EDA.ipynb

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    "colab": {
      "name": "EDA.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmtsgz2I9l1h",
        "colab_type": "code",
        "outputId": "bcb858c0-89b7-48fc-cf46-bb352a77a6d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!pip install vaderSentiment"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: vaderSentiment in /usr/local/lib/python3.6/dist-packages (3.2.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-roo_6DzVUe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGxeu6phCX9s",
        "colab_type": "text"
      },
      "source": [
        "First, lets just have a look at given data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjyZ2Dqay-_L",
        "colab_type": "code",
        "outputId": "3f439989-4a56-496f-d9b6-187c25b09e7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_json(\"/content/drive/My Drive/test_task/reviews.json\", lines=True)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>overall</th>\n",
              "      <th>verified</th>\n",
              "      <th>reviewTime</th>\n",
              "      <th>reviewerID</th>\n",
              "      <th>asin</th>\n",
              "      <th>style</th>\n",
              "      <th>reviewerName</th>\n",
              "      <th>reviewText</th>\n",
              "      <th>summary</th>\n",
              "      <th>unixReviewTime</th>\n",
              "      <th>vote</th>\n",
              "      <th>image</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>False</td>\n",
              "      <td>05 1, 2015</td>\n",
              "      <td>A8WEXFRWX1ZHH</td>\n",
              "      <td>0209688726</td>\n",
              "      <td>{'Color:': ' AC'}</td>\n",
              "      <td>Goldengate</td>\n",
              "      <td>After I wrote the below review, the manufactur...</td>\n",
              "      <td>Works well if you place phone in horizontally ...</td>\n",
              "      <td>1430438400</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>04 19, 2018</td>\n",
              "      <td>ABCA1A8E4DGV1</td>\n",
              "      <td>0209688726</td>\n",
              "      <td>{'Color:': ' Blue'}</td>\n",
              "      <td>noe</td>\n",
              "      <td>It sucks barely picks up anything definitely n...</td>\n",
              "      <td>sucks</td>\n",
              "      <td>1524096000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>04 16, 2018</td>\n",
              "      <td>A1NX8HM89FRQ32</td>\n",
              "      <td>0209688726</td>\n",
              "      <td>{'Color:': ' Black'}</td>\n",
              "      <td>Eduard</td>\n",
              "      <td>Well to write a short one, it blew 2 fuses of ...</td>\n",
              "      <td>Defective</td>\n",
              "      <td>1523836800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>04 13, 2018</td>\n",
              "      <td>A1X77G023NY0KY</td>\n",
              "      <td>0209688726</td>\n",
              "      <td>{'Color:': ' CA'}</td>\n",
              "      <td>Lauren</td>\n",
              "      <td>I have absolutely no memory of buying this but...</td>\n",
              "      <td>Looks cool! Probably works</td>\n",
              "      <td>1523577600</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>True</td>\n",
              "      <td>04 8, 2018</td>\n",
              "      <td>A3GK37JO2MGW6Q</td>\n",
              "      <td>0209688726</td>\n",
              "      <td>{'Color:': ' Black'}</td>\n",
              "      <td>danny</td>\n",
              "      <td>it ok it does it job</td>\n",
              "      <td>Five Stars</td>\n",
              "      <td>1523145600</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   overall  verified   reviewTime  ... unixReviewTime vote image\n",
              "0        4     False   05 1, 2015  ...     1430438400  NaN   NaN\n",
              "1        1      True  04 19, 2018  ...     1524096000  NaN   NaN\n",
              "2        1      True  04 16, 2018  ...     1523836800  NaN   NaN\n",
              "3        3      True  04 13, 2018  ...     1523577600  NaN   NaN\n",
              "4        5      True   04 8, 2018  ...     1523145600  NaN   NaN\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIxjy4DTDBAH",
        "colab_type": "text"
      },
      "source": [
        "Some overall info:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CY7BKLEN0n3m",
        "colab_type": "code",
        "outputId": "ac5ccb6e-a027-4864-adbd-b55a9f77cb08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000000 entries, 0 to 999999\n",
            "Data columns (total 12 columns):\n",
            "overall           1000000 non-null int64\n",
            "verified          1000000 non-null bool\n",
            "reviewTime        1000000 non-null object\n",
            "reviewerID        1000000 non-null object\n",
            "asin              1000000 non-null object\n",
            "style             358331 non-null object\n",
            "reviewerName      999917 non-null object\n",
            "reviewText        999636 non-null object\n",
            "summary           999862 non-null object\n",
            "unixReviewTime    1000000 non-null int64\n",
            "vote              121947 non-null object\n",
            "image             16345 non-null object\n",
            "dtypes: bool(1), int64(2), object(9)\n",
            "memory usage: 84.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7DFrfKyGENtn",
        "colab_type": "text"
      },
      "source": [
        "Let's consider at column 'summary'. I think that this is a customer's review in short. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BokBlcr0QEp",
        "colab_type": "code",
        "outputId": "389d3223-e997-4800-a617-28ac54c1d3e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.summary.value_counts().iloc[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Five Stars       246158\n",
              "Four Stars        41040\n",
              "Three Stars       13583\n",
              "One Star           7035\n",
              "Two Stars          4950\n",
              "Great product      3069\n",
              "Great              2680\n",
              "Perfect            2290\n",
              "Good product       2128\n",
              "Works great        2039\n",
              "Name: summary, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QW9DliChExah",
        "colab_type": "text"
      },
      "source": [
        "Hmm, the first five values look like default values for this field. Let's take a closer look: I suppose, that correlation between target and summary field will be hight in rows with these values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5uHAzCtzbdh",
        "colab_type": "code",
        "outputId": "a99d6429-15fe-427f-c355-47d2122a5bbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "five = df[df['summary']=='Five Stars']['overall'].value_counts()\n",
        "four = df[df['summary']=='Four Stars']['overall'].value_counts()\n",
        "three = df[df['summary']=='Three Stars']['overall'].value_counts()\n",
        "two = df[df['summary']=='Two Stars']['overall'].value_counts()\n",
        "one = df[df['summary']=='One Star']['overall'].value_counts()\n",
        "\n",
        "frame = {'Five Stars': five, 'Four Stars': four,'Three Stars': three, 'Two Star': two, 'One Star': one}\n",
        "pd.DataFrame(frame)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>Five Stars</th>\n",
              "      <th>Four Stars</th>\n",
              "      <th>Three Stars</th>\n",
              "      <th>Two Star</th>\n",
              "      <th>One Star</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67</td>\n",
              "      <td>14</td>\n",
              "      <td>15</td>\n",
              "      <td>23</td>\n",
              "      <td>7003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "      <td>4912</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>32</td>\n",
              "      <td>11</td>\n",
              "      <td>13520</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>40964</td>\n",
              "      <td>19</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>245990</td>\n",
              "      <td>46</td>\n",
              "      <td>16</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Five Stars  Four Stars  Three Stars  Two Star  One Star\n",
              "1          67          14           15        23      7003\n",
              "2          37           5           13      4912         2\n",
              "3          32          11        13520         8         5\n",
              "4          32       40964           19         4         5\n",
              "5      245990          46           16         3        20"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPeJgBjrFp3R",
        "colab_type": "text"
      },
      "source": [
        "As I expected :)\n",
        "\n",
        "We even can avoid ML approaches to these rows... So, will see."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jcjrs7OmGLWD",
        "colab_type": "text"
      },
      "source": [
        "Let's have a look at columns: vote, verified, image, style too.\n",
        "\n",
        "Probably reviews with the high vote will be positive. I suppose that verified can also be correlated with the target. Style - don't know what exactly means, but let's try. The same point with the image field."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ms7jWtdZ2rK0",
        "colab_type": "code",
        "outputId": "6c2762f8-dacf-492a-b7cf-25292a1043bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.vote.value_counts().iloc[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2     45953\n",
              "3     23375\n",
              "4     13601\n",
              "5      8583\n",
              "6      5997\n",
              "7      4107\n",
              "8      3168\n",
              "9      2408\n",
              "10     1897\n",
              "11     1528\n",
              "Name: vote, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwETNILx4_06",
        "colab_type": "code",
        "outputId": "03cb6ecb-6f5f-4e2c-f61b-83a8ba8a7374",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df.verified.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     947137\n",
              "False     52863\n",
              "Name: verified, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CAsAyMsA5fAO",
        "colab_type": "code",
        "outputId": "a3fd99bc-a30a-4d0e-84b0-6190c625f0f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df['style'].astype(str).nunique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11703"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-pOHGNAHu8U",
        "colab_type": "text"
      },
      "source": [
        "I will pick these features to separate dataframe to visualize the correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sfQ8zxkz54Ww",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image = df.image.isna()\n",
        "style = df['style'].astype(str)\n",
        "verified = df.verified.astype(int)\n",
        "vote = df.vote.iloc[:].str.replace(',', '.').astype(float).fillna(0)\n",
        "target = df.overall\n",
        "\n",
        "frame = {'image': image, 'style': style, 'vote': vote, 'verified': verified, 'target': target}\n",
        "corr = pd.DataFrame(frame)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RfPSlYst7yxs",
        "colab_type": "code",
        "outputId": "d4dfa6ae-b321-4eef-d69b-1322b487899f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        }
      },
      "source": [
        "plt.figure(figsize = (9, 8))\n",
        "\n",
        "sns.heatmap(corr.corr(), cmap = plt.cm.RdYlBu_r, vmin = -1.0, annot = True, vmax = 1.0)\n",
        "plt.title('Correlation plot')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F9xU7BJUTLCl2CKqk2Wo7FHyQQ3pnYEG+q2Lzc6uNPSL2B64FVgPuSCkNiIj+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\nX10JAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cBsAOzXIIHcK",
        "colab_type": "text"
      },
      "source": [
        "Ops, my assumptions were wrong. Maybe these features are useless in our case."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EvB8QXmHIfVh",
        "colab_type": "text"
      },
      "source": [
        "Also, I would like to try Vader. Using this tool you can extract how much text is positive, negative and so on. I think that it have a sense."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvGHSOEn80fu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "analyzer = SentimentIntensityAnalyzer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A1WulA1oJPZQ",
        "colab_type": "text"
      },
      "source": [
        "Let's make subsample as it can take much time to calculate metrics for all million rows. We will use StratifiedShuffleSplit to get a good representative subsample."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNxeEaUmBgyb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sss = StratifiedShuffleSplit(n_splits=2, test_size=0.1, random_state=0)\n",
        "_, sample_index = next(sss.split(df['summary'], target))\n",
        "\n",
        "sub_sample = df.iloc[sample_index]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yzx_J47z9jAU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "target = pd.DataFrame(target.iloc[sample_index])\n",
        "\n",
        "for column in ['reviewText', 'summary']:\n",
        "\n",
        "    sent_list = []\n",
        "    for sentiment in sub_sample[column].astype(str):\n",
        "        sentiments = analyzer.polarity_scores(sentiment)\n",
        "        list_temp=[]\n",
        "        for key, value in sentiments.items():\n",
        "            temp = value\n",
        "            list_temp.append(temp)\n",
        "        sent_list.append(list_temp)\n",
        "\n",
        "    temp_df = pd.DataFrame(sent_list, columns=['compound','neg','neu','pos'], index=sub_sample.index)\n",
        "    target = pd.merge(target, temp_df, left_index=True,right_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6rnnPpAJ4D2",
        "colab_type": "text"
      },
      "source": [
        "And the same visualization:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxzfC-A3-hmB",
        "colab_type": "code",
        "outputId": "b8572fa6-40e5-454d-dfbc-c93d32f58470",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        }
      },
      "source": [
        "plt.figure(figsize = (9, 8))\n",
        "\n",
        "sns.heatmap(target.corr(), cmap = plt.cm.RdYlBu_r, vmin = -1.0, annot = True, vmax = 1.0)\n",
        "plt.title('Correlation plot');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uzgwa/i4VK6e9L08aP5MVS9cTdSmaDX/9atec7pVOUE6mxc59ErRtMyHBwYz9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\n5dZ0sKOUUkopt/Z/DNKH8nJAaWkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LyFp4DhTKOkj",
        "colab_type": "text"
      },
      "source": [
        "Hmm, I wouldn't say that even 0.36 is a good reason to waste time on building the model on these features. Yes, probably there is a non-linear correlation, but as I expected linear I will not stop at this step."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpo15bgnMDC7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}