text_classifyer.ipynb

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              "     Unnamed: 0                                          Denuncias  \\\n",
              "0             0  EN EL 2018 PAGABA A MI VECINO MM POR UN TERREN...   \n",
              "1             1  DENUNCIA REALIZADA POR MMMM, REFIERE QUE EN EL...   \n",
              "2             2  DENUNCIA MMMM QUE SU HIJA DE 15 Aï¿½OS DE EDAD...   \n",
              "3             3  DENUNCIA REALIZADA POR MMMM SOBRE UN HECHO DE ...   \n",
              "4             4  DENUNCIA REALIZADA POR MMM, OCURRIO UN ACCIDEN...   \n",
              "..          ...                                                ...   \n",
              "277         277  Robo denunciado en la ciudad de asuncion un ho...   \n",
              "278         278  Robo denunciado en la ciudad de asuncion un ho...   \n",
              "279         279  Robo denunciado en la ciudad de asuncion un ho...   \n",
              "280         280  Asalto denunciado en la ciudad de asuncion un ...   \n",
              "281         281  Asalto denunciado en la ciudad de asuncion a p...   \n",
              "\n",
              "               tipo  category_id  \n",
              "0    denuncia-Legal            0  \n",
              "1    denuncia-Legal            0  \n",
              "2    denuncia-Legal            0  \n",
              "3    denuncia-Legal            0  \n",
              "4    denuncia-Legal            0  \n",
              "..              ...          ...  \n",
              "277  denuncia-Legal            0  \n",
              "278  denuncia-Legal            0  \n",
              "279  denuncia-Legal            0  \n",
              "280  denuncia-Legal            0  \n",
              "281  denuncia-Legal            0  \n",
              "\n",
              "[282 rows x 4 columns]"
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          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "datos_inf = pd.read_csv(\"denuncias_inf.csv\")\n",
        "datos_LEG = pd.read_csv(\"denuncias_LEG.csv\")\n",
        "datos_LEG[\"tipo\"] = \"denuncia-Legal\"\n",
        "datos_LEG['category_id'] = datos_LEG['tipo'].factorize()[0]\n",
        "datos_LEG"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datos_inf[\"tipo\"] = \"denuncia_informatica\"\n",
        "datos_inf[\"category_id\"] = 1\n",
        "datos_inf\n",
        "df = pd.concat([datos_LEG,datos_inf])\n",
        "df = df[df['Denuncias'].notna()]\n"
      ],
      "metadata": {
        "id": "vEe5hlIPLQwU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from spacy.lang.es import STOP_WORDS\n",
        "\n",
        "tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', encoding='latin-1', ngram_range=(1, 2), stop_words=list(STOP_WORDS))\n",
        "\n",
        "features = tfidf.fit_transform(df.Denuncias).toarray()\n",
        "\n",
        "features.shape\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3OIjlOJWF7Xb",
        "outputId": "6f27212a-e1e6-4370-fcb1-e6a9f73f3e63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(505, 622)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure(figsize=(8,6))\n",
        "df.groupby('tipo').Denuncias.count().plot.bar(ylim=0)\n",
        "plt"
      ],
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        "id": "ohwQniYXbGLI",
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      "execution_count": null,
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        {
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            "text/plain": [
              "<module 'matplotlib.pyplot' from '/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py'>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from io import StringIO\n",
        "category_id_df = df[['tipo', 'category_id']].drop_duplicates().sort_values('category_id')\n",
        "category_to_id = dict(category_id_df.values)\n",
        "id_to_category = dict(category_id_df[['category_id', 'tipo']].values)\n",
        "labels = df.category_id\n"
      ],
      "metadata": {
        "id": "ZYtU3yOjLE-d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_selection import chi2\n",
        "import numpy as np\n",
        "from spacy.lang.es import STOP_WORDS\n",
        "\n",
        "# Vocabulario personalizado para denuncias legales\n",
        "legal_tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', ngram_range=(1, 2), stop_words=list(STOP_WORDS))\n",
        "legal_tfidf.fit(df[df['tipo'] == 'denuncia-Legal']['Denuncias'])\n",
        "legal_vocab = legal_tfidf.vocabulary_\n",
        "\n",
        "# Vocabulario personalizado para denuncias informáticas\n",
        "informatica_tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', ngram_range=(1, 2), stop_words=list(STOP_WORDS))\n",
        "informatica_tfidf.fit(df[df['tipo'] == 'denuncia_informatica']['Denuncias'])\n",
        "informatica_vocab = informatica_tfidf.vocabulary_\n",
        "\n",
        "# Matriz de características para denuncias legales\n",
        "legal_features = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', ngram_range=(1, 2), stop_words=list(STOP_WORDS), vocabulary=legal_vocab).fit_transform(df[df['tipo'] == 'denuncia-Legal']['Denuncias'])\n",
        "legal_labels = labels[df['tipo'] == 'denuncia-Legal']\n",
        "\n",
        "# Matriz de características para denuncias informáticas\n",
        "informatica_features = TfidfVectorizer(sublinear_tf=True, min_df=5, norm='l2', ngram_range=(1, 2), stop_words=list(STOP_WORDS), vocabulary=informatica_vocab).fit_transform(df[df['tipo'] == 'denuncia_informatica']['Denuncias'])\n",
        "informatica_labels = labels[df['tipo'] == 'denuncia_informatica']\n",
        "\n",
        "# Calcular chi2 para denuncias legales\n",
        "legal_chi2 = chi2(legal_features, legal_labels == 0)\n",
        "\n",
        "# Calcular chi2 para denuncias informáticas\n",
        "informatica_chi2 = chi2(informatica_features, informatica_labels == 1)\n",
        "\n",
        "# Función para imprimir los términos más correlacionados\n",
        "def print_most_correlated_terms(features, labels, n=2):\n",
        "    features_chi2 = chi2(features, labels)[0]\n",
        "    indices = np.argsort(features_chi2)[::-1]\n",
        "    feature_names = np.array(tfidf.get_feature_names_out())[indices]\n",
        "    unigrams = [v for v in feature_names if len(v.split(' ')) == 1][:n]\n",
        "    bigrams = [v for v in feature_names if len(v.split(' ')) == 2][:n]\n",
        "    print(\". Most correlated unigrams:\\n. {}\".format('\\n. '.join(unigrams)))\n",
        "    print(\". Most correlated bigrams:\\n. {}\".format('\\n. '.join(bigrams)))\n",
        "\n",
        "# Imprimir los términos más correlacionados para denuncias legales\n",
        "print(\"# 'denuncia-Legal':\")\n",
        "print_most_correlated_terms(legal_features, legal_labels)\n",
        "\n",
        "# Imprimir los términos más correlacionados para denuncias informáticas\n",
        "print(\"# 'denuncia_informatica':\")\n",
        "print_most_correlated_terms(informatica_features, informatica_labels)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PhMDPKv64tP",
        "outputId": "aed54210-207c-48e4-b633-17da941f9439"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# 'denuncia-Legal':\n",
            ". Most correlated unigrams:\n",
            ". desconocidas\n",
            ". aplicaciï\n",
            ". Most correlated bigrams:\n",
            ". asaltado delincuente\n",
            ". aplicaciï ½n\n",
            "# 'denuncia_informatica':\n",
            ". Most correlated unigrams:\n",
            ". pagos\n",
            ". computadora\n",
            ". Most correlated bigrams:\n",
            ". codigos tenia\n",
            ". compaã ia\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Entrenamiento del modelo para el aprendizaje automatico"
      ],
      "metadata": {
        "id": "4dXMnvyx3ouf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(df['Denuncias'], df['tipo'], random_state = 0)\n",
        "count_vect = CountVectorizer()\n",
        "X_train_counts = count_vect.fit_transform(X_train)\n",
        "tfidf_transformer = TfidfTransformer()\n",
        "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
        "\n",
        "clf = MultinomialNB().fit(X_train_tfidf, y_train)"
      ],
      "metadata": {
        "id": "AglI0VozY-YU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U_5MzjN31NId"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Crear una lista de denuncias\n",
        "Denuncias = [\"Un grupo de usuarios de una red social denunció que sus datos personales fueron filtrados debido a una falla de seguridad en la plataforma.\",    \"Una empresa de comercio electrónico denunció que fue víctima de un ataque cibernético que comprometió la seguridad de los datos de sus clientes.\",    \"Un ciudadano denunció que su cuenta bancaria fue hackeada y que el atacante realizó varias transacciones fraudulentas en su nombre.\",    \"Un trabajador de una empresa de tecnología denunció que la compañía no tomó medidas adecuadas para proteger la información personal de los usuarios.\",    \"Una organización de derechos digitales denunció que un proveedor de servicios de internet estaba monitoreando ilegalmente el tráfico en línea de sus usuarios.\",    \"Un grupo de consumidores presentó una denuncia ante una agencia de protección de datos por la recolección y uso ilegal de información personal por parte de una aplicación de salud.\",    \"Un ciudadano denunció que un sitio web de compras en línea le vendió un producto falso y que su información personal fue utilizada sin su consentimiento.\",    \"Una empresa de tecnología denunció que un competidor había robado su propiedad intelectual y estaba utilizando su tecnología sin permiso.\",    \"Un periodista denunció que su correo electrónico fue hackeado y que el atacante había accedido a información confidencial.\",    \"Un grupo de usuarios de una aplicación de mensajería denunció que sus conversaciones privadas estaban siendo monitoreadas y analizadas por la empresa sin su consentimiento.\" ,\"Un vecino denunció a su comunidad de vecinos por incumplir las normas de convivencia y perturbar el descanso de los demás vecinos.\",    \"Un grupo de trabajadores presentó una denuncia contra su empresa por no cumplir con las medidas de seguridad necesarias para prevenir accidentes laborales.\",    \"Un ciudadano presentó una demanda contra el ayuntamiento por negligencia en el mantenimiento de las calles, lo que provocó un accidente de tráfico.\",    \"Un grupo de mujeres presentó una denuncia contra una empresa por discriminación de género en la selección de personal.\",    \"Un cliente presentó una denuncia contra una tienda de electrónica por venderle un producto defectuoso y negarse a hacerse responsable.\",    \"Un grupo de vecinos presentó una denuncia contra una empresa por contaminar el aire y provocar problemas respiratorios en la población.\",    \"Un trabajador presentó una denuncia contra su jefe por acoso laboral y discriminación.\",    \"Un ciudadano presentó una demanda contra una aerolínea por cancelar su vuelo sin previo aviso y no ofrecer una alternativa adecuada.\",    \"Un grupo de usuarios de redes sociales presentó una denuncia contra una plataforma por violar su privacidad y compartir sus datos sin consentimiento.\",    \"Un ciudadano presentó una denuncia contra una cadena de supermercados por vender productos caducados y en mal estado.\"]\n",
        "# Crear un DataFrame para almacenar las denuncias y las predicciones\n",
        "resultados = pd.DataFrame(columns=['Denuncia', 'Prediccion'])\n",
        "\n",
        "# Iterar sobre cada denuncia y hacer la predicción\n",
        "for denuncia in Denuncias:\n",
        "    # Realizar la predicción utilizando el modelo\n",
        "    prediccion = clf.predict(count_vect.transform([denuncia]))[0]\n",
        "\n",
        "    # Agregar la denuncia y la predicción al DataFrame de resultados\n",
        "    resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
        "\n",
        "# Guardar los resultados en un archivo Excel\n",
        "resultados.to_excel('resultados_predicciones.xlsx', index=False)\n",
        "print(\"hi\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlWBMf9bZAIS",
        "outputId": "fd7b24a5-00eb-4b74-8ef9-c663dba0e598"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-8-5afc0ce4d0ee>:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hi\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Dividir los datos en conjuntos de entrenamiento y prueba\n",
        "X_train, X_test, y_train, y_test = train_test_split(df['Denuncias'], df['tipo'], random_state=0)\n",
        "\n",
        "# Crear el vectorizador de conteo\n",
        "count_vect = CountVectorizer()\n",
        "X_train_counts = count_vect.fit_transform(X_train)\n",
        "\n",
        "# Aplicar la transformación TF-IDF\n",
        "tfidf_transformer = TfidfTransformer()\n",
        "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
        "\n",
        "# Crear y entrenar el modelo RandomForestClassifier\n",
        "clf = RandomForestClassifier(n_estimators=100, random_state=0)\n",
        "clf.fit(X_train_tfidf, y_train)\n",
        "\n",
        "# Crear una lista de denuncias\n",
        "Denuncias = [\"Un grupo de usuarios de una red social denunció que sus datos personales fueron filtrados debido a una falla de seguridad en la plataforma.\",    \"Una empresa de comercio electrónico denunció que fue víctima de un ataque cibernético que comprometió la seguridad de los datos de sus clientes.\",    \"Un ciudadano denunció que su cuenta bancaria fue hackeada y que el atacante realizó varias transacciones fraudulentas en su nombre.\",    \"Un trabajador de una empresa de tecnología denunció que la compañía no tomó medidas adecuadas para proteger la información personal de los usuarios.\",    \"Una organización de derechos digitales denunció que un proveedor de servicios de internet estaba monitoreando ilegalmente el tráfico en línea de sus usuarios.\",    \"Un grupo de consumidores presentó una denuncia ante una agencia de protección de datos por la recolección y uso ilegal de información personal por parte de una aplicación de salud.\",    \"Un ciudadano denunció que un sitio web de compras en línea le vendió un producto falso y que su información personal fue utilizada sin su consentimiento.\",    \"Una empresa de tecnología denunció que un competidor había robado su propiedad intelectual y estaba utilizando su tecnología sin permiso.\",    \"Un periodista denunció que su correo electrónico fue hackeado y que el atacante había accedido a información confidencial.\",    \"Un grupo de usuarios de una aplicación de mensajería denunció que sus conversaciones privadas estaban siendo monitoreadas y analizadas por la empresa sin su consentimiento.\" ,\"Un vecino denunció a su comunidad de vecinos por incumplir las normas de convivencia y perturbar el descanso de los demás vecinos.\",    \"Un grupo de trabajadores presentó una denuncia contra su empresa por no cumplir con las medidas de seguridad necesarias para prevenir accidentes laborales.\",    \"Un ciudadano presentó una demanda contra el ayuntamiento por negligencia en el mantenimiento de las calles, lo que provocó un accidente de tráfico.\",    \"Un grupo de mujeres presentó una denuncia contra una empresa por discriminación de género en la selección de personal.\",    \"Un cliente presentó una denuncia contra una tienda de electrónica por venderle un producto defectuoso y negarse a hacerse responsable.\",    \"Un grupo de vecinos presentó una denuncia contra una empresa por contaminar el aire y provocar problemas respiratorios en la población.\",    \"Un trabajador presentó una denuncia contra su jefe por acoso laboral y discriminación.\",    \"Un ciudadano presentó una demanda contra una aerolínea por cancelar su vuelo sin previo aviso y no ofrecer una alternativa adecuada.\",    \"Un grupo de usuarios de redes sociales presentó una denuncia contra una plataforma por violar su privacidad y compartir sus datos sin consentimiento.\",    \"Un ciudadano presentó una denuncia contra una cadena de supermercados por vender productos caducados y en mal estado.\"]\n",
        "\n",
        "# Crear un DataFrame para almacenar las denuncias y las predicciones\n",
        "resultados = pd.DataFrame(columns=['Denuncia', 'Prediccion'])\n",
        "\n",
        "# Iterar sobre cada denuncia y hacer la predicción\n",
        "for denuncia in Denuncias:\n",
        "    # Realizar la predicción utilizando el modelo\n",
        "    prediccion = clf.predict(count_vect.transform([denuncia]))[0]\n",
        "\n",
        "    # Agregar la denuncia y la predicción al DataFrame de resultados\n",
        "    resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
        "\n",
        "# Guardar los resultados en un archivo Excel\n",
        "resultados.to_excel('resultados_predicciones_Randomforrest.xlsx', index=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rE5VS5Ft8Oxc",
        "outputId": "fd1da836-0567-49c7-a2e7-ed1a188c85ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
            "<ipython-input-9-eafa82642dd6>:35: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# # Dividir los datos en conjuntos de entrenamiento y prueba\n",
        "# X_train, X_test, y_train, y_test = train_test_split(df['Denuncias'], df['tipo'], random_state=0)\n",
        "\n",
        "# # Vectorizar los datos de texto\n",
        "# count_vect = CountVectorizer()\n",
        "# X_train_counts = count_vect.fit_transform(X_train)\n",
        "# tfidf_transformer = TfidfTransformer()\n",
        "# X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
        "\n",
        "# # Entrenar el modelo de regresión logística\n",
        "# clf = LogisticRegression(random_state=0).fit(X_train_tfidf, y_train)\n",
        "\n",
        "# # Crear una lista de denuncias\n",
        "# Denuncias = [\"Un grupo de usuarios de una red social denunció que sus datos personales fueron filtrados debido a una falla de seguridad en la plataforma.\",    \"Una empresa de comercio electrónico denunció que fue víctima de un ataque cibernético que comprometió la seguridad de los datos de sus clientes.\",    \"Un ciudadano denunció que su cuenta bancaria fue hackeada y que el atacante realizó varias transacciones fraudulentas en su nombre.\",    \"Un trabajador de una empresa de tecnología denunció que la compañía no tomó medidas adecuadas para proteger la información personal de los usuarios.\",    \"Una organización de derechos digitales denunció que un proveedor de servicios de internet estaba monitoreando ilegalmente el tráfico en línea de sus usuarios.\",    \"Un grupo de consumidores presentó una denuncia ante una agencia de protección de datos por la recolección y uso ilegal de información personal por parte de una aplicación de salud.\",    \"Un ciudadano denunció que un sitio web de compras en línea le vendió un producto falso y que su información personal fue utilizada sin su consentimiento.\",    \"Una empresa de tecnología denunció que un competidor había robado su propiedad intelectual y estaba utilizando su tecnología sin permiso.\",    \"Un periodista denunció que su correo electrónico fue hackeado y que el atacante había accedido a información confidencial.\",    \"Un grupo de usuarios de una aplicación de mensajería denunció que sus conversaciones privadas estaban siendo monitoreadas y analizadas por la empresa sin su consentimiento.\" ,\"Un vecino denunció a su comunidad de vecinos por incumplir las normas de convivencia y perturbar el descanso de los demás vecinos.\",    \"Un grupo de trabajadores presentó una denuncia contra su empresa por no cumplir con las medidas de seguridad necesarias para prevenir accidentes laborales.\",    \"Un ciudadano presentó una demanda contra el ayuntamiento por negligencia en el mantenimiento de las calles, lo que provocó un accidente de tráfico.\",    \"Un grupo de mujeres presentó una denuncia contra una empresa por discriminación de género en la selección de personal.\",    \"Un cliente presentó una denuncia contra una tienda de electrónica por venderle un producto defectuoso y negarse a hacerse responsable.\",    \"Un grupo de vecinos presentó una denuncia contra una empresa por contaminar el aire y provocar problemas respiratorios en la población.\",    \"Un trabajador presentó una denuncia contra su jefe por acoso laboral y discriminación.\",    \"Un ciudadano presentó una demanda contra una aerolínea por cancelar su vuelo sin previo aviso y no ofrecer una alternativa adecuada.\",    \"Un grupo de usuarios de redes sociales presentó una denuncia contra una plataforma por violar su privacidad y compartir sus datos sin consentimiento.\",    \"Un ciudadano presentó una denuncia contra una cadena de supermercados por vender productos caducados y en mal estado.\"]\n",
        "\n",
        "# # Crear un DataFrame para almacenar las denuncias y las predicciones\n",
        "# resultados = pd.DataFrame(columns=['Denuncia', 'Prediccion'])\n",
        "\n",
        "# # Iterar sobre cada denuncia y hacer la predicción\n",
        "# for denuncia in Denuncias:\n",
        "#     # Realizar la predicción utilizando el modelo\n",
        "#     prediccion = clf.predict(count_vect.transform([denuncia]))[0]\n",
        "\n",
        "#     # Agregar la denuncia y la predicción al DataFrame de resultados\n",
        "#     resultados = resultados.append({'Denuncia': denuncia, 'Prediccion': prediccion}, ignore_index=True)\n",
        "\n",
        "# # Guardar los resultados en un archivo Excel\n",
        "# resultados.to_excel('resultados_predicciones_LogisticRegression.xlsx', index=False)"
      ],
      "metadata": {
        "id": "rCwq9R29_S8y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "\n",
        "models = [\n",
        "    RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0),\n",
        "    LinearSVC(),\n",
        "    MultinomialNB(),\n",
        "    LogisticRegression(random_state=0),\n",
        "]\n",
        "CV = 10\n",
        "cv_df = pd.DataFrame(index=range(CV * len(models)))\n",
        "entries = []\n",
        "for model in models:\n",
        "  model_name = model.__class__.__name__\n",
        "  accuracies = cross_val_score(model, features, labels, scoring='accuracy', cv=CV)\n",
        "  for fold_idx, accuracy in enumerate(accuracies):\n",
        "    entries.append((model_name, fold_idx, accuracy))\n",
        "cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])\n",
        "cv_df"
      ],
      "metadata": {
        "id": "xUxCYuEpiUZo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "11ee4bca-85f3-4fe5-b93d-1d6845d7b11d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                model_name  fold_idx  accuracy\n",
              "0   RandomForestClassifier         0  0.901961\n",
              "1   RandomForestClassifier         1  0.941176\n",
              "2   RandomForestClassifier         2  0.960784\n",
              "3   RandomForestClassifier         3  0.941176\n",
              "4   RandomForestClassifier         4  0.921569\n",
              "5   RandomForestClassifier         5  0.840000\n",
              "6   RandomForestClassifier         6  0.740000\n",
              "7   RandomForestClassifier         7  0.560000\n",
              "8   RandomForestClassifier         8  0.560000\n",
              "9   RandomForestClassifier         9  0.560000\n",
              "10               LinearSVC         0  0.941176\n",
              "11               LinearSVC         1  0.980392\n",
              "12               LinearSVC         2  0.980392\n",
              "13               LinearSVC         3  1.000000\n",
              "14               LinearSVC         4  0.960784\n",
              "15               LinearSVC         5  0.960000\n",
              "16               LinearSVC         6  0.960000\n",
              "17               LinearSVC         7  0.980000\n",
              "18               LinearSVC         8  0.980000\n",
              "19               LinearSVC         9  0.860000\n",
              "20           MultinomialNB         0  0.901961\n",
              "21           MultinomialNB         1  0.941176\n",
              "22           MultinomialNB         2  1.000000\n",
              "23           MultinomialNB         3  1.000000\n",
              "24           MultinomialNB         4  0.960784\n",
              "25           MultinomialNB         5  0.980000\n",
              "26           MultinomialNB         6  0.900000\n",
              "27           MultinomialNB         7  0.900000\n",
              "28           MultinomialNB         8  0.920000\n",
              "29           MultinomialNB         9  0.840000\n",
              "30      LogisticRegression         0  0.941176\n",
              "31      LogisticRegression         1  0.980392\n",
              "32      LogisticRegression         2  0.980392\n",
              "33      LogisticRegression         3  1.000000\n",
              "34      LogisticRegression         4  0.921569\n",
              "35      LogisticRegression         5  0.960000\n",
              "36      LogisticRegression         6  0.920000\n",
              "37      LogisticRegression         7  0.900000\n",
              "38      LogisticRegression         8  0.960000\n",
              "39      LogisticRegression         9  0.800000"
            ],
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              "\n",
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              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>model_name</th>\n",
              "      <th>fold_idx</th>\n",
              "      <th>accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>0</td>\n",
              "      <td>0.901961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>1</td>\n",
              "      <td>0.941176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>2</td>\n",
              "      <td>0.960784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>3</td>\n",
              "      <td>0.941176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>4</td>\n",
              "      <td>0.921569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>5</td>\n",
              "      <td>0.840000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>6</td>\n",
              "      <td>0.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>7</td>\n",
              "      <td>0.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>8</td>\n",
              "      <td>0.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "      <td>9</td>\n",
              "      <td>0.560000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>0</td>\n",
              "      <td>0.941176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>1</td>\n",
              "      <td>0.980392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>2</td>\n",
              "      <td>0.980392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>4</td>\n",
              "      <td>0.960784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>5</td>\n",
              "      <td>0.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>6</td>\n",
              "      <td>0.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>7</td>\n",
              "      <td>0.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>8</td>\n",
              "      <td>0.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>LinearSVC</td>\n",
              "      <td>9</td>\n",
              "      <td>0.860000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>0</td>\n",
              "      <td>0.901961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>1</td>\n",
              "      <td>0.941176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>2</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>4</td>\n",
              "      <td>0.960784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>5</td>\n",
              "      <td>0.980000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>6</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>7</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>8</td>\n",
              "      <td>0.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>MultinomialNB</td>\n",
              "      <td>9</td>\n",
              "      <td>0.840000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0</td>\n",
              "      <td>0.941176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>1</td>\n",
              "      <td>0.980392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>2</td>\n",
              "      <td>0.980392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>3</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>4</td>\n",
              "      <td>0.921569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>5</td>\n",
              "      <td>0.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>6</td>\n",
              "      <td>0.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>7</td>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>8</td>\n",
              "      <td>0.960000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>9</td>\n",
              "      <td>0.800000</td>\n",
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              "</table>\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
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              "\n",
              "      <script>\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    }\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",
              "    [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",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-50fefef8-63b6-4fd0-a86e-293efd293038');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.boxplot(x='model_name', y='accuracy', data=cv_df)\n",
        "sns.stripplot(x='model_name', y='accuracy', data=cv_df,\n",
        "              size=8, jitter=True, edgecolor=\"gray\", linewidth=2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "c1u4tn8ribL2",
        "outputId": "38cbdfa4-d79f-41cd-b920-349735ea52f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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CDgAISEj+vez2o1dtNUUaEZF1sSWnGZo8eTISExNhMBhuO3RRo9EgLCzMxhXTnUrLVZudsbzU3hn5br1h0LWCpvAG9NknYFeSh1Ih4Z+/6NFGe5MtOkTU5LElpxny9fVFREQENBoNAMhDF91+/RYtMw6bBZyIiAie2kEB/nPeUQ44+a498ZvfNOR4B6HAtQdyvIPwm9805Lv2AACUCglfnXe0ZblERFbBlpxmKjg4GGvWrMHmzZsRHx9f6dxVAQEBCAsLa9YBp9gowdpTjwsBlDRw/97fi1T4+feyQFvbuZGSf9fgfK4KrbW3L9ZeBdTHtEhlrz8RkeUYcpoxX19fREdHIzMzE8nJyWZnIWcfHGB6XEtbl2A1KtX/WnFqOzcSJEQccWnAComIrI8hh+Dp6QlPT09bl0ENgHMjEVFzwpBDVEF9z1tU8RQbDeW///2vRXMjzZo1CyNGjLjt9h0cHOr9NB6ce4mILMGQQ1RBQ8xb5OjYsJ16hwwZgvfee6/OcyMFBQWhVStOT0ZETRdHVxEpnKenJ+dGIqJmiS05RM0A50YiouaILTlEzQDnRiKi5oghh6iZKJ8bKTAwsFJHYUmSEBgYiDVr1iA4ONhGFRIRWRcPVxE1I5wbiYiaE4YcomaIcyMRUXPAw1VERESkSAw5REREpEg2Dzlr166Fj48PtFot/P39ceTIkWrXNRgMeP3119G5c2dotVr07dsXu3fvbsBqiYiIqKmwacjZunUrwsPDERkZiWPHjqFv374ICQnB1atXq1x/8eLF+OCDD7BmzRqcOnUKzz33HB555BEcP368gSsnIiKixs6mIWfVqlWYNm0apkyZgh49emDdunVwdHTExo0bq1z/448/xiuvvIIxY8agU6dOeP755zFmzBisXLmygSsnIiKixs5mIaekpASJiYkYOXLkn8WoVBg5ciQOHz5c5X2Ki4srnahPp9Phxx9/rPZxiouLkZuba3YhIiIi5bNZyLl27RqMRiPc3d3Nlru7u+PKlStV3ickJASrVq3C2bNnYTKZsGfPHnz55ZfIzMys9nGio6Ph4uIiX7y9va36PIiIiKhxsnnH47pYvXo17r77bvj6+sLe3h4zZszAlClToFJV/zQWLlyImzdvypf09PQGrJiIiIhsxWYhx9XVFWq1GllZWWbLs7Kyqp151c3NDV999RUKCgpw8eJFpKamQq/Xo1OnTtU+joODA1q0aGF2ISIiIuWzWcixt7fHgAEDsHfvXnmZyWTC3r17ERgYWON9tVotvLy8UFpaiu3bt+Ohhx6q73KJiIioibHpaR3Cw8MRFhaGgQMHYvDgwXjnnXdQUFCAKVOmAABCQ0Ph5eWF6OhoAEBCQgIyMjLg5+eHjIwMvPrqqzCZTHjppZds+TSIiIioEbJpyJkwYQKys7OxZMkSXLlyBX5+fti9e7fcGfnSpUtm/W2KioqwePFipKWlQa/XY8yYMfj444/RsmVLGz0DIiIiaqxsfoLOGTNmYMaMGVXetn//frPrw4YNw6lTpxqgKiIiImrqbB5yqH5lZmbi6NGj8vWsrCz4+PjYriAiIqrkBoALAIoAaAH4AGhlw3qUgiFHoVJTUxETE4OEhAQIIeTDflOmTIG/vz8mT54MX19fG1dJRNS8ZQDYLwFnAQhJkpdLQuBuAMMF4GWr4hSgSc2TQ7UTFxeHmTNnIj4+HkIIs9uEEIiPj8fMmTMRFxdnowqJiOgUgA0ScEaSzAIOUBZ4zkgSNkhl65FlGHIUJjU1FVFRUTAYDACAUntn5HgNQXaXscjxGoJSe2cAZWd0j4qKQmpqqi3LJSJqljIAfCEBxv+Fm+r21UZJwhdS2fpUdzxcpTAxMTFywMl37YlrnUIAlVq+PccrAK5pu6G/dgoGgwGbN2+Wh+gTEVHD2F8h4NxuX22UJOyHwCRR3daoOgw5tyGEQFFRka3LqJWsrCwkJCQAKPtVcOuHBgCgUuNap9HQ5qbDriQP8fHxuHDhQqVziDV2Wq0W0i3Nu0RENal4+L6kbIlN6sgBcAZ/tuDUZl99BkA2BFwauth6UlLh71u7VVgTQ85tFBUVISQkxNZl1Fp5B+N8t96VPzTySmrku/VCy4zDEEIgNDS0ASu0jtjYWOh0OluXQURNSHFxsfz3chvWAQCq8lacWu6rIUl4tx7DgC0VFxfD0dGxXrbNPjkKZdDVPPjQoGvdQJUQEVF1uK+uX2zJqYOC/pMAVeN9yex+PwfdxUMAAE3hjRrX1RRel/8u7jgUpW0612ttVmEqhdOxT2xdBRE1UQ4ODvLfLwOwt1EdyQC++d/fddlXPwSgT71V1bBK8GdrWsX3xdoa7zd2Y6SyA9QaW1dRLaOLFwQACYA++wRyvAKqbgY1GaHPPgmg7Ii00aV9o35eRETWULEfnz0Ae9imX18XlM2DIySp1vtqSQh0gWSzYGZ9fx56q8/+lTxcpSDCwRlGF28AgF1JHlzTdgMmo/lKJiNc03bDriQPAGB08YZw0Dd0qUREzVYrAHf/7+/a7qvvBtCyAWtUCrbkKExxOz+oczMgCRP0105Bm5uOfLdeMOhaQ1N4Hfrsk/KHRkgqFLfzs23BRETN0HABnIOAUZJuu69WC4HhyuxzXO8YchTGpHdDUafh0KbthyRMsCvJK+uZfwshqVDUaThMejcbVElE1Lx5AfibAL74X9Cpbl+tFgJ/46kdLMaQo0ClrX3wh/1YOFxOgvpmutlR57I+ON4obufHgENEZEM9AEwVwH4InruqnjDkKJRJ74bCrqMgFedBnXcFkrEEQm0Po7Mn++AQETUSXgAmibKzkF8Uwuws5C1tWJdSMOQonHBwRqmDsxx27G5chLCzh9HZA8LB2dblERERyjoj1zxjTvVuALgAmAUkS7elNAw5CqfKz+ZhKyIiBcpA2TmweKiregw5CmZ3/YLcAflWEgC7m+lQ52agqNNwlLb2afD6iIjIMqdgfhbzioQk4QzKRm/9TZT1/WmuGHIUSpWfbRZwSu2dke/WGwZdK2gKb0CffQJ2JXmQhAnatP34w34sW3SIiJqADJgHnOr270ZJwhcQmNqMW3QYchTK4XKSHHDyXXtWOsttjlcAXNN2Q3/tFCRhgsPlJBR2HWWrcomIqJb2Vwg4t9u/GyUJ+yEwqZnOs8OQcxtmp4A3GmxXSB1IxflQ30wHUJbwb/0AAABUalzrNBra3HTYleSV9dn540bjHnlV4fUXCj0bLxE1jBIAFU8t0FTcAHAGf7bg1Gb/fgZANgRcGrza6pU00OMw5NxGcXGx/LfT8U9tWEndSKqyM3bku/Wu+pwoAKBSI9+tF1pmHIYEwPHE9oYr8A4VFxfD0dHR1mUQURO1/ParNFqq8lacWu7fIUl4t5n+MOS5qxTOoKt5IKFB17qBKiEiImvi/v322JJzGxVPAV/Qb2KTOFu33e/noLt4CACgKbxR47qawuvy38Udh6K0Ted6re2OGA1ya1rF94Wo2SgApGwJkkGC0AgINwE42bqopkOr1SI2NtbWZdyRnTt3Ys2aNQDqtn+fN28eRo1qnP0utVptvW2bIec2zE4Br9Y0iZBjdPGCQNkwcX32CeR4BVTdpGkyQp99EkD5vDntm8TzA255X4gUTrouQZWignRFglRhxisBAeEhYOpugmjdPA9H1IUkSdDpdLYu444MGDAAQghIklTr/bskSRg8eHCTf+6W4OEqBRIOzjC6eAMA7Ery4Jq2GzAZzVcyGeGatls+y63RxbtxdzomaqakDAnqH9RQXVGZBRwAkCBBdUUF9Q9qSBkM/s2Bu7u7/Hdt9+8BAQHw8PBoyDIbDbbkKFRxOz+oczMgCRP0105Bm5uOfLdeMOhaQ1N4Hfrsk/IHQEgqFLfzs23BRFSJdF2C+ogakqnm+VAkU9l6xmFGtug0A+WjSyVJuu3+XaPRICwszJbl2hRDjkKZ9G4o6jRcnhDQriSvrJf9LYSkQlGn4ZwIkKgRUqWo5IBz2/muTGWHtIxBxuo2RwoihIBGo0FpaWm1+3eNRoOIiAj4+vraoMLGgSFHwUpb++AP+7E8dxVRNczmWyq1XR1V+gOQrtRtPhTpigTkAmiKsytUeP05D1btrFixAlu3bkV8fLzZayZJEgICAhAWFtasAw7AkKN4Jr0bCruOks9CLhlLINT2MDp7sg8ONXsV58FS/181843YkKSq23woEiSoYxvf86grzoNVO926dUN0dDQyMzORnJyMgoICODk5wc/Pr9n2wbkVQ04zIRycUergbOsyiMgCnA+FauLp6QlPT09bl9EoMeQQUbNVcb4l4zhjo9ojSpckqI6XDYCty3wopgEmiLua4OGe0j9b0zgPFllLI/pIExE1LLP5luzQqPaIwl1AQEBC7edDERAQ7qJRPQ9LcB4sshbOk0NE1Bg5AcKjrEWmtvOhCA/OgExUURPP+0REymXqboJ0VYJkuv18KEJVNvMxEf2JIYeIqJESrQWMg43yhIDVznelKluPEwESmePhKiKiRkx4CRiHGWHyMEHAPMQICJg8TGUzHXsx4BDdii05RESNnGgtymYyLgCkaxXOQu7KPjhENWHIISJqKpwA4SQqtejUSgEgZVcISG4MSKR8DDlERAomXS87p5V0RTI7i7mAgPAo66zMvjykVAw5REQKJWWYn8Xc7DZIZcHnqlTWaZl9ekiBGHKIiBRIum4ecErtnZHv1hsGXStoCm9An32i7KSeprL1jMM4OouUhyGHiEiBVCkqOeDku/asdBbzHK8AuKbthv7aKUimskNaxiBjdZsjapIYcoiIAKDU1gVYSAAozyZqABKAPwDpyp8tOLcGHACASo1rnUZDm5te1qJzRQJyAdjq5N9N9fWnRo0hh4gIf54cUikk1f9acdx6V33OKwBQqZHv1gstMw5DggR1rLJeAyKGHCIiBTPoWt3m9tYNVAnVh8zMTCQlJaGgoABOTk7w8/ODp6enrctqNBhyiKjZ0mq1iI2NtXUZd6SoqAgPPfQQAGDHjh3QarXYs2cPVq1aBQDQFN6o8f6awuvy3/PmzcOoUaPqr9ha0mq1ti6hSYiMjMTRo0chxJ8dxiVJgr+/PyZPngxfX18bVtc4MOQQUbMlSRJ0Op2ty7AarVYLnU6HQYMGQZIkCCGgzz6BHK+Aqg9ZmYzQZ58EUPZaDB48WFGvh5JJkoQjR45UWi6EQHx8PBITExEREYHg4GAbVNd48NxVREQK4+npCX9/fwCAXUkeXNN2A6ZbRk6ZjHBN2y2fxTwgIAAeHh4NXSrV0enTpyFJEiTpz47lOV5DkN1lLHK8hqDU3hkAYDAYEBUVhdTUVFuWa3NsySEiUqDJkycjMTERBoMB+munoM1NR75bLxh0raEpvA599kk54Gg0GoSFhdm4YqqNTz/9VA44t5sawGAwYPPmzYiOjrZVuTbHkENEpEC+vr6IiIhAVFQUDAYD7Ery0DLjcKX1NBoNIiIi2H/DCoQQKCoqqrftZ2Vl4ejRowBqPzVAfHw8Lly4AHd3d4seU6vVyqGqKWLIISJSqODgYKxZswabN29GfHx8pQ6qAQEBCAsLY8CxkqKiIoSEhNTrY6hUZb1Majs1gBACoaGhFj9ebGxsk+6nxZBDRKRgvr6+iI6ORmZmJpKTk82GGrMPTtPFqQFqhyGHiKgZ8PT05Pwp9ay+pySwxdQATX04P0MOERGRFdT3lAScGqDuOISciIioCeDUAHVnUUvOvn37cO+991q7FiIiIqoBpwaoG4tackaPHo3OnTtj6dKlSE9Pt3ZNREREVIXyqQE0Gg0AyFMDuP36LVpmHDYLOJwawMKQk5GRgRkzZuCLL75Ap06dEBISgm3btqGkpMTa9REREVEF5VMDBAYGVprDRpIkBAYGYs2aNc3+lA6AhYerXF1dMWfOHMyZMwfHjh3Dpk2b8MILL+CFF17AxIkTMXXqVPTt29fatRIRERE4NUBt3fHoqv79+8PDwwNt2rTBsmXLsHHjRrz//vsIDAzEunXr0LNnT2vUSUR1kJmZiaSkJLMdH4cPEykPpwaomcUhx2AwYMeOHdi4cSP27NmDgQMH4r333sMTTzyB7OxsLF68GOPHj8epU6esWS8R1SA1NRUxMTFISEioNLutv78/Jk+e3OyP0RNR82FRyJk5cyY+++wzCCHw5JNP4q233kKvXr3k252cnLBixQq0a9fOaoUSUc3i4uLk8xTdSgiB+Ph4JCYmIiIigsfqiahZsCjknDp1CmvWrMGjjz4KBweHKtdxdXXFvn377qg4Iqqd1NRUs4BTau+MfLfeMOhaQVN4A/rsE7AryYPBYEBUVBTWrFnDFh0iUjyLRlft3bsXTzzxRLUBBwDs7OwwbNiw225r7dq18PHxgVarhb+/P44cOVLj+u+88w66desGnU4Hb29vzJkzp17P+krUFMTExMgBJ9+1J37zm4Yc7yAUuPZAjncQfvObhnzXHgDKDjVv3rzZluUSETUIi0JOdHQ0Nm7cWGn5xo0bsXz58lpvZ+vWrQgPD0dkZCSOHTuGvn37IiQkBFevXq1y/U8//RQLFixAZGQkUlJSsGHDBmzduhWvvPKKJU+DSBEyMzORkJAAoKwF51qnkMpTvavUuNZpNErtnQEA8fHxuHLlSkOXSkTUoCwKOR988EGVTd09e/bEunXrar2dVatWYdq0aZgyZQp69OiBdevWwdHRscoABQCHDh1CUFAQJk6cCB8fH9x///144oknbtv6Q6RkSUlJcifjfLfeVZ/LBgBUauS7lfWdE0IgKSmpgSokIrINi0LOlStXqhyy5ubmhszMzFpto6SkBImJiRg5cuSfxahUGDlyJA4fPlzlfYYMGYLExEQ51KSlpWHnzp0YM2aMBc+CSBkKCgrkvw26VjWua9C1rvJ+RERKZFHHY29vbxw8eBAdO3Y0W37w4MFaj6i6du0ajEYj3N3dzZa7u7sjNTW1yvtMnDgR165dw9ChQyGEQGlpKZ577rkaD1cVFxejuLhYvp6bm1ur+oiaCicnJ/lvTeGNGtfVFF6v8n7UtFScB8ne3t7W5RA1WhaFnGnTpmH27NkwGAwYMWIEgLLOyC+99BLmzp1r1QIr2r9/P9588028//778Pf3x6+//opZs2YhKioKERERVd4nOjoar732Wr3VRGRrfn5+kCQJQgjos08gxyug6kNWJiP02ScBlM2b4+fn17CF0h2raR4kADh9+jTfV6IKLAo58+fPx++//44XXnhBPl+VVqvFyy+/jIULF9ZqG66urlCr1cjKyjJbnpWVVe2U1BEREXjyySfx9NNPAwB69+6NgoICPPPMM1i0aBFUqspH3xYuXIjw8HD5em5uLry9vWtVI1FT4OnpCX9/f8THx8OuJA+uabtxrdNo86BjMsI1bbd88r6AgABO/d7E1DQPUnnImTdvHpYsWcJ5kIj+x6I+OZIkYfny5cjOzkZ8fDySk5Nx/fp1LFmypNbbsLe3x4ABA7B37155mclkwt69exEYGFjlff74449KQUatLtuRV/xVU5GDgwNatGhhdiFSmsmTJ8tnJdZfO4X2SevRMv1HOF07hZbpP6J90nror5XNPq7RaBAWFmbLcqmOqpoHKcdrCLK7jEWO1xB51FxpaSmioqKqPeRP1Nzc0bmr9Ho9Bg0aZPH9w8PDERYWhoEDB2Lw4MF45513UFBQgClTpgAAQkND4eXlhejoaADAuHHjsGrVKvTr108+XBUREYFx48bJYYeoOfL19UVERIT8RWhXkoeWGZU78Gs0GkRERHAiwCbm1nmQbp0mIMcrAK5pu6G/dkqeB6l8v0nUnFkccn766Sds27YNly5dkg9Zlfvyyy9rtY0JEyYgOzsbS5YswZUrV+Dn54fdu3fLnZEvXbpk1nKzePFiSJKExYsXIyMjA25ubhg3bhzeeOMNS58GkWIEBwdjzZo12Lx5M+Lj4yv12QgICEBYWBgDTgMSQtzxZKVZWVm1ngdJm5sOu5I8xMfH48KFC5UGdliDVquVD48RNXaSqO44Tw22bNmC0NBQhISE4LvvvsP999+PM2fOICsrC4888gg2bdpUH7VaRW5uLlxcXHDz5s1aHboqLCxESEgIAKBgYBig1tR3iVQdowFOP5XN1BsbGwudTmfjghqvzMxMJCcnm52FnH1wGl7F/cedKP+xl+M1BDneQdWu1zL9R7kFz2Qy3fHjVoWfPbKVun5/Axa25Lz55pt4++23MX36dDg7O2P16tXo2LEjnn32WZ7ynagR8PT05GdRgeoyDxIRWRhyzp07h7FjxwIo60BcUFAASZIwZ84cjBgxgkO2iYhQdmgnNjb2jraxZ88erFq1CkDd5kGaN28eRo0adUePXRWtVmv1bRLVF4tCTqtWrZCXVzYU1cvLCydPnkTv3r2Rk5ODP/74w6oFEhE1VZIk3fGhnUGDBlk0D9LgwYN5WImaPYuGkAcHB2PPnj0AgPHjx2PWrFmYNm0annjiCdx3331WLZCIqDkrnwcJgDwPEkxG85U4DxJRlSxqyXnvvffkEQOLFi2CRqPBoUOH8Ne//hWLFy+2aoFERM3d5MmTkZiYCIPBAP21U9DmpiPfrRcMutbQFF6HPvukHHA4DxLRn+occkpLS/HNN9/IIwZUKhUWLFhg9cKIiKgM50EiskydD1fZ2dnhueeeu+O5H4iIqPbK50EKDAysNE+NJEkIDAzEmjVreEoHogosOlw1ePBgJCUloUOHDtauh4iIquHr64vo6GjOg0RUSxaFnBdeeAHh4eFIT0/HgAED4OTkZHZ7nz59rFIcERFVxnmQiGrHopDz+OOPAwBefPFFeVn5EEdJkmA0Gqu7KxEREVGDsCjknD9/3tp1EBEREVmVRSGHfXGIiIiosbMo5PzrX/+q8fbQ0FCLiiEiIiKyFotCzqxZs8yuGwwG/PHHH7C3t4ejoyNDDhEREdmcRad1uHHjhtklPz8fp0+fxtChQ/HZZ59Zu0YiIiKiOrMo5FTl7rvvxrJlyyq18hARERHZgtVCDlA2G/Lly5etuUkiIiIii1jUJ+frr782uy6EQGZmJt577z0EBQVZpTAiIiKiO2FRyHn44YfNrkuSBDc3N4wYMQIrV660Rl1EREREd8SikGMymaxdBxEREZFVWbVPDhEREVFjYVHI+etf/4rly5dXWv7WW29h/Pjxd1wUERER0Z2yKOTExcVhzJgxlZY/8MADiIuLu+OiiIiIiO6URSEnPz8f9vb2lZZrNBrk5ubecVFEREREd8qikNO7d29s3bq10vItW7agR48ed1wUERER0Z2yaHRVREQEHn30UZw7dw4jRowAAOzduxefffYZPv/8c6sWSERERGQJi0LOuHHj8NVXX+HNN9/EF198AZ1Ohz59+uD777/HsGHDrF0jERERUZ1ZFHIAYOzYsRg7dqw1ayEiIiKyGov65Bw9ehQJCQmVlickJOCnn36646KIiIiI7pRFIWf69OlIT0+vtDwjIwPTp0+/46KIiIiI7pRFIefUqVPo379/peX9+vXDqVOn7rgoIiIiojtlUchxcHBAVlZWpeWZmZmws7O4mw8RERGR1VgUcu6//34sXLgQN2/elJfl5OTglVdewahRo6xWHBEREZGlLGp2WbFiBYKDg9GhQwf069cPAJCUlAR3d3d8/PHHVi2QiIiIyBIWhRwvLy/8/PPP+OSTT5CcnAydTocpU6bgiSeegEajsXaNRERERHVmcQcaJycnDB06FHfddRdKSkoAALt27QIA/OUvf7FOdUREREQWsijkpKWl4ZFHHsGJEycgSRKEEJAkSb7daDRarUAiIiIiS1jU8XjWrFno2LEjrl69CkdHR5w8eRI//PADBg4ciP3791u5RCIiIqK6s6gl5/Dhw/jvf/8LV1dXqFQqqNVqDB06FNHR0XjxxRdx/Phxa9dJREREVCcWteQYjUY4OzsDAFxdXXH58mUAQIcOHXD69GnrVUdERERkIYtacnr16oXk5GR07NgR/v7+eOutt2Bvb48PP/wQnTp1snaNRERERHVmUchZvHgxCgoKAACvv/46HnzwQdxzzz1o06YNtm7datUCiYiIiCxhUcgJCQmR/+7SpQtSU1Nx/fp1tGrVymyUFREREZGtWO1EU61bt7bWpoiIiIjumEUdj4mIiIgaO4YcIiIiUiSGHCIiIlIkhhwiIiJSJIYcIiIiUiSGHCIiIlIkhhwiIiJSJIYcIiIiUiSGHCIiIlIkhhwiIiJSJIYcIiIiUiSGHCIiIlIkhhwiIiJSJIYcIiIiUiSGHCIiIlIkhhwiIiJSJIYcIiIiUiSGHCIiIlIkhhwiIiJSJIYcIiIiUiSGHCIiIlKkRhFy1q5dCx8fH2i1Wvj7++PIkSPVrjt8+HBIklTpMnbs2AasmIiIiBo7m4ecrVu3Ijw8HJGRkTh27Bj69u2LkJAQXL16tcr1v/zyS2RmZsqXkydPQq1WY/z48Q1cORERETVmNg85q1atwrRp0zBlyhT06NED69atg6OjIzZu3Fjl+q1bt4aHh4d82bNnDxwdHRlyiIiIyIxNQ05JSQkSExMxcuRIeZlKpcLIkSNx+PDhWm1jw4YNePzxx+Hk5FTl7cXFxcjNzTW7EBERkfLZNORcu3YNRqMR7u7uZsvd3d1x5cqV297/yJEjOHnyJJ5++ulq14mOjoaLi4t88fb2vuO6iYiIqPGz+eGqO7Fhwwb07t0bgwcPrnadhQsX4ubNm/IlPT29ASskIiIiW7Gz5YO7urpCrVYjKyvLbHlWVhY8PDxqvG9BQQG2bNmC119/vcb1HBwc4ODgcMe1EhERUdNi05Yce3t7DBgwAHv37pWXmUwm7N27F4GBgTXe9/PPP0dxcTH+/ve/13eZRERE1ATZtCUHAMLDwxEWFoaBAwdi8ODBeOedd1BQUIApU6YAAEJDQ+Hl5YXo6Giz+23YsAEPP/ww2rRpY4uyiYiIqJGzeciZMGECsrOzsWTJEly5cgV+fn7YvXu33Bn50qVLUKnMG5xOnz6NH3/8Ed99950tSiYiIqImwOYhBwBmzJiBGTNmVHnb/v37Ky3r1q0bhBD1XBURERE1ZU16dBURERFRdRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiR7GxdANUPqTgP6rwrkEpLIOzsYXT2gHBwtnVZREREDYYhR2FU+dlwuJwE9c10SBWWCwBGF28Ut/ODSe9mq/KIiIgaDEOOgthdvwBt2n5IwlTpNgmA3c10qHMzUNRpOEpb+zR4fURERA2JIUchVPnZZgGn1N4Z+W69YdC1gqbwBvTZJ2BXkgdJmKBN248/7MeyRYeIiBSNIUchHC4nyQEn37UnrnUKAVRq+fYcrwC4pu2G/topSMIEh8tJKOw6ylblEhER1TuGnLowldq6gipJxflQ30wHUNaCc2vAAQCo1LjWaTS0uemwK8kr67Pzxw0IB70NKrZQI339iYiocWLIqQOnY5/YuoRqSaqy2QDy3XpXDjjlVGrku/VCy4zDkAA4ntjecAUSERE1MM6TozAGXavb3N66gSohIiKyLbbk3IZWq0VsbKyty6jRnj17sGrVKgCApvBGjetqCq/Lf8+bNw+jRjXNfjlardbWJRARUSPHkHMbkiRBp9PZuowaDRo0CJIkQQgBffYJ5HgFVH3IymSEPvskgLLnNXjw4Eb/3IiIiCzFw1UK4OnpCX9/fwCAXUkeXNN2Ayaj+UomI1zTdsOuJA8AEBAQAA8Pj4YulYiIqMGwJUchJk+ejMTERBgMBuivnYI2Nx35br1g0LWGpvA69Nkn5YCj0WgQFhZm44qJiIjqF1tyFMLX1xcRERHQaDQAylp0WmYchtuv36JlxmE54NjZ2SEiIgK+vr62LJeIiKjeMeQoSHBwMNasWYPAwEBIkmR2mxACQgisWLECwcHBNqqQiIio4UhCCGHrIhpSbm4uXFxccPPmTbRo0cLW5dSbzMxMJCcno6CgABqNBitWrAAAxMbGsrMxERE1OZZ8f7NPjkJ5enrC09MTAFBYWCiHHCIiouaCh6uIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRbB5y1q5dCx8fH2i1Wvj7++PIkSM1rp+Tk4Pp06fD09MTDg4O6Nq1K3bu3NlA1RIREVFTYWfLB9+6dSvCw8Oxbt06+Pv745133kFISAhOnz6Ntm3bVlq/pKQEo0aNQtu2bfHFF1/Ay8sLFy9eRMuWLRu+eCIiImrUbBpyVq1ahWnTpmHKlCkAgHXr1uHbb7/Fxo0bsWDBgkrrb9y4EdevX8ehQ4eg0WgAAD4+Pg1ZMhERETURNjtcVVJSgsTERIwcOfLPYlQqjBw5EocPH67yPl9//TUCAwMxffp0uLu7o1evXnjzzTdhNBqrfZzi4mLk5uaaXYiIiEj5bBZyrl27BqPRCHd3d7Pl7u7uuHLlSpX3SUtLwxdffAGj0YidO3ciIiICK1euxNKlS6t9nOjoaLi4uMgXb29vqz4PIiIiapxs3vG4LkwmE9q2bYsPP/wQAwYMwIQJE7Bo0SKsW7eu2vssXLgQN2/elC/p6ekNWDERERHZis365Li6ukKtViMrK8tseVZWFjw8PKq8j6enJzQaDdRqtbyse/fuuHLlCkpKSmBvb1/pPg4ODnBwcLBu8URERNTo2awlx97eHgMGDMDevXvlZSaTCXv37kVgYGCV9wkKCsKvv/4Kk8kkLztz5gw8PT2rDDhERETUfNn0cFV4eDjWr1+PzZs3IyUlBc8//zwKCgrk0VahoaFYuHChvP7zzz+P69evY9asWThz5gy+/fZbvPnmm5g+fbqtngIRERE1UjYdQj5hwgRkZ2djyZIluHLlCvz8/LB79265M/KlS5egUv2Zw7y9vREbG4s5c+agT58+8PLywqxZs/Dyyy/b6ikQERFRIyUJIYSti2hIubm5cHFxwc2bN9GiRQtbl9MgCgsLERISAgCIjY2FTqezcUVERER1Y8n3d5MaXUVERERUWww5REREpEgMOURERKRIDDlERESkSAw5REREpEgMOURERKRIDDlERESkSAw5REREpEgMOURERKRIDDlERESkSAw5REREpEgMOURERKRIDDlERESkSAw5REREpEgMOURERKRIDDlERESkSAw5REREpEgMOURERKRIDDlERESkSAw5REREpEgMOURERKRIdrYugOpPZmYmkpKSkJOTY+tSiIiIGhxDjgKlpqYiJiYGCQkJEEIAAFQqFYQQiIyMxFNPPQVfX18bV0lERFS/eLhKYeLi4jBz5kzEx8fLAaecJEk4cuQIZs6cibi4OBtVSERE1DAYchQkNTUVUVFRMBgMAIBSe2fkeA1BdpexyPEaglJ7ZwCAwWBAVFQUUlNTbVkuERFRvWLIUZCYmBg54OS79sRvftOQ4x2EAtceyPEOwm9+05Dv2gNAWdDZvHmzLcslIiKqV+yTY2NCCBQVFd3xdrKyspCQkACgrAXnWqcQQKU2X0mlxrVOo6HNTYddSR7i4+Nx4cIFuLu73/HjV6TVaiFJklW3SUREVFcMOTZWVFSEkJAQq2xLpSprmMt361054MgrqZHv1gstMw5DCIHQ0FCrPHZFsbGx0Ol0Vt8uERFRXfBwlQIZdK1uc3vrBqqEiIjIdtiSY2NarRaxsbF3vJ09e/Zg1apVAABN4Y0a19UUXpf/njdvHkaNGnXHj1+RVqu16vaIiIgswZBjY5IkWeXQzqBBgyBJEoQQ0GefQI5XQNWHrExG6LNPyo89ePBgHloiIiJF4uEqhfD09IS/vz8AwK4kD65puwGT0XwlkxGuabthV5IHAAgICICHh0dDl0pERNQg2JKjIJMnT0ZiYiIMBgP0105Bm5uOfLdeMOhaQ1N4Hfrsk3LA0Wg0CAsLs3HFRERE9YctOQri6+uLiIgIaDQaAGUtOi0zDsPt12/RMuOwWcCJiIjgqR2IiEjRGHIUJjg4GGvWrEFgYGCluWokSUJgYCDWrFmD4OBgG1VIRETUMCRx6wmOFC43NxcuLi64efMmWrRoYety6lVmZiaSk5NRUFAAJycn+Pn5sQ8OERE1SZZ8f7NPjoJ5enrC09PT1mUQERHZBA9XERERkSIx5BAREZEiMeQQERGRIjHkEBERkSIx5BAREZEiMeQQERGRIjHkEBERkSI1u3lyyuc+zM3NtXElREREVFvl39t1mcO42YWcvLyy8zd5e3vbuBIiIiKqq7y8PLi4uNRq3WZ3WgeTyYTLly/D2dm50rmdlCw3Nxfe3t5IT09X/OksiO93c8P3u3lpru+3EAJ5eXlo164dVKra9bZpdi05KpUK7du3t3UZNtOiRYtm9aFo7vh+Ny98v5uX5vh+17YFpxw7HhMREZEiMeQQERGRIjHkNBMODg6IjIyEg4ODrUuhBsD3u3nh+9288P2uvWbX8ZiIiIiaB7bkEBERkSIx5BAREZEiMeTUkiRJ+Oqrr2xdRpMzfPhwzJ49u0Ee69b3KDU1FQEBAdBqtfDz88OFCxcgSRKSkpIapJ7a1EjKVJv3efLkyXj44YcbpJ7bqev/5auvvgo/P796q6ex8PHxwTvvvGPx/WNiYtCyZUur1dNU7d+/H5IkIScnp8Efu0mFnMmTJ0OSJEiSBI1Gg44dO+Kll15CUVGRrUuzmvLnV/EydOhQm9dU1Q6wpKQEb731Fvr27QtHR0e4uroiKCgImzZtgsFgaPA6MzMz8cADD8jXIyMj4eTkhNOnT2Pv3r3w9vZGZmYmevXqVa911PTldWuNtmQ0GrFs2TL4+vpCp9OhdevW8Pf3x0cffQQAGDduHEaPHl3lfQ8cOABJkvDzzz/Ly7Zv347hw4fDxcUFer0effr0weuvv47r1683yPO5E+X7lueee67SbdOnT4ckSZg8ebJF264uXK9evRoxMTEWbdPa7vT/8tVXX63y9UtKSoIkSbhw4QKAP1+L8ou9vT26dOmCpUuX1mmq/nL1HRSPHj2KZ555plbrVhWIJkyYgDNnztT68YYPHy6/NlqtFl27dkV0dLRFr01jMmTIEGRmZtZ5jhtraFIhBwBGjx6NzMxMpKWl4e2338YHH3yAyMhIW5dlVZs2bUJmZqZ8+frrry3eVn2FjZKSEoSEhGDZsmV45plncOjQIRw5cgTTp0/HmjVr8Msvv9TL49bEw8PDbLTBuXPnMHToUHTo0AFt2rSBWq2Gh4cH7OwsnwOzpKTEqjXaghACpaWleO211/D2228jKioKp06dwr59+/DMM8/Iv7amTp2KPXv24Lfffqu0jU2bNmHgwIHo06cPAGDRokWYMGECBg0ahF27duHkyZNYuXIlkpOT8fHHHzfk07OYt7c3tmzZgsLCQnlZUVERPv30U9x1111WfzwXF5dG8yvfGv+XWq0WGzZswNmzZ2+77vfff4/MzEycPXsWr732Gt544w1s3Ljxjh6/Pri5ucHR0dHi++t0OrRt27ZO95k2bRoyMzNx+vRpLFy4EEuWLMG6dessrqE27nS/djv29vbw8PCwzVkGRBMSFhYmHnroIbNljz76qOjXr58QQohr166Jxx9/XLRr107odDrRq1cv8emnn5qtP2zYMDFz5kwxf/580apVK+Hu7i4iIyPN1jlz5oy45557hIODg+jevbv47rvvBADxn//8R17n559/Fvfee6/QarWidevWYtq0aSIvL69SrW+88YZo27atcHFxEa+99powGAxi3rx5olWrVsLLy0ts3LjR7LFvfZyKjEajeO2114SXl5ewt7cXffv2Fbt27ZJvP3/+vAAgtmzZIoKDg4WDg4PYtGmTEEKI9evXC19fX+Hg4CC6desm1q5dK9+vuLhYTJ8+XXh4eAgHBwdx1113iTfffFMIIUSHDh0EAPnSoUMHIYQQy5cvFyqVShw7dqxSnSUlJSI/P19+vWfNmiXf9q9//UsMGDBA6PV64e7uLp544gmRlZUl3379+nUxceJE4erqKrRarejSpYv8GtVU562vXcWaAYjIyEj59Tl+/Lh8nxMnTojRo0cLJycn0bZtW/H3v/9dZGdny7cPGzZMTJ8+XcyaNUu0adNGDB8+vMr3pqKq/k+rqrG8nu3bt4vhw4cLnU4n+vTpIw4dOmR2nwMHDoihQ4cKrVYr2rdvL2bOnCm/vrV5Tfft2ycAiJ07d4r+/fsLjUYj9u3bJ/r27SteffXVap+HwWAQ7u7uIioqymx5Xl6e0Ov14p///KcQQoiEhAQBQLzzzjtVbufGjRvVPkZjUf6e9erVS/z73/+Wl3/yySeiT58+4qGHHhJhYWFCiLLPxNtvv212/759+5rtR2r6Xxw2bJjZY5arzb7p4sWL4i9/+YtwcnISzs7OYvz48eLKlSvy7ZGRkaJv375iw4YNwtvbWzg5OYnnn39elJaWiuXLlwt3d3fh5uYmli5darbdW/c7L730krj77ruFTqcTHTt2FIsXLxYlJSWVHufW66NGjRLjx4+Xlx8/flwAEOfPnxdCiCo/g0IIcd9994kXXnhB1FVNn7X9+/eLQYMGCXt7e+Hh4SFefvllYTAY5Ntzc3PFxIkThaOjo/Dw8BCrVq2qtL+q+F6bTCYRGRkpvL29hb29vfD09BQzZ84UQpS9d7e+z0IIsWnTJuHi4mJW19dffy0GDhwoHBwcRJs2bcTDDz8s33br4wshRP/+/cUjjzwiXy8qKhJz584V7dq1E46OjmLw4MFi3759Zvf58MMPRfv27YVOpxMPP/ywWLlypVkd5e/X+vXrhY+Pj5AkSQhR9lmdOnWqcHV1Fc7OzuLee+8VSUlJ8v2SkpLE8OHDhV6vF87OzqJ///7i6NGjQgghLly4IB588EHRsmVL4ejoKHr06CG+/fZbIcSf+6CK+4IvvvhC9OjRQ9jb24sOHTqIFStWmD2HDh06iDfeeENMmTJF6PV64e3tLT744ANRV02uJaeikydP4tChQ7C3twdQ9qtrwIAB+Pbbb3Hy5Ek888wzePLJJ3HkyBGz+23evBlOTk5ISEjAW2+9hddffx179uwBUHZuq0cffRT29vZISEjAunXr8PLLL5vdv6CgACEhIWjVqhWOHj2Kzz//HN9//z1mzJhhtt5///tfXL58GXFxcVi1ahUiIyPx4IMPolWrVkhISMBzzz2HZ599tspfylVZvXo1Vq5ciRUrVuDnn39GSEgI/vKXv1T65bRgwQLMmjULKSkpCAkJwSeffIIlS5bgjTfeQEpKCt58801ERERg8+bNAIB3330XX3/9NbZt24bTp0/jk08+gY+PD4Cy5lrgz9al8uuffPIJRo4ciX79+lWqU6PRwMnJqcrnYDAYEBUVheTkZHz11Ve4cOGC2WGAiIgInDp1Crt27UJKSgr++c9/wtXV9bZ13iozMxM9e/bE3LlzkZmZiXnz5lVaJycnByNGjEC/fv3w008/Yffu3cjKysJjjz1mtt7mzZthb2+PgwcP1ssvqkWLFmHevHlISkpC165d8cQTT6C0tBRAWWvU6NGj8de//hU///wztm7dih9//NHsf+12r2m5BQsWYNmyZUhJSUGfPn3g4eGB//73v8jOzq6yLjs7O4SGhiImJsasufzzzz+H0WjEE088AaDsf0Gv1+OFF16ocjuNpbWiNp566ils2rRJvr5x40ZMmTLljrZZvv8pb7348ssvq133dvumhx56CNevX8cPP/yAPXv2IC0tDRMmTDDbxrlz57Br1y7s3r0bn332GTZs2ICxY8fit99+ww8//IDly5dj8eLFSEhIqLYOZ2dnxMTE4NSpU1i9ejXWr1+Pt99++7bPddmyZdi+fTt++umn2rw0AICffvoJiYmJ8Pf3r/V9bicjIwNjxozBoEGDkJycjH/+85/YsGEDli5dKq8THh6OgwcP4uuvv8aePXtw4MABHDt2rNptbt++XT56cPbsWXz11Vfo3bs3AODLL79E+/bt8frrr8st8FX59ttv8cgjj2DMmDE4fvw49u7di8GDB1e5rhACBw4cQGpqqvwdBwAzZszA4cOHsWXLFvz8888YP348Ro8eLX8PHDx4EM899xxmzZqFpKQkjBo1Cm+88Ual7f/666/Yvn07vvzyS/lQ6vjx43H16lXs2rULiYmJ6N+/P+677z75kPOkSZPQvn17HD16FImJiViwYAE0Gg2AssO6xcXFiIuLw4kTJ7B8+XLo9foqn1tiYiIee+wxPP744zhx4gReffVVREREVDp8u3LlSgwcOBDHjx/HCy+8gOeffx6nT5+u5h2qRp1jkQ2FhYUJtVotnJychIODgwAgVCqV+OKLL6q9z9ixY8XcuXPl68OGDRNDhw41W2fQoEHi5ZdfFkIIERsbK+zs7ERGRoZ8+65du8x+6Xz44YeiVatWZr+mv/32W6FSqeRfVWFhYaJDhw7CaDTK63Tr1k3cc8898vXS0lLh5OQkPvvsM3kZAKHVaoWTk5N8KX/cdu3aiTfeeKNS7eW/gMp/Jd36i7pz586VWrSioqJEYGCgEEKImTNnihEjRgiTyVTlawhUbl3S6XTixRdfrHL9iqr6ZVLR0aNHBQC5FWzcuHFiypQpVa5b1zpv/XV966/IqKgocf/995ttIz09XQAQp0+flusvbymsrbq25Hz00Ufy7b/88osAIFJSUoQQQkydOlU888wzZts4cOCAUKlUorCwsMrHuPU1Lf8V9dVXX5mt98svv4ju3bsLlUolevfuLZ599lmxc+dOs3VSUlIEALNfivfcc4/4+9//Ll9/4IEHRJ8+fap/QZqA8vfs6tWrwsHBQVy4cEFcuHBBaLVakZ2dfUctOdW1XlTVklPTvum7774TarVaXLp0Sb69/P/lyJEjQoiyX+iOjo4iNzdXXickJET4+PhU2hdFR0dXWW9V/vGPf4gBAwbI16tryRFCiMcff1yMGDFCCFF9S45OpxNOTk5Co9EIAJX+x2urus/aK6+8Irp162a2r1i7dq3Q6/XCaDSK3NxcodFoxOeffy7fnpOTIxwdHattyVm5cqXo2rWrWYtWRVX9X9zakhMYGCgmTZpU7fMZNmyY0Gg0Zq+NVqsVBw8eFEKUteSp1Wqz7ychylrCFi5cKIQQYsKECWLs2LFmt0+aNKlSS45GoxFXr16Vlx04cEC0aNFCFBUVmd23c+fOcguKs7OziImJqbL23r17V9syfGtLzsSJE8WoUaPM1pk/f77o0aOHfL1Dhw5m+xmTySTatm0rtyDXVpNrybn33nuRlJSEhIQEhIWFYcqUKfjrX/8KoKwjZVRUFHr37o3WrVtDr9cjNjYWly5dMttGeT+Ccp6enrh69SoAICUlBd7e3mjXrp18e2BgoNn6KSkp6Nu3r1lrRVBQEEwmk1nK7Nmzp9mZUt3d3eXkDwBqtRpt2rSRH7vc22+/jaSkJPkyatQo5Obm4vLlywgKCjJbNygoCCkpKWbLBg4cKP9dUFCAc+fOYerUqdDr9fJl6dKlOHfuHICyzntJSUno1q0bXnzxRXz33Xe4HWFhR7jExESMGzcOd911F5ydnTFs2DAAkN+j559/Hlu2bIGfnx9eeuklHDp0SL6vJXXWJDk5Gfv27TN7XXx9fQFAfm0AYMCAAXf0OLdT8f/R09MTAOT/ieTkZMTExJjVGBISApPJhPPnzwO4/WtaruL/BQD06NEDJ0+eRHx8PJ566ilcvXoV48aNw9NPPy2v4+vriyFDhsj9JX799VccOHAAU6dOldex9H+hMXJzc8PYsWMRExODTZs2YezYsXJLYkOozb7J29tbvr1Hjx5o2bKl2T7Ax8cHzs7O8nV3d3f06NGj0r7o1v1ORVu3bkVQUBA8PDyg1+uxePHiSv9P1Vm6dCkOHDhQ4+dz69atSEpKQnJyMrZt24YdO3ZgwYIFtdp+baSkpCAwMNCsD0hQUBDy8/Px22+/IS0tDQaDwawVxcXFBd26dat2m+PHj0dhYSE6deqEadOm4T//+Y/c4lpbSUlJuO+++2pcZ9KkSUhKSsLBgwfxwAMPYNGiRRgyZAgA4MSJEzAajejatavZPuGHH36Q91mnT5+u1DpUVWtRhw4d4ObmJl9PTk5Gfn4+2rRpY7bt8+fPy9sODw/H008/jZEjR2LZsmVm+8kXX3wRS5cuRVBQECIjI80GJdwqJSWlyu+ys2fPwmg0yssqfh4kSYKHh0eN/7dVaXIhx8nJCV26dEHfvn2xceNGJCQkYMOGDQCAf/zjH1i9ejVefvll7Nu3D0lJSQgJCanUqaq8ea2cJEkwmUxWr7Wqx6nNY3t4eKBLly7ypbpDP9WpuH5+fj4AYP369WbBqfzLDQD69++P8+fPIyoqCoWFhXjsscfwt7/9rcbH6Nq1K1JTU+tUV/lhvhYtWuCTTz7B0aNH8Z///AfAnx3fHnjgAVy8eBFz5szB5cuXcd9998mHmiypsyb5+fkYN26c2euSlJSEs2fPIjg4WF6vrq9/XVX8nyjfKZf/T+Tn5+PZZ581qy85ORlnz55F586da/Wa1vQ8VCoVBg0ahNmzZ+PLL79ETEwMNmzYIAcooKwD8vbt25GXl4dNmzahc+fOcpACyv4Xyr80lOCpp55CTEwMNm/ejKeeeqrS7SqVqlKws9Zzt8a+ydL9TrnDhw9j0qRJGDNmDL755hscP34cixYtqnXn1M6dO2PatGlYsGBBtQHY29sbXbp0Qffu3TF+/HjMnj0bK1eubNQjZb29vXH69Gm8//770Ol0eOGFFxAcHFyn916n0912HRcXF3Tp0gWDBg3Ctm3b8N577+H7778HULY/UKvVSExMNNsnpKSkYPXq1XV6PrfuD/Lz8+Hp6Vlpf3j69GnMnz8fQNkoul9++QVjx47Ff//7X/To0UPe3zz99NNIS0vDk08+iRMnTmDgwIFYs2ZNnWq6lTU+D00u5FSkUqnwyiuvYPHixSgsLMTBgwfx0EMP4e9//zv69u2LTp061Wn4HgB0794d6enpZsdUy8NAxXWSk5NRUFAgLzt48CBUKlWNvwTuRIsWLdCuXTscPHjQbPnBgwfRo0ePau/n7u6Odu3aIS0tzSw4denSBR07djTb/oQJE7B+/Xps3boV27dvl4/DajQas3QNABMnTsT333+P48ePV3pMg8Fg9tqUS01Nxe+//45ly5bhnnvuga+vb5Wp3M3NDWFhYfj3v/+Nd955Bx9++GGt6qyr/v3745dffoGPj0+l16a+g01t9e/fH6dOnapUX5cuXWBvb1/r17S2yv+XKr5/jz32GFQqFT799FP861//wlNPPWX2C3nixInIz8/H+++/X+U2bTE3xp0YPXo0SkpKYDAYEBISUul2Nzc3s/1Dbm6uWSi8VXl/ils/Q3VVvm9KT0+Xl506dQo5OTk17gPq6tChQ+jQoQMWLVqEgQMH4u6778bFixfrtI0lS5bgzJkz2LJlS63WV6vVKC0ttdoon+7du+Pw4cNmIevgwYNwdnZG+/bt0alTJ2g0GrmPIQDcvHnztt8XOp0O48aNw7vvvov9+/fj8OHDOHHiBICy9/l273GfPn2wd+/eWj8PvV6PWbNmYd68eRBCoF+/fjAajbh69Wql/YGHhwcAoFu3bmbPC0Cl61Xp378/rly5Ajs7u0rbrtia2bVrV8yZMwffffcdHn30UbM+bN7e3njuuefw5ZdfYu7cuVi/fn2Vj9W9e/cqv8u6du0KtVpd69enNpp0yAHKmhDVajXWrl2Lu+++G3v27MGhQ4eQkpKCZ599FllZWXXa3siRI9G1a1eEhYUhOTkZBw4cwKJFi8zWmTRpErRaLcLCwnDy5Ens27cPM2fOxJNPPgl3d3drPj0z8+fPx/Lly7F161acPn0aCxYsQFJSEmbNmlXj/V577TVER0fj3XffxZkzZ3DixAls2rQJq1atAgCsWrUKn332GVJTU3HmzBl8/vnn8PDwkDuM+vj4YO/evbhy5Qpu3LgBAJg9ezaCgoJw3333Ye3atUhOTkZaWhq2bduGgICAKoeR3nXXXbC3t8eaNWuQlpaGr7/+GlFRUWbrLFmyBDt27MCvv/6KX375Bd988w26d+9eqzrravr06bh+/TqeeOIJHD16FOfOnUNsbCymTJlyx19IN2/erPSLqOKXU229/PLLOHToEGbMmCG3Mu3YsUPueFyb17Q6f/vb3/D2228jISEBFy9exP79+zF9+nR07dpVPmwHlO1oJ0yYgIULFyIzM7NSp2Z/f3+89NJLmDt3Ll566SUcPnwYFy9exN69ezF+/Hi5g3tToVarkZKSglOnTlW5wx0xYgQ+/vhjHDhwACdOnEBYWFiNO+a2bdtCp9PJHdtv3rxpUV0jR45E7969MWnSJBw7dgxHjhxBaGgohg0bVulQ5J24++67cenSJWzZsgXnzp3Du+++K/9ary13d3eEh4fj3XffrfL233//HVeuXMFvv/2GXbt2YfXq1bj33nvRokWLOtdb1WftmWeeQXp6OmbOnInU1FTs2LEDkZGRCA8Ph0qlgrOzM8LCwjB//nzs27cPv/zyC6ZOnQqVSlXtMOfyVs6TJ08iLS0N//73v6HT6dChQwcAZfvJuLg4ZGRk4Nq1a1VuIzIyEp999hkiIyORkpIid9CtybPPPoszZ85g+/bt6Nq1KyZNmoTQ0FB8+eWXOH/+PI4cOYLo6Gh8++23AICZM2di586dWLVqFc6ePYsPPvgAu3btuu3w7ZEjRyIwMBAPP/wwvvvuO1y4cAGHDh3CokWL8NNPP6GwsBAzZszA/v37cfHiRRw8eBBHjx6V98+zZ89GbGwszp8/j2PHjmHfvn3ybbeaO3cu9u7di6ioKJw5cwabN2/Ge++9V+UAkTvV5EOOnZ0dZsyYgbfeegtz585F//79ERISguHDh8PDw6POE0WpVCr85z//QWFhIQYPHoynn366Us90R0dHxMbG4vr16xg0aBD+9re/4b777sN7771nxWdW2Ysvvojw8HDMnTsXvXv3xu7du/H111/j7rvvrvF+Tz/9ND766CNs2rQJvXv3xrBhwxATEyO35Dg7O+Ott97CwIEDMWjQIFy4cAE7d+6Uj+GvXLkSe/bsgbe3tzyaysHBAXv27MFLL72EDz74AAEBARg0aBDeffddvPjii1VOuOfm5oaYmBh8/vnn6NGjB5YtW4YVK1aYrWNvb4+FCxeiT58+CA4Ohlqtln8N3q7OuipvGTMajbj//vvRu3dvzJ49Gy1btrR4m+X279+Pfv36mV1ee+21Om+nT58++OGHH3DmzBncc8896NevH5YsWSL3GavNa1qdkJAQ/N///R/GjRsnB3tfX1989913leYSmjp1Km7cuIGQkBCz/mrlli9fjk8//RQJCQkICQlBz549ER4ejj59+iAsLKzOz9vWWrRoUe0X7sKFCzFs2DA8+OCDGDt2LB5++GF07ty52m3Z2dnh3XffxQcffIB27drhoYcesqgmSZKwY8cOtGrVCsHBwRg5ciQ6deqErVu3WrS96vzlL3/BnDlzMGPGDPj5+eHQoUOIiIio83bmzZtX7eiakSNHwtPTEz4+PnjmmWcwZswYi59HVZ+1qKgo7Ny5E0eOHEHfvn3x3HPPYerUqVi8eLF8v1WrViEwMBAPPvggRo4ciaCgIHTv3h1arbbKx2nZsiXWr1+PoKAg9OnTB99//z3+7//+D23atAEAvP7667hw4QI6d+5s1telouHDh+Pzzz/H119/DT8/P4wYMaLS6N9btW7dGqGhoXj11VdhMpmwadMmhIaGYu7cuejWrRsefvhhHD16VJ7LKSgoCOvWrcOqVavQt29f7N69G3PmzKn2eZWTJAk7d+5EcHAwpkyZgq5du+Lxxx/HxYsX4e7uDrVajd9//x2hoaHo2rUrHnvsMTzwwAPyfs1oNGL69Ono3r07Ro8eja5du1bbutu/f39s27YNW7ZsQa9evbBkyRK8/vrrFk+4WePzEkrqNUhERGSBgoICeHl5YeXKlWYd65Vg2rRpSE1NxYEDB2xdSoOzfOpXIiKiJur48eNITU3F4MGDcfPmTbz++usAYHFLW2OyYsUKjBo1Ck5OTti1axc2b95cbauK0jHkEBFRs7RixQqcPn0a9vb2GDBgAA4cONCgUwbUlyNHjuCtt95CXl4eOnXqhHfffddsaojmhIeriIiISJGafMdjIiIioqow5BAREZEiMeQQERGRIjHkEBERkSIx5BAREZEiMeQQUaMxfPhwzJ49u9brx8TEWHxaDyJSPoYcIiIiUiSGHCIiIlIkhhwiuq3hw4dj5syZmD17Nlq1agV3d3esX78eBQUFmDJlCpydndGlSxfs2rVLvs8PP/yAwYMHw8HBAZ6enliwYAFKS0vl2wsKChAaGgq9Xg9PT0+sXLmy0uMWFxdj3rx58PLygpOTE/z9/bF//36LnsOrr74KPz8/fPzxx/Dx8YGLiwsef/xx5OXlyevs3r0bQ4cORcuWLdGmTRs8+OCDOHfunHz7hQsXIEkStm3bhnvuuQc6nQ6DBg3CmTNncPToUQwcOBB6vR4PPPAAsrOzzR7/o48+kk8A6evr22yn2SdqSAw5RFQrmzdvhqurK44cOYKZM2fi+eefx/jx4zFkyBAcO3YM999/P5588kn88ccfyMjIwJgxYzBo0CAkJyfjn//8JzZs2IClS5fK25s/fz5++OEH7NixA9999x3279+PY8eOmT3mjBkzcPjwYWzZsgU///wzxo8fj9GjR+Ps2bMWPYdz587hq6++wjfffINvvvkGP/zwA5YtWybfXlBQgPDwcPz000/Yu3cvVCoVHnnkEZhMJrPtREZGYvHixTh27Bjs7OwwceJEvPTSS1i9ejUOHDiAX3/9FUuWLJHX/+STT7BkyRK88cYbSElJwZtvvomIiAhs3rzZoudBRLUkiIhuY9iwYWLo0KHy9dLSUuHk5CSefPJJeVlmZqYAIA4fPixeeeUV0a1bN2EymeTb165dK/R6vTAajSIvL0/Y29uLbdu2ybf//vvvQqfTiVmzZgkhhLh48aJQq9UiIyPDrJb77rtPLFy4UAghxKZNm4SLi0utnkNkZKRwdHQUubm58rL58+cLf3//au+TnZ0tAIgTJ04IIYQ4f/68ACA++ugjeZ3PPvtMABB79+6Vl0VHR4tu3brJ1zt37iw+/fRTs21HRUWJwMDAWtVORJbhCTqJqFb69Okj/61Wq9GmTRv07t1bXubu7g4AuHr1KlJSUhAYGAhJkuTbg4KCkJ+fj99++w03btxASUkJ/P395dtbt26Nbt26yddPnDgBo9GIrl27mtVRXFyMNm3aWPQcfHx84OzsLF/39PTE1atX5etnz57FkiVLkJCQgGvXrsktOJcuXUKvXr2qfC3Kn/etr0X5dgsKCnDu3DlMnToV06ZNk9cpLS2Fi4uLRc+DiGqHIYeIakWj0ZhdlyTJbFl5oLn10I6l8vPzoVarkZiYCLVabXabXq+3aJtVPYeK9Y4bNw4dOnTA+vXr0a5dO5hMJvTq1QslJSXVbqf8ed+6rHy7+fn5AID169ebhToAlZ4XEVkXQw4RWV337t2xfft2CCHkEHDw4EE4Ozujffv2aN26NTQaDRISEnDXXXcBAG7cuIEzZ85g2LBhAIB+/frBaDTi6tWruOeee+q95t9//x2nT5/G+vXr5cf78ccf73i77u7uaNeuHdLS0jBp0qQ73h4R1R5DDhFZ3QsvvIB33nkHM2fOxIwZM3D69GlERkYiPDwcKpUKer0eU6dOxfz589GmTRu0bdsWixYtgkr151iIrl27YtKkSQgNDcXKlSvRr18/ZGdnY+/evejTpw/Gjh1r1ZpbtWqFNm3a4MMPP4SnpycuXbqEBQsWWGXbr732Gl588UW4uLhg9OjRKC4uxk8//YQbN24gPDzcKo9BRJUx5BCR1Xl5eWHnzp2YP38++vbti9atW2Pq1KlYvHixvM4//vEP5OfnY9y4cXB2dsbcuXNx8+ZNs+1s2rQJS5cuxdy5c5GRkQFXV1cEBATgwQcftHrNKpUKW7ZswYsvvohevXqhW7duePfddzF8+PA73vbTTz8NR0dH/OMf/8D8+fPh5OSE3r1712l2ZyKqO0kIIWxdBBEREZG1cZ4cIiIiUiSGHCJShJ49e0Kv11d5+eSTT2xdHhHZAA9XEZEiXLx4EQaDocrb3N3dzebHIaLmgSGHiIiIFImHq4iIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkRhyiIiISJEYcoiIiEiRGHKIiIhIkf4fo1ZCcoe3hLoAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_df.groupby('model_name').accuracy.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYFGKoudigwt",
        "outputId": "8e4b6023-c739-459d-c640-27ce53593c28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "model_name\n",
              "LinearSVC                 0.922772\n",
              "LogisticRegression        0.899010\n",
              "MultinomialNB             0.887129\n",
              "RandomForestClassifier    0.776238\n",
              "Name: accuracy, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "model = LinearSVC()\n",
        "\n",
        "#Aqui hay que pasarle los legal features, y los inf features y lo mismo con los labels\n",
        "X_train, X_test, y_train, y_test, indices_train, indices_test = train_test_split(features,labels, df.index, test_size=0.33, random_state=0)\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "5_bRjcF_inuv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "conf_mat = confusion_matrix(y_test, y_pred)\n",
        "fig, ax = plt.subplots(figsize=(8,6))\n",
        "sns.heatmap(conf_mat, annot=True, fmt='d',\n",
        "            xticklabels=category_id_df.tipo.values, yticklabels=category_id_df.tipo.values)\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Predicted')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "i7ohhOZiIrl-",
        "outputId": "e8a1b40e-c963-45ed-bafb-cf298ffb6e64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display\n",
        "for predicted in category_id_df.category_id:\n",
        "  for actual in category_id_df.category_id:\n",
        "    if predicted != actual and conf_mat[actual, predicted] >= 10:\n",
        "      print(\"'{}' predicted as '{}' : {} examples.\".format(id_to_category[actual], id_to_category[predicted], conf_mat[actual, predicted]))\n",
        "      display(df.loc[indices_test[(y_test == actual) & (y_pred == predicted)]][['tipo', 'Denuncias']])\n",
        "      print('')"
      ],
      "metadata": {
        "id": "0yTEABBiiqfg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display\n",
        "\n",
        "for predicted in category_id_df.category_id:\n",
        "  for actual in category_id_df.category_id:\n",
        "    if predicted != actual and conf_mat[actual, predicted] >= 10:\n",
        "      print(\"'{}' predicted as '{}' : {} examples.\".format(id_to_category[actual], id_to_category[predicted], conf_mat[actual, predicted]))\n",
        "      display(df.loc[indices_test[(y_test == actual) & (y_pred == predicted)]][['tipo', 'Denuncias']])\n",
        "      print('')"
      ],
      "metadata": {
        "id": "kKeT845Lj_Li"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print(metrics.classification_report(y_test, y_pred,\n",
        "                                    target_names=df['tipo'].unique()))"
      ],
      "metadata": {
        "id": "XZ7bRz-3t2Em"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creamos un Dataset con los resultados que nos vaya arrojando nuestro modelo."
      ],
      "metadata": {
        "id": "a8T0z5prt-Sh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Datos de entrada\n",
        "texts = [    \"Un grupo de usuarios de una red social denunció que sus datos personales fueron filtrados debido a una falla de seguridad en la plataforma.\",    \"Una empresa de comercio electrónico denunció que fue víctima de un ataque cibernético que comprometió la seguridad de los datos de sus clientes.\",    \"Un ciudadano denunció que su cuenta bancaria fue hackeada y que el atacante realizó varias transacciones fraudulentas en su nombre.\",    \"Un trabajador de una empresa de tecnología denunció que la compañía no tomó medidas adecuadas para proteger la información personal de los usuarios.\",    \"Una organización de derechos digitales denunció que un proveedor de servicios de internet estaba monitoreando ilegalmente el tráfico en línea de sus usuarios.\",    \"Un grupo de consumidores presentó una denuncia ante una agencia de protección de datos por la recolección y uso ilegal de información personal por parte de una aplicación de salud.\",    \"Un ciudadano denunció que un sitio web de compras en línea le vendió un producto falso y que su información personal fue utilizada sin su consentimiento.\",    \"Una empresa de tecnología denunció que un competidor había robado su propiedad intelectual y estaba utilizando su tecnología sin permiso.\",    \"Un periodista denunció que su correo electrónico fue hackeado y que el atacante había accedido a información confidencial.\",    \"Un grupo de usuarios de una aplicación de mensajería denunció que sus conversaciones privadas estaban siendo monitoreadas y analizadas por la empresa sin su consentimiento.\" ,\"Un vecino denunció a su comunidad de vecinos por incumplir las normas de convivencia y perturbar el descanso de los demás vecinos.\",    \"Un grupo de trabajadores presentó una denuncia contra su empresa por no cumplir con las medidas de seguridad necesarias para prevenir accidentes laborales.\",    \"Un ciudadano presentó una demanda contra el ayuntamiento por negligencia en el mantenimiento de las calles, lo que provocó un accidente de tráfico.\",    \"Un grupo de mujeres presentó una denuncia contra una empresa por discriminación de género en la selección de personal.\",    \"Un cliente presentó una denuncia contra una tienda de electrónica por venderle un producto defectuoso y negarse a hacerse responsable.\",    \"Un grupo de vecinos presentó una denuncia contra una empresa por contaminar el aire y provocar problemas respiratorios en la población.\",    \"Un trabajador presentó una denuncia contra su jefe por acoso laboral y discriminación.\",    \"Un ciudadano presentó una demanda contra una aerolínea por cancelar su vuelo sin previo aviso y no ofrecer una alternativa adecuada.\",    \"Un grupo de usuarios de redes sociales presentó una denuncia contra una plataforma por violar su privacidad y compartir sus datos sin consentimiento.\",    \"Un ciudadano presentó una denuncia contra una cadena de supermercados por vender productos caducados y en mal estado.\"]\n",
        "\n",
        "\n",
        "# Predecir las categorías\n",
        "text_features = tfidf.transform(texts)\n",
        "predictions = model.predict(text_features)\n",
        "\n",
        "# Crear listas para cada columna del DataFrame\n",
        "denuncia_list = []\n",
        "tipo_list = []\n",
        "\n",
        "# Llenar las listas con los datos\n",
        "for text, predicted in zip(texts, predictions):\n",
        "    denuncia_list.append(text)\n",
        "    tipo_list.append(id_to_category[predicted])\n",
        "\n",
        "# Crear el DataFrame\n",
        "df = pd.DataFrame({'Denuncia': denuncia_list, 'tipo': tipo_list})\n",
        "\n",
        "# Guardar el DataFrame en un archivo CSV\n",
        "df.to_excel('denuncias.xlsx')\n",
        "\n",
        "df"
      ],
      "metadata": {
        "id": "Y63KhTv64BsU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "text = 'Un robo en una vivienda de asuncion, entraron dos delincuentes y se llevaron objetos de valor'\n",
        "\n",
        "text_features = tfidf.transform(text)\n",
        "predictions = model.predict(text_features)\n",
        "\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "9iEMar5DeJry",
        "outputId": "c55074cc-f4d3-40e5-b951-72434eb3af36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-2bda435ef3a0>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Un robo en una vivienda de asuncion, entraron dos delincuentes y se llevaron objetos de valor'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtext_features\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtfidf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, raw_documents)\u001b[0m\n\u001b[1;32m   2155\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"The TF-IDF vectorizer is not fitted\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2157\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_documents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2158\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tfidf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, raw_documents)\u001b[0m\n\u001b[1;32m   1425\u001b[0m         \"\"\"\n\u001b[1;32m   1426\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_documents\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1427\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m   1428\u001b[0m                 \u001b[0;34m\"Iterable over raw text documents expected, string object received.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1429\u001b[0m             )\n",
            "\u001b[0;31mValueError\u001b[0m: Iterable over raw text documents expected, string object received."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# End of tesis"
      ],
      "metadata": {
        "id": "OmPtrOQoXGEo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import time\n",
        "import random\n",
        "from datetime import date, datetime"
      ],
      "metadata": {
        "id": "1M3I6pjhep7n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "zillow = \"https://www.zillow.com/redding-ca/fsbo/?searchQueryState=%7B%22usersSearchTerm%22%3A%22Redding%2C%20CA%22%2C%22mapBounds%22%3A%7B%22west%22%3A-122.64684715332031%2C%22east%22%3A-121.97530784667968%2C%22south%22%3A40.42663065068017%2C%22north%22%3A40.861148566020944%7D%2C%22regionSelection%22%3A%5B%7B%22regionId%22%3A47322%2C%22regionType%22%3A6%7D%5D%2C%22isMapVisible%22%3Afalse%2C%22category%22%3A%22cat2%22%2C%22filterState%22%3A%7B%22sort%22%3A%7B%22value%22%3A%22globalrelevanceex%22%7D%2C%22ah%22%3A%7B%22value%22%3Atrue%7D%2C%22fore%22%3A%7B%22value%22%3Afalse%7D%2C%22fsba%22%3A%7B%22value%22%3Afalse%7D%2C%22nc%22%3A%7B%22value%22%3Afalse%7D%2C%22cmsn%22%3A%7B%22value%22%3Afalse%7D%2C%22auc%22%3A%7B%22value%22%3Afalse%7D%7D%2C%22isListVisible%22%3Atrue%2C%22mapZoom%22%3A11%7D\"\n"
      ],
      "metadata": {
        "id": "Kc4AoWtRRv1A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "headers = {\n",
        "        'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n",
        "        'accept-encoding': 'gzip, deflate, br',\n",
        "        'accept-language': 'en-US,en;q=0.8',\n",
        "        'cache-control': 'no-cache',\n",
        "        \"cookie\":'zguid=24|$7f42baba-788a-454e-acd8-c7e70f6bcd0a; zgsession=1|07ad1397-f7a6-4531-a105-d4bca175efea; _ga=GA1.2.223205435.1670447769; zjs_user_id=null; zg_anonymous_id=\"f81804b7-21eb-436f-bad0-f01b703651c1\"; zjs_anonymous_id=\"7f42baba-788a-454e-acd8-c7e70f6bcd0a\"; pxcts=5e493448-7674-11ed-b2dc-644241697947; _pxvid=5e4921bb-7674-11ed-b2dc-644241697947; _gcl_au=1.1.526233822.1670447769; DoubleClickSession=true; _cs_c=0; utag_main=v_id:0184ee72268b006125c4b5717eb005075001706d00bd0$_sn:1$_se:1$_ss:1$_st:1670449569228$ses_id:1670447769228;exp-session$_pn:1;exp-session$dcsyncran:1;exp-session$tdsyncran:1;exp-session$dc_visit:1$dc_event:1;exp-session$dc_region:us-west-2;exp-session; __pdst=09ff59cdf472426eacb1952bcb738760; _fbp=fb.1.1670447770893.1427663025; _pin_unauth=dWlkPU4yUmlPR05tTW1NdFpqTmtPQzAwTVRCbExXRmxNVFl0WWpsallUTXhObU5qTURSbQ; __gads=ID=d928549d0d10c8e9:T=1670447789:S=ALNI_MZ2vCJ2ydoGKLwyiyQYWMmyMCEYJQ; _clck=1qbb1tk|1|f7c|0; _gid=GA1.2.1791235879.1670882459; KruxPixel=true; _hp2_ses_props.1215457233={\"ts\":1670882460214,\"d\":\"www.zillow.com\",\"h\":\"/\"}; JSESSIONID=B88E7D43186DA90EE0DE5F8DA7AACC79; __gpi=UID=0000090d94d3217f:T=1670447789:RT=1670882470:S=ALNI_MaAipz_xEdVRpITS8FF3nykzhwmoQ; KruxAddition=true; _cs_id=a94aef50-32ce-a2eb-9bc6-a30e415f9d22.1670447769.2.1670882869.1670882460.1.1704611769485; _cs_s=2.5.0.1670884669842; _hp2_id.1215457233={\"userId\":\"8071024779595274\",\"pageviewId\":\"163331852153630\",\"sessionId\":\"6773580954950247\",\"identity\":null,\"trackerVersion\":\"4.0\"}; _px3=d7187c16d3b4aa8e6a17c41fd3c7a5b01734926532028f17f4d634859aeba7fa:dUo86PtbNlNKA8CZqLHYaoXgh8ycBIjgE9SPY4Cik8ptyERaZb2wmlvQx86ZWq4RXRqf9W2XnMPML2zorVfU8Q==:1000:fVEHHukaM2fXQlbQ7Vgi7fmbR8b7+Az/0OH6IafLOJBgN7Nm4QWTZFGBJvTbUMo9tlP5Th/oUV2NTPep5G6H8DYBq5FXNEuwJlD2I7Ua+RLl01AwAY12KgHGMfqnAo/5AKhS4oSCizJNnEIW04hByD7kcbhyjwJVhSOaIY4+e90W2FqwkfD7qRkI1271cG+KFSxJItlwJHMOP5OW9ZNeBw==; _uetsid=77596cd07a6811eda4312d337bb09dfe; _uetvid=5f656990767411edbbd27b674a5aa2f4; _gat=1; AWSALB=Xn+HEaMwzpjce9C0eM27aLDTttvXqk1xmaOaQzeLWyndx8vXSE9sl1B65prYLU+Hd7B8hMMVXixgBvD1rQPOu/OZppgEgTJdGIDR05QI+u6gmeguex1fD5iSpw5x; AWSALBCORS=Xn+HEaMwzpjce9C0eM27aLDTttvXqk1xmaOaQzeLWyndx8vXSE9sl1B65prYLU+Hd7B8hMMVXixgBvD1rQPOu/OZppgEgTJdGIDR05QI+u6gmeguex1fD5iSpw5x; search=6|1673475124255|rect=39.04111161492053%2C-121.19050515136719%2C38.174031858212814%2C-121.74119484863282&rid=20288&disp=map&mdm=auto&p=1&z=1&lt=fsbo&fs=1&fr=0&mmm=0&rs=0&ah=0&singlestory=0&housing-connector=0&abo=0&garage=0&pool=0&ac=0&waterfront=0&finished=0&unfinished=0&cityview=0&mountainview=0&parkview=0&waterview=0&hoadata=1&zillow-owned=0&3dhome=0&featuredMultiFamilyBuilding=0&commuteMode=driving&commuteTimeOfDay=now\t\t20288\t\t\t\t\t\t; _clsk=1ufr8i|1670883124606|37|0|e.clarity.ms/collect',\n",
        "        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36'}"
      ],
      "metadata": {
        "id": "IvqIq99rR6xY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params ={\"searchQueryState\":'{\"usersSearchTerm\":\"Redding, CA\",\"mapBounds\":{\"west\":-122.64684715332031,\"east\":-121.97530784667968,\"south\":40.42663065068017,\"north\":40.861148566020944},\"regionSelection\":[{\"regionId\":47322,\"regionType\":6}],\"isMapVisible\":false,\"category\":\"cat2\",\"filterState\":{\"sort\":{\"value\":\"globalrelevanceex\"},\"ah\":{\"value\":true},\"fore\":{\"value\":false},\"fsba\":{\"value\":false},\"nc\":{\"value\":false},\"cmsn\":{\"value\":false},\"auc\":{\"value\":false}},\"isListVisible\":true,\"mapZoom\":11}'}\n"
      ],
      "metadata": {
        "id": "RCfzxPiySFw9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = requests.get(zillow, headers=headers, params = params)"
      ],
      "metadata": {
        "id": "1gxyupavSJQA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response.status_code"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXTiUvi-SLCN",
        "outputId": "76bcd442-8758-4d68-f8b5-0df89ada8173"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "200"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encod = response.encoding\n",
        "contents = response.content.decode(encod)"
      ],
      "metadata": {
        "id": "zXclUGDUSM3n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "soup = BeautifulSoup(contents, 'html.parser') #this line here contain all the html code"
      ],
      "metadata": {
        "id": "mghJmYvqSO1p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Print out the title of the pages\n",
        "titles = soup.title.text\n",
        "print(titles)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ZeJbZFfSQh1",
        "outputId": "4a577e0d-fc8f-4ba3-8f70-548bebc61a49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Redding CA For Sale by Owner (FSBO) - 7 Homes | Zillow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time.sleep(random.uniform(15, 119)) # wait untill page loads completely #2022\n",
        "today = date.today().strftime(\"%m/%d/%y\")\n",
        "table1 = pd.DataFrame(columns = [\"Address\", \"Price\", \"Link\", \"Posted_Time\", \"Detail\"])\n",
        "\n",
        "houses = soup.find_all(\"article\", {\"class\": \"StyledPropertyCard-c11n-8-73-8__sc-jvwq6q-0 gHGLmX srp__sc-15y3msw-0 epgJFL property-card list-card_not-saved\"})\n",
        "\n",
        "for house in houses:\n",
        "    try:\n",
        "        address = house.find(\"address\", {\"data-test\":\"property-card-addr\"}).text\n",
        "        price = house.find(\"span\", {\"data-test\":\"property-card-price\" }).text\n",
        "        link = house.find(\"div\", {\"class\":\"StyledPropertyCardDataWrapper-c11n-8-73-8__sc-1omp4c3-0 gXNuqr property-card-data\" }).a[\"href\"]\n",
        "        posted = house.find(\"div\", class_ = \"StyledPropertyCardBadgeArea-c11n-8-73-8__sc-wncxdw-0 hlsElW\").text + \" From \" + str(today)\n",
        "        detail = house.find(\"span\", {\"class\":\"StyledPropertyCardHomeDetails-c11n-8-73-8__sc-1mlc4v9-0 jlVIIO\" }).text\n",
        "    except Exception as e:\n",
        "        address = None\n",
        "        price = None\n",
        "        link = None\n",
        "        posted = None\n",
        "        detail = None\n",
        "\n",
        "\n",
        "    table1 = table1.append({\"Address\": address, \"Price\": price, \"Link\": link,\n",
        "                            \"Posted_Time\": posted, \"Detail\": detail }, ignore_index=True)\n",
        "\n",
        "table1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "QogWopSUSnY_",
        "outputId": "3ec59701-bbd7-40eb-d0ec-20f7c1a1b4d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Address, Price, Link, Posted_Time, Detail]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-f95a19a6-8b62-4c4d-a377-8dd5121b00e3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Address</th>\n",
              "      <th>Price</th>\n",
              "      <th>Link</th>\n",
              "      <th>Posted_Time</th>\n",
              "      <th>Detail</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f95a19a6-8b62-4c4d-a377-8dd5121b00e3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-ca09d6bf-65a3-485f-ab7a-e23792896282\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ca09d6bf-65a3-485f-ab7a-e23792896282')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-ca09d6bf-65a3-485f-ab7a-e23792896282 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-f95a19a6-8b62-4c4d-a377-8dd5121b00e3 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-f95a19a6-8b62-4c4d-a377-8dd5121b00e3');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "table1 = pd.DataFrame(columns = [\"Address\", \"Price\", \"Detail\", \"Link\", \"Posted_Time\"])\n",
        "houses = soup.find_all(\"article\", {\"class\": \"list-card list-card-additional-attribution list-card_not-saved\"})\n",
        "\n",
        "for house in houses:\n",
        "    try:\n",
        "        time.sleep(random.uniform(15, 60)) # wait untill page loads completely\n",
        "        address = house.find(\"address\", {\"class\":\"list-card-addr\" }).text\n",
        "        link = house.find(\"div\", {\"class\":\"list-card-info\" }).a[\"href\"]\n",
        "        price = house.find(\"div\", {\"class\":\"list-card-price\" }).text\n",
        "        detail = house.find(\"ul\", {\"class\":\"list-card-details\" }).text\n",
        "        posted = house.find(\"div\", class_ = \"list-card-variable-text list-card-img-overlay\").text + \" From \" + str(today)\n",
        "\n",
        "    except Exception as e:\n",
        "        address = None\n",
        "        link = None\n",
        "        price = None\n",
        "        detail = None\n",
        "        posted = None\n",
        "\n",
        "\n",
        "    table1 = table1.append({\"Address\": address, \"Price\": price,\n",
        "                            \"Detail\": detail, \"Link\": link, \"Posted_Time\": posted}, ignore_index=True)\n",
        "\n",
        "table1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "bhUo8ImISoD1",
        "outputId": "ff8e2ee7-6027-486a-d005-5d5213d7df0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Address, Price, Detail, Link, Posted_Time]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-231753bb-0c01-450e-88e5-c033dd226828\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Address</th>\n",
              "      <th>Price</th>\n",
              "      <th>Detail</th>\n",
              "      <th>Link</th>\n",
              "      <th>Posted_Time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-231753bb-0c01-450e-88e5-c033dd226828')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-356adc82-eac7-468c-813c-817db682dba6\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-356adc82-eac7-468c-813c-817db682dba6')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-356adc82-eac7-468c-813c-817db682dba6 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-231753bb-0c01-450e-88e5-c033dd226828 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-231753bb-0c01-450e-88e5-c033dd226828');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "city = [\"Redding\", \"Anderson\", \"Cottonwood\", \"Red-Bluff\", \"Corning\", \"Orland\" ]\n",
        "first_part = \"https://www.zillow.com/homes/\"\n",
        "last_part = \",-CA_rb/\"\n",
        "urls = []\n",
        "for i in city:\n",
        "    urls.append(first_part + i + last_part)\n",
        "urls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VAQbb9q6TV34",
        "outputId": "c1898c5c-a724-47cd-8522-d5b180ae75b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['https://www.zillow.com/homes/Redding,-CA_rb/',\n",
              " 'https://www.zillow.com/homes/Anderson,-CA_rb/',\n",
              " 'https://www.zillow.com/homes/Cottonwood,-CA_rb/',\n",
              " 'https://www.zillow.com/homes/Red-Bluff,-CA_rb/',\n",
              " 'https://www.zillow.com/homes/Corning,-CA_rb/',\n",
              " 'https://www.zillow.com/homes/Orland,-CA_rb/']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time.sleep(8)\n",
        "def city_scraping(u):\n",
        "    header = {\"User-Agent\":\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36\" }\n",
        "    response = requests.get(u, headers = header )\n",
        "    soup = BeautifulSoup(response.content, 'html.parser')\n",
        "    #Search for data and store them in a table\n",
        "    table = pd.DataFrame(columns = [\"Address\", \"Price\", \"Detail\", \"Links\"])\n",
        "    #print(soup.prettify())\n",
        "\n",
        "    houses = soup.find_all(\"div\", class_ = \"list-card-info\")\n",
        "\n",
        "    for house in houses:\n",
        "        try:\n",
        "            address = house.find(\"address\", {\"class\":\"list-card-addr\" }).text\n",
        "            price = house.find(\"div\", {\"class\":\"list-card-price\" }).text.split(\"$\")[1].replace(\",\",\"\") #split to take price only, not $ sign\n",
        "            detail = house.find(\"ul\", {\"class\":\"list-card-details\" }).text\n",
        "            link = house.a[\"href\"]\n",
        "        except Exception as e:\n",
        "            address = None\n",
        "            link = None\n",
        "            detail = None\n",
        "            price = None\n",
        "\n",
        "\n",
        "        table = table.append({\"Address\": address, \"Price\": price,\n",
        "                                \"Detail\": detail, \"Links\":link}, ignore_index=True)\n",
        "    return table"
      ],
      "metadata": {
        "id": "fI98RJ4kT6SO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "urls[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "4bnH5KK1UQog",
        "outputId": "74830154-1ffa-4707-c217-bec2140b6592"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://www.zillow.com/homes/Redding,-CA_rb/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time.sleep(30)\n",
        "redding = city_scraping(urls[0])\n",
        "redding"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "id": "xWqD8VtWT9up",
        "outputId": "433bcadb-7d3b-49a2-e5e9-a12d2324fbe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Address, Price, Detail, Links]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-9c7cfaaf-dd9b-4b51-a863-8e68813bb479\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Address</th>\n",
              "      <th>Price</th>\n",
              "      <th>Detail</th>\n",
              "      <th>Links</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c7cfaaf-dd9b-4b51-a863-8e68813bb479')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-95d30267-d979-4fe5-9301-8d0683b9a55c\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-95d30267-d979-4fe5-9301-8d0683b9a55c')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-95d30267-d979-4fe5-9301-8d0683b9a55c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-9c7cfaaf-dd9b-4b51-a863-8e68813bb479 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-9c7cfaaf-dd9b-4b51-a863-8e68813bb479');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time.sleep(30)\n",
        "anderson = city_scraping(urls[1])\n",
        "anderson"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "hXlIl9-OUCg0",
        "outputId": "b84f3dee-4e5c-4b53-ea07-b038be2a36db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Address, Price, Detail, Links]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-08a4c5a4-88fb-4361-b87e-b1ae43c9f510\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Address</th>\n",
              "      <th>Price</th>\n",
              "      <th>Detail</th>\n",
              "      <th>Links</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08a4c5a4-88fb-4361-b87e-b1ae43c9f510')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-5cbbf1c3-cbde-4690-b675-0dbc1bfc7205\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5cbbf1c3-cbde-4690-b675-0dbc1bfc7205')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-5cbbf1c3-cbde-4690-b675-0dbc1bfc7205 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-08a4c5a4-88fb-4361-b87e-b1ae43c9f510 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-08a4c5a4-88fb-4361-b87e-b1ae43c9f510');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time.sleep(30)\n",
        "cottonwood = city_scraping(urls[2])\n",
        "cottonwood"
      ],
      "metadata": {
        "id": "oo-9_jyKUFAi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "time.sleep(30)\n",
        "red_bluff = city_scraping(urls[3])\n",
        "red_bluff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "8CcLXJ7dUHSu",
        "outputId": "8d348d9d-7639-4ae1-ece9-40f526fc0d31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-3c80aea87c62>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mred_bluff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_scraping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mred_bluff\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time.sleep(35)\n",
        "corning = city_scraping(urls[4])\n",
        "corning"
      ],
      "metadata": {
        "id": "gxoL6S4IUJUR"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}