Beer_Review.ipynb

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  "metadata": {
    "colab": {
      "name": "Beer_Review.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "1poajGP8DEIQh-cXLL871yRDUTekfzUtW",
      "authorship_tag": "ABX9TyNdShmTJlc0vFax0BxSk9ZK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chintanshrinath/beer_review/blob/main/Beer_Review.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dpqqyvq9cXXV"
      },
      "source": [
        "# **Evolent Health Technical Solution**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0S6a-1n4clPN"
      },
      "source": [
        "#Import the libraries and Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltjB2SP9Z0FM"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "plt.style.use(['dark_background'])\n",
        "sns.set(color_codes=True)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NI7XEf7Scd_h"
      },
      "source": [
        "beer_data = pd.read_csv('/content/drive/My Drive/Beer Review/BeerDataScienceProject.csv', encoding='latin-1')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-Ch2CwS-1di"
      },
      "source": [
        "#Inspecting the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kngZgMGHpyH",
        "outputId": "37f9b227-18de-4f37-b7f7-5c5534339534",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "beer_data.head()"
      ],
      "execution_count": 3,
      "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>beer_ABV</th>\n",
              "      <th>beer_beerId</th>\n",
              "      <th>beer_brewerId</th>\n",
              "      <th>beer_name</th>\n",
              "      <th>beer_style</th>\n",
              "      <th>review_appearance</th>\n",
              "      <th>review_palette</th>\n",
              "      <th>review_overall</th>\n",
              "      <th>review_taste</th>\n",
              "      <th>review_profileName</th>\n",
              "      <th>review_aroma</th>\n",
              "      <th>review_text</th>\n",
              "      <th>review_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.0</td>\n",
              "      <td>47986</td>\n",
              "      <td>10325</td>\n",
              "      <td>Sausa Weizen</td>\n",
              "      <td>Hefeweizen</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>stcules</td>\n",
              "      <td>1.5</td>\n",
              "      <td>A lot of foam. But a lot. In the smell some ba...</td>\n",
              "      <td>1234817823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.2</td>\n",
              "      <td>48213</td>\n",
              "      <td>10325</td>\n",
              "      <td>Red Moon</td>\n",
              "      <td>English Strong Ale</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>stcules</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dark red color, light beige foam, average. In ...</td>\n",
              "      <td>1235915097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.5</td>\n",
              "      <td>48215</td>\n",
              "      <td>10325</td>\n",
              "      <td>Black Horse Black Beer</td>\n",
              "      <td>Foreign / Export Stout</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>stcules</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Almost totally black. Beige foam, quite compac...</td>\n",
              "      <td>1235916604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>47969</td>\n",
              "      <td>10325</td>\n",
              "      <td>Sausa Pils</td>\n",
              "      <td>German Pilsener</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>stcules</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Golden yellow color. White, compact foam, quit...</td>\n",
              "      <td>1234725145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.7</td>\n",
              "      <td>64883</td>\n",
              "      <td>1075</td>\n",
              "      <td>Cauldron DIPA</td>\n",
              "      <td>American Double / Imperial IPA</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>johnmichaelsen</td>\n",
              "      <td>4.5</td>\n",
              "      <td>According to the website, the style for the Ca...</td>\n",
              "      <td>1293735206</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   beer_ABV  ...  review_time\n",
              "0       5.0  ...   1234817823\n",
              "1       6.2  ...   1235915097\n",
              "2       6.5  ...   1235916604\n",
              "3       5.0  ...   1234725145\n",
              "4       7.7  ...   1293735206\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2K_UWmZHvyS",
        "outputId": "2d6a861c-0c73-4839-ccd9-5ddf690b6f5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(beer_data['beer_brewerId'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1803"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iuyAoB9z-w2j",
        "outputId": "bedc0358-84fb-4eab-cf7c-f95fdb572864",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "beer_data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 528870 entries, 0 to 528869\n",
            "Data columns (total 13 columns):\n",
            " #   Column              Non-Null Count   Dtype  \n",
            "---  ------              --------------   -----  \n",
            " 0   beer_ABV            508590 non-null  float64\n",
            " 1   beer_beerId         528870 non-null  int64  \n",
            " 2   beer_brewerId       528870 non-null  int64  \n",
            " 3   beer_name           528870 non-null  object \n",
            " 4   beer_style          528870 non-null  object \n",
            " 5   review_appearance   528870 non-null  float64\n",
            " 6   review_palette      528870 non-null  float64\n",
            " 7   review_overall      528870 non-null  float64\n",
            " 8   review_taste        528870 non-null  float64\n",
            " 9   review_profileName  528755 non-null  object \n",
            " 10  review_aroma        528870 non-null  float64\n",
            " 11  review_text         528751 non-null  object \n",
            " 12  review_time         528870 non-null  int64  \n",
            "dtypes: float64(6), int64(3), object(4)\n",
            "memory usage: 52.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c28HRjnd-9FH"
      },
      "source": [
        "#Checking missing values analysis, if it is there and drop observation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vno3PhB2-0Ux",
        "outputId": "513216d4-acb0-4069-af25-1659d3789b70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "beer_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "beer_ABV              20280\n",
              "beer_beerId               0\n",
              "beer_brewerId             0\n",
              "beer_name                 0\n",
              "beer_style                0\n",
              "review_appearance         0\n",
              "review_palette            0\n",
              "review_overall            0\n",
              "review_taste              0\n",
              "review_profileName      115\n",
              "review_aroma              0\n",
              "review_text             119\n",
              "review_time               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BgCNU4oF_Iai"
      },
      "source": [
        "beer_data.dropna(inplace =  True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNrPU7qE_M53",
        "outputId": "5cc54e52-6027-448e-b519-e8a6d28b4991",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "beer_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "beer_ABV              0\n",
              "beer_beerId           0\n",
              "beer_brewerId         0\n",
              "beer_name             0\n",
              "beer_style            0\n",
              "review_appearance     0\n",
              "review_palette        0\n",
              "review_overall        0\n",
              "review_taste          0\n",
              "review_profileName    0\n",
              "review_aroma          0\n",
              "review_text           0\n",
              "review_time           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IkfmWpLAQvk4"
      },
      "source": [
        "# **Question : 1 Rank top 3 Breweries which produce the strongest beers?**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_eqmsz3JGCm"
      },
      "source": [
        "As per column headers, we will use beer_brewerId to get the strongest beers\n",
        "\n",
        "Moreover, the strongest factor, we can decide beased on **Alcohol by volume (ABV)**, as there is only one factor, present in the dataset.\n",
        "\n",
        "Therefore, we will use beer_ABV to dervied strongest beers\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCJ8XWSJ_aPn",
        "outputId": "91f819c4-0b56-4a81-83c5-bddc6061217b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "beer_data['beer_name'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sierra Nevada Celebration Ale                 2998\n",
              "Sierra Nevada Pale Ale                        2586\n",
              "Founders Breakfast Stout                      2501\n",
              "Sierra Nevada Bigfoot Barleywine Style Ale    2491\n",
              "La Fin Du Monde                               2480\n",
              "                                              ... \n",
              "Malaprop 8                                       1\n",
              "Folklore (Coffee)                                1\n",
              "Farfadet                                         1\n",
              "Svaneke Choco Stout                              1\n",
              "Croaking Lizard Brown Ale                        1\n",
              "Name: beer_name, Length: 14028, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1fJaQeUv6sy"
      },
      "source": [
        "#Peforming outlier analysis using box plot\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2wUMRFNtsYEF"
      },
      "source": [
        "factors = pd.DataFrame(beer_data[['beer_ABV','beer_brewerId']])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_vM-7F0Zsz3",
        "outputId": "43ea2671-8659-46b4-a46a-9405afba5e84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "factors.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['beer_ABV', 'beer_brewerId'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjJfS04eslrZ",
        "outputId": "6289291b-0785-422b-b12d-95fb84a4bfda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "factors.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>beer_ABV</th>\n",
              "      <th>beer_brewerId</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.0</td>\n",
              "      <td>10325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.2</td>\n",
              "      <td>10325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.5</td>\n",
              "      <td>10325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>10325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.7</td>\n",
              "      <td>1075</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   beer_ABV  beer_brewerId\n",
              "0       5.0          10325\n",
              "1       6.2          10325\n",
              "2       6.5          10325\n",
              "3       5.0          10325\n",
              "4       7.7           1075"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZyNAa485s1XD",
        "outputId": "568ec140-20d3-4c8c-a1bf-7b1ffeafed3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "sns.boxplot(x=factors['beer_ABV'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0234790ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyZugJi9TnCM",
        "outputId": "aecbe9ca-7c18-4312-ca00-701cdcd4d495",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "Q1 = factors.quantile(0.25)\n",
        "Q3 = factors.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "print(IQR)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "beer_ABV            3.2\n",
            "beer_brewerId    1183.0\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7p6dWnyXumLN",
        "outputId": "9a70923f-e172-4a88-9235-42b6fce018db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "factors.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(508358, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cuz2ZRnVaOfr"
      },
      "source": [
        "factors_out = factors[~((factors < (Q1 - 1.5 * IQR)) |(factors > (Q3 + 1.5 * IQR))).any(axis=1)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPIq0bi7T9eb"
      },
      "source": [
        "factor_out= factors['beer_ABV'][~((factors['beer_ABV'] < (Q1 - 1.5 * IQR)) |(factors['beer_ABV'] > (Q3 + 1.5 * IQR))).any(axis=1)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NzIAXfndCCj"
      },
      "source": [
        "factor_out= factors[~((factors < (Q1 - 1.5 * IQR)) |(factors > (Q3 + 1.5 * IQR))).any(axis=1)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2m7HwGLAujr5",
        "outputId": "914c580f-c3ff-4cb3-b68e-8eda86450214",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "factors_out.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(412318, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_w5NdzJ_ZdjQ",
        "outputId": "f5e6d81d-7024-46cd-96c5-c2a7f055f58c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "factors.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['beer_ABV', 'beer_brewerId'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-GXzATEMVhk"
      },
      "source": [
        "q1_beer_data = factors.groupby('beer_brewerId')['beer_ABV'].mean()\n",
        "strong_brewer = pd.DataFrame(q1_beer_data.reset_index())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zpyyHgmRNiXZ"
      },
      "source": [
        "strong_brewer = pd.DataFrame(q1_beer_data.reset_index())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsZpBNdVNs7e",
        "outputId": "893f63f4-59f4-49be-832a-64f3f35d0972",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "strong_brewer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>beer_brewerId</th>\n",
              "      <th>beer_ABV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4.438717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>5.537475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>4.790072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>4.973214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14</td>\n",
              "      <td>6.939831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1602</th>\n",
              "      <td>27870</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1603</th>\n",
              "      <td>27879</td>\n",
              "      <td>5.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1604</th>\n",
              "      <td>27917</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1605</th>\n",
              "      <td>27922</td>\n",
              "      <td>7.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1606</th>\n",
              "      <td>27980</td>\n",
              "      <td>4.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1607 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      beer_brewerId  beer_ABV\n",
              "0                 1  4.438717\n",
              "1                 3  5.537475\n",
              "2                 5  4.790072\n",
              "3                10  4.973214\n",
              "4                14  6.939831\n",
              "...             ...       ...\n",
              "1602          27870  6.000000\n",
              "1603          27879  5.500000\n",
              "1604          27917  8.000000\n",
              "1605          27922  7.450000\n",
              "1606          27980  4.666667\n",
              "\n",
              "[1607 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IH-mHn2jMqlR"
      },
      "source": [
        "sort_by_ABV_value = strong_brewer.sort_values(['beer_ABV','beer_brewerId'], ascending =[False,False])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6qHJgAgSPJso"
      },
      "source": [
        "As per analysis, we can say that the top 3 breweries are 6513, 736 and 24215  and beer_ABV values 19.22, 13.75 and 12.46 respectively"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3j956RyWN1Q4",
        "outputId": "08de7217-272c-4345-a239-a540e6339cd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "sort_by_ABV_value[:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>beer_brewerId</th>\n",
              "      <th>beer_ABV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>699</th>\n",
              "      <td>6513</td>\n",
              "      <td>19.228824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>736</td>\n",
              "      <td>13.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1466</th>\n",
              "      <td>24215</td>\n",
              "      <td>12.466667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      beer_brewerId   beer_ABV\n",
              "699            6513  19.228824\n",
              "165             736  13.750000\n",
              "1466          24215  12.466667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzRTy3CjfYT8"
      },
      "source": [
        "# Answer 1 As per analysis, we can say that the top 3 breweries are 6513, 736 and 24215  and beer_ABV values 19.22, 13.75 and 12.46 respectively\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zta9f5-9Qkes"
      },
      "source": [
        "# Question 2 Which year did beers enjoy the highest ratings?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtOxVsJRRGJN"
      },
      "source": [
        "#Answer 2 As there is no specific column that mentioned data and time to derived the answer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNhJLgTURQe0"
      },
      "source": [
        "# Question 3 Based on the user’s ratings which factors are important among taste, aroma, appearance, and palette?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9IN7rBmeOwW"
      },
      "source": [
        "There are four features on basis of users have rate the beer. These are taste, aroma, apperance and palette"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8E9XeWhSRVma"
      },
      "source": [
        "q3_sol = beer_data[['review_appearance','review_palette','review_taste','review_aroma']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rBrRI3sivPg",
        "outputId": "615cf073-24ff-45f4-8959-4a26fa43ecf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "sns.violinplot(data = q3_sol);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VqxDllbnZqPP",
        "outputId": "bcc73f69-5670-4d15-fc30-a1a28433b742",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "q3_sol.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 508358 entries, 0 to 528796\n",
            "Data columns (total 4 columns):\n",
            " #   Column             Non-Null Count   Dtype  \n",
            "---  ------             --------------   -----  \n",
            " 0   review_appearance  508358 non-null  float64\n",
            " 1   review_palette     508358 non-null  float64\n",
            " 2   review_taste       508358 non-null  float64\n",
            " 3   review_aroma       508358 non-null  float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 19.4 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0q6KxJzXfHBw",
        "outputId": "64a99dd8-1e39-4c6d-903f-f1027ecaf68c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "q3_sol.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review_appearance</th>\n",
              "      <th>review_palette</th>\n",
              "      <th>review_taste</th>\n",
              "      <th>review_aroma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   review_appearance  review_palette  review_taste  review_aroma\n",
              "0                2.5             2.0           1.5           1.5\n",
              "1                3.0             2.5           3.0           3.0\n",
              "2                3.0             2.5           3.0           3.0\n",
              "3                3.5             3.0           2.5           3.0\n",
              "4                4.0             4.5           4.0           4.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FDdsxekufin4",
        "outputId": "536ba29e-c958-431a-fd21-f3c7e3641365",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "correaltion_analysis = q3_sol.corr()\n",
        "sns.heatmap(correaltion_analysis)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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++eYbVFZWYty4cXjuuecwbNgwqFQqTJ48GQBw7NgxREREYOfOnXXeW11djbCwMERFRcHZ2RllZWUICAiAm5sbIiIi8PXXX+Obb76BlZUVAODo0aPo3bs3/vM//xMAcPLkSRw6dAjbt2+HhYUFkpOTsXjxYnzzzTeP7P8DEdEjYUQFr6mMrjD6+fnpniclJSE9PR2bNm0CANy5cwcODg4AAJVKhYSEBCxatAgJCQlQqVRQKBR1rpWSkoIrV65g4sSJumPV1dW4fv06PD09ERcXB7VajatXr2LKlCk4duwYnJycMGDAgEZzqlQqtGrVCq1atYK3tzfS0tIwbNgwnD59Gl988QVu3boFhUKBnJyceu/NyclBVlYWQkNDdceqqqpw6dIlODs7N/rZSUlJyMzMxCuvvAKgZpPekpKSRt9HRCQ3Y2oJNpXRFca2bdvqnkuShHXr1qFLly71zlOpVAgKCkJoaCgSExOxY8eOBq83aNAgrF69usHXtFotDhw4ADc3NyiVSsyfPx+dOnWCUql8oOxqtRqzZs3Ctm3b0KtXLxQUFGDw4MH1zpMkCXZ2dti3b98DfY4kSQgICMCsWbMe6P1ERHKRHmy6iFBGNcb4V15eXoiLi9ON/RUXFyM3NxcA4OTkBBcXF0RGRsLFxQWdOnWq9/4XXngBKSkpuHDhgu6Y/iQWT09PREdHY+DAgXB0dMTNmzfx008/Nakwfvvtt6iurkZ5eTkOHToET09PqNVqVFdXw9HREQDw9ddfN/jebt26oU2bNrrxQqCma7asrAwAYGVlpXsOANbW1igtLa3zc9m3bx+uXr0KANBoNDh9+nSjmYmI5NYSxxiNujAuXrwYZmZm8PX1hY+PD6ZOnYqCggLd635+fti5c2ed7ld9Xbt2RVRUFMLDwzF27FiMGjWqTstSqVTizz//hKenJwDA3d0dVlZWuu7a+3n66ad1E2SGDh2KYcOGwdraGjNnzkRgYCD8/f3rtH71tWrVCrGxsTh48CB8fHwwevRovP/++7olGpMnT8Zrr70GX19flJSUYOzYsUhMTNRNvvHw8MDs2bMxbdo0jB07FmPGjMHhw4eb/HMlIpJLSyyMCqkl3kVSsFdffRWTJ0/GsGHDREd5INZt7z+xyFTcuMJfJu4a/I8poiMYjYlm9XufTNn03G0P9f6CoUObfK7DkSMP9VmPitGNMRIR0d+HMbUEm4qF8R6Sk5PrrE+8KzQ0FF999ZWARERELY+kVTR+0gPIzs7GwoULcfPmTbRv3x6rVq1C165d65xTVFSERYsWIT8/H9XV1RgwYADeffddtGp1/9LHwngPQ4YMwZAhQ0THICJq0bQawxTGiIgITJgwAb6+vti3bx+WLFmCrVu31jknNjYWzs7OiIuLQ1VVFSZMmIAffvgB3t7e9722UU++ISKils0Qk2+Kiopw5swZjBkzBgAwZswYnDlzBsXFxXXOUygUuH37NrRaLdRqNaqqqpo0uZItRiIiMpjmdKWWlJQ0uFmJra0tbG1tdV/n5+fDwcEB5ubmAABzc3PY29sjPz8fHTp00J339ttvY8aMGXjxxRdRUVGBiRMnwt3dvdEcLIxERGQwzVn3sGXLFsTExNQ7Pn36dMyYMaPZn/3dd9+hR48e2LJlC27fvo2QkBB89913GDly5H3fx8JIREQG05wW46RJkxpcl67fWgQAR0dHFBQUQKPRwNzcHBqNBoWFhbrNVe7atm0bli9fDjMzM9jY2MDLywvHjx9nYSQiInGaM/nmr12m99KxY0f07NlTt/FJYmIievbsWacbFQA6d+6Mo0ePom/fvlCr1UhNTcXLL7/c6PU5+YaIiAxG0iqa/GiOpUuXYtu2bRgxYgS2bdumu2VfSEgIMjIyANTsnvbzzz/Dx8cHKpUKXbt2RVBQUKPX5s43Jog739Tgzje1uPNNLe58U9fD7nyT1XtEk891Pv39Q33Wo8KuVCIiMhjufENERKRHKxlmgb8hsTASEZHBSCyMREREtQy1JZwhsTASEZHBGGoTcUNiYSQiIoPhGCMREZEejjESERHpaYkr5VkYiYjIYNiVSkREpEfLyTfUEliY8387wG3Q9B39bYPoCEZjZ98loiP8rbDFSEREpIeTb4iIiPSwxUhERKSnBU5KZWEkIiLD0Whb3m1/WRiJiMhgWuBdp1gYiYjIcCRwjJGIiEhH2wIHGVkYiYjIYLRsMRIREdViVyoREZEeDQsjERFRLc5KJSIi0sPCSEREpIdjjERERHpa4F2nWBiJiMhwuFyDiIhIj0Z0gAfAwkhERAajVbDFSEREpNMCd4RjYSQiIsPhcg0iIiI9nJVKRESkh1vCERER6WGLkYiISA/HGImIiPS0xFmpZqIDiJSRkYG5c+ca9DP27NmD7Ozsh7rG5s2bUVRU9IgSERHJR6to+sNY/C0KY3V19QO9r0+fPlizZs0jTlNXQkICcnJyHuoaW7duZWEkohZJ24yHsWixhbFHjx6Ijo5GQEAAYmJiUFZWhvDwcAQGBsLHxweRkZHQaDQ4efIkVCpVnff6+/vjxIkTOH78OPz9/XXHk5OTMW7cOPj7+yM4OBinTp0CAISGhuLQoUMAgPXr18Pd3R0aTc1GR97e3vdsEcbHx+P06dOIjIyEr68vjh07hnPnzmHChAnw8/ODt7c3Nm/erDt/x44dGDVqFHx9feHj44OsrCx8/vnnKCwsxMyZM+Hr64uLFy9CrVZj1apVCAwMxNixYzFv3jzcvn37Uf54iYgeCY2i6Y/myM7ORnBwMEaMGIHg4OAGGyDz58+Hr6+v7uHq6orDhw83eu0WPcZoaWmJ+Ph4AEB4eDg8PDywbNkyaLVahIWFIT4+HkFBQSgvL0dmZiZcXV1x7tw5lJSUwMPDAydOnNBd68qVK1i3bh02bNgAa2trXLhwASEhIThy5AiUSiVSU1MxatQopKWloXv37sjIyICTkxPKy8vRrVu3BvMFBARg7969mDx5MoYNGwYAKCsrw+bNm2FhYYHbt2/jlVdewaBBg+Ds7IzVq1fj0KFDsLe3h1qthkajwbRp07Br1y58+umneOaZZwAA69atg42NDXbv3g0AiIqKQlxcHObMmWPIHzcRUbMZqiUYERGBCRMmwNfXF/v27cOSJUuwdevWOuesXr1a9zwzMxOTJk3CoEGDGr12iy6Mfn5+uudJSUlIT0/Hpk2bAAB37tyBg4MDAEClUiEhIQGLFi1CQkICVCoVFH/Zvy8lJQVXrlzBxIkTdceqq6tx/fp1eHp6Ii4uDmq1GlevXsWUKVNw7NgxODk5YcCAAc3KfOfOHSxduhTnzp2DQqFAYWEhMjMz4ezsDE9PTyxcuBDDhg3D0KFD0aVLlwavkZSUhLKyMnz//fcAALVaDVdX12blICKSgyEKY1FREc6cOaP7937MmDH48MMPUVxcjA4dOjT4nt27d8PHxwcWFhaNXr9FF8a2bdvqnkuShHXr1jVYTFQqFYKCghAaGorExETs2LGjwesNGjSozm8Y+rRaLQ4cOAA3NzcolUrMnz8fnTp1glKpbFbmtWvX4vHHH8fKlSvRqlUrTJ48GZWVlQCAmJgYZGRkIC0tDa+99hqWLl2KIUOG1LuGJEmIiIho9mcTEcmtObNSS0pKUFJSUu+4ra0tbG1tdV/n5+fDwcEB5ubmAABzc3PY29sjPz+/wcKoVquxf//+OkNX99Nixxj/ysvLC3Fxcbqxv+LiYuTm5gIAnJyc4OLigsjISLi4uKBTp0713v/CCy8gJSUFFy5c0B1LT0/XPff09ER0dDQGDhwIR0dH3Lx5Ez/99FOjxcnKygqlpaW6r0tLS/HEE0+gVatWOH/+PE6ePAmgpnWam5uLvn374o033sALL7yAs2fPNngNLy8vbN68GXfu3AFQ0z2blZXVrJ8XEZEcmjMrdcuWLXjppZfqPbZs2fJQGX788Uc4OTmhZ8+eTTq/RbcY9S1evBhRUVHw9fWFQqFA69atsXjxYl0L0s/PD/Pnz79ni7Br166IiopCeHg47ty5g6qqKvTr1w99+/YFACiVSsTHx8PT0xMA4O7ujtTUVF137b0EBwdj5cqV2LBhAxYsWIBp06Zh/vz52L17N7p16wYPDw8ANS3ShQsXorS0FAqFAo6OjrqlJK+99hoWL16MNm3aYM2aNXjjjTcQExODwMBAKBQKKBQKTJ8+Hc7Ozo/kZ0lE9Kg0pyv19UmT6gyR3aXfWgQAR0dHFBQUQKPRwNzcHBqNBoWFhXB0dGzwuvHx8QgICGhyDoUkSS1x/SU9hA423UVHMAo9bDuLjmA0jv62QXQEo7Gz7xLREYzKxD+3PdT7P3ryP5t8btiVpn/Wq6++isDAQN3km927d+Orr76qd97Vq1cxcuRIJCcno127dk269t+mK5WIiIyPoRb4L126FNu2bcOIESOwbds2vP/++wCAkJAQZGRk6M5LSEjAsGHDmlwUgb9RV6pIycnJWLt2bb3joaGhDU6eISIyFYZaruHs7Ixdu3bVO75+/fo6X0+bNq3Z12ZhfASGDBnCAkhE1ICWOFbHwkhERAajbYGlkYWRiIgMRiM6wANgYSQiIoMxps3Bm4qFkYiIDMaYbifVVCyMRERkMBxjJCIi0tPyyiILIxERGRDHGImIiPRoWmCbkYWRiIgMhi1GIiIiPZx8Q0REpKfllUUWRiIiMiB2pRIREenh5BsiIiI9HGMkIiLS0/LKIgsjEREZEFuMREREejj5hlqEkspy0RGMwkSzTqIjGI2dfZeIjmA0gtI/EB3hb0Vii5GIiKgWZ6USERHpYVcqERGRHq3EFiMREZFOyyuLLIxERGRAXK5BRESkh7NSiYiI9FSzMBIREdVii5GIiEgPl2sQERHpkbhcg4iIqBZnpRIREenhlnBERER62GIkIiLSwzFGIiIiPZyVSkREpIfrGImIiPRwjJGIiEiPRmp5naksjEREZDDsSiUiItJjqBsVZ2dnY+HChbh58ybat2+PVatWoWvXrvXOO3jwID7//HNIkgSFQoFNmzbhP/7jP+57bRZGIiIyGEO1FyMiIjBhwgT4+vpi3759WLJkCbZu3VrnnIyMDMTExGDLli14/PHHUVpaCgsLi0avzcJIREQG05zJNyUlJSgpKal33NbWFra2trqvi4qKcObMGWzatAkAMGbMGHz44YcoLi5Ghw4ddOdt3rwZkydPxuOPPw4AsLGxaVIOFkYiIjKY5hTGLVu2ICYmpt7x6dOnY8aMGbqv8/Pz4eDgAHNzcwCAubk57O3tkZ+fX6cwZmVloXPnzpg4cSLKy8vx8ssvY9q0aVAoFPfNwcJIREQG05xZqZMmTYKfn1+94/qtxWZ9tkaDc+fOYdOmTVCr1Zg6dSqcnJygUqnu+z6zB/q0FiQjIwNz584VHYOIyCRJzfjP1tYWnTt3rvf4a2F0dHREQUEBNBoNgJoCWFhYCEdHxzrnOTk5YeTIkbCwsIC1tTVeeuklpKenN5q5xRTG6urqB3pfnz59sGbNmkec5sE86PdARNRSSZLU5EdTdezYET179kRiYiIAIDExET179qzTjQrUjD3+9NNPkCQJVVVVSEtLg6ura6PXN+rC2KNHD0RHRyMgIAAxMTEoKytDeHg4AgMD4ePjg8jISGg0Gpw8ebJe09jf3x8nTpzA8ePH4e/vrzuenJyMcePGwd/fH8HBwTh16hQAIDQ0FIcOHQIArF+/Hu7u7rrfRry9vZGdnX3PnBs3bkRAQABUKhWCg4Nx9uzZe34P169fxzvvvAMfHx/4+Phg7969unO9vLzw8ccfIzg4GEOHDsX+/fuxefNmBAYG4uWXX8b//u//AqgpsFOmTIG/vz9Gjx6NRYsWQa1WP+RPm4jo0dNCavKjOZYuXYpt27ZhxIgR2LZtG95//30AQEhICDIyMgAAo0ePRseOHeHt7Q2VSgUXFxcEBgY2em2jH2O0tLREfHw8ACA8PBweHh5YtmwZtFotwsLCEB8fj6CgIJSXlyMzMxOurq44d+4cSkpK4OHhgRMnTuiudeXKFaxbtw4bNmyAtbU1Lly4gJCQEBw5cgRKpRKpqakYNWoU0tLS0L17d2RkZMDJyQnl5eXo1gGESD0AABoDSURBVK3bPTOqVCpMnjwZAHDs2DFERERg586dDX4Ps2fPRvfu3fHZZ5+hsLAQ/v7+ePbZZ/HMM88AANRqNXbs2IH09HS89tprmDdvHnbv3o2DBw9i7dq1+O///m+Ym5vjo48+gp2dHSRJwoIFCxAfH4/x48c/8p8/EdHDMNTdNZydnbFr1656x9evX697bmZmhkWLFmHRokXNurbRF0b9gdikpCSkp6frpujeuXMHDg4OAGqKU0JCAhYtWoSEhASoVKp6M49SUlJw5coVTJw4UXesuroa169fh6enJ+Li4qBWq3H16lVMmTIFx44dg5OTEwYMGHDfjKdPn8YXX3yBW7duQaFQICcn557fQ2pqKhYuXAgAsLe3x5AhQ3D8+HFdYfT29gYA9OrVCxUVFRg1ahQAoHfv3rhy5QoAQKvVYuPGjTh69Ci0Wi1u3bqFNm3aNO0HSkQkI00LvL+G0RfGtm3b6p5LkoR169ahS5cu9c5TqVQICgpCaGgoEhMTsWPHjgavN2jQIKxevbrB17RaLQ4cOAA3NzcolUrMnz8fnTp1glKpvGc+tVqNWbNmYdu2bejVqxcKCgowePDge34PjbG0tAQA3TTku1+bmZnpxij379+Pn3/+Gdu3b4e1tTViY2PrFWMiImNgqJ1vDMmoxxj/ysvLC3Fxcbqxv+LiYuTm5gKomX3k4uKCyMhIuLi4oFOnTvXe/8ILLyAlJQUXLlzQHdOfoeTp6Yno6GgMHDgQjo6OuHnzJn766adGC2N1dbVuNtTXX3993+9BqVTqulmvXbuG5ORkeHp6NvEnUKO0tBR2dnawtrZGaWmpbgCaiMjYNGdWqrFoUYVx8eLFMDMzg6+vL3x8fDB16lQUFBToXvfz88POnTsbXAcDAF27dkVUVBTCw8MxduxYjBo1qk7LUqlU4s8//9QVKnd3d1hZWem6axtibW2NmTNnIjAwEP7+/o22Dt99911kZmbCx8cHkydPRlhYGLp3796cHwNUKhVu376NkSNH4q233oK7u3uz3k9EJBetJDX5YSwUkqFGRslotbKo35o2RZ84DBMdwWjYaUQnMB5B6R+IjmBUWv/H0w/1fld7jyafm1n4vw/1WY+K0Y8xEhFRy2VMLcGmYmFsouTkZKxdu7be8dDQUAwZMkRAIiIi48cbFf+NDRkyhAWQiKiZjGlSTVOxMBIRkcFIbDESERHVau5Wb8aAhZGIiAymJS58YGEkIiKDYYuRiIhIj0bLMUYiIiIdzkolIiLSwzFGIiIiPRxjJCIi0sMWIxERkR5OviEiItLDrlQiIiI97EolIiLSw9tOERER6eE6RiIiIj1sMRIREenR8rZTREREtTj5hoiISE9LLIwKqSWmJiIiMhAz0QGIiIiMCQsjERGRHhZGIiIiPSyMREREelgYiYiI9LAwEhER6WFhJCIi0sPCSEREpIeFkYiISA8LIxERkR4WRiIiIj0sjCS77Oxs/PjjjwCA27dv4+bNm4ITERHV4t01SFYJCQn44osvUFVVheHDh6OgoAAffPABNm/eLDqa7HJycrBo0SIUFBQgKSkJv//+O5KSkjBjxgzR0WRXUVGB2NhY5OXlYc2aNcjKykJ2djaGDx8uOprsqqurER8fj7Nnz6KyslJ3fMWKFQJTmRa2GElWW7ZsQXx8PGxsbAAATz/9NK5fvy44lRhLly7FtGnTdD+Lnj174rvvvhOcSoylS5dCo9EgMzMTAPDEE08gJiZGcCoxlixZgl9++QVHjhxB165dcfr0abRp00Z0LJPCwkiyat26NaysrOocMzc3F5RGrNLSUgwePBgKhQIAYGZmhtatWwtOJca5c+cQFham+/6trKyg1ba8O78/ChkZGVi1ahVsbGzw5ptv4uuvv8bFixdFxzIpLIwkq/bt2yM7O1tXDPbt24cnnnhCcCoxzM3NUVVVpftZFBQUwMzMNP9KWlhY1Pm6srKyRd7g9lGwtLQEUPPno6KiAjY2NigqKhKcyrRwjJFktXjxYsydOxfZ2dnw8vJCmzZtEBsbKzqWEBMmTMD06dNx48YNREdHY+/evZgzZ47oWEL0798fsbGxUKvVOH78ODZt2gQvLy/RsYRo164dbt26hUGDBiEkJAR2dnZwcHAQHcukKCRT/bWMhNFoNMjJyYEkSejWrZvJdqUCwMmTJ/Hvf/8bkiTBy8sL/fv3Fx1JiKqqKnz55ZdISkrS/SzefPNNk/yzodFoYG5uDq1Wi2+//RZlZWVQqVSwtrYWHc1ksDCSrI4dO4Y+ffroJpyUlJTg999/h1KpFJxMfvv27YOvr2+jx0xBVlYWnJ2dGz1GJAfTHNAgYVavXl3nN19ra2usXr1aYCJxGlqiYorLVgAgLCysScdMwcmTJzFhwgS8+OKLUCqVugfJh2OMJCtJknSTTYCamZgajUZgIvllZGQgPT0dN27cwPbt23XHy8rKUFVVJTCZ/IqLi1FcXIzKykpkZWXpJtyUlpaivLxccDoxwsPDMXv2bPTu3dtkJ2OJxsJIsrKyssJvv/2Gf/zjHwCA3377DW3bthWcSl4FBQU4ffo0KioqcPr0ad1xKysrk1vEvX//fmzZsgWFhYUICQnRHbexscHUqVMFJhPH1tYWo0aNEh3DpHGMkWT166+/YsaMGXBxcYEkScjKykJMTAzc3NxER5PdwYMH4e3tXedYWVmZSU6yiI2NxVtvvSU6hlHYsmULLCwsMGrUKN3SDQB47LHHBKYyLSyMJLtbt27h1KlTAAA3Nze0a9dOcCIx/Pz8kJCQ0OgxU5CdnQ0nJydYWloiJSUFZ8+eRXBwsEn+2UhMTMR7772HO3fuAKgdfjh79qzgZKaDHdgku3bt2kGpVOL555+HhYUFKioqREeSVXV1NSoqKqDVanHnzh1UVFSgoqIChYWFJvezuGv27NkwMzNDbm4uIiIikJubiwULFoiOJcTatWuxdetW/P777zh79iwyMzNZFGXGMUaS1Q8//IDIyEhcu3YNgGn+NhwbG4uYmBgoFIo6XcjW1tZ4/fXXBSYT5+52eMnJyRg/fjxCQkJMctkKANjb26NPnz6iY5g0FkaSVVRUFD755BO4ubmZ7Iy76dOnY/r06fjggw+wZMkS0XGMQmVlJa5fv45///vfmD17NgCY7JZwnp6eiIqKgre3d50xRhcXF4GpTAsLI8mqXbt26Nevn+gYRmHJkiXIzs5GVlYWhg8fjtu3b6Oqqgrt27cXHU12kyZNwsiRI6FUKtGnTx/k5ubqNoEwNd9++y0A4NChQ7pjCoUChw8fFhXJ5HDyDcnqiy++gI2NTb3fhk1xxp3+vSkPHz6MS5cumey9Kf9Kq9Wiurq63ubiRHJgYSRZubq66p4rFAqTHGO8S6VSYfv27Zg4cSL27t0LABgzZgwSExMFJxPj0qVLyMzMhFqt1h1TqVQCE4lz8eJFHD9+HEBN1yq3xpMXu1JJVndvREu8N6W+rVu3YseOHbh27Rr69OmDkydPwsPDwyQL4969e7FmzRoMGTIEQE0vS1hYGMaOHSs4melgYSQShPemrLVz507s2rUL48ePx4YNG3D+/Hl89tlnomMJsXHjRuzZswePP/44AODatWuYMmUKC6OMWBhJVpmZmYiIiKjXZWaKXam8N2UtCwsLtG3bFlqtFpIk4ZlnnkFOTo7oWMLcLYp/fU7yYGEkWS1duhSzZ8/GihUr8OWXX2L79u31uhNNRbdu3bBr1y7emxI1k6+qqqrg6uqKqKgoODo6QqvVio4lxJNPPolPP/0UwcHBAIBdu3ahS5cuglOZFk6+IVn5+/tjz5498PHxwf79+wEAAQEBiI+PF5xMPo3tbmOKM3TPnz+Pzp07o6KiAmvXrkVpaSmmTZuGnj17io4mu6KiIkRGRuLYsWNQKBQYOHAgwsPD0bFjR9HRTAZbjCSru4v627Vrh8zMTDg4OODGjRuCU8nrueee083IvcvUZ+gWFRXhmWeeQdu2bbFs2TIAQGpqquBU8tNoNNi+fTs+/vhj0VFMGgsjyWr06NG4ceMG3njjDYwfPx5arRYzZ84UHUtWnJlb3+rVq+ttnt7Qsb87c3NzHD161OT+ThgbFkaSjVarhVKphJ2dHQYPHowTJ06gsrLSJG+zRDUuX76MnJwclJWVITk5WXe8tLTUZDdUHzp0KDZs2ACVSlXnXqWm2MUuCscYSVb6Y4umjjN0a3b/2bNnD06fPo3evXvrjltbWyM4OBhDhw4VF04QboIhHluMJKunnnoKeXl56Ny5s+gownGGbs39J/38/LBnzx74+/vf87yjR49i8ODBMiYTh13t4rEwkqxu376NsWPHwt3dvU430b/+9S+BqcRQq9VQKpWQJAn29vaYM2cOAgIC8MYbb4iOJrv7FUUA+Pjjj02mMJJ4LIwkq7Fjx3IHj/93d82iKc/QbSpTGvFhF7t4LIwkKz8/P9ERjIa3t7fJz9Btqrvb5pkCdrGLx8JIsqqurkZ8fDzOnj2LyspK3fEVK1YITCXG66+/DgCcoUt1sItdPBZGktWSJUug0Whw/PhxjB8/HomJiejfv7/oWMIkJycjLS0NQM3the7eUYHqMqWuVHaxi2cmOgCZloyMDKxatQo2NjZ488038fXXX+PixYuiYwnx8ccf46OPPkK7du3Qrl07rF27Fp988onoWEJcvnz5vq/PmTNHpiTi/bWLfejQoZg4caLoWCaFLUaSlaWlJYCa34orKipgY2ODoqIiwanE+O6775CQkKCbnfvaa6/Bz88Ps2fPFpxMfm+//TYqKiowYMAAeHp6QqlUwt7eXve6KbWkG+tiN6WlK6KwMJKs2rVrh1u3bmHQoEEICQmBnZ0dHBwcRMcSwtbWts5uJpaWlrC1tRWYSJwDBw7g2rVrOHbsGNLS0rBmzRpYW1vj4MGDoqMJ1bp1a7Ru3brOMS5dMTwWRpJVXFwczM3NMWfOHOzfvx+lpaUmeZd2oGYz8alTp+pm6n777bdwd3fXbY1mSq0kSZKQn5+PP//8E3/88Qfat28Pd3d30bGMkimNt4rCLeFIdmVlZbh8+TJ69eolOopQr7766j1fUygU2Lp1q4xpxOrfvz+6d++O4OBgKJVKk+1FaAo/Pz+T21xdbiyMJKvk5GQsWbIE5ubmSEpKQkZGBj777DOTvXP9/ZjSWNL69euRlpaGgoICPPfccxg4cCAGDBiADh06iI5mdFgYDY+zUklWn376KXbv3q0bS+vTpw+uXLkiOJVxMqV78oWEhGDDhg3Ys2cP3N3dERUVhRdffFF0LKPEtozhcYyRZPf444/X+drCwkJQEuNmSv8Afv/990hNTUVqaiq0Wi0GDhwIpVIpOpYQly9fxlNPPXXP101p6YooLIwkKysrK1y/fl23xdfx48dhY2MjOJVxMqVt0H744Qd4enpi6tSpJn/nFS5dEY9jjCSr9PR0REREIC8vD66ursjJycHnn39e5158VMMUx5KKi4s5rgjUWbryP//zP1y6IjO2GElWffv2xdatW/HLL78AqFmyYKpr9xpjSr+zpqenY9asWdBqtUhOTkZGRgZ27tyJDz/8UHQ02XHpingsjCS7qqoqaLVaADWbipsqjiXVWr58OdavX4+wsDAANZOyFi5cKDiVGB4eHrqlK1FRUVy6IgBnpZKsfvjhB4waNQrbtm3DV199hdGjR+PHH38UHUuIt99+G15eXli0aBH27duHwsLCOq+b0lhSVVUVXFxc6hz7644vpuLNN99E27Zt8eWXXyImJgaHDh1CcXGx6FgmhS1GktXHH3+Mb775Bt26dQMA5OTkYNq0aRg+fLjgZPLjNmi1LCwscPv2bd2Eo4sXL+r21TU1ISEhCAkJgVqtxsGDBxEVFYWrV6/izJkzoqOZDBZGkpWlpaWuKAJA165d0aZNG4GJxOFYUq233noLU6ZMQWFhIRYuXIiUlBRERUWJjiUEl66Ix1mpJKvo6Gi0atUKgYGBkCQJe/bsQXV1NaZOnQpJkupsqv13x23Q6srNzUVKSgokScKLL7543/HXv7O5c+fqlmmY+tIVUVgYSVaurq73fE2hUODs2bMyphGL26DR/XDpijgsjESC3R1L+vTTT01uLGnevHmIiopCQEBAgxsa7N69W0Aqsbh0RTyOMZLsiouL8dtvvwEA3NzcYGdnJziRGBxLAiZNmgQAWLBggeAkxoNLV8RjYSRZ/fDDD3jvvfd0t5xavHgxPvzwQ5Oclcpt0KDb8cjMzAz9+/cXnMY4cOmKeCyMJCsu16i1Zs0aAOAaNQArVqzQ3bTaz88Pjo6OoiMJw6Ur4nGBP8mKyzVqpaenY9iwYfDz8wMAZGRk4L333hOcSoz4+HhER0ejtLQUQUFBmDx5MhITE0XHEuKvS1cmTZqEWbNmiY5lUjj5hmTF5Rq1xo0bh8jISISFhWHv3r0AgNGjR+PAgQOCk4mlVqsRGRmJXbt2mdQsZX1cuiIWu1JJVp999hkA4F//+led4zExMSa3XINjSXWdP38eCQkJOHDgAFxcXLBq1SrRkYTp0qULJkyYIDqGyWJhJFllZmaKjmA0OJZUy8/PD+Xl5VCpVNixY4dJjjFy6YrxYFcqkSDJycn4/PPPkZubi0GDBum2QRs4cKDoaLL7+eefTXY7vLtOnz6N3r1748SJEw2+/vzzz8ucyHSxMJKsMjMzERERgczMTKjVat1xU+pC1cexpBqSJGH37t3IycnBvHnzkJeXh8LCQvTr1090NNmdPHmSS1cEY2EkWY0bNw6zZs3CihUr8OWXX2L79u2wsrLCG2+8IToaCbR8+XIUFRXh999/x3fffYcbN24gJCTEJLsPAwICuHRFMI4xkqzUajWUSiUkSYK9vT3mzJmDgIAAkyqMHEuq7/jx49i7d69u6YqdnR0qKysFpxIjPj4e586dw969exEUFITu3bvD398fY8aMER3NZLAwkqzMzc0BAO3atUNmZiYcHBxw48YNwankxW3Q6rO0tKzzS4JWqxWYRrwePXpgwYIFmDNnDiIjIzFv3jwWRhmxMJKsvL29cePGDbzxxhsYP348tFotZs6cKTqWrLgNWn3PPPMMvv32W0iShLy8PMTFxZn0ZBwuXRGLY4wkTFVVFSorK2Ftba07dvToUQwePFhgKvlwLKlWWVkZVq5ciaSkJACAl5cXFi9ejLZt2wpOJj/9pSsqlcqk/1yIwsJIRsXPzw8JCQmiY8jm7lhSYmKiyY4laTQafPbZZybXc3AvXLoiHvdKJaNiar+n3R1LOnz4MDp37ox58+aJjiQ7c3NzHD16VHQMo9GvXz/s2rULUVFRAIC8vDz88ssvglOZFhZGMioNzdL8Ozt//jxWrVqF4cOHIy8vz2THkoYOHYoNGzagqKgIFRUVuocpWrFiBdLS0nD48GEAgJWVFZYvXy44lWnh5BsiQbgNWq2YmBgAQFRUFBQKBSRJMrm9c+/i0hXxWBjJqJhSV+q7777LsaT/xz10a3HpinjsSiVZXb58+b6vz5kzR6Yk4nEsiRry16UrS5cu5S9QMuOsVJLV6NGjUVFRgQEDBsDT0xNKpRL29vaiYwnBbdCoIVy6Ih67UklWBw4cwLVr13Ds2DGkpaVhzZo1sLa2xsGDB0VHkx3HkuivNBoNNm7ciMjISNFRTBq7UklWkiQhPz8ff/75J/744w+0b98eAwYMEB1LCI4l0V9x6YpxYIuRZOXh4YHu3bsjODgYUVFRcHBwEB1JGG6DRg25u3RFpVLV6T597LHHBKYyLRxjJFmtX78eaWlpKCgowHPPPYeBAwdiwIAB6NChg+hosuNYEjXE1dVV99zUl66IwsJIQqjVahw8eBCffvoprl69ijNnzoiOJCtug0ZkvNiVSrL6/vvvkZqaitTUVGi1WgwcOBBKpVJ0LNndHUtiYSQyPmwxkqzmzp2rW6bRuXNn0XGEiomJwWOPPcaxJCIjw8JIQhQXF5vkuKI+jiURGScWRpJVeno6Zs2aBa1Wi+TkZGRkZGDnzp348MMPRUcjIgLAdYwks+XLl2P9+vWws7MDAPTp04fboBGRUWFhJFlVVVXBxcWlzrHWrVsLSkNEVB8LI8nKwsICt2/f1u34cvHiRVhaWgpORURUi2OMJKvk5GR8/vnnyM3NxaBBg5CSkoKoqCgMHDhQdDQiIgAsjCRAbm4uUlJSIEkSXnzxRTz11FOiIxER6bAwEhER6eHONySLefPmISoqCgEBAXXuKHEX70FIRMaCLUaSxenTp9G7d2+cOHGiwdeff/55mRMRETWMLUaSRe/evQEAZmZm6N+/v+A0RET3xhYjySogIAClpaVQqVTw8/ODo6Oj6EhERHWwMJLszp07h7179yIxMRHdu3eHv78/xowZIzoWEREAFkYSSK1WIzIyErt27eLG2URkNDjGSLI7f/48EhIScODAAbi4uGDVqlWiIxER6bDFSLLy8/NDeXk5VCoVVCoVxxiJyOiwMJKsfv75Z7i7u4uOQUR0T9xEnGTVr18/7Nq1C1FRUQCAvLw83naKiIwKCyPJasWKFUhLS8Phw4cBAFZWVli+fLngVEREtVgYSVbHjx/HRx99hDZt2gAA7OzsUFlZKTgVEVEtFkaSlaWlZZ29UrVarcA0RET1cbkGyeqZZ57Bt99+C0mSkJeXh7i4OE7GISKjwlmpJKuysjKsXLkSSUlJAAAvLy8sXrwYbdu2FZyMiKgGCyPJRqPR4LPPPsPMmTNFRyEiuieOMZJszM3NcfToUdExiIjui4WRZDV06FBs2LABRUVFqKio0D2IiIwFu1JJVq6urrrnCoUCkiRBoVBwE3EiMhosjERERHrYlUpERKSHhZGIiEgPCyMREZEeFkYiIiI9LIxERER6/g++vzmeOoygVAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCGPpGC7gFmN",
        "outputId": "1644a79f-d359-4723-d8ed-d4c027cac9af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "display(q3_sol.corr())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "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>review_appearance</th>\n",
              "      <th>review_palette</th>\n",
              "      <th>review_taste</th>\n",
              "      <th>review_aroma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>review_appearance</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.544724</td>\n",
              "      <td>0.551979</td>\n",
              "      <td>0.531204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>review_palette</th>\n",
              "      <td>0.544724</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.600842</td>\n",
              "      <td>0.703428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>review_taste</th>\n",
              "      <td>0.551979</td>\n",
              "      <td>0.600842</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.722737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>review_aroma</th>\n",
              "      <td>0.531204</td>\n",
              "      <td>0.703428</td>\n",
              "      <td>0.722737</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   review_appearance  ...  review_aroma\n",
              "review_appearance           1.000000  ...      0.531204\n",
              "review_palette              0.544724  ...      0.703428\n",
              "review_taste                0.551979  ...      0.722737\n",
              "review_aroma                0.531204  ...      1.000000\n",
              "\n",
              "[4 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OkGkiQ15hMAc"
      },
      "source": [
        "As there is review_aroma and review_taste, highly corelate to each other. Therefore, we should remove review_taste from the dataframe and consider as predictor to derive impportnat factor for beer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4RAM8_Yi5ss"
      },
      "source": [
        "revised_q3_sol = q3_sol.drop(['review_taste'], axis = 1).columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLTITF7jhl1Q"
      },
      "source": [
        "rnd_clf = RandomForestRegressor(n_estimators=500, n_jobs=-1, random_state=42)\n",
        "rnd_clf.fit(q3_sol[revised_q3_sol].values, q3_sol[\"review_taste\"].values)\n",
        "feature_importance = rnd_clf.feature_importances_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ss_Tizjlikl",
        "outputId": "f7525ca6-27b2-40de-b86e-6dd84e18499d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(revised_q3_sol,feature_importance)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['review_appearance', 'review_palette', 'review_aroma'], dtype='object') [0.07565518 0.01709533 0.90724949]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dx_AhTF0l0ld"
      },
      "source": [
        "feature_name_list = []\n",
        "feature_score_list = []\n",
        "for feature_name, feature_score in zip(revised_q3_sol,feature_importance):\n",
        "  feature_name_list.append(feature_name)\n",
        "  feature_score_list.append(feature_score)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmqVoyYYnq8_"
      },
      "source": [
        "dict_data = {'feature_name':feature_name_list,'feature_score':feature_score_list}\n",
        "plot_data = pd.DataFrame(dict_data, index = ['review_appearance','review_palette','review_aroma'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NI4gZzJ8qFZR",
        "outputId": "61a86d22-2ed2-42c1-87c3-ae223d7a598a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "plot_data['feature_score']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "review_appearance    0.075655\n",
              "review_palette       0.017095\n",
              "review_aroma         0.907249\n",
              "Name: feature_score, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_RaswlEmvYW",
        "outputId": "c23a04ab-2042-4c59-9ac7-4f5096bad56e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "sns.barplot(x = plot_data.index, y = plot_data['feature_score'], data = plot_data);\n",
        "plt.xlabel('Name of Features')\n",
        "plt.ylabel('Values')\n",
        "plt.title('Feature Importance');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cIjJhwo8wiW9"
      },
      "source": [
        "# Answer 3 Based on  the analysis, we can say that review_aroma is important factor amongst the all features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2ij4hd4ws7s"
      },
      "source": [
        "# Question 4 If you were to recommend 3 beers to your friends based on this data which ones will you recommend?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0EvmBKCLnlb"
      },
      "source": [
        "There are many factors on those factors, we can recommend best beer.\n",
        "Such factors are beer_ABV, review_appearance, review_palette, review_overall, review_taste and review_aroma"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLr5nhQ1LQPY",
        "outputId": "9fcfe3d2-f642-4594-bcfa-179db6e1dfa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "beer_data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['beer_ABV', 'beer_beerId', 'beer_brewerId', 'beer_name', 'beer_style',\n",
              "       'review_appearance', 'review_palette', 'review_overall', 'review_taste',\n",
              "       'review_profileName', 'review_aroma', 'review_text', 'review_time'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ap7_VtEMkIm"
      },
      "source": [
        "recommend_beer = beer_data.groupby('beer_name').agg({'review_overall':np.mean,\n",
        "                                                     'beer_ABV':np.mean,\n",
        "                                                     'review_appearance':np.mean,\n",
        "                                                     'review_palette':np.mean,\n",
        "                                                     'review_taste':np.mean,\n",
        "                                                     'review_aroma':np.mean})\n",
        "selection_of_beer = pd.DataFrame(recommend_beer.reset_index())\n",
        "\n",
        "selection_of_top_3 = selection_of_beer.sort_values(['review_overall','beer_ABV','review_appearance','review_palette','review_taste'],\n",
        "                                                   ascending = [False, False, False, False, False])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Xm0mqs3M0ZV",
        "outputId": "a458661d-a0ed-4783-ee06-0264c93056ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        }
      },
      "source": [
        "selection_of_top_3[:3]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>beer_name</th>\n",
              "      <th>review_overall</th>\n",
              "      <th>beer_ABV</th>\n",
              "      <th>review_appearance</th>\n",
              "      <th>review_palette</th>\n",
              "      <th>review_taste</th>\n",
              "      <th>review_aroma</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>444</th>\n",
              "      <td>AleSmith Speedway Stout - Oak Aged</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9624</th>\n",
              "      <td>Pilot Series Imperial Sweet Stout - Palm Ridge...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1330</th>\n",
              "      <td>Bees Knees Barleywine</td>\n",
              "      <td>5.0</td>\n",
              "      <td>11.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              beer_name  ...  review_aroma\n",
              "444                  AleSmith Speedway Stout - Oak Aged  ...           4.5\n",
              "9624  Pilot Series Imperial Sweet Stout - Palm Ridge...  ...           4.5\n",
              "1330                              Bees Knees Barleywine  ...           4.5\n",
              "\n",
              "[3 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JzbxVWUMf4tx"
      },
      "source": [
        "# Answer 4 The top 3 beer recommend\n",
        "\n",
        "1.   AleSmith Speedway Stout - Oak Aged\t\n",
        "2.   Pilot Series Imperial Sweet Stout - Palm Ridge...\t\n",
        "3. Bees Knees Barleywine\t\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZnuPWoGmUyC"
      },
      "source": [
        "# Question 5.\tWhich Beer style seems to be the favorite based on reviews written by users? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgel-6T8mdLG"
      },
      "source": [
        "Here, we count total review for each beer style.\n",
        "\n",
        "Then we take review_overall to find mean for each beer_style category to derive best beer style"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izG16ZGVmT2x"
      },
      "source": [
        "s = beer_data.groupby(['beer_style'])['review_text'].count().sort_values(ascending= False)\n",
        "#s = beer_data.groupby('beer_style')['review_text'].count().sort_values(ascending= True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2UxEHhMwl5w",
        "outputId": "b98e72af-6a23-4051-f952-463ea5caf485",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        }
      },
      "source": [
        "s = s[0:10]\n",
        "plt.figure(figsize=(20,4)) \n",
        "sns.barplot(x = s.index, y= s.values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0231e3f320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fLJZKNbva1q"
      },
      "source": [
        "list_of_beer_style = s.index[0:10]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aLsSaz9vj0P"
      },
      "source": [
        "list_beer_mean = []\n",
        "for i in range(len(list_of_beer_style)):\n",
        "  x = beer_data[beer_data['beer_style']==list_of_beer_style[i]]['review_overall'].mean()\n",
        "  list_beer_mean.append([x, list_of_beer_style[i]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bx624377Akz1",
        "outputId": "31945a97-ece7-4260-9780-936111382a73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "list_beer_mean"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[3.9948683835408154, 'American IPA'],\n",
              " [3.953221364570808, 'American Double / Imperial IPA'],\n",
              " [4.10059503276992, 'American Double / Imperial Stout'],\n",
              " [3.884397800571006, 'American Pale Ale (APA)'],\n",
              " [3.879104647199182, 'American Amber / Red Ale'],\n",
              " [4.029481132075472, 'Russian Imperial Stout'],\n",
              " [3.888120475698035, 'American Porter'],\n",
              " [4.009565160616349, 'Belgian Strong Dark Ale'],\n",
              " [3.5638116070818238, 'Fruit / Vegetable Beer'],\n",
              " [3.7659759509944792, 'Witbier']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYFQ7FqvzCeQ"
      },
      "source": [
        "list_beer_mean.sort(key = lambda i: i[0], reverse = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCE0IYxK9Itm",
        "outputId": "e743c2e6-2d6a-4b34-9dd4-757fd460dd8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "list_beer_mean"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[4.10059503276992, 'American Double / Imperial Stout'],\n",
              " [4.029481132075472, 'Russian Imperial Stout'],\n",
              " [4.009565160616349, 'Belgian Strong Dark Ale'],\n",
              " [3.9948683835408154, 'American IPA'],\n",
              " [3.953221364570808, 'American Double / Imperial IPA'],\n",
              " [3.888120475698035, 'American Porter'],\n",
              " [3.884397800571006, 'American Pale Ale (APA)'],\n",
              " [3.879104647199182, 'American Amber / Red Ale'],\n",
              " [3.7659759509944792, 'Witbier'],\n",
              " [3.5638116070818238, 'Fruit / Vegetable Beer']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLlYwqkAAwcu"
      },
      "source": [
        "# Answer 5 : - As we have derived first count of review text for each beer_style and then we have used overall_rating for each beer_style as mean\n",
        "\n",
        "So, we now derived there are three main beer_Style we can ask to user to taste\n",
        "\n",
        "\n",
        "\n",
        "*   American Double / Imperial Stout\n",
        "*   Russian Imperial Stout\n",
        "* Belgian Strong Dark Ale\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X53BCITdHUfs"
      },
      "source": [
        "#Question 6.\tHow does written review compare to overall review score for the beer styles?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waYl7AzgIvBI"
      },
      "source": [
        "First we will count review on basis of beer style.\n",
        "Then sort the beer style by counts \n",
        "\n",
        "In next step, we will retrieved review_overall by each beer style to understand which beer style is favourite brand.\n",
        "\n",
        "Then we will plot the data on historgram.\n",
        "\n",
        "As per analysis, we can say that American IPA is one of the best beer style"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bplaLru_Br2V"
      },
      "source": [
        "s = beer_data.groupby('beer_style')['review_text'].count().sort_values(ascending= False)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dyUzalvksF9",
        "outputId": "a6ea3139-46a3-494e-9ea3-9b7e229c66d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "sns.swarmplot(s);"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v//EOPF5fWJ1fIKxSGgSudleUZ5GTkcita4pDV8Fnq7F9gN9tqaG3b5hrlxWGZki+UHw+P//8u/20Br+kahp7sdng5tUaDCISLdMaum63j85eV9iy1u4h/tcDK3j1/Tr6h9xsXFzAgtIMHn/dDHte76Cb+rbwCS29Pj9t3cO4PT7yM5LISo0P3QBLSYzhqoX5bDvQxIHjgYEYfj/sPtrG1+9bRmlBCsMjgavXE819rDRymJmTREFmMrMLU/jKI9vCttXQPkBOWgKtY0bIjR2k8fbeBh577fQ+1zT18pf3Lj3rc9M7OMI/Pr47NLpuR1ULf//ZVRe0D3B9W38ocEftOdo2Zej6/X5efPcE2w81k5oUy8evKaNiVlq0d1XksjGtoRsX62BecTqH67pCy5bOyWLnkdbQsNqS/BSWlmexaHYmLbvrQ8+bmZ3E6nk5oZtjAGnJsWyrbGTr/ib8QGlBCpuWFWK321g7P5fU5LiwpoRRLV2DzC1O55GnD4T2pbF9gPWOPD6ytgS/3x9WThJgzsw0PnFtGS9sq6W9e4hV83LDuoSNL6x+MFhjIn1GHE0dAwwMe5hdkDLpT/N91e2hwIXAF8qOqpYLGroZKfE4HfbQTU8gYk3isTbva+T5bbUAtHYN8f2n9/Pwn6wnIS4qBetELjvT/pfyxTsX8PSW4zS2DzKnMCUUfqOee6eG2QUpfOLaMiBQE7cwK4lPXl9Odmo8I24fOw4Hbm6tqMjiZy8dDr22trGXlUZ22JXYsvIs3th5KtRuG+O0s2h2JiNub1j4A+w/FrgittlsfOmuBfznK0c42drHgtIM7r+hnNTkOL5w+/yIx5U8rpua02EjLsbOL189Egrkwuwkvn7fMmYkxk54fUrS2S2zUnJCDPfdUM6Tb1Yz4vFRmJXERzeWTvmasSMLAYZcXo439JyxXKaIBEx/aUe3l96BEbr6hukbjKemoWfCc2oae2npHKSypgOnw86quTnkpAW6hDV2DNDd7yI53jlhoAKcLobzbmUTr7xfh98P1ywtoLlzEKfTTm56Ir97+xgVs9LCmiOAULGbvdVtPL+1liGXh09cU8ata4vZV93O81v3M+jycO2yQj6ytpj9x9p5bmsNg8MeFpZmEBfbG+qHfPu6Elq6hsKugBvaBnhzdz0f3RjomTE47OE3fzjKoROdzMxOwihKwzwZuJIvyklm4yQ34rw+H89sqWFHVQsZKXHcu2kO5TPP/BP+wPEOnn3nOIPDHjYuKeCOKWpNdPYO8+s3jlLT2MuC0gxuXlNEeWFqeGlMYMTt5cm3jrG3uo28jERyM8KvhO0226RFhFxuL0+8Wc2+Y+0UZCZx3w3lEXuBjHWkrounNh+nZ8DFugV53H317A91Y+/A8Q6ee6eGgWE3Gxfnc8f6qb9UpjL2nFXMSuP+mypIifAFK5cun8/Ps+/U8H5VM2nJcdxzbRlGUfq0bmPaQ/cnzx+krjlwM+y9Qy3Mj1CHNtZp5/HXj4Ye/+zFKmZmJ/PW3ga2Bfvz9vSP0N4zTEKcgyFXIOhsELzZ1st/vHz6Cri5c5C/fmAFWw808mawyeKDw62smpvDiMdH78AIWanx3H9jBe09Q/z4uYOhm2ZPbT5OfKyD3/yhOrTs6eCyJ96sDt1827yvkQdvqSDG7qCjd5js9ATaIjRtdI4pa/nbN4+y/WBz6HiKcpP53l9cTWtbH+Wz0iYNkzd21vP7HYGqaV19Lh55+gAP/8l64iL0BBnV3e/ih89WhpoKnnunhqyUeNYFB3eM9/OXqjgS/ALYW90OQMXHF0943n9tq+XtvQ3BbYzQ2jXIkrJM9h/vICHOyT2byshIiVx0/tktNaGBJj39I/zwmUq+88W1kx73wJCb7z9zIPTF9vJ7daQlx4XNq3cuesafk621ZKbGc9XC8+t1Mvac7TzSitfn579/bNF5vZdcnN7cXc8r79cB0Nnr4pFnDkx789m0hu7gsDsUuKMa2vu57/pyXtkROJCPrCmecAXrJ9CftupE+E/Xlq4hvnrvErYdaGLA5WbJ7CzmFKby+s6TE7Z9qLYjFHCjDtd18c//fT1dfS4yU+Ox22y8W9k0oZfCruAfUPiytgmDIY7UdVNd3xOqF1yQlcSMxJjQ6DkbgZ4Zo6pOhDdvnGzpJz8rifSEqU/7+PMwMOyhrqVvyhtW1fU9YW2zo+8TKXS9Pl8oPELPHdcUM9kxdPaN8JefnMMX7phPrNNBjHPyHhxVdeHH0do9RHvPMDlpkWd6Nk92TRjRWHWi87xD92jEc9J1XqEb8ZyN+3eSS9/4f9Mhl5fapt6IF4/na1pnA06Ic06o+FWcO4PkhBhsBH5uN3cNMitn4s/RotxkinNnhC1LS45lfkk6N6ycSX3rAL99s5r/8aN3I14pFeelkDRmdmCAGYkxOB12stMSQq8Zvw2A8ghhVjFrYncul9sXVqC9sX2A29YVs3FxPkvnZHHnhtKwn5vjt5WVGh9WknIyReNe53TYyc+c+gZXUU7yhIkyxr/PKIfdzsxxAzuKJ7mhV5Qbvjwp3hk6jqkCN7BP4dufkRhDxoy4SZ9fnDcDhz38KCL9e52tczknZxLpnJ3ve8nFqzgv/N/UYbdReIYa3OfK8Y1vfGOydWnAV4aGRjjbiRNsNhuleSmYJ7sZGPZQkjeDu6+ezQ+frWRoxIvP5+dEUx9zi9LISU/kVGsfTqed29YVs2FxAWUFKdQ09tLZ5yIzJZ4v3D6fnPREHnn6AG3BrlwjnkDwXb2kgBPNfdhtNjYszicrNZ5ZuclU1XbhJ3Cj61M3VJCTnhj2h5ySFEt8rIPjDT34/bB+YT73XjeHpPgYjjf24PMF6i3ce90ckhNjON7Qi8/nZ93CPPIzEsN6V0Dgynb9onxeef8Ee6vb2by3ga4+F0vLsygrSOFYQy/d/S6y0+J56I75FBWkMjg4sa16rNL8FBrbB2jpHGRGYgwP3myElbuMJDkhhuSEGI419ODz+Vm7IDfQHmqP/FN+dkEqR0910z/kZmZ2Ml+4Y37EG4BlBamcaO6lvWeY9BlxfO4j8yg8Q7ts6LWFKdQ29dHRO0xGShyfv20eeRmTf4CzMpKIc9ioru/B7fWxoiKbezfNOef+0KPGn5M1C3K5e+PsCcF+tmYXpGKeHHPObp8X8ZxNt6SkuDN+Zq5E0TgvJfkzQn97yQkxPHBTRcSLsqnYbDYSA5+L7wPdE9ZPMRVNCVDb0dGPz3eWqRu0o6qZytouslLiyE5LCGt/hcDghIfuWMCRuk5e3XES14iXa5cVsnZBHiNuLy+8W0ttUx9zClO5bV0xf/7I1lCRcwjcvPn5X23C7fGy/WAzj75m4vcHvpUeuKmC5PgYdh1tY9eRVpxOO3euL+HWNcVh+3CiqZeX3qtjyOVhw+J81i3ICw2QiBszP1tn7zBPbT7OyZY+inJncOB4B0OuQNevzJR4vvn51Tz7Tk2oLXnUNz+/OnTTaHDYQ0KcA5vNRnb2DNrawptgJjM84iHGacdhP/vQ8Xh9eL3+Kdt/xxqdHPRMhlwe4mId53VT62xfO3puvD4fbo+P+Njpaf0613MyFb/fz5DLe1bnbLqcy2fmShLN83I+f3uj7HYbmZnJAKXAifHrp/2Ts3lvA4+OGURQGvzJOLbNtHxWGj0DI3z/6cpQXd2j9T0kJ8awo6olNGfa4bouOnqHWV6eHTZZ5LKKQJEZp8POc+/UhK7EvT4/r+88xc2ri9gRfL53xMtTbx9nQUlG6OfgkMvD957cF+o3e7iui/hYB8vKs3GO+7v8z1cOcyjYrtnUMcjy8iyK8mYQ63SwflEeCXFO+iJ82/YNjEBwbMX5/oGeT+g4HfYJxzCVs923D3Mj4Vxf67DbccROX8vXuZ6TqdhsNksDVy6M6frCj2Ra23Qh0JVrrNrmvkAf3LR4EuKc3Lx6FlcvKaCqtjMUuKP2Hm0PTSI56oPDrTx4i8FNq2ZRkjeDqxbmsX5hPm6PF78fBl2esOf3D7mpa5n47Te2EM6Ruq6wgQoAu802Rtxe6pr7Qvvl8fpCgRt67clu7lxfyi1rikI/LcffmMlOiz/nnyQicmWY9jgf38blsNtYPS8Ht9vL3up2+ofcdPW6yMmYeAc7Nz2BjJT4sMI2gaI5Tv7o+nJe3H6C57fWsP1gM6nJsXz9vmVctTA/rK/sxsUFlOan8PaehrB9mFt0OgSzI9w9t9ngf/54O/1DbhLjnHz5owtZUJpBTnpC2FDZggiVxRaXZfLVe5fw3sFmUpJiuXl10Xm3Q4rI5W1ab6QB5GUksvNwK+5gV527NpRS3zbA05uP09nn4lRrP1UnOvnY1bMZHvFS29SHH1hQmsG9182hMCuJvdXteIJtcH982zxy0xPpHRjhB88cYLSVwjXiZWDYw2dvncuMxBgSYh2smZ/HnRtKKcxKCtTE7R4iMyWeT98UfiMqJSmWEbeX442BgRuzC1Jo7xmmozcwkMLt9VHT2MMNK2cxKyeZg7WduEa8ZKbEsaIim8qaDmKcdjLH1P3NTU9khZHDwtmZU/6c1k2RyencRKbzEtnFel4uyI20nn4XWyqbcdpg07JCHn5ib6h62KhvP7SG/MwkuvpdvP7BSQ7VdpGREsfHrykjKzWeyppAv9vmjkEWl2WybmEe/+dXu8Leo2JWGv/r/uVsO9DEr/9wFNeIl8LsJL7yiSVhgTiZrj4Xg8NuCrOT+fL3toQ1d9htNn729Wux2Wx4fT66el08+dYxdh8N1JCwAV+8awGr5+VO8u6R6abI5HRuItN5iexiPS+W30gbHvHw3d/uDZVN3LKvgVk5M8JCNzbGTlpw9t1dR1p57YNTQKDqVV1zH9/98lW8uP0EDcGqY38I9gwoykkOlYOEQC+IIZeHx98wQ70bGtoGeG5rDV+4fT5DLg92uy2sN8JY6TPiSA/2G11pZPPumMEVy43s0JBYh92Ow2EPBS4EBnT8YXf9OYeuiFzZpj10dx5uDQUuBGrSrp2fS31boG001mnnvuvLQz/BDxxrD3t9z8AIlcc7QoE76mBtJ391/3Jefb+Ols5BVszNZsOiAhra+sO6kwE0dQzw85eqeO9QMzGOQD/gM425f+Amg5SkWKrre8hJTyA1KZY3d9ezflEe8bFOHHYbNhthTS0xarcVkXM07aE7ftglQHyck298bhUvb69j0BWYSn1UflZSWA8Bp8NGSf6MsOG1ECj9mBDrCIRyTSc1Tb3YsLFuYd6E2SLSkuNCQ4JHPD6e21rLorJMSvJSJt3vuFgH92yaQ21TL//4+J7QcWyrbOLvPrOSlKRYNi0r5K3gDTqnw8ZH1hVP+n4iIpFM+6Xayrk5pI4pWZicEMPa+Xn85PlDvPx+HW/vbeT//mYvO48EuobdcVVJaAaFuFgHn7qhgoyUeD5/27xQ6cOi3GTu3TSHN3adYkdVCz6/n75BN7989Qg9/SN89d4lrJ6XQ3HuDO7eWBpqMhjr1Li50ybz9p6GsC+OuuY+qoOj0B64yeBrf7SUT99UwbcfWsuCaRyPLSJXhqh0GfuHz61i59F2mtv6uGV1EW6vj8qajrDnvb2nnlVzc5iRGMs9m8rYur8pND06wOKyLB7+k6voH3KH2n9PjLsZ5/X5OdXaz+KyTL5018LQ8n3V7aErUgh0GZtXfHbl2ZyOiaOmHGOaEeaVZDBPYSsi5ykqwy5qGnt5fstxhlwe9lS38/mPzJvQHhobvLlVWdPBvz61P7Ru37F2/vbBFdhsNpyO0zfcAOYWp4fdzIqNsTO7YGKTwdLyLO6/sYK39tQT67SzbmHepDUIxrt+5Sx2HG4NDfWdX5J+wecyE5HLx7SHrs/n57HXzFBo9fSP8PJ7dVy7rDA0YCE2WOQGAsOGx4ZxbVMvJ5r7KM2fGKablhfS2TccGByRFMs9184hOSFy1a7rV8xkhZHNw0/s44k3j/HkW8e4bV0xH7u6bMr9L8xK4jsPrWHP0TZSkmJZMifrfE6DiEhE0x66Lrd3Qr3c1u4h/ur+5ayem8OJpl5qm/v43VvHWDwnK2J3rkjTtkOg7+zCkgxOtvTj8fgYHld7dbxX3z9JY3ugF4TfDy9vr2PDonxyzjAPWGpyHGjAUr8AAA4DSURBVJuWn18NVxGRqUz7jbSEOCfzS8LbT0dn1TWK0tlxuJUPDrdyvLGX596pIS7GETaCa92CPPIzI5f/a+0e4l+e2s+h2k7MU938+LlKjkeYDmjU6AizUX4C08KLiFwoUWnT/dJdC/n9zlOYdZ3ML87g9uBcXZ29w5wYN7NEdUMP//TFtbxb2URXn4tl5dkR3jGg8nhH2GwOfgJtwJPVml09L4c9Y9qA02fEUT5T7bMicuFEJXQHh900tPXT2D5ISmIsQy4PMc5YkhNiSIhzhtp7AXLSEqhr7uOZLTV4fX7e2FXPjStncd8N5RPeNzd9YqGanAjLRq2el4vH6+O9Qy2kJsVwx/pSFaIRkQsqKgn00xcOsb+6nSGXh73V7TwWrK8bG+PggZsqQu24Wanx3LOpjJfeqwurt/vWnnr6h9wT3ndBaQZXLylgtBb2kjmZZKXGMziuTONYM7OT6R0YYfvBFn7x8mHaeyZOJikiYpVpv9IdcXsnFLc5cvL0iLN1C/JITYrl/UMtzC5IISs1AbcnfBSbz+efMFEkBKr3fPbWuXx0YymH67p4/HWT/cc6iI2x86U7F7K0fGJPg5+9WEVD8GZadX0Pj79+lK/cs2Q6DlVE5JxN+5VubIxjwkRupWP60u6tbuN7T+xjW2UTj75m8qPnKrlhZXhPgVXzwke1jZeWHMcr79eFpmYfcfv4zR+OTniey+0NBe6omsbecz4mEZHpEpXmhYfumE9JsJ/tnMJUHrzJCK17c3c9Y69hDxzvoKwgha/ft5QNi/N54MYKvnD7/DNuo3Ncz4SuPhe+cWUq42IcE2b3NDSjg4hcQFEJ3Zk5ydy8tpglczJZUJoRNpvE+BtZNqB3YITHXj/KtgNN/G7zsVCxmqmML6m4am5OxIkPv3TnAuYWpQXnQMvigZuNCc8REbFKVHovPL+1lpe2nwBg/7EOTrb08WcfXwzAR9YWc7iuK9SOu35xPm/vbQiVgxxtKlhp5Ew5AeCnbqggfUYc1ae6KS1IDY1wGy83I5Gvf2r5NB6diMj5i0rovjfuSnVfsCdDQpyTillpfOehtRw43k5OeiLzS9L59mO7w54/4vbR1e+aMnRjnHbuPEONXBGRi01UmhfSksNvgiXGO4lxnt5UZmo8m5bPZEFpBjabjeUV4QMi8jISKciceqiuiMilKCpXup+4toxHnqlkyOXBYbdx73VzphyUcMuaIvx+P3uOtpObkcDdG2eHpsoREbmcROVK1yhK50df28SNK2fxsWtms6Ji8qG9EChkk52WwKDLw+G6rgnNEyIil4uoXOkODLv5P4++T2tn4ObY23sa+IfPrSIpPnIZxtauQf79hapQl6/nt9WSl5moSR9F5LITlSvd9w+1hAIXoL1nmB1VLZM+v7q+Z0If2yMnJ0wXLyJyyYtK6Pr9E4fw+iIM6x1VMm4AA0BphGUiIpe6qITumvm5ZKTEhx6nJceG5j6LpDA7mftvrCAp3onTYWf1vBzqWvp44s1qWrsGJ32diMilJiptujMSY/n+X17Li1uqae0eonxmWlih8kiuXzGTTcsLae4Y4Ju/2sWIOzB4YvvBZr710BpSEievxSAicqmIWnHZGUmxVNZ0snlvIz97sYq/+48PIpZrDNsZm42dR9pCgQvQP+Rmj9k2xatERC4dUQvdPUdaOFx3uqRjS+cgW/Y1TPGKgEij0KYamSYicimJWuj2DU68qh2Yotj4qPUL88NKQ84pTJ0wYk1E5FIVtUvI1QvySEmKpTc4M7DTYeeqBXlnfF1ivJN/+NwqDtV24nTYmVeSHrF6mIjIpShqoZucEMPffnoFb+1poHdwBGNWGrkZZ1dPwemws2TOxFkgREQudVGdpTErLYG05MDUPP/56hH+6t+2T5jJQUTkShLV0B0YdvP0lprQaLPu/hGe31oTzU2KiFzUohq6/YNuPN7wSSe7+lzR3KSIyEUtqqGbm5E4YYjvVCPTREQud1HvAPuVe5bw0nsnaOkcYllFFtcuLYz2JkVELlpRD92UpFg+dUNFtDcjInJJiGrzgoiIhFPoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYSKErImIhha6IiIUUuiIiFlLoiohYyDnFOgeA3W477zf/MK+9nOm8TE7nJjKdl8guxvMyZp8ckdbb/H7/ZK/dAGyNwj6JiFwJNgLbxi+cKnTjgFVAE+CN3n6JiFxWHEA+sBNwjV85VeiKiMg00400ERELKXRFRCyk0BURsZBCV0TEQgpdERELKXRFRCyk0BURsdBUw4DPi2EYFcCvgEygA3jQNM3q6d7OhWIYxsPAx4ESYJFpmgeDyyc97misu9gYhpEJPAaUASNANfBF0zTbDMNYC/wUSABOAA+YptkafN20r7vYGIbxPFAK+IB+4M9M09x3pX9mRhmG8Q/ANwj+PV3un5doXOn+G/Aj0zQrgB8RONDLyfPA1UDduOVTHXc01l1s/MB3TdM0TNNcBBwH/skwDDvwOPCnweN4B/gngGisu0h9xjTNJaZpLgMeBn4RXH6lf2YwDGM5sJbg39OV8HmZ1tA1DCMHWA78Nrjot8BywzCyp3M7F5JpmttM0zw1dtlUxx2NddE6tg/DNM1O0zQ3j1n0PlAMrACGTdMcHYP+b8C9wf+PxrqLjmmaPWMepgI+fWbAMIw4Al8MXx6z+LL/vEz3le4soME0TS9A8L+NweWXs6mOOxrrLmrBK4svAy8ARYz5VWCaZjtgNwwjI0rrLkqGYfzcMIyTwLeBz6DPDMA3gcdN0zwxZtll/3nRjTSJhh8QaLv84YXekYuFaZpfME2zCPhr4P9d6P250AzDWAesBH58offFatMduqeAQsMwHADB/xYEl1/OpjruaKy7aAVvNJYDnzRN0wecJNDMMLo+C/CZptkZpXUXNdM0HwM2AfVc2Z+Za4B5QK1hGCeAmcBrwBwu88/LtIZu8G7gPuC+4KL7gL2mabZN53YuNlMddzTWRf+Izo9hGN8h0H72UdM0R0va7QYSDMPYEHz8JeCpKK67qBiGkWwYxqwxj+8AOoEr+jNjmuY/maZZYJpmiWmaJQS+hG4m8Cvgsv68THtpR8Mw5hLospIOdBHosmJO60YuIMMwHgE+BuQB7UCHaZoLpjruaKy72BiGsQA4CBwFhoKLa03TvNswjKsI3EWP53R3nZbg66Z93cXEMIxc4L+AJAJ1qTuB/2ma5p4r/TMzVvBq9/Zgl7HL+vOieroiIhbSjTQREQspdEVELKTQFRGxkEJXRMRCCl0REQspdEVELKTQlQvGMIx/Mwzj7y70fohYSf105YpgGIYfKDdN89jF9F5y5dGVrnwohmFMeyF8kcuZrnTlnAWHbP4EuB8wgOuB7wLzCZTQ+wvTNDcbhvFJ4Gumaa4c89qvAptM07zTMIxfAvWmaf5tcN3twLcIzMpRBXzJNM0DhmF8DviYaZp3BJ9XDewzTfOe4ONTwB2mae6bZH/fATYCgwSKrX/eNM0np9jeJwkUuV5immavYRi3Av8JLAKeifReH+Z8ypVFoSvnLBi63cAdBKagOQB8Gvg9gQB+ApgLDAAtwHLz9HQyO4Hvmab5xNjQNQxjGYEqU3cAu4AHgP9NINQLgT1ABoGaF+8BDtM0ZxqGMZtAQZPMYFWzyfY5rElgqu2ZpukyDOPXgBv4H0Al8N9M03wp0nuJnAs1L8j5eiQ4g8YDwCumab5imqbPNM03CITYR0zTHCRQ7OU+AMMwygmE8QsR3u+/AT81TXOHaZpe0zR/BbiAtaZp1gB9wFICUyW9BjQGi7tcA2ydKnAnMen2guv/FLgO2Ay8OBq4Ih+W2uPkfI3WaC0G7gmWLBwVA7wd/P/fAN8jMEvAp4Dng2E8XjHwGcMw/mzMslgC9WABtgDXEqi3uoXAlfY1wLrg43M15fZM0+w2DOMp4C8JTEQqMi0UunK+RtulTgGPmab50CTPewPINgxjKYEr3q9O8rxTwLdN0/z2JOu3EGgKKAW+QyB07ycQuuczQ8WU2wvu7x8TmGfsEeCW89iGyAQKXfmwHgd2GoZxM/AHAle5a4FjpmnWm6bpDl4x/j8CbbJvTPI+PwOeMwzjD8AHQCKBK9t3TNPsIxC6/wy0mKZZbxhGL4Ep353A3rPYzxZgNjDaDjvp9gi05T5OYGqdXwC7DMP4E9M0fzzJe4mcNbXpyocSbNe9i0BAtRG4gvwa4Z+t3wA3AE+ZpumZ5H12AQ8RuGrtIhBonx2z/iiBede2Bh/3AjXAu6MTMZ7BN4BfGYbRbRjGvWfY3j8Cp0zT/ElwBowHgG8F26QnvNdZbFskRL0XREQspCtdERELqU1XLguGYWwEXo20zjTNZIt3R2RSal4QEbGQmhdERCyk0BURsZBCV0TEQgpdERELKXRFRCz0/wFzgiJpye6nXgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "um2blj18lQsb",
        "outputId": "d9fd33c1-696d-4fd7-b2e4-bd2b90d3e5b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "sns.swarmplot(s.head(10));"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yKpw4KSByrf",
        "outputId": "848a72ce-099a-462c-e16b-ee72c08dc46d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "s.head(10)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "beer_style\n",
              "American IPA                        43364\n",
              "American Double / Imperial IPA      26101\n",
              "American Double / Imperial Stout    23352\n",
              "American Pale Ale (APA)             20514\n",
              "American Amber / Red Ale            18728\n",
              "Russian Imperial Stout              17183\n",
              "American Porter                     16597\n",
              "Belgian Strong Dark Ale             15403\n",
              "Fruit / Vegetable Beer              15144\n",
              "Witbier                             13533\n",
              "Name: review_text, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iatMkJvoDaS-"
      },
      "source": [
        "new = s.index"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fJ13LJhDpaC"
      },
      "source": [
        "new=new[:10]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBDRuHMKDcyx"
      },
      "source": [
        "best_beer = []\n",
        "for i in range(len(new)):\n",
        "\n",
        "  #print(new[i])\n",
        "  best_beer.append([new[i], beer_data[beer_data['beer_style']==new[i]]['review_overall'].sum()])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WV33iUI1EUQ-",
        "outputId": "9463abf1-234b-45c1-8df3-083a4c7f9db8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "best_beer"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['American IPA', 173033.0],\n",
              " ['American Double / Imperial IPA', 103150.0],\n",
              " ['American Double / Imperial Stout', 95763.5],\n",
              " ['American Pale Ale (APA)', 79408.0],\n",
              " ['American Amber / Red Ale', 72260.5],\n",
              " ['Russian Imperial Stout', 69209.0],\n",
              " ['American Porter', 64290.5],\n",
              " ['Belgian Strong Dark Ale', 61770.5],\n",
              " ['Fruit / Vegetable Beer', 53917.0],\n",
              " ['Witbier', 50940.5]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrXWXmAJCBPT"
      },
      "source": [
        "convert_best_beer = pd.DataFrame(best_beer,columns = ['Beer_style','count'])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mmk4Jj3ggmxN",
        "outputId": "d35d80e9-9a68-4704-ece8-a0c89c5ca2fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "convert_best_beer"
      ],
      "execution_count": 16,
      "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>Beer_style</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>American IPA</td>\n",
              "      <td>173033.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>American Double / Imperial IPA</td>\n",
              "      <td>103150.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>American Double / Imperial Stout</td>\n",
              "      <td>95763.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>American Pale Ale (APA)</td>\n",
              "      <td>79408.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>American Amber / Red Ale</td>\n",
              "      <td>72260.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Russian Imperial Stout</td>\n",
              "      <td>69209.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>American Porter</td>\n",
              "      <td>64290.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Belgian Strong Dark Ale</td>\n",
              "      <td>61770.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Fruit / Vegetable Beer</td>\n",
              "      <td>53917.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Witbier</td>\n",
              "      <td>50940.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Beer_style     count\n",
              "0                      American IPA  173033.0\n",
              "1    American Double / Imperial IPA  103150.0\n",
              "2  American Double / Imperial Stout   95763.5\n",
              "3           American Pale Ale (APA)   79408.0\n",
              "4          American Amber / Red Ale   72260.5\n",
              "5            Russian Imperial Stout   69209.0\n",
              "6                   American Porter   64290.5\n",
              "7           Belgian Strong Dark Ale   61770.5\n",
              "8            Fruit / Vegetable Beer   53917.0\n",
              "9                           Witbier   50940.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HFs_yMjdCPS7",
        "outputId": "f2a42116-6538-4e58-98b8-437909995f45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "plt.figure(figsize=(20,4))\n",
        "sns.barplot(x = convert_best_beer['Beer_style'], y = convert_best_beer['count'], data = convert_best_beer);\n",
        "plt.xlabel('Beer Style')\n",
        "plt.ylabel('Count')\n",
        "plt.title('favourite Beer style on basis of review' )\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'favourite Beer style on basis of review')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0WPUbTWhIgm"
      },
      "source": [
        "#Answer 6 As per over all review score against wrtten review,we can say that below are best beer style\n",
        "\n",
        "\n",
        "\n",
        "*   American Double / Imperial IPA\t\n",
        "*   American Double / Imperial Stout\t\n",
        "* American Pale Ale (APA)\t\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h7HS6tnEImWd",
        "outputId": "582e23d6-4b2b-4999-fdfd-e1342b3f33f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "beer_data.head()"
      ],
      "execution_count": 4,
      "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>beer_ABV</th>\n",
              "      <th>beer_beerId</th>\n",
              "      <th>beer_brewerId</th>\n",
              "      <th>beer_name</th>\n",
              "      <th>beer_style</th>\n",
              "      <th>review_appearance</th>\n",
              "      <th>review_palette</th>\n",
              "      <th>review_overall</th>\n",
              "      <th>review_taste</th>\n",
              "      <th>review_profileName</th>\n",
              "      <th>review_aroma</th>\n",
              "      <th>review_text</th>\n",
              "      <th>review_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.0</td>\n",
              "      <td>47986</td>\n",
              "      <td>10325</td>\n",
              "      <td>Sausa Weizen</td>\n",
              "      <td>Hefeweizen</td>\n",
              "      <td>2.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>stcules</td>\n",
              "      <td>1.5</td>\n",
              "      <td>A lot of foam. But a lot. In the smell some ba...</td>\n",
              "      <td>1234817823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.2</td>\n",
              "      <td>48213</td>\n",
              "      <td>10325</td>\n",
              "      <td>Red Moon</td>\n",
              "      <td>English Strong Ale</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>stcules</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Dark red color, light beige foam, average. In ...</td>\n",
              "      <td>1235915097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6.5</td>\n",
              "      <td>48215</td>\n",
              "      <td>10325</td>\n",
              "      <td>Black Horse Black Beer</td>\n",
              "      <td>Foreign / Export Stout</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>stcules</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Almost totally black. Beige foam, quite compac...</td>\n",
              "      <td>1235916604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>47969</td>\n",
              "      <td>10325</td>\n",
              "      <td>Sausa Pils</td>\n",
              "      <td>German Pilsener</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>stcules</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Golden yellow color. White, compact foam, quit...</td>\n",
              "      <td>1234725145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.7</td>\n",
              "      <td>64883</td>\n",
              "      <td>1075</td>\n",
              "      <td>Cauldron DIPA</td>\n",
              "      <td>American Double / Imperial IPA</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>johnmichaelsen</td>\n",
              "      <td>4.5</td>\n",
              "      <td>According to the website, the style for the Ca...</td>\n",
              "      <td>1293735206</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   beer_ABV  ...  review_time\n",
              "0       5.0  ...   1234817823\n",
              "1       6.2  ...   1235915097\n",
              "2       6.5  ...   1235916604\n",
              "3       5.0  ...   1234725145\n",
              "4       7.7  ...   1293735206\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChGqCF-DItSb"
      },
      "source": [
        "q_6 = beer_data[['beer_style','review_appearance','review_palette','review_overall','review_taste','review_aroma']]"
      ],
      "execution_count": 6,
      "outputs": []
    }
  ]
}