv1VTL0CWDbzFzQ2BgRpwGTqZ418iqZOQboqILymDOP7qPuaWUMGNCPi079FJf8diJX/v4vADw964Ul+8de/kcuP/1IAKY//Rw33/EQM5+r/u/16psns8NWm3DDbQ/wxN+qeQ0vzp3PJb+dyD9t+3p+/pvbmvxtpFXPpDvv4IYbfs/NN93I/PnzmTPnRT54wH4MXG0g+++zFwAvvTSP/fbek99cPR6AW/78J34y5izOOfcCBg4c2MruS22rr4qhxcBGVA9VazSi3qdV0FknfJT7H3mS0y/4/ZK2DdcbwpPPzAbggHe/mXsfmgHA+D/dy7GHvYdBa6zGywsW8a4d/4EfXHA9/fv3Y521BjHzuTkMGNCPfXfdjt/fen9Lvo+0qjnm2OM45tjjAJhw262cd+7Ype4mA9j5rTssKYSmTLmXb3z9a/zoxz9h3XXXbXp/1f7aPbFplr4qhj4HXBcRDwKP122bAv8AGPesgt6x/RZ8dL+3cfcD07nl4mqk84QzxnHQe9/Km944kszksRmz+OzJFwHw3AvzOP2C33PzBV8iM7nm5slcffNkXrfGQMb98ChWG9Cf/v37cf2t9zH28j+28qtJr1nfO/U7zJ07ly8eewwAG44Ywek/PKvFvZLaT1QPfu2DE0f0A3Zi6QnUEzJzUeefWkoO2sG6SWq2eXeewUsLW90LqTxrVPFEU6Oa7U+8rmm3+U46cY+2jaH67G6yzFwM3NJX55ckSeoNLrooSVKhnDNUcdFFSZJUNJMhSZIKZTBUMRmSJElFsxiSJElFc5hMkqRCOYG6YjIkSZKKZjIkSVKhDIYqJkOSJKloJkOSJBXKOUMVkyFJklQ0kyFJkgplMFQxGZIkSUUzGZIkqVDOGaqYDEmSpKKZDEmSVCiDoYrJkCRJKprJkCRJhXLOUMVkSJIkFc1kSJKkQhkMVUyGJElS0SyGJElS0RwmkySpUE6grpgMSZKkopkMSZJUKIOhismQJEkqmsmQJEmFcs5QxWRIkiQVzWRIkqRCmQxVTIYkSVLRTIYkSSqUwVDFZEiSJBXNZEiSpEI5Z6hiMiRJkopmMiRJUqEMhiomQ5IkqWgmQ5IkFco5QxWTIUmSVDSLIUmSVDSHySRJKpSjZBWTIUmS1HIRMTYino6IexraLomISfX2aERMqts3i4h5DfvOavjMjhFxd0RMjYjTowcTo0yGJEkqVL/2iobOBc4Azu9oyMwPd7yOiO8Czzcc/1Bmbr+c85wJfAq4FbgK2Bv4bVcXNhmSJEktl5k3ArOWt69Odw4CLurqHBExAhiSmbdkZlIVVh/o7toWQ5IkFSqimVuMjoiJDdvoFejqu4CnMvPBhrbNI+LOiPhDRLyrbtsYmNZwzLS6rUsOk0mSpD6XmWOAMSv58UNYOhWaAWyamTMjYkfgVxGx7cr2zWJIkqRCrQqLLkbEAOCDwI4dbZk5H5hfv749Ih4CtgSmAyMbPj6ybuuSw2SSJKmdvQe4LzOXDH9FxPCI6F+/3gIYBTycmTOA2RGxcz3P6FDgyu4uYDIkSVKh+rVRMBQRFwG7AetFxDTghMw8BziYV0+c3hU4KSIWAIuBIzOzY/L1Z6juTBtEdRdZl3eSgcWQJElqA5l5SCftH19O22XAZZ0cPxHYbkWubTEkSVKhVoU5Q83gnCFJklQ0kyFJkgplMFQxGZIkSUUzGZIkqVCB0RCYDEmSpMJZDEmSpKI5TCZJUqHaadHFVjIZkiRJRTMZkiSpUC66WDEZkiRJRTMZkiSpUAZDFZMhSZJUNJMhSZIK1c9oCDAZkiRJhTMZkiSpUAZDFZMhSZJUNJMhSZIK5TpDFZMhSZJUNJMhSZIKZTBUMRmSJElFMxmSJKlQrjNUMRmSJElFsxiSJElFc5hMkqRCOUhWMRmSJElFMxmSJKlQLrpYMRmSJElFMxmSJKlQ/QyGAJMhSZJUuE6ToYh4AciOt/WfWb/OzBzSx32TJEl9yDlDlU6Locxcq5kdkSRJaoUezRmKiHcCozLzpxGxHrBWZj7St12TJEl9yWCo0u2coYg4AfgycHzdNBC4oC87JUmS1Cw9SYYOBHYA7gDIzCciwiE0SZJWcc4ZqvTkbrKXMzOpJ1NHxJp92yVJkqTm6UkydGlE/BhYJyI+BRwBnN233ZIkSX3NdYYq3RZDmXlqROwJzAa2BL6WmeP7vGeSJElN0NMVqO8GBlENld3dd92RJEnN4pyhSk/uJvskcBvwQeBDwC0RcURfd0ySJKkZepIMfRHYITNnAkTEusCfgLF92TFJkqRm6EkxNBN4oeH9C3WbJElahTlIVunq2WSfr19OBW6NiCup5gwdANzVhL5JkiT1ua6SoY6FFR+qtw5X9l13JElSs/RzAjXQ9YNav97MjkiSJLVCt3OGImI48CVgW2CNjvbMfHcf9kuSJPUxg6FKTx7HcSFwH7A58HXgUWBCH/ZJkiSpaXpyN9m6mXlORByTmX8A/hARFkOSJK3iXHSx0pNiaEH954yIeB/wBDCs77okSZLUPD0phk6OiLWB44AfAEOAY/u0V5Ikqc8ZDFV68qDW39Qvnwd279vuSJIkNVdXiy7+gGqRxeXKzH/vkx5JkqSmcJ2hSlfJ0MSm9UKSJKlFulp08bxmdkSSJDWXwVClJ+sMSZIkvWZZDEmSVKiIaNrWg76MjYinI+KehrYTI2J6REyqt30b9h0fEVMj4v6IeG9D+95129SI+EpPfoee3FrfMvPuPKPVXZCKtEZb/5dB0mvUucAZwPnLtH8vM09tbIiIbYCDqR4VthHwu4jYst79Q2BPYBowISLGZea9XV3Yu8kkSSpUOw0PZeaNEbFZDw8/ALg4M+cDj0TEVGCnet/UzHwYICIuro9duWKINrib7K+z5re6C1JxNh22Oh/66R2t7oZUnF8e/pZWd6FPRcRoYHRD05jMHNODjx4dEYdS1SXHZeazwMbALQ3HTKvbAB5fpv1t3V3Au8kkSVKfqwufnhQ/jc4EvkE1UvUN4LvAEb3cte7nDEXEcODLwDbAGh3tmfnu3u6MJElqnnZ/UGtmPtXxOiLOBjqeijEd2KTh0JF1G120d6onw4UXAlOAzYGvA48CPrVekiT1qYgY0fD2QKDjTrNxwMERsXpEbA6MAm6jqk9GRcTmETGQapL1uO6u05N7RtbNzHMi4pjM/APwh4iwGJIkaRXXr42CoYi4CNgNWC8ipgEnALtFxPZUw2SPAp8GyMzJEXEp1cTohcBRmbmoPs/RwDVAf2BsZk7u7to9KYYW1H/OiIj3AU8Aw3r87SRJkrqRmYcsp/mcLo7/JvDN5bRfBVy1ItfuSTF0ckSsDRwH/AAYAhy7IheRJEntp52SoVbqthjKzI7JSs8Du/dtdyRJkpqrJ3eT/ZTlLL6Ymb1+a5skSWqedr+brFl6Mkz2m4bXa1DN5n6ib7ojSZLUXD0ZJrus8X092/vmPuuRJElqCucMVVbmsSSjgPV7uyOSJEmt0JM5Qy+w9JyhJ6lWpJYkSaswpwxVejJMtlYzOiJJktQK3Q6TRcR1PWmTJEmrln4RTdvaWafJUESsAbyOalnsoUDHNxkCbNyEvkmSJPW5robJPg18DtgIuJ1XiqHZwBl93C9JktTHVuYuqteiTouhzPxf4H8j4rOZ+YMm9kmSJKlpelIULo6IdTreRMTQiPhMH/ZJkiSpaXpSDH0qM5/reJOZzwKf6rsuSZKkZoho3tbOelIM9Y+Gh5dERH9gYN91SZIkqXl68myyq4FLIuLH9ftP122SJGkV1u63vDdLT4qhLwOjgX+r348Hzu6zHkmSJDVRt8Nkmbk4M8/KzA9l5oeAewHvLpMkaRXnnKFKT5IhImIH4BDgIOAR4PK+7JQkSVKzdLUC9ZZUBdAhwDPAJUBk5u5N6pskSepD/do8sWmWrpKh+4CbgP0ycypARBzblF5JkiQ1SVfF0AeBg4HrI+Jq4GJeeSSHJElaxXk3WaXTCdSZ+avMPBjYCrie6jll60fEmRGxV7M6KEmS1Jd6cjfZnMz8eWbuD4wE7qS63V6SJK3CvJusskIPrM3MZzNzTGbu0VcdkiRJaqYe3VovSZJee7ybrLJCyZAkSdJrjcmQJEmFCm8SB0yGJElS4SyGJElS0RwmkySpUE6grpgMSZKkopkMSZJUKJOhismQJEkqmsmQJEmFinZ/TkaTmAxJkqSimQxJklQo5wxVTIYkSVLRTIYkSSqUU4YqJkOSJKloJkOSJBWqn9EQYDIkSZIKZzIkSVKhvJusYjIkSZKKZjIkSVKhnDJUMRmSJElFsxiSJElFc5hMkqRC9cNxMjAZkiRJhTMZkiSpUE6grpgMSZKkopkMSZJUKBddrJgMSZKkopkMSZJUKB/UWjEZkiRJRbMYkiSpUBHN27rvS4yNiKcj4p6GtlMi4r6IuCsiroiIder2zSJiXkRMqrezGj6zY0TcHRFTI+L0iO6vbjEkSZLawbnA3su0jQe2y8w3AQ8Axzfseygzt6+3IxvazwQ+BYyqt2XP+SoWQ5IkFapfRNO27mTmjcCsZdquzcyF9dtbgJFdnSMiRgBDMvOWzEzgfOAD3f4O3fZOkiTp7xQRoyNiYsM2egVPcQTw24b3m0fEnRHxh4h4V922MTCt4ZhpdVuXvJtMkqRCNfNmsswcA4xZmc9GxFeBhcCFddMMYNPMnBkROwK/iohtV7ZvFkOSJKltRcTHgf2APeqhLzJzPjC/fn17RDwEbAlMZ+mhtJF1W5ccJpMkqVD9mritjIjYG/gS8P7MnNvQPjwi+tevt6CaKP1wZs4AZkfEzvVdZIcCV3Z3HZMhSZLUchFxEbAbsF5ETANOoLp7bHVgfH2H/C31nWO7AidFxAJgMXBkZnZMvv4M1Z1pg6jmGDXOM1ouiyFJktRymXnIcprP6eTYy4DLOtk3EdhuRa5tMSRJUqF6sB5hEZwzJEmSimYyJElSocyFKiZDkiSpaCZDkiQVqiePySiByZAkSSqayZAkSYUyF6qYDEmSpKKZDEmSVCinDFVMhiRJUtFMhiRJKpQrUFdMhiRJUtFMhiRJKpSJSMXfQZIkFc1kSJKkQjlnqGIyJEmSimYxJEmSiuYwmSRJhXKQrGIyJEmSimYyJElSoZxAXTEZkiRJRTMZkiSpUCYiFX8HSZJUNJMhSZIK5ZyhismQJEkqmsmQJEmFMheqmPjh1ScAABDRSURBVAxJkqSimQxJklQopwxVTIYkSVLRTIYkSSpUP2cNASZDkiSpcCZDkiQVyjlDFZMhSZJUNIshSZJUNIfJJEkqVDiBGjAZkiRJhTMZkiSpUE6grlgMaaW8+MJsTvvvE3n0oakQwRe+ehKXX3IBj//1UQDmvPACa661Fj8+/xcsXLiA0751Ig/eP4VFixax5z77c8hhn2ztF5BWEZ/ZZVN23GRtnn9pIZ//1ZQl7ftsPZy9txrO4kxunzabCyZOZ/jggXz/wG144vmXAHjwb3MY8+fHWWNAP76x75ZLPrvumgO58aFZnHvbtKZ/H6kdWQxppfzoe//DW3feha996zQWLFjA/Jfm8Z8nn7Jk/1mnn8qaaw4G4MbrrmXBggWcfeHlvPTSPD55yIHsvtc+bDhi41Z1X1plXD91Fr+972989l2bLWnbdsPB/NOma3PclVNYuDgZssYr/yl/6oX5fHHcfUud46WFi5dq+5/9t+LWx57r876r/bnoYsU5Q1phc158gbsn3c4++38QgNVWW43Baw1Zsj8zufG6a9h9r32qhghemjeXRQsX8vL8+QxYbTVe97rBrei6tMqZ8tSLvDh/0VJt791qOFfc9RQLFycAs19a2OPzjRiyOmsPGsCUp17s1X5Kq7KmJ0MRcXhm/rTZ11XvmfHEdNZeZxinnPxfPPzgA4zaams+c+yXGTTodQDcPel21hm2LiM3eT0Au757T/580w18eP89mP/SPI485ksMWXvtVn4FaZU2YsjqbL3BYD6y40a8vGgx50+YzkPPzAVg/cEDOeX9WzF3wSIuvuMJpjw1Z6nP7rL5UP70yLOt6LbakHOGKq1Ihr7e2Y6IGB0REyNi4pgxY5rZJ62ARYsW8eADU9j/gwdx1vmXssagQVxy/tgl+68f/1t233OfJe/vm3wP/fr14+Jf/47zL/stv7zoPGZMd66CtLL69wsGr96f439zPz+bMJ3P77Y5AM/OXcCRv7iHL467j/Num8Yx/7w5g1Zb+j/zu2wxlJsfntWKbkttq0+SoYi4q7NdwAadfS4zxwAdVVD+ddb83u6aesHw9Tdg+PAN2HrbNwGw6+57cvHPqmJo0cKF3HzDdfzo3IuXHP/7a6/irTvvwoABqzF02Lps+4878MCUyYzYeGRL+i+t6mbOeXnJnJ+pz8wlE4asPoDZ8xcuGVJ7eOY8npo9n42GrMFDM6vU6PVDB9E/godnzmtZ39VeTIYqfZUMbQAcCuy/nG1mH11TTTJs3fUYvsEGPP7YIwDcOfFWXr/ZFgDcMeEWNnn95gxff8Mlx6+/4Qgm3X4bAPPmzWXK5LvYZLPNm99x6TViwl+fZ7sRawHVkNmA/sHs+QsZsvoA+tV/ua0/eCAbDlmdp1545X8q37nFUG5+2CEyaVl9NWfoN8DgzJy07I6IuKGPrqkmOurzx/PfJx7PwgULGLHxSL7w1W8AcP3vrl5qiAzggH85mFNO/i8++ZEDyUze+74D2OIftlzeaSUt43P/vBnbbrgWa60xgB8ftB2X3DmD3z84k8+88/Wc9oGtWbg4OeOmRwHYesPBHLzDCBYuThIY8+fHefHlVyZfv2PzoXxz/NTWfBG1JVegrkRmtroPnXGYTGqBTYetzod+ekeruyEV55eHvwVobnUyfsozTSsC9tx6vbatvFxnSJKkQvVr2/KkuVxnSJIkFc1kSJKkQjlnqGIyJEmSimYxJEmSiuYwmSRJhXLRxYrJkCRJKprJkCRJhXICdcVkSJIkFc1iSJKkQvWL5m3diYixEfF0RNzT0DYsIsZHxIP1n0Pr9oiI0yNiakTcFRFvafjMYfXxD0bEYT36HVb8p5MkSep15wJ7L9P2FeC6zBwFXFe/B9gHGFVvo4EzoSqegBOAtwE7ASd0FFBdsRiSJKlQ0cR/upOZNwKzlmk+ADivfn0e8IGG9vOzcguwTkSMAN4LjM/MWZn5LDCeVxdYr2IxJEmS+lxEjI6IiQ3b6B58bIPMnFG/fhLYoH69MfB4w3HT6rbO2rvk3WSSJBWqmesMZeYYYMzf8fmMiOzFLi1hMiRJktrVU/XwF/WfT9ft04FNGo4bWbd11t4liyFJkgoVTdxW0jig446ww4ArG9oPre8q2xl4vh5OuwbYKyKG1hOn96rbuuQwmSRJarmIuAjYDVgvIqZR3RX2beDSiPgE8BhwUH34VcC+wFRgLnA4QGbOiohvABPq407KzGUnZb+KxZAkSYXq10YPJ8vMQzrZtcdyjk3gqE7OMxYYuyLXdphMkiQVzWRIkqRCtU8u1FomQ5IkqWgWQ5IkqWgOk0mSVCrHyQCTIUmSVDiTIUmSCtWTB6iWwGRIkiQVzWRIkqRCtdGaiy1lMiRJkopmMiRJUqEMhiomQ5IkqWgmQ5IklcpoCDAZkiRJhTMZkiSpUK4zVDEZkiRJRTMZkiSpUK4zVDEZkiRJRTMZkiSpUAZDFZMhSZJUNIshSZJUNIfJJEkqleNkgMmQJEkqnMmQJEmFctHFismQJEkqmsmQJEmFctHFismQJEkqmsmQJEmFMhiqmAxJkqSimQxJklQqoyHAZEiSJBXOZEiSpEK5zlDFZEiSJBXNZEiSpEK5zlDFZEiSJBXNZEiSpEIZDFVMhiRJUtFMhiRJKpXREGAyJEmSCmcxJEmSiuYwmSRJhXLRxYrJkCRJKprJkCRJhXLRxYrJkCRJKprJkCRJhTIYqpgMSZKkopkMSZJUKqMhwGRIkiQVzmRIkqRCuc5QxWRIkiQVzWRIkqRCuc5QxWRIkiQVzWRIkqRCGQxVTIYkSVLRTIYkSSqV0RBgMiRJklosIt4YEZMattkR8bmIODEipje079vwmeMjYmpE3B8R7/17rm8yJEmSWioz7we2B4iI/sB04ArgcOB7mXlq4/ERsQ1wMLAtsBHwu4jYMjMXrcz1TYYkSSpUNPGfFbAH8FBmPtbFMQcAF2fm/Mx8BJgK7LSyv4PFkCRJ6nMRMToiJjZsozs59GDgoob3R0fEXRExNiKG1m0bA483HDOtblspFkOSJBUqonlbZo7JzLc2bGNe3Z8YCLwf+EXddCbwBqohtBnAd/vid7AYkiRJ7WIf4I7MfAogM5/KzEWZuRg4m1eGwqYDmzR8bmTdtlIshiRJKlQ0ceuhQ2gYIouIEQ37DgTuqV+PAw6OiNUjYnNgFHBbzy+zNO8mkyRJLRcRawJ7Ap9uaP5ORGwPJPBox77MnBwRlwL3AguBo1b2TjKwGJIkqVxttOhiZs4B1l2m7WNdHP9N4Ju9cW2HySRJUtFMhiRJKtQKrv/zmmUyJEmSimYyJElSocJgCDAZkiRJhTMZkiSpUAZDFZMhSZJUNJMhSZJKZTQEmAxJkqTCWQxJkqSiOUwmSVKhXHSxYjIkSZKKZjIkSVKhXHSxYjIkSZKKZjIkSVKhDIYqJkOSJKloJkOSJBXKOUMVkyFJklQ0kyFJkoplNAQmQ5IkqXAmQ5IkFco5QxWTIUmSVDSTIUmSCmUwVInMbHUfOtO2HZMkqY80tT554rmXm/Z37UbrDGzb2qudk6G2/dHUvYgYnZljWt0PqTT+u6cV4ZyhinOG1FdGt7oDUqH8d09aQRZDkiSpaO08TCZJkvpQOCMFMBlS33HOgtQa/rsnraB2vptMkiT1oSdnL2haEbDhkNXaNoYyGZIkSUVzzpAkSYVq26imyUyG1KsiYu+IuD8ipkbEV1rdH6kUETE2Ip6OiHta3RdpVWMxpF4TEf2BHwL7ANsAh0TENq3tlVSMc4G9W90JrVoimre1M4sh9aadgKmZ+XBmvgxcDBzQ4j5JRcjMG4FZre6HtCpyzpB608bA4w3vpwFva1FfJEndcJ2hismQJEkqmsmQetN0YJOG9yPrNklSOzIYAkyG1LsmAKMiYvOIGAgcDIxrcZ8kSeqSxZB6TWYuBI4GrgGmAJdm5uTW9koqQ0RcBPwZeGNETIuIT7S6T2p/0cStnfk4DkmSCvXMiwubVgSsN3hA29ZEzhmSJKlQ7b7+T7M4TCZJkopmMSRJkormMJkkSYVy0cWKyZAkSSqaxZDUYhGxKCImRcQ9EfGLiHjd33GucyPiQ/Xrn3T1oNyI2C0i3rES13g0Itbrafsyx7y4gtc6MSK+sKJ9lNQzPqi1YjEktd68zNw+M7cDXgaObNwZESs1nJ2Zn8zMe7s4ZDdghYshSXqtsRiS2stNwD/Uqc1NETEOuDci+kfEKRExISLuiohPA0TljIi4PyJ+B6zfcaKIuCEi3lq/3jsi7oiIv0TEdRGxGVXRdWydSr0rIoZHxGX1NSZExC71Z9eNiGsjYnJE/IQerJ8WEb+KiNvrz4xeZt/36vbrImJ43faGiLi6/sxNEbFVb/yYktQTTqCW2kSdAO0DXF03vQXYLjMfqQuK5zPznyJideCPEXEtsAPwRmAbYAPgXmDsMucdDpwN7Fqfa1hmzoqIs4AXM/PU+rifA9/LzJsjYlOqlcS3Bk4Abs7MkyLifUBPVjY+or7GIGBCRFyWmTOBNYGJmXlsRHytPvfRwBjgyMx8MCLeBvwIePdK/IyStMIshqTWGxQRk+rXNwHnUA1f3ZaZj9TtewFv6pgPBKwNjAJ2BS7KzEXAExHx++Wcf2fgxo5zZeasTvrxHmCbeGVwf0hEDK6v8cH6s/8XEc/24Dv9e0QcWL/epO7rTGAxcEndfgFweX2NdwC/aLj26j24hqS/U7vP5WkWiyGp9eZl5vaNDXVRMKexCfhsZl6zzHH79mI/+gE7Z+ZLy+lLj0XEblSF1dszc25E3ACs0cnhWV/3uWV/A0lqFucMSauGa4B/i4jVACJiy4hYE7gR+HA9p2gEsPtyPnsLsGtEbF5/dljd/gKwVsNx1wKf7XgTER3FyY3AR+q2fYCh3fR1beDZuhDaiiqZ6tAP6Ei3PkI1/DYbeCQi/l99jYiIN3dzDUm9IJr4TzuzGJJWDT+hmg90R0TcA/yYKtm9Aniw3nc+1VPLl5KZfwNGUw1J/YVXhql+DRzYMYEa+HfgrfUE7Xt55a62r1MVU5Ophsv+2k1frwYGRMQU4NtUxViHOcBO9Xd4N3BS3f5R4BN1/yYDB/TgN5GkXuFT6yVJKtTslxY3rQgYska/to2HTIYkSVLL1Qu33l2n1RPrtmERMT4iHqz/HFq3R0ScHhFT6zT7LX/PtS2GJEkqVDRx66Hd60Vo31q//wpwXWaOAq6r30O1DMmoehsNnLmCX30pFkOSJKldHQCcV78+D/hAQ/v5WbkFWKe+iWSlWAxJklSqJkZDETE6IiY2bEutTk+11Ma19Ur0Hfs2yMwZ9esnqRaXBdgYeLzhs9PqtpXiOkOSJKnPZeYYqtXmO/POzJweEesD4yPivmU+nxHRJxO+TYYkSVLLZeb0+s+nqZYN2Ql4qmP4q/7z6frw6VSr23cYWbetFIshSZIK1S6LLkbEmhGxVsdrqkcQ3QOMAw6rDzsMuLJ+PQ44tL6rbGeqZzfOYCU5TCZJklptA+CK+vE/A4CfZ+bVETEBuDQiPgE8BhxUH38VsC8wFZgLHP73XNxFFyVJKtScl5tXBKw5sH0fC+swmSRJKprDZJIkFapto5omMxmSJElFMxmSJKlURkOAyZAkSSqcyZAkSYXqbv2fUpgMSZKkopkMSZJUqPZd+ae5TIYkSVLRXIFakiQVzWRIkiQVzWJIkiQVzWJIkiQVzWJIkiQVzWJIkiQVzWJIkiQV7f8DcV6W9+s4jC8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9em55pVhr_WO",
        "outputId": "4c3e2b21-8f67-40c5-d800-2d88a4345715",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(model.score(X_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7903406963684013\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n350zvh4tgT7"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}