yelp_business_logistic_regression.ipynb

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      "provenance": [],
      "authorship_tag": "ABX9TyOEk4YWDCs2wC2dN34rfuZF",
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    },
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      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EAName/yelp-reviews/blob/main/yelp_business_logistic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CeWdVgtzrM_b",
        "outputId": "40790ffd-246a-4ecb-c12d-30eadf709717"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "BOCtCsBsrQVM"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_business_linear =pd.read_csv('/content/gdrive/MyDrive/yelp_dataset/yelp_business_clean_version6.csv')\n"
      ],
      "metadata": {
        "id": "115kEhhKrQYh"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_business_linear\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "_-uymNforQbS",
        "outputId": "3abb5b10-3007-4e61-c544-1435e4eed71a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                  business_id                           name  \\\n",
              "0      k0hlBqXX-Bt0vf1op7Jr1w          Tsevi's Pub And Grill   \n",
              "1      il_Ro8jwPlHresjw9EGmBg                        Denny's   \n",
              "2      MUTTqe8uqyMdBl186RmNeA                       Tuna Bar   \n",
              "3      ROeacJQwBeh05Rqg7F6TCg                            BAP   \n",
              "4      9OG5YkX1g2GReZM0AskizA        Romano's Macaroni Grill   \n",
              "...                       ...                            ...   \n",
              "30925  gPr1io7ks0Eo3FDsnDTYfg                      Tata Cafe   \n",
              "30926  wVxXRFf10zTTAs11nr4xeA                   PrimoHoagies   \n",
              "30927  2MAQeAqmD8enCT2ZYqUgIQ    The Melting Pot - Nashville   \n",
              "30928  w_4xUt-1AyY2ZwKtnjW0Xg           Bittercreek Alehouse   \n",
              "30929  WnT9NIzQgLlILjPT0kEcsQ  Adelita Taqueria & Restaurant   \n",
              "\n",
              "                    address          city state  postal_code   latitude  \\\n",
              "0         8025 Mackenzie Rd        Affton    MO      63123.0  38.565165   \n",
              "1              8901 US 31 S  Indianapolis    IN      46227.0  39.637133   \n",
              "2               205 Race St  Philadelphia    PA      19106.0  39.953949   \n",
              "3             1224 South St  Philadelphia    PA      19147.0  39.943223   \n",
              "4        5505 S Virginia St          Reno    NV      89502.0  39.476117   \n",
              "...                     ...           ...   ...          ...        ...   \n",
              "30925   7201 Germantown Ave  Philadelphia    PA      19119.0  40.060414   \n",
              "30926        6024 Ridge Ave  Philadelphia    PA      19128.0  40.032483   \n",
              "30927  166 2nd Ave N, Ste A     Nashville    TN      37201.0  36.163875   \n",
              "30928          246 N 8th St         Boise    ID      83702.0  43.616590   \n",
              "30929         1108 S 9th St  Philadelphia    PA      19147.0  39.935982   \n",
              "\n",
              "        longitude  stars  review_count  ...  Chicken Wings Sports Bars  \\\n",
              "0      -90.321087    3.0            19  ...              0           0   \n",
              "1      -86.127217    2.5            28  ...              0           0   \n",
              "2      -75.143226    4.0           245  ...              0           0   \n",
              "3      -75.162568    4.5           205  ...              0           0   \n",
              "4     -119.789339    2.5           339  ...              0           0   \n",
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              "30925  -75.191084    4.0            21  ...              0           0   \n",
              "30926  -75.214430    3.0            55  ...              0           0   \n",
              "30927  -86.776311    4.0           204  ...              0           0   \n",
              "30928 -116.202383    4.5           998  ...              0           0   \n",
              "30929  -75.158665    4.5            35  ...              0           0   \n",
              "\n",
              "      JapaneseTotal HasTV_NA  RestaurantsPriceRange2_NA  \\\n",
              "0                 0      0.0                        0.0   \n",
              "1                 0      0.0                        0.0   \n",
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              "       RestaurantsReservations_NA  garage_NA validated_NA  lot_NA  valet_NA  \n",
              "0                             0.0        0.0          0.0     0.0       0.0  \n",
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              "      <td>k0hlBqXX-Bt0vf1op7Jr1w</td>\n",
              "      <td>Tsevi's Pub And Grill</td>\n",
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              "      <th>2</th>\n",
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              "      <td>205 Race St</td>\n",
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              "      <td>19106.0</td>\n",
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              "      <th>3</th>\n",
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              "      <td>Romano's Macaroni Grill</td>\n",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_business_linear.isna().any()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-EOXgDqrQc8",
        "outputId": "579f6d90-8adb-4c18-8d37-3cbb2d8e69dc"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "business_id                   False\n",
              "name                          False\n",
              "address                        True\n",
              "city                          False\n",
              "state                         False\n",
              "                              ...  \n",
              "RestaurantsReservations_NA    False\n",
              "garage_NA                     False\n",
              "validated_NA                  False\n",
              "lot_NA                        False\n",
              "valet_NA                      False\n",
              "Length: 69, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_business_linear.fillna({'address':0,\n",
        " 'postal_code':0,\n",
        " 'RestaurantsTakeOut':0,\n",
        " 'HasTV':0,\n",
        " 'BusinessAcceptsCreditCards':0,\n",
        " 'BusinessParking':0,\n",
        " 'RestaurantsPriceRange2':0,\n",
        " 'RestaurantsReservations':0,\n",
        " },\n",
        " inplace=True)\n",
        "df_business_linear.isna().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "COVN69lKrQgn",
        "outputId": "2ac03036-97c0-47c5-af76-74482d75f434"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "business_id                   False\n",
              "name                          False\n",
              "address                       False\n",
              "city                          False\n",
              "state                         False\n",
              "                              ...  \n",
              "RestaurantsReservations_NA    False\n",
              "garage_NA                     False\n",
              "validated_NA                  False\n",
              "lot_NA                        False\n",
              "valet_NA                      False\n",
              "Length: 69, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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    {
      "cell_type": "code",
      "source": [
        "df_business_linear"
      ],
      "metadata": {
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          "height": 721
        },
        "id": "zKWVCCzhrQia",
        "outputId": "cf305bc0-d130-457c-c97d-3046efe8ce97"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                  business_id                           name  \\\n",
              "0      k0hlBqXX-Bt0vf1op7Jr1w          Tsevi's Pub And Grill   \n",
              "1      il_Ro8jwPlHresjw9EGmBg                        Denny's   \n",
              "2      MUTTqe8uqyMdBl186RmNeA                       Tuna Bar   \n",
              "3      ROeacJQwBeh05Rqg7F6TCg                            BAP   \n",
              "4      9OG5YkX1g2GReZM0AskizA        Romano's Macaroni Grill   \n",
              "...                       ...                            ...   \n",
              "30925  gPr1io7ks0Eo3FDsnDTYfg                      Tata Cafe   \n",
              "30926  wVxXRFf10zTTAs11nr4xeA                   PrimoHoagies   \n",
              "30927  2MAQeAqmD8enCT2ZYqUgIQ    The Melting Pot - Nashville   \n",
              "30928  w_4xUt-1AyY2ZwKtnjW0Xg           Bittercreek Alehouse   \n",
              "30929  WnT9NIzQgLlILjPT0kEcsQ  Adelita Taqueria & Restaurant   \n",
              "\n",
              "                    address          city state  postal_code   latitude  \\\n",
              "0         8025 Mackenzie Rd        Affton    MO      63123.0  38.565165   \n",
              "1              8901 US 31 S  Indianapolis    IN      46227.0  39.637133   \n",
              "2               205 Race St  Philadelphia    PA      19106.0  39.953949   \n",
              "3             1224 South St  Philadelphia    PA      19147.0  39.943223   \n",
              "4        5505 S Virginia St          Reno    NV      89502.0  39.476117   \n",
              "...                     ...           ...   ...          ...        ...   \n",
              "30925   7201 Germantown Ave  Philadelphia    PA      19119.0  40.060414   \n",
              "30926        6024 Ridge Ave  Philadelphia    PA      19128.0  40.032483   \n",
              "30927  166 2nd Ave N, Ste A     Nashville    TN      37201.0  36.163875   \n",
              "30928          246 N 8th St         Boise    ID      83702.0  43.616590   \n",
              "30929         1108 S 9th St  Philadelphia    PA      19147.0  39.935982   \n",
              "\n",
              "        longitude  stars  review_count  ...  Chicken Wings Sports Bars  \\\n",
              "0      -90.321087    3.0            19  ...              0           0   \n",
              "1      -86.127217    2.5            28  ...              0           0   \n",
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              "3      -75.162568    4.5           205  ...              0           0   \n",
              "4     -119.789339    2.5           339  ...              0           0   \n",
              "...           ...    ...           ...  ...            ...         ...   \n",
              "30925  -75.191084    4.0            21  ...              0           0   \n",
              "30926  -75.214430    3.0            55  ...              0           0   \n",
              "30927  -86.776311    4.0           204  ...              0           0   \n",
              "30928 -116.202383    4.5           998  ...              0           0   \n",
              "30929  -75.158665    4.5            35  ...              0           0   \n",
              "\n",
              "      JapaneseTotal HasTV_NA  RestaurantsPriceRange2_NA  \\\n",
              "0                 0      0.0                        0.0   \n",
              "1                 0      0.0                        0.0   \n",
              "2                 1      0.0                        0.0   \n",
              "3                 0      0.0                        0.0   \n",
              "4                 0      0.0                        0.0   \n",
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              "      <td>Philadelphia</td>\n",
              "      <td>PA</td>\n",
              "      <td>19147.0</td>\n",
              "      <td>39.935982</td>\n",
              "      <td>-75.158665</td>\n",
              "      <td>4.5</td>\n",
              "      <td>35</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "<p>30930 rows × 69 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import sklearn.metrics as metrics"
      ],
      "metadata": {
        "id": "_EgLym76rQj-"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_business_linear.copy()\n",
        "X = X.drop( [\"stars\",\"business_id\",\"name\",\"address\",\"city\",\"state\",\"postal_code\",\"latitude\",\"longitude\",\"attributes\",\"categories\",\"BusinessParking\",\"market\"], axis=1 )\n",
        "Y = df_business_linear[\"stars\"]"
      ],
      "metadata": {
        "id": "u_JaV7vIrzlH"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "varNames = list( X.columns.values )\n",
        "m = LinearRegression()\n",
        "m.fit(X,Y)\n",
        "\n",
        "\n",
        "coef_dict = {}\n",
        "coef_dict[\"INTERCEPT\"] = m.intercept_\n",
        "for coef, feat in zip(m.coef_,varNames):\n",
        "    coef_dict[feat] = coef\n",
        "\n",
        "for i in coef_dict :\n",
        "    print( i, \" = \", coef_dict[i]  )\n",
        "\n",
        "\n",
        "PRED = m.predict( X )\n",
        "print(\" Predicted Values \")\n",
        "print( PRED )\n",
        "print(\" --------- \")\n",
        "\n",
        "\n",
        "r2 = metrics.r2_score( Y, PRED )\n",
        "print( \"r2=\", r2 )\n",
        "\n",
        "mse = metrics.mean_squared_error(Y,PRED)\n",
        "print( \"MSE=\", mse )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXOxxigbr8F2",
        "outputId": "f6022cc5-d65d-4d02-c7f7-7a5bae3e53d9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INTERCEPT  =  3.9378538790057926\n",
            "review_count  =  0.0003916845828781254\n",
            "is_open  =  0.13331329350008225\n",
            "RestaurantsTakeOut  =  -0.027337747350925534\n",
            "HasTV  =  -0.09548191664314803\n",
            "BusinessAcceptsCreditCards  =  -0.22702532096378525\n",
            "RestaurantsPriceRange2  =  -0.015536851556807674\n",
            "RestaurantsReservations  =  0.038349408688232986\n",
            "AgesAllowed_21plus  =  -0.38267021909704446\n",
            "AgesAllowed_allages  =  -0.7155522724394714\n",
            "Alcohol_None  =  0.06306787392416675\n",
            "Alcohol_beer_and_wine  =  0.185708887742425\n",
            "Alcohol_full_bar  =  -0.10554397432332918\n",
            "BYOBCorkage_no  =  -0.12418612913200923\n",
            "BYOBCorkage_yes_corkage  =  0.05491920582789786\n",
            "BYOBCorkage_yes_free  =  -0.08880520420410651\n",
            "NoiseLevel_average  =  -0.019729628779493275\n",
            "NoiseLevel_loud  =  -0.189759423140979\n",
            "NoiseLevel_quiet  =  0.05699430178420352\n",
            "NoiseLevel_very_loud  =  -0.3996163745609313\n",
            "RestaurantsAttire_casual  =  -0.23336960539761434\n",
            "RestaurantsAttire_dressy  =  -0.032062761853418775\n",
            "RestaurantsAttire_formal  =  -0.2971910982460051\n",
            "Smoking_no  =  0.1917921568382873\n",
            "Smoking_outdoor  =  0.16541203522629272\n",
            "Smoking_yes  =  0.16297442544853538\n",
            "WiFi_free  =  0.11241421532675022\n",
            "WiFi_no  =  0.04619325578493483\n",
            "WiFi_paid  =  0.05385904832356761\n",
            "garage  =  -0.044228247929632244\n",
            "street  =  0.20490652253526237\n",
            "validated  =  -0.04783418223550967\n",
            "lot  =  0.08099965176258057\n",
            "valet  =  -0.0860768486940297\n",
            "Nightlife  =  -0.041274602578981835\n",
            "Bars  =  0.06349112025914772\n",
            "American (Traditional)  =  -0.16173482840630724\n",
            "Pizza  =  -0.24627695637367963\n",
            "American (New)  =  -0.02014892685924806\n",
            "Sandwiches  =  0.03996944224524035\n",
            "Italian  =  0.006671641188912322\n",
            "Breakfast & Brunch  =  -0.02617046595449063\n",
            "Mexican  =  -0.0006794194144569415\n",
            "Seafood  =  0.017579430638890337\n",
            "ChineseTotal  =  -0.27726701130866505\n",
            "Burgers  =  -0.0868760039585361\n",
            "Salad  =  0.016121444304724467\n",
            "Chicken Wings  =  -0.3493403506028776\n",
            "Sports Bars  =  -0.14191053094695016\n",
            "JapaneseTotal  =  -0.0021983257657807453\n",
            "HasTV_NA  =  -0.18086490903413313\n",
            "RestaurantsPriceRange2_NA  =  0.1480284744140357\n",
            "RestaurantsReservations_NA  =  0.11788155274172632\n",
            "garage_NA  =  -0.015914290103212714\n",
            "validated_NA  =  0.003013000590224274\n",
            "lot_NA  =  0.12086079325588184\n",
            "valet_NA  =  -0.15953512042285423\n",
            " Predicted Values \n",
            "[3.28183804 3.4728371  3.86362045 ... 3.71666843 3.96093656 3.83739574]\n",
            " --------- \n",
            "r2= 0.20860622974361687\n",
            "MSE= 0.3820562072000432\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import sklearn.metrics as metrics\n",
        "\n",
        "from sklearn import preprocessing"
      ],
      "metadata": {
        "id": "UPV20vXSvcZT"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#le = preprocessing.LabelEncoder()"
      ],
      "metadata": {
        "id": "KlNmEyyEIdO9"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#le.fit([\"paris\", \"paris\", \"tokyo\", \"amsterdam\"])"
      ],
      "metadata": {
        "id": "Qbu0H0TTIdTm"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y = Y * 2"
      ],
      "metadata": {
        "id": "0DR5scbSwIwY"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "varNames = list( X.columns.values )\n",
        "m = LogisticRegression()\n",
        "m.fit(X,Y)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "coef_dict = {}\n",
        "coef_dict[\"INTERCEPT\"] = m.intercept_[0]\n",
        "for coef, feat in zip(m.coef_[0],varNames):\n",
        "    coef_dict[feat] = coef\n",
        "\n",
        "for i in coef_dict :\n",
        "    print( i, \" = \", coef_dict[i]  )\n",
        "\n",
        "\n",
        "\n",
        "PRED_FLAG = m.predict( X )\n",
        "print(\" Predicted Flag Values \")\n",
        "print( PRED_FLAG )\n",
        "print(\" --------- \")\n",
        "\n",
        "PRED_PROB = m.predict_proba( X )\n",
        "print(\" Predicted Probabilities\")\n",
        "print( PRED_PROB )\n",
        "print(\" --------- \")\n",
        "\n",
        "P1 = PRED_PROB[:,1]\n",
        "print(\" Predicted Probability of Class\")\n",
        "print( P1 )\n",
        "print(\" --------- \")\n",
        "\n",
        "\n",
        "\n",
        "fpr_train, tpr_train, threshold = metrics.roc_auc_score( Y, P1, multi_class='ovr')\n",
        "auc = metrics.auc(fpr_train, tpr_train)\n",
        "print(\"AUC=\",auc)\n",
        "\n",
        "accuracy = metrics.accuracy_score( Y, PRED_FLAG )\n",
        "print(\"ACCURACY=\",accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2-kpxWzQvjVP",
        "outputId": "1fb54267-8713-4c78-b501-8f83ec3ac376"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INTERCEPT  =  -0.029130297525635167\n",
            "review_count  =  -0.4761374266988408\n",
            "is_open  =  -0.012023816415641951\n",
            "RestaurantsTakeOut  =  -0.0274642661281927\n",
            "HasTV  =  -0.019242533584344065\n",
            "BusinessAcceptsCreditCards  =  -0.02687031525877314\n",
            "RestaurantsPriceRange2  =  -0.03892970556772892\n",
            "RestaurantsReservations  =  -0.008676774852636984\n",
            "AgesAllowed_21plus  =  -8.334214482801133e-06\n",
            "AgesAllowed_allages  =  -2.0137712551072288e-05\n",
            "Alcohol_None  =  -0.011537068210580629\n",
            "Alcohol_beer_and_wine  =  -0.0038968537394530145\n",
            "Alcohol_full_bar  =  -0.0091844691683954\n",
            "BYOBCorkage_no  =  -0.0004140222464574285\n",
            "BYOBCorkage_yes_corkage  =  -4.0244616229966546e-05\n",
            "BYOBCorkage_yes_free  =  -0.0002933978723133375\n",
            "NoiseLevel_average  =  -0.010317179555423424\n",
            "NoiseLevel_loud  =  -0.00061208991806647\n",
            "NoiseLevel_quiet  =  -0.007448661368681282\n",
            "NoiseLevel_very_loud  =  -0.000987697970000788\n",
            "RestaurantsAttire_casual  =  -0.02442748417021868\n",
            "RestaurantsAttire_dressy  =  -0.0002893452024885353\n",
            "RestaurantsAttire_formal  =  -0.00010223028004326855\n",
            "Smoking_no  =  -0.0006429260603693689\n",
            "Smoking_outdoor  =  -0.000893828673179309\n",
            "Smoking_yes  =  -0.0002617203733354318\n",
            "WiFi_free  =  -0.008960654422854382\n",
            "WiFi_no  =  -0.01004802400131219\n",
            "WiFi_paid  =  0.00011427619461412488\n",
            "garage  =  -0.0008277678661456128\n",
            "street  =  -0.005223035511874589\n",
            "validated  =  0.0008087484394475757\n",
            "lot  =  -0.011682297941485266\n",
            "valet  =  -0.0005055506530264572\n",
            "Nightlife  =  -0.00566199585351608\n",
            "Bars  =  -0.005624856001560129\n",
            "American (Traditional)  =  -0.0058694587562061525\n",
            "Pizza  =  -0.0010113294623395807\n",
            "American (New)  =  -0.003818611607469609\n",
            "Sandwiches  =  -0.0023030328200236796\n",
            "Italian  =  0.0002614626174650841\n",
            "Breakfast & Brunch  =  -0.0025476841379673994\n",
            "Mexican  =  -0.0030070257550957953\n",
            "Seafood  =  -0.0017451793225870872\n",
            "ChineseTotal  =  -0.003392780250602641\n",
            "Burgers  =  -0.0016479604539216012\n",
            "Salad  =  -0.0014693912820331887\n",
            "Chicken Wings  =  0.0008129302143111634\n",
            "Sports Bars  =  -0.0015334835904602497\n",
            "JapaneseTotal  =  -0.001165554012991427\n",
            "HasTV_NA  =  -0.004014222330352869\n",
            "RestaurantsPriceRange2_NA  =  -0.004615859786619583\n",
            "RestaurantsReservations_NA  =  -0.003970272172877023\n",
            "garage_NA  =  -0.0034519217875737087\n",
            "validated_NA  =  -0.0036080585335547207\n",
            "lot_NA  =  -0.0034695398313528522\n",
            "valet_NA  =  -0.0029705961570798555\n",
            " Predicted Flag Values \n",
            "[6. 8. 8. ... 8. 8. 8.]\n",
            " --------- \n",
            " Predicted Probabilities\n",
            "[[2.34051504e-006 2.29456750e-002 3.45832476e-002 ... 1.83103907e-001\n",
            "  7.06482938e-002 1.81286367e-002]\n",
            " [1.94999923e-008 1.81171440e-002 3.48571451e-002 ... 2.70263388e-001\n",
            "  1.58448057e-001 1.98926494e-002]\n",
            " [1.15582549e-059 2.00729659e-006 3.62648064e-003 ... 4.14058827e-001\n",
            "  1.98312524e-001 5.56025936e-003]\n",
            " ...\n",
            " [5.76957838e-050 1.14370748e-005 5.83887759e-003 ... 3.56695256e-001\n",
            "  1.39384236e-001 7.05286897e-003]\n",
            " [1.32362106e-237 3.38550919e-020 1.68888032e-006 ... 5.60610252e-001\n",
            "  2.82970924e-001 7.47075243e-005]\n",
            " [3.82084293e-010 1.10258582e-002 2.47792191e-002 ... 3.11193895e-001\n",
            "  1.86903202e-001 1.46158467e-002]]\n",
            " --------- \n",
            " Predicted Probability of Class\n",
            "[2.29456750e-02 1.81171440e-02 2.00729659e-06 ... 1.14370748e-05\n",
            " 3.38550919e-20 1.10258582e-02]\n",
            " --------- \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AxisError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-e1ac0217c8d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mfpr_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtpr_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreshold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc_score\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mP1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_class\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ovr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0mauc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfpr_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtpr_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"AUC=\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mauc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_ranking.py\u001b[0m in \u001b[0;36mroc_auc_score\u001b[0;34m(y_true, y_score, average, sample_weight, max_fpr, multi_class, labels)\u001b[0m\n\u001b[1;32m    560\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"multi_class must be in ('ovo', 'ovr')\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m         return _multiclass_roc_auc_score(\n\u001b[0;32m--> 562\u001b[0;31m             \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_class\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    563\u001b[0m         )\n\u001b[1;32m    564\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_ranking.py\u001b[0m in \u001b[0;36m_multiclass_roc_auc_score\u001b[0;34m(y_true, y_score, labels, multi_class, average, sample_weight)\u001b[0m\n\u001b[1;32m    625\u001b[0m     \"\"\"\n\u001b[1;32m    626\u001b[0m     \u001b[0;31m# validation of the input y_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 627\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    628\u001b[0m         raise ValueError(\n\u001b[1;32m    629\u001b[0m             \u001b[0;34m\"Target scores need to be probabilities for multiclass \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m     46\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[1;32m     47\u001b[0m          initial=_NoValue, where=True):\n\u001b[0;32m---> 48\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\n",
            "\u001b[0;31mAxisError\u001b[0m: axis 1 is out of bounds for array of dimension 1"
          ]
        }
      ]
    }
  ]
}