Case1_Quiz_4.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
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      "start_time": "2021-04-18T17:02:07.955274",
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      "name": "Case1_Quiz_4.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/capajaro/Quiz--4/blob/main/Case1_Quiz_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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          "start_time": "2021-04-18T17:02:14.828804",
          "status": "completed"
        },
        "tags": [],
        "id": "intermediate-tobacco"
      },
      "source": [
        "# Quiz 4 - Asignatura Mineria de Datos Uninorte; Case-1\n",
        "#### **_Estudiante: César Augusto Pájaro Miranda_**\n",
        "\n",
        "En este notebook lo que haremos es utilizar las funcionalidades de limpieza de datos y visualización de Python para generar los archivos base que se necesitan para generar los modelos usando las librerias de **_\"R\"_**. Por comodidad en la carga de los datos, se utilizan repositorios públicos de _GitHub_.\n",
        "\n",
        "# Agenda📃:\n",
        "1. Limpieza de datos y preprocesamiento\n",
        "2. Exploración de Base de Datos\n",
        "3. Generacion modelos usando **_\"R\"_**\n",
        "4. Resultados modelo **_\"R\"_**\n",
        "\n",
        "Muchas de las secciones del codigo de python son adaptadas del notebook de hannaliavoshka alojado en: \n",
        "https://www.kaggle.com/hannaliavoshka/bert-with-disaster-tweets"
      ],
      "id": "intermediate-tobacco"
    },
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:15.003029Z",
          "iopub.status.busy": "2021-04-18T17:02:15.001416Z",
          "iopub.status.idle": "2021-04-18T17:02:16.094331Z",
          "shell.execute_reply": "2021-04-18T17:02:16.095117Z"
        },
        "papermill": {
          "duration": 1.152896,
          "end_time": "2021-04-18T17:02:16.095450",
          "exception": false,
          "start_time": "2021-04-18T17:02:14.942554",
          "status": "completed"
        },
        "tags": [],
        "id": "applicable-coordinate",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8fc863a-b7c5-4645-9b48-b35ce821da03"
      },
      "source": [
        " # linear algebra and modeling\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "# data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import pandas as pd\n",
        "#visualization\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "# tools for text processing\n",
        "import re\n",
        "import string\n",
        "from nltk.corpus import stopwords\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "  "
      ],
      "id": "applicable-coordinate",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pVqHahfdYPVQ"
      },
      "source": [
        "Se cargan el archivo de excel alojado en el repositorio de GitHub con la base de datos y se divide la data entre Train y Testing con una proporcion de 70%/30% respectivamente, usando la libreria de scikit-learn https://scikit-learn.org/stable/."
      ],
      "id": "pVqHahfdYPVQ"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:16.213200Z",
          "iopub.status.busy": "2021-04-18T17:02:16.212145Z",
          "iopub.status.idle": "2021-04-18T17:02:16.302285Z",
          "shell.execute_reply": "2021-04-18T17:02:16.301224Z"
        },
        "papermill": {
          "duration": 0.150162,
          "end_time": "2021-04-18T17:02:16.302466",
          "exception": false,
          "start_time": "2021-04-18T17:02:16.152304",
          "status": "completed"
        },
        "tags": [],
        "id": "spare-genetics"
      },
      "source": [
        "#train and test set\n",
        "Data = pd.read_excel(\"https://github.com/capajaro/Quiz--4/blob/main/Data_yelp.xlsx?raw=true\", header = None)\n",
        "Data.columns = ['text', 'target']\n",
        "\n",
        "X = Data.text\n",
        "y = Data.target\n",
        "\n",
        "train_X,test_X,train_y,test_y = train_test_split(X,y,test_size=0.3, random_state=10);\n",
        "\n",
        "train = pd.DataFrame()\n",
        "train['text'] = train_X\n",
        "train['target'] = train_y\n",
        "\n",
        "test = pd.DataFrame()\n",
        "test['text'] = test_X\n",
        "test['target'] = test_y\n",
        "\n",
        "df = pd.concat([train,test])"
      ],
      "id": "spare-genetics",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.057319,
          "end_time": "2021-04-18T17:02:17.365273",
          "exception": false,
          "start_time": "2021-04-18T17:02:17.307954",
          "status": "completed"
        },
        "tags": [],
        "id": "numeric-circular"
      },
      "source": [
        "# 2. Limpieza de datos y preprocesamiento🧹"
      ],
      "id": "numeric-circular"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:17.076029Z",
          "iopub.status.busy": "2021-04-18T17:02:17.075255Z",
          "iopub.status.idle": "2021-04-18T17:02:17.249788Z",
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          "duration": 0.234868,
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          "exception": false,
          "start_time": "2021-04-18T17:02:17.015066",
          "status": "completed"
        },
        "tags": [],
        "id": "southeast-floor",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "8fb8dfc8-f169-4941-aee8-2873e78acb41"
      },
      "source": [
        "#distribution reviews\n",
        "sns.set_style(\"darkgrid\")\n",
        "plt.figure(num=10, figsize=(8, 6))\n",
        "sns.countplot(x=df.target)\n",
        "plt.xlabel('Reviews', fontweight='bold', fontsize= 14)\n",
        "plt.xticks(ticks = [0,1], labels = [\"Negative\", \"Positive\"])\n",
        "plt.tight_layout()\n"
      ],
      "id": "southeast-floor",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:19.340561Z",
          "iopub.status.busy": "2021-04-18T17:02:19.338865Z",
          "iopub.status.idle": "2021-04-18T17:02:19.347390Z",
          "shell.execute_reply": "2021-04-18T17:02:19.346624Z"
        },
        "papermill": {
          "duration": 0.075276,
          "end_time": "2021-04-18T17:02:19.347541",
          "exception": false,
          "start_time": "2021-04-18T17:02:19.272265",
          "status": "completed"
        },
        "tags": [],
        "id": "japanese-germany"
      },
      "source": [
        "# Se uniran los datasets para mayor operabilidad\n",
        "df = pd.concat([train,test])\n",
        "ntrain = train.shape[0]\n",
        "ntest = test.shape[0]"
      ],
      "id": "japanese-germany",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:19.474290Z",
          "iopub.status.busy": "2021-04-18T17:02:19.473550Z",
          "iopub.status.idle": "2021-04-18T17:02:19.477336Z",
          "shell.execute_reply": "2021-04-18T17:02:19.476548Z"
        },
        "papermill": {
          "duration": 0.070463,
          "end_time": "2021-04-18T17:02:19.477478",
          "exception": false,
          "start_time": "2021-04-18T17:02:19.407015",
          "status": "completed"
        },
        "tags": [],
        "id": "wireless-estimate"
      },
      "source": [
        "# function to Make text lowercase, remove text in square brackets,remove links,remove punctuation and remove words containing numbers\n",
        "# Special thanks to https://www.kaggle.com/andreshg for this function. Check his kernel out - it's great!\n",
        "def clean_text(text):\n",
        "    text = str(text).lower()\n",
        "    text = re.sub('\\[.*?\\]', '', text)\n",
        "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
        "    text = re.sub('<.*?>+', '', text)\n",
        "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "    text = re.sub('\\n', '', text)\n",
        "    text = re.sub('\\w*\\d\\w*', '', text)\n",
        "    return text"
      ],
      "id": "wireless-estimate",
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:19.607572Z",
          "iopub.status.busy": "2021-04-18T17:02:19.606758Z",
          "iopub.status.idle": "2021-04-18T17:02:19.617187Z",
          "shell.execute_reply": "2021-04-18T17:02:19.617933Z"
        },
        "papermill": {
          "duration": 0.079282,
          "end_time": "2021-04-18T17:02:19.618134",
          "exception": false,
          "start_time": "2021-04-18T17:02:19.538852",
          "status": "completed"
        },
        "tags": [],
        "id": "regular-beverage"
      },
      "source": [
        "#Se definen las frases a filtrar\n",
        "stop_words = stopwords.words('english') + [\"u\", \"im\", \"ur\", \"dont\", \"doin\", \"ure\"]"
      ],
      "id": "regular-beverage",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:19.751025Z",
          "iopub.status.busy": "2021-04-18T17:02:19.749985Z",
          "iopub.status.idle": "2021-04-18T17:02:19.753346Z",
          "shell.execute_reply": "2021-04-18T17:02:19.752789Z"
        },
        "papermill": {
          "duration": 0.071242,
          "end_time": "2021-04-18T17:02:19.753489",
          "exception": false,
          "start_time": "2021-04-18T17:02:19.682247",
          "status": "completed"
        },
        "tags": [],
        "id": "facial-suffering"
      },
      "source": [
        "#final cleaning function presente en el Notebook original de Kaggle\n",
        "def preprocess_data(text):\n",
        "    # Clean puntuation, urls, and so on\n",
        "    text = clean_text(text)\n",
        "    # Remove stopwords\n",
        "    text = ' '.join(word for word in text.split(' ') if word not in stop_words)\n",
        "    return text"
      ],
      "id": "facial-suffering",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:19.889831Z",
          "iopub.status.busy": "2021-04-18T17:02:19.884407Z",
          "iopub.status.idle": "2021-04-18T17:02:20.635957Z",
          "shell.execute_reply": "2021-04-18T17:02:20.635342Z"
        },
        "papermill": {
          "duration": 0.822185,
          "end_time": "2021-04-18T17:02:20.636119",
          "exception": false,
          "start_time": "2021-04-18T17:02:19.813934",
          "status": "completed"
        },
        "tags": [],
        "id": "minus-choice"
      },
      "source": [
        "#Se crea la variable clean text\n",
        "df[\"clean_txt\"] = df[\"text\"].apply(preprocess_data)"
      ],
      "id": "minus-choice",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:20.767958Z",
          "iopub.status.busy": "2021-04-18T17:02:20.764308Z",
          "iopub.status.idle": "2021-04-18T17:02:20.772731Z",
          "shell.execute_reply": "2021-04-18T17:02:20.772037Z"
        },
        "papermill": {
          "duration": 0.074446,
          "end_time": "2021-04-18T17:02:20.772878",
          "exception": false,
          "start_time": "2021-04-18T17:02:20.698432",
          "status": "completed"
        },
        "tags": [],
        "id": "interim-decade",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10008728-462e-40a9-e10a-9e28849b3c50"
      },
      "source": [
        "#se comparan los textos original y procesados\n",
        "df[[\"text\",\"clean_txt\"]].head().values\n"
      ],
      "id": "interim-decade",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[\"I'm not sure how long we stood there but it was long enough for me to begin to feel awkwardly out of place.\",\n",
              "        'sure long stood long enough begin feel awkwardly place'],\n",
              "       [\"I can't wait to go back.\", 'cant wait go back'],\n",
              "       ['We recently witnessed her poor quality of management towards other guests as well.',\n",
              "        'recently witnessed poor quality management towards guests well'],\n",
              "       ['On the up side, their cafe serves really good food.',\n",
              "        'side cafe serves really good food'],\n",
              "       [\"Today is the second time I've been to their lunch buffet and it was pretty good.\",\n",
              "        'today second time ive lunch buffet pretty good']], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.059908,
          "end_time": "2021-04-18T17:02:20.892504",
          "exception": false,
          "start_time": "2021-04-18T17:02:20.832596",
          "status": "completed"
        },
        "tags": [],
        "id": "sound-james"
      },
      "source": [
        "# 3. Exploración Base de Datos🧭"
      ],
      "id": "sound-james"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.063762,
          "end_time": "2021-04-18T17:02:21.017338",
          "exception": false,
          "start_time": "2021-04-18T17:02:20.953576",
          "status": "completed"
        },
        "tags": [],
        "id": "funny-flash"
      },
      "source": [
        "Para explorar los datos, lo que haremos sera revisar:\n",
        "* longitud de cada mensaje\n",
        "* numero de palabras en cada mensaje\n",
        "* longitud promedio por palabra\n",
        "* palabras únicas en cada mensaje\n",
        "* numero de stopwords por mensaje\n"
      ],
      "id": "funny-flash"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:21.155654Z",
          "iopub.status.busy": "2021-04-18T17:02:21.154958Z",
          "iopub.status.idle": "2021-04-18T17:02:21.655198Z",
          "shell.execute_reply": "2021-04-18T17:02:21.654467Z"
        },
        "papermill": {
          "duration": 0.577844,
          "end_time": "2021-04-18T17:02:21.655355",
          "exception": false,
          "start_time": "2021-04-18T17:02:21.077511",
          "status": "completed"
        },
        "tags": [],
        "id": "noble-university",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "2b3603c9-52a1-40cc-871b-d584ee49868d"
      },
      "source": [
        "#longitud de cada mensaje\n",
        "df.text = df.text.astype('str')\n",
        "train.text = train.text.astype('str')\n",
        "test.text = test.text.astype('str')\n",
        "\n",
        "df[\"Message_Length\"] = df.text.apply(lambda x: len(x))\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "train.text.loc[train.target==1].apply(lambda x: len(x)).plot(bins=35, kind='hist', color='blue', label='Positive', alpha=0.4)\n",
        "train.text.loc[train.target==0].apply(lambda x: len(x)).plot(bins=35, kind='hist', color='red', label='Negative', alpha=0.3)\n",
        "plt.legend()\n",
        "plt.xlabel(\"Message Length\")\n",
        "\n",
        "print('Como podemos ver la distribución de palabras entre las reviews positivas y negativas ')\n",
        "print('en el dataset de entrenamiento es similar')"
      ],
      "id": "noble-university",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Como podemos ver la distribución de palabras entre las reviews positivas y negativas \n",
            "en el dataset de entrenamiento es similar\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:21.943789Z",
          "iopub.status.busy": "2021-04-18T17:02:21.810146Z",
          "iopub.status.idle": "2021-04-18T17:02:22.366312Z",
          "shell.execute_reply": "2021-04-18T17:02:22.366919Z"
        },
        "papermill": {
          "duration": 0.650151,
          "end_time": "2021-04-18T17:02:22.367126",
          "exception": false,
          "start_time": "2021-04-18T17:02:21.716975",
          "status": "completed"
        },
        "tags": [],
        "id": "strange-universal",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "d60cd361-03b9-4792-8322-c30c17428b61"
      },
      "source": [
        "# numero de palabras en cada mensaje\n",
        "df[\"Word_Count\"] = df.text.str.split().apply(lambda x: len(x))\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "train.text.loc[train.target==1].str.split().apply(lambda x: len(x)).plot(bins=25, kind='hist', color='blue', label='Positive', alpha=0.4)\n",
        "train.text.loc[train.target==0].str.split().apply(lambda x: len(x)).plot(bins=25, kind='hist', color='red', label='Negative', alpha=0.2)\n",
        "plt.legend()\n",
        "plt.xlabel(\"Word Count\")\n",
        "print('Como podemos ver el número de palabras de las reviews positivas y negativas ')\n",
        "print('en el dataset de entrenamiento es similar')"
      ],
      "id": "strange-universal",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Como podemos ver el número de palabras de las reviews positivas y negativas \n",
            "en el dataset de entrenamiento es similar\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:22.574800Z",
          "iopub.status.busy": "2021-04-18T17:02:22.524526Z",
          "iopub.status.idle": "2021-04-18T17:02:23.377959Z",
          "shell.execute_reply": "2021-04-18T17:02:23.377274Z"
        },
        "papermill": {
          "duration": 0.937656,
          "end_time": "2021-04-18T17:02:23.378110",
          "exception": false,
          "start_time": "2021-04-18T17:02:22.440454",
          "status": "completed"
        },
        "tags": [],
        "id": "grand-relevance",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "ae8ad064-3857-44b5-b480-1c733c726c63"
      },
      "source": [
        "# longitud promedio por palabra\n",
        "df[\"Avg_Word\"] = df.text.str.split().apply(lambda x: [len(i) for i in x]).map(lambda x: np.mean(x))\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "train.text.loc[train.target==1].str.split().apply(lambda x: [len(i) for i in x]).map(lambda x: np.mean(x)).plot(bins=25, kind='hist', color='blue', label='Positive', alpha=0.4)\n",
        "train.text.loc[train.target==0].str.split().apply(lambda x: [len(i) for i in x]).map(lambda x: np.mean(x)).plot(bins=25, kind='hist', color='red', label='Negative', alpha=0.2)\n",
        "plt.legend()\n",
        "plt.xlabel(\"Average Word Length\")\n",
        "print('Como podemos ver la longitud promedio por palabra entre las reviews positivas y negativas ')\n",
        "print('en el dataset de entrenamiento presenta una media similar, sin embargo la media de las')\n",
        "print('reviews negativas parece ser ligeramente menor a la de las reviews positivas')"
      ],
      "id": "grand-relevance",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Como podemos ver la longitud promedio por palabra entre las reviews positivas y negativas \n",
            "en el dataset de entrenamiento presenta una media similar, sin embargo la media de las\n",
            "reviews negativas parece ser ligeramente menor a la de las reviews positivas\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:23.519998Z",
          "iopub.status.busy": "2021-04-18T17:02:23.519197Z",
          "iopub.status.idle": "2021-04-18T17:02:24.040299Z",
          "shell.execute_reply": "2021-04-18T17:02:24.039524Z"
        },
        "papermill": {
          "duration": 0.596544,
          "end_time": "2021-04-18T17:02:24.040466",
          "exception": false,
          "start_time": "2021-04-18T17:02:23.443922",
          "status": "completed"
        },
        "tags": [],
        "id": "aggressive-nelson"
      },
      "source": [
        "# palabras únicas en cada mensaje\n",
        "df[\"Unique_Words\"] = df.text.str.split().apply(lambda x: len(set(str(x).split())))\n",
        "plt.figure(figsize=(8, 6))\n",
        "train.text.loc[train.target==1].str.split().apply(lambda x: len(set(str(x).split()))).plot(bins=25, kind='hist', color='blue', label='Positive', alpha=0.4)\n",
        "train.text.loc[train.target==0].str.split().apply(lambda x: len(set(str(x).split()))).plot(bins=25, kind='hist', color='red', label='Negative', alpha=0.2)\n",
        "plt.legend()\n",
        "plt.xlabel(\"Unique Words\")\n",
        "\n",
        "print('Como podemos ver el número de palabras únicas en ambos tipos de review es similar en el dataset de entrenamiento')"
      ],
      "id": "aggressive-nelson",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:24.187388Z",
          "iopub.status.busy": "2021-04-18T17:02:24.186656Z",
          "iopub.status.idle": "2021-04-18T17:02:24.666344Z",
          "shell.execute_reply": "2021-04-18T17:02:24.666918Z"
        },
        "papermill": {
          "duration": 0.557616,
          "end_time": "2021-04-18T17:02:24.667105",
          "exception": false,
          "start_time": "2021-04-18T17:02:24.109489",
          "status": "completed"
        },
        "tags": [],
        "id": "corrected-amazon",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "outputId": "d27d926a-a13f-41f8-a065-5856608f8395"
      },
      "source": [
        "#numero de stopwords por mensaje\n",
        "from wordcloud import STOPWORDS\n",
        "df[\"Stopwords\"] = df.text.apply(lambda x: len([w for w in str(x).lower().split() if w in STOPWORDS]))\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "train.text.loc[train.target==1].apply(lambda x: len([w for w in str(x).lower().split() if w in STOPWORDS])).plot(bins=20, kind='hist', color='blue', label='Positive', alpha=0.4)\n",
        "train.text.loc[train.target==0].apply(lambda x: len([w for w in str(x).lower().split() if w in STOPWORDS])).plot(bins=20, kind='hist', color='red', label='Negative', alpha=0.2)\n",
        "plt.legend()\n",
        "plt.xlabel(\"Count Stopwords\")\n",
        "\n",
        "print('Como podemos ver el número de stopwords entre las reviews positivas y negativas ')\n",
        "print('en el dataset de entrenamiento presenta una media similar, sin embargo la media de las')\n",
        "print('reviews positivas parece ser ligeramente menor a la de las reviews negativas')"
      ],
      "id": "corrected-amazon",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Como podemos ver el número de stopwords entre las reviews positivas y negativas \n",
            "en el dataset de entrenamiento presenta una media similar, sin embargo la media de las\n",
            "reviews positivas parece ser ligeramente menor a la de las reviews negativas\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:26.252206Z",
          "iopub.status.busy": "2021-04-18T17:02:26.251452Z",
          "iopub.status.idle": "2021-04-18T17:02:26.254956Z",
          "shell.execute_reply": "2021-04-18T17:02:26.254369Z"
        },
        "papermill": {
          "duration": 0.082268,
          "end_time": "2021-04-18T17:02:26.255104",
          "exception": false,
          "start_time": "2021-04-18T17:02:26.172836",
          "status": "completed"
        },
        "tags": [],
        "id": "virtual-collectible"
      },
      "source": [
        "# Se separan de nuevo las datasets de entrenamiento y validación\n",
        "train = df[:ntrain]\n",
        "test = df[ntrain:]\n",
        "train.to_csv( 'Train_Cleaned.csv',index= False)\n",
        "train.to_csv( 'Test_Cleaned.csv',index= False)\n",
        "df.to_csv( 'Data_Cleaned.csv',index= False)"
      ],
      "id": "virtual-collectible",
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.071472,
          "end_time": "2021-04-18T17:02:26.397189",
          "exception": false,
          "start_time": "2021-04-18T17:02:26.325717",
          "status": "completed"
        },
        "tags": [],
        "id": "chinese-fifteen"
      },
      "source": [
        "Para la generación de los modelos se utilizaron los textos provesados alojados en la variable Clean_text"
      ],
      "id": "chinese-fifteen"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:26.551914Z",
          "iopub.status.busy": "2021-04-18T17:02:26.551187Z",
          "iopub.status.idle": "2021-04-18T17:02:26.553911Z",
          "shell.execute_reply": "2021-04-18T17:02:26.554538Z"
        },
        "papermill": {
          "duration": 0.081746,
          "end_time": "2021-04-18T17:02:26.554749",
          "exception": false,
          "start_time": "2021-04-18T17:02:26.473003",
          "status": "completed"
        },
        "tags": [],
        "id": "oriental-slovakia"
      },
      "source": [
        "#function to process each text seperatly \n",
        "def create_corpus(target):\n",
        "    corpus=[]\n",
        "    \n",
        "    for x in train[train['target']==target]['clean_txt'].str.split():\n",
        "        for i in x:\n",
        "            corpus.append(i)\n",
        "    return corpus"
      ],
      "id": "oriental-slovakia",
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:26.703667Z",
          "iopub.status.busy": "2021-04-18T17:02:26.702983Z",
          "iopub.status.idle": "2021-04-18T17:02:26.740157Z",
          "shell.execute_reply": "2021-04-18T17:02:26.740673Z"
        },
        "papermill": {
          "duration": 0.112051,
          "end_time": "2021-04-18T17:02:26.740891",
          "exception": false,
          "start_time": "2021-04-18T17:02:26.628840",
          "status": "completed"
        },
        "tags": [],
        "id": "falling-error",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "86dece8f-9d53-48e6-b9d3-42f6c565458c"
      },
      "source": [
        "# Se hace una lista para ver la frecuencia de las palabras en las reviews negativas\n",
        "from collections import defaultdict\n",
        "\n",
        "corpus=create_corpus(0)\n",
        "\n",
        "dic=defaultdict(int)\n",
        "for word in corpus:\n",
        "        dic[word]+=1\n",
        "        \n",
        "top=sorted(dic.items(), key=lambda x:x[1],reverse=True)[:10]\n",
        "\n",
        "#plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "x,y=zip(*top)\n",
        "plt.bar(x,y, color=\"red\")\n",
        "plt.title(label=\"Most common words in Negative Reviews\", fontweight='bold', fontsize= 14)\n",
        "\n",
        "print('Como podemos ver entre las palabras con mas frecuencia en las reviews negativas ')\n",
        "print('se encuentran el lugar, la comida, volver, el servicio')\n"
      ],
      "id": "falling-error",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Como podemos ver entre las palabras con mas frecuencia en las reviews negativas \n",
            "se encuentran el lugar, la comida, volver, el servicio\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-04-18T17:02:27.253873Z",
          "iopub.status.busy": "2021-04-18T17:02:27.253160Z",
          "iopub.status.idle": "2021-04-18T17:02:27.285488Z",
          "shell.execute_reply": "2021-04-18T17:02:27.286037Z"
        },
        "papermill": {
          "duration": 0.108752,
          "end_time": "2021-04-18T17:02:27.286245",
          "exception": false,
          "start_time": "2021-04-18T17:02:27.177493",
          "status": "completed"
        },
        "tags": [],
        "id": "packed-russian",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "2127b3a8-b869-4edd-95e5-7d6327a07b33"
      },
      "source": [
        "# Se hace una lista para ver la frecuencia de las palabras en las reviews positivas\n",
        "corpus=create_corpus(1)\n",
        "\n",
        "dic=defaultdict(int)\n",
        "for word in corpus:\n",
        "        dic[word]+=1\n",
        "        \n",
        "top=sorted(dic.items(), key=lambda x:x[1],reverse=True)[:10]\n",
        "\n",
        "#plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "x,y=zip(*top)\n",
        "plt.bar(x,y, color=\"blue\", )\n",
        "plt.title(label=\"Most common words in Positive Reviews\", fontweight='bold', fontsize= 14)\n",
        "\n",
        "print('Como podemos ver entre las palabras con mas frecuencia en las reviews positivas ')\n",
        "print('se encuentran los adjetivos bueno, grandioso. Además, se encuentran palabras esperadas tales como')\n",
        "print('comida, lugar y servicio')\n"
      ],
      "id": "packed-russian",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Como podemos ver entre las palabras con mas frecuencia en las reviews positivas \n",
            "se encuentran los adjetivos bueno, grandioso. Además, se encuentran palabras esperadas tales como\n",
            "comida, lugar y servicio\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.057319,
          "end_time": "2021-04-18T17:02:17.365273",
          "exception": false,
          "start_time": "2021-04-18T17:02:17.307954",
          "status": "completed"
        },
        "tags": [],
        "id": "96y9Y4QvCtGU"
      },
      "source": [
        "# 2. Generación de modelos usando **_R_**"
      ],
      "id": "96y9Y4QvCtGU"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LmTXzTpCsH8"
      },
      "source": [
        "# Activando R magic \n",
        "%load_ext rpy2.ipython"
      ],
      "id": "0LmTXzTpCsH8",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUeDaKhJG2Ct"
      },
      "source": [
        "%%R \n",
        "if(!require(tidyverse)) install.packages('tidyverse')\n",
        "if(!require(text2vec)) install.packages('text2vec')\n",
        "if(!require(glmnet)) install.packages('glmnet')\n",
        "if(!require(MLmetrics)) install.packages('MLmetrics')\n",
        "if(!require(MLmetrics)) install.packages('MLmetrics')\n",
        "if(!require(tm)) install.packages('tm')\n",
        "if(!require(SnowballC)) install.packages('SnowballC')\n",
        "if(!require(e1071)) install.packages('e1071')\n",
        "if(!require(caret)) install.packages('caret')\n",
        "if(!require(rpart)) install.packages('rpart')\n",
        "\n",
        "\n",
        "library(rpart)\n",
        "library(e1071)\n",
        "library(tidyverse)\n",
        "library(text2vec) # text vectorization\n",
        "library(glmnet) # building model cv.gmlnet()\n",
        "library(tm)\n",
        "library(SnowballC)\n",
        "library(caret)\n"
      ],
      "id": "tUeDaKhJG2Ct",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNkc1-FXDk0S"
      },
      "source": [
        "%%R\n",
        "#cargando la data Train\n",
        "Data_Yelp_train = read.csv(\"https://raw.githubusercontent.com/capajaro/Quiz--4/main/Train_Cleaned.csv\")\n",
        "Data_Yelp_Cleaned_train = Data_Yelp_train[c(\"clean_txt\", \"target\")]\n",
        "colnames(Data_Yelp_Cleaned_train) = c(\"text\", \"target\")\n",
        "id = 1:nrow(Data_Yelp_Cleaned_train)\n",
        "Data_Yelp_Cleaned_train = cbind(id,Data_Yelp_Cleaned_train)\n",
        "Data_Yelp_Cleaned_train$target = as.factor(Data_Yelp_Cleaned_train$target)\n",
        "\n",
        "# Creando la matriz de palabras\n",
        "corpus = Corpus(VectorSource(Data_Yelp_Cleaned_train$text))\n",
        "frequencies = DocumentTermMatrix(corpus)\n",
        "\n",
        "Matrix_train = as.data.frame(as.matrix(frequencies))\n",
        "colnames(Matrix_train)= make.names(colnames(frequencies))\n",
        "Matrix_train$target = as.factor(Data_Yelp_Cleaned_train$target)\n"
      ],
      "id": "iNkc1-FXDk0S",
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rq71Lw6DMXr"
      },
      "source": [
        "%%R\n",
        "\n",
        "#cargando la data Test\n",
        "Data_Yelp_test = read.csv(\"https://raw.githubusercontent.com/capajaro/Quiz--4/main/Test_Cleaned.csv\")\n",
        "Data_Yelp_Cleaned_test = cbind(Data_Yelp_test[\"clean_txt\"],Data_Yelp_test[\"target\"])\n",
        "colnames(Data_Yelp_Cleaned_test) = c(\"text\", \"target\")\n",
        "id = 1:nrow(Data_Yelp_Cleaned_test)\n",
        "Data_Yelp_Cleaned_test = as.data.frame(cbind(id,Data_Yelp_Cleaned_test))\n",
        "\n",
        "# Creando la matriz de palabras\n",
        "corpus = Corpus(VectorSource(Data_Yelp_Cleaned_test$text))\n",
        "frequencies = DocumentTermMatrix(corpus)\n",
        "\n",
        "Matrix_test = as.data.frame(as.matrix(frequencies))\n",
        "colnames(Matrix_test)= make.names(colnames(frequencies))\n",
        "Matrix_test$target = as.factor(Data_Yelp_Cleaned_test$target)\n"
      ],
      "id": "8rq71Lw6DMXr",
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iUZLhQu8GCWG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfbf5c1a-5a1f-46a6-e8dc-8267411d4bc5"
      },
      "source": [
        "%%R\n",
        "Matrix_train$next. = NULL\n",
        "Matrix_train$else. = NULL\n",
        "modelo_svm <- svm(formula = target ~., data = Matrix_train, kernel = \"linear\",\n",
        "                  cost = 10, scale = FALSE)\n",
        "\n",
        "predictSVM=predict(modelo_svm,newdata=Matrix_test)\n",
        "\n",
        "TP_Sp = as.data.frame(table(Matrix_test$target, predictSVM))$Freq[4]\n",
        "TF_Sp = as.data.frame(table(Matrix_test$target, predictSVM))$Freq[1]\n",
        "paste0(\"Accuracy SVM = \" ,\": \",signif(100*(TP_Sp+TF_Sp)/nrow(Matrix_test), digits = 4), \" %\")\n"
      ],
      "id": "iUZLhQu8GCWG",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1] \"Accuracy SVM = : 99.57 %\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4EeLm82fjj7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f690176-f4e8-415a-8e26-c6e52a27ac3a"
      },
      "source": [
        "%%R\n",
        "Matrix_train$next. = NULL\n",
        "Matrix_train$else. = NULL\n",
        "\n",
        "modelo_rpart = rpart(target ~ ., data=Matrix_train, method=\"class\")\n",
        "\n",
        "predictRpart=predict(modelo_rpart,newdata=Matrix_test,type=\"class\")\n",
        "\n",
        "TP_Sp = as.data.frame(table(Matrix_test$target, predictRpart))$Freq[4]\n",
        "TF_Sp = as.data.frame(table(Matrix_test$target, predictRpart))$Freq[1]\n",
        "paste0(\"Accuracy Rpart = \" ,\": \",signif(100*(TP_Sp+TF_Sp)/nrow(Matrix_test), digits = 4), \" %\")\n"
      ],
      "id": "x4EeLm82fjj7",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1] \"Accuracy Rpart = : 73.29 %\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7wkN-y5fi-v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75cd73c8-6795-4e9d-ad77-c40197c64ca7"
      },
      "source": [
        "%%R\n",
        "Matrix_train$next. = NULL\n",
        "Matrix_train$else. = NULL\n",
        "set.seed(123)\n",
        "modelo_Lasso <- train(\n",
        "  target ~., data = Matrix_train, method = \"glmnet\",\n",
        "  trControl = trainControl(\"cv\", number = 30),\n",
        "  tuneLength = 30\n",
        ")\n",
        "\n",
        "predict_Lasso=predict(modelo_Lasso,newdata=Matrix_test)\n",
        "\n",
        "TP_Sp = as.data.frame(table(Matrix_test$target, predict_Lasso))$Freq[4]\n",
        "TF_Sp = as.data.frame(table(Matrix_test$target, predict_Lasso))$Freq[1]\n",
        "paste0(\"Accuracy Lasso = \" ,\": \",signif(100*(TP_Sp+TF_Sp)/nrow(Matrix_test), digits = 4), \" %\")\n"
      ],
      "id": "n7wkN-y5fi-v",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1] \"Accuracy Lasso = : 98 %\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B4EuDzw-xHQX"
      },
      "source": [
        "Como podemos notar el modelo de SVM presenta la mayor precisión de los modelos testeados, y de mas del 99%. El modelo SVM es el que se recomienda para el analisis de las reviews de los restaurantes."
      ],
      "id": "B4EuDzw-xHQX"
    }
  ]
}