Porto_safe_driver.ipynb

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      "name": "Porto_safe_driver.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hxtruong/fb-developer-circles/blob/master/Porto_safe_driver.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "quD_tS3PgRo5",
        "colab_type": "text"
      },
      "source": [
        "Porto Seguro's Safe Driver Prediction\n",
        "===="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vZX8VXugRo_",
        "colab_type": "text"
      },
      "source": [
        "## Introduction\n",
        "[This competition](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/data) is hosted by the third largest insurance company in Brazil: Porto Seguro with the task of predicting the probability that a driver will initiate an insurance claim in the next year.\n",
        "\n",
        "In the train data, features that belong to similar groupings are tagged as such in the feature names (e.g., ind, reg, car, calc). In addition, feature names include the postfix bin to indicate binary features and cat to indicate categorical features. Features without these designations are either continuous or ordinal. **Values of -1 indicate that the feature was missing** from the observation. The target columns signifies whether or not a claim was filed for that policy holder\n",
        "\n",
        "train.csv contains the training data, where each row corresponds to a policy holder, and the target columns signifies that a claim was filed.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vRLf9PHgRpB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "sns.set_style('whitegrid')\n",
        "\n",
        "# To avoid Warning message inbetween ...\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fV2N9TEbiq6Z",
        "colab_type": "code",
        "outputId": "433a2993-c4af-4365-c363-6b7a6682207f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0y3tIeZgRpF",
        "colab_type": "code",
        "outputId": "a2d2f7c6-d57b-4228-e26c-ea3c67662740",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "# Read csv file and take a look at it\n",
        "train = pd.read_csv('gdrive/My Drive/Dataset/train.csv')\n",
        "train.head()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  target  ps_ind_01  ...  ps_calc_18_bin  ps_calc_19_bin  ps_calc_20_bin\n",
              "0   7       0          2  ...               0               0               1\n",
              "1   9       0          1  ...               0               1               0\n",
              "2  13       0          5  ...               0               1               0\n",
              "3  16       0          0  ...               0               0               0\n",
              "4  17       0          0  ...               1               1               0\n",
              "\n",
              "[5 rows x 59 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsrUYH88gRpI",
        "colab_type": "code",
        "outputId": "04595a98-23f0-41f6-fa03-840e10aeae81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Taking a look at how many rows and columns the train dataset contains\n",
        "rows = train.shape[0]\n",
        "columns = train.shape[1]\n",
        "print(\"The train dataset contains {} rows and {} columns\".format(rows, columns))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The train dataset contains 595212 rows and 59 columns\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sPRuY94zQ2j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e9084a69-c71b-4df6-fc41-c6aa12eb7867"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 595212 entries, 0 to 595211\n",
            "Data columns (total 59 columns):\n",
            "id                595212 non-null int64\n",
            "target            595212 non-null int64\n",
            "ps_ind_01         595212 non-null int64\n",
            "ps_ind_02_cat     595212 non-null int64\n",
            "ps_ind_03         595212 non-null int64\n",
            "ps_ind_04_cat     595212 non-null int64\n",
            "ps_ind_05_cat     595212 non-null int64\n",
            "ps_ind_06_bin     595212 non-null int64\n",
            "ps_ind_07_bin     595212 non-null int64\n",
            "ps_ind_08_bin     595212 non-null int64\n",
            "ps_ind_09_bin     595212 non-null int64\n",
            "ps_ind_10_bin     595212 non-null int64\n",
            "ps_ind_11_bin     595212 non-null int64\n",
            "ps_ind_12_bin     595212 non-null int64\n",
            "ps_ind_13_bin     595212 non-null int64\n",
            "ps_ind_14         595212 non-null int64\n",
            "ps_ind_15         595212 non-null int64\n",
            "ps_ind_16_bin     595212 non-null int64\n",
            "ps_ind_17_bin     595212 non-null int64\n",
            "ps_ind_18_bin     595212 non-null int64\n",
            "ps_reg_01         595212 non-null float64\n",
            "ps_reg_02         595212 non-null float64\n",
            "ps_reg_03         595212 non-null float64\n",
            "ps_car_01_cat     595212 non-null int64\n",
            "ps_car_02_cat     595212 non-null int64\n",
            "ps_car_03_cat     595212 non-null int64\n",
            "ps_car_04_cat     595212 non-null int64\n",
            "ps_car_05_cat     595212 non-null int64\n",
            "ps_car_06_cat     595212 non-null int64\n",
            "ps_car_07_cat     595212 non-null int64\n",
            "ps_car_08_cat     595212 non-null int64\n",
            "ps_car_09_cat     595212 non-null int64\n",
            "ps_car_10_cat     595212 non-null int64\n",
            "ps_car_11_cat     595212 non-null int64\n",
            "ps_car_11         595212 non-null int64\n",
            "ps_car_12         595212 non-null float64\n",
            "ps_car_13         595212 non-null float64\n",
            "ps_car_14         595212 non-null float64\n",
            "ps_car_15         595212 non-null float64\n",
            "ps_calc_01        595212 non-null float64\n",
            "ps_calc_02        595212 non-null float64\n",
            "ps_calc_03        595212 non-null float64\n",
            "ps_calc_04        595212 non-null int64\n",
            "ps_calc_05        595212 non-null int64\n",
            "ps_calc_06        595212 non-null int64\n",
            "ps_calc_07        595212 non-null int64\n",
            "ps_calc_08        595212 non-null int64\n",
            "ps_calc_09        595212 non-null int64\n",
            "ps_calc_10        595212 non-null int64\n",
            "ps_calc_11        595212 non-null int64\n",
            "ps_calc_12        595212 non-null int64\n",
            "ps_calc_13        595212 non-null int64\n",
            "ps_calc_14        595212 non-null int64\n",
            "ps_calc_15_bin    595212 non-null int64\n",
            "ps_calc_16_bin    595212 non-null int64\n",
            "ps_calc_17_bin    595212 non-null int64\n",
            "ps_calc_18_bin    595212 non-null int64\n",
            "ps_calc_19_bin    595212 non-null int64\n",
            "ps_calc_20_bin    595212 non-null int64\n",
            "dtypes: float64(10), int64(49)\n",
            "memory usage: 267.9 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lwxjmUOOgRpO",
        "colab_type": "text"
      },
      "source": [
        "## 1. Data Quality Checks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78QS6rXXgRpP",
        "colab_type": "text"
      },
      "source": [
        "### Null or missing values check\n",
        "Let check whether there are any null values in the train dataset as follows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HCvZq8QNgRpQ",
        "colab_type": "code",
        "outputId": "87e55884-0339-4b96-8678-2075d28442f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train.isnull().any().any()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fsgM30QzgRpS",
        "colab_type": "text"
      },
      "source": [
        "**Note:** Our null values check returns False but however, this does not really mean that this case has been closed as the data is also described as *\"Values of -1 indicate that the feature was missing from the observation\"*. Therefore I take it that Porto Seguro has simply conducted a blanket replacement of all null values in the data with the value of -1. Let us now inspect if there where any missing values in the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGFgMh6LgRpT",
        "colab_type": "text"
      },
      "source": [
        "Here we can see that which columns contained -1 in their values so we could easily for example make a blanket replacement of all -1 with nulls first as follows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1-o9AfrgRpU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_copy = train.copy()\n",
        "train_copy = train_copy.replace(-1, np.NaN)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeGlII_jgRpX",
        "colab_type": "text"
      },
      "source": [
        "We can use \"Missingno\" to visualize missing values in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pkd7rKOBgRpY",
        "colab_type": "code",
        "outputId": "802b2b4d-2b89-431d-8c5a-9526574fecd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 811
        }
      },
      "source": [
        "# If you don't have \"Missingno\" package, use this command to install\n",
        "# (Linux) conda install -c conda-forge missingno\n",
        "import missingno as msno\n",
        "\n",
        "msno.matrix(df=train_copy.iloc[:, 2:39], figsize=(18, 12), color=(0.42, 0.1, 0.05))"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f06191f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YqLV8kjxw4ACdnJwEHZmZmZRIJDQ3N+fEiRN5/vx54d6rV68yISGBZ86ceaps0LeOZ10+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\nlKF///7d6q9PUyEvGfL8e2AlDRhqovMBu+2228Z73vOeGD9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05ddffz3dfPPNaY899kif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\nAAAAQI39f8acmOtVYJy2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1296x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thkqME0lgRpc",
        "colab_type": "code",
        "outputId": "c1d73ff9-ac9e-42a3-db3d-49f0811b1947",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "# List null columns name\n",
        "# Hint: isnull()\n",
        "nullCols = dict(train_copy.isnull().sum())\n",
        "for k,v in nullCols.items():\n",
        "  if v>0:\n",
        "    print(str(k) + ': '+ str(v)) "
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ps_ind_02_cat: 216\n",
            "ps_ind_04_cat: 83\n",
            "ps_ind_05_cat: 5809\n",
            "ps_reg_03: 107772\n",
            "ps_car_01_cat: 107\n",
            "ps_car_02_cat: 5\n",
            "ps_car_03_cat: 411231\n",
            "ps_car_05_cat: 266551\n",
            "ps_car_07_cat: 11489\n",
            "ps_car_09_cat: 569\n",
            "ps_car_11: 5\n",
            "ps_car_12: 1\n",
            "ps_car_14: 42620\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lRY2lopEgRpr",
        "colab_type": "text"
      },
      "source": [
        "#### Target variable inspection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSWkksH7zYvq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "6dd02ae2-a3f3-4ee9-e603-cae6d91ab5db"
      },
      "source": [
        "sns.countplot(x='target', hue='target', data=train, palette=\"pastel\")"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f029a64e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KCwtxOp0AOJ1OCgsLATh48CA33ngjNpsNp9NJSUkJXV1d1NXVUVtby/XXX09cXBy1tbXU\n1dXR1dVFSUkJTqcTm83Waw0REQmNfn/OZtOmTbzwwgu4XC5aWlrIzMwEICMjg5aWFlwuFy+88AIP\nP/wwANOnT+eWW25h8eLFrF27li1btjB8+HDCwsLYsmULa9euZfHixdxyyy1Mnz69zxoiIhIaNsuy\nrFA3MRC43e6gXGMoqPo6CN38/GXMnRDqFkSkHwT62akVBERExDiFjYiIGKewERER4xQ2IiJinMJG\nRESMU9iIiIhxChsRETFOYSMiIsYpbERExDiFjYiIGKewERER4xQ2IiJinMJGRESMU9iIiIhxChsR\nETFOYSMiIsYpbERExDiFjYiIGBdQ2KxevTqgMRERkUsJ6+uXnZ2dfPvttzQ3N9Pa2oplWQC0t7fj\n8Xj6pUEREfn56zNsXnnlFfbu3YvX62XFihX+sImMjOT222/vlwZFROTnr8+wWb16NatXr+bFF1/k\njjvu6K+eRERkkOkzbC644447OHbsGF999RU+n88/np6ebqwxEREZPAIKm02bNlFXV8eMGTMYPnw4\nADabTWEjIiIBCShsjh8/zhtvvIHNZjPdj4iIDEIB3fo8ffp0vv76a9O9iIjIIBXQkU1zczNLlizh\n+uuvZ8SIEf7xv/zlL8YaExGRwSOgsFm/fv2Pnrizs5Nf//rXdHV14fP5SE1N5f7776euro6NGzfS\n0tLCtddeyx//+EfCw8Pp6urid7/7HSdOnCA6Oppnn32WSZMmAfD8889TUFDAsGHD+P3vf89NN90E\nQHl5OU8++SQ9PT1kZmZy1113AfRaQ0REQiOg02hz58695H99CQ8PZ+/evbz66qsUFRVRUVFBdXU1\nu3bt4s477+TQoUOMGTOGgoICAPbv38+YMWM4dOgQd955J7t27QLg5MmTlJSUUFJSQn5+Pk888QQ+\nnw+fz8f27dvJz8+npKSE119/nZMnTwL0WkNEREIjoLD55S9/yezZs5k9ezZxcXHMnDmT2bNn97mP\nzWZj9OjRAHR3d9Pd3Y3NZuPIkSOkpqYCsHz5csrKygB45513WL58OQCpqam8//77WJZFWVkZS5Ys\nITw8nMmTJzNlyhRqamqoqalhypQpTJ48mfDwcJYsWUJZWRmWZfVaQ0REQiOg02gfffSR/98XAqC6\nuvoH9/P5fKxYsYIvv/ySX/3qV0yePJkxY8YQFna+rMPh8C974/F4uPLKK883FRZGVFQUzc3NeDwe\nZs2a5Z/Tbrf793E4HBeN19TU0Nzc3GuNvnR2duJ2u39wu77MnDnzsvYfbC73/RSRwSOgsPlvNpuN\nhQsXkpuby8MPP9zntsOHD6e4uJi2tjbWrVvH559//pMbNS0iIkJhEWR6P0UGv0D/qAwobEpLS/3/\n7unp4fjx40RERATczJgxY5g3bx7V1dW0tbXR3d1NWFgYDQ0N2O124PyRyenTp3E4HHR3d3PmzBnG\njRuH3W6noaHBP5fH4/Hvc6nxcePG9VpDRERCI6BrNu+++67/v8rKSkaPHs2f//znPvdpamqira0N\ngI6ODg4fPsw111zDvHnzOHjwIACFhYU4nU4AnE4nhYWFABw8eJAbb7wRm82G0+mkpKSErq4u6urq\nqK2t5frrrycuLo7a2lrq6uro6uqipKQEp9OJzWbrtYaIiIRGQEc2Tz311I+e2Ov18uijj+Lz+bAs\ni0WLFnHzzTczbdo0HnzwQfbs2cPMmTPJzMwEICMjg02bNuFyuRg7dizPPvsscP6B0ltuuYXFixcz\nfPhwtmzZ4l8yZ8uWLaxduxafz8fKlSuZPn06cH55nUvVEBGR0LBZF743oA8NDQ3k5ORw7NgxAObM\nmcPmzZsvukD/c+d2u4NyjaGgSistAGTMnRDqFkSkHwT62RnQabTs7GycTicVFRVUVFRw8803k52d\nfdlNiojI0BBQ2DQ1NbFy5UrCwsIICwtjxYoVNDU1me5NREQGiYDCJjo6muLiYv+T+8XFxURHR5vu\nTUREBomAwmbnzp28+eabJCYmkpSUxMGDB/nDH/5gujcRERkkArob7U9/+hNPP/00Y8eOBaClpYWn\nn376J92lJiIiQ09ARzaffvqpP2jg/Gk1LUUiIiKBCihsenp6aG1t9f/c0tKCz+cz1pSIiAwuAZ1G\n+81vfsOqVatYtGgRAG+99Rb33HOP0cZERGTwCChs0tPTue666zhy5AgAubm5TJs2zWhjIiIyeAS8\n6vO0adMUMCIi8pMEdM1GRETkcihsRETEOIWNiIgYp7ARERHjFDYiImKcwkZERIxT2IiIiHEKGxER\nMU5hIyIixilsRETEOIWNiIgYp7ARERHjFDYiImKcwkZERIxT2IiIiHEKGxERMU5hIyIixhkLm9On\nT3PHHXewePFilixZwt69ewFoaWkhKyuLlJQUsrKyaG1tBcCyLHbs2IHL5SItLY0TJ0745yosLCQl\nJYWUlBQKCwv948ePHyctLQ2Xy8WOHTuwLKvPGiIiEhrGwmb48OE8+uijvPHGG/z973/nb3/7GydP\nniQvL4+EhARKS0tJSEggLy8PgPLycmprayktLSUnJ4dt27YB54MjNzeXffv2sX//fnJzc/3hsW3b\nNnJycigtLaW2tpby8nKAXmuIiEhoGAub2NhYrr32WgAiIyOZOnUqHo+HsrIy0tPTAUhPT+ftt98G\n8I/bbDbi4+Npa2vD6/VSWVlJYmIi0dHRjB07lsTERCoqKvB6vbS3txMfH4/NZiM9PZ2ysrKL5vpu\nDRERCY1+uWZTX1+P2+1m1qxZNDY2EhsbC8CECRNobGwEwOPx4HA4/Ps4HA48Hs/3xu12+yXHL2wP\n9FpDRERCI8x0gbNnz3L//ffz2GOPERkZedHvbDYbNpvNaP1Aa3R2duJ2uy+r1syZMy9r/8Hmct9P\nERk8jIbNuXPnuP/++0lLSyMlJQWAmJgYvF4vsbGxeL1exo8fD5w/YmloaPDv29DQgN1ux263U1VV\n5R/3eDzMnTu31+37qtGXiIgIhUWQ6f0UGfwC/aPS2Gk0y7LYvHkzU6dOJSsryz/udDopKioCoKio\niAULFlw0blkW1dXVREVFERsbS1JSEpWVlbS2ttLa2kplZSVJSUnExsYSGRlJdXU1lmVdcq7v1hAR\nkdAwdmTzr3/9i+LiYn7xi1+wbNkyADZu3Mhdd93Fhg0bKCgoYOLEiezZsweA+fPn89577+FyuRg1\nahQ7d+4EIDo6mnvvvZeMjAwA1q1bR3R0NABbt24lOzubjo4OkpOTSU5OBui1hoiIhIbNuvBwyhDn\ndruDctqnoOrrIHTz85cxd0KoWxCRfhDoZ6dWEBAREeMUNiIiYpzCRkREjFPYiIiIcQobERExTmEj\nIiLGKWxERMQ4hY2IiBinsBEREeMUNiIiYpzCRkREjFPYiIiIcQobERExTmEjIiLGKWxERMQ4hY2I\niBinsBEREeMUNiIiYpzCRkREjFPYiIiIcQobERExTmEjIiLGKWxERMQ4hY2IiBinsBEREeMUNiIi\nYpzCRkREjDMWNtnZ2SQkJLB06VL/WEtLC1lZWaSkpJCVlUVraysAlmWxY8cOXC4XaWlpnDhxwr9P\nYWEhKSkppKSkUFhY6B8/fvw4aWlpuFwuduzYgWVZfdYQEZHQMRY2K1asID8//6KxvLw8EhISKC0t\nJSEhgby8PADKy8upra2ltLSUnJwctm3bBpwPjtzcXPbt28f+/fvJzc31h8e2bdvIycmhtLSU2tpa\nysvL+6whIiKhYyxsbrjhBsaOHXvRWFlZGenp6QCkp6fz9ttvXzRus9mIj4+nra0Nr9dLZWUliYmJ\nREdHM3bsWBITE6moqMDr9dLe3k58fDw2m4309HTKysr6rCEiIqHTr9dsGhsbiY2NBWDChAk0NjYC\n4PF4cDgc/u0cDgcej+d743a7/ZLjF7bvq4aIiIROWKgK22w2bDbbgKnR2dmJ2+2+rHozZ868rP0H\nm8t9P0Vk8OjXsImJicHr9RIbG4vX62X8+PHA+SOWhoYG/3YNDQ3Y7XbsdjtVVVX+cY/Hw9y5c3vd\nvq8aPyQiIkJhEWR6P0UGv0D/qOzX02hOp5OioiIAioqKWLBgwUXjlmVRXV1NVFQUsbGxJCUlUVlZ\nSWtrK62trVRWVpKUlERsbCyRkZFUV1djWdYl5/puDRERCR1jRzYbN26kqqqK5uZmkpOTWb9+PXfd\ndRcbNmygoKCAiRMnsmfPHgDmz5/Pe++9h8vlYtSoUezcuROA6Oho7r33XjIyMgBYt24d0dHRAGzd\nupXs7Gw6OjpITk4mOTkZoNcaIiISOjbrwgMqQ5zb7Q7KaZ+Cqq+D0M3PX8bcCaFuQUT6QaCfnVpB\nQEREjFPYiIiIcQobERExTmEjIiLGKWxERMQ4hY2IiBinsBEREeMUNiIiYpzCRkREjFPYiIiIcQob\nERExTmEjIiLGKWxERMQ4hY2IiBinsBEREeMUNiIiYpzCRkREjFPYiIiIcQobERExTmEjIiLGKWxE\nRMQ4hY2IiBinsBEREeMUNiIypFi+7lC3MGD053sR1m+VREQGANvwMFre+t9QtzEgRC9a32+1dGQj\nIiLGKWxERMS4QRs25eXlpKam4nK5yMvLC3U7IiJD2qAMG5/Px/bt28nPz6ekpITXX3+dkydPhrot\nEZEha1CGTU1NDVOmTGHy5MmEh4ezZMkSysrKQt2WiMiQNSjDxuPx4HA4/D/b7XY8Hk8IOxIRGdp0\n6/P/6+zsxO12X/Y810YFoZlBwO3+JtQtiPRuysJQdzAgnA7CZ15nZ2dA2w3KsLHb7TQ0NPh/9ng8\n2O32PveJj4833ZaIyJA1KE+jxcXFUVtbS11dHV1dXZSUlOB0OkPdlojIkDUoj2zCwsLYsmULa9eu\nxefzsXLlSqZPnx7qtkREhiybZVlWqJsQEZHBbVCeRhMRkYFFYSMiIsYpbCSotEyQDFTZ2dkkJCSw\ndOnSULcyJClsJGi0TJAMZCtWrCA/Pz/UbQxZChsJGi0TJAPZDTfcwNixY0PdxpClsJGg0TJBItIb\nhY2IiBinsJGg+SnLBInI0KCwkaDRMkEi0hutICBB9d5777Fz507/MkG//e1vQ92SCAAbN26kqqqK\n5uZmYmJiWL9+PZmZmaFua8hQ2IiIiHE6jSYiIsYpbERExDiFjYiIGKewERER4xQ2IiJinMJGpB+0\ntbXx0ksvGa/zwQcfcOzYMeN1RH4shY1IP2hra+Pll18OeHvLsujp6fnRdaqqqvjoo49+9H4ipuk5\nG5F+8OCDD1JWVsbVV1/NvHnz+PTTT2lra6O7u5sHHniAhQsXUl9fz5o1a5g1axYnTpwgLy+Pw4cP\nk5+fT1RUFDNmzCA8PJwtW7bQ1NTE1q1bOXXqFACPPfYYdrudVatWMWzYMMaPH8/jjz/OnDlzQvzK\nRc4LC3UDIkPBQw89xGeffUZxcTHd3d10dHQQGRlJU1MTq1atYsGCBQB88cUXPP3008THx+PxeHju\nuec4cOAAo0ePZvXq1cyYMQOAJ598ktWrVzNnzhxOnTrFmjVrePPNN7n11lu54oorWLNmTShfrsj3\nKGxE+pllWezevZujR48ybNgwPB4P33zzDQATJ04kPj4egI8//pgbbriB6OhoABYtWkRtbS0Ahw8f\nvuiL6drb2zl79mz/vhCRH0FhI9LPXnvtNZqamjhw4AAjRozA6XTS2dkJwBVXXBHQHD09Pezbt4+I\niAiTrYoEjW4QEOkHo0eP9h95nDlzhpiYGEaMGMGRI0f46quvLrlPXFwcR48epbW1le7ubkpLS/2/\nS0pK4sUXX/T/7Ha7v1dHZCBR2Ij0g3HjxjF79myWLl3KJ598wvHjx0lLS6O4uJipU6dech+73c7d\nd99NZmYmt912G1dddRVRUVEAbN682T/H4sWL/Xe63XzzzRw6dIhly5bx4Ycf9tvrE/khuhtNZAA7\ne/Yso0ePpru7m/vuu4+VK1ficrlC3ZbIj6ZrNiIDWG5uLocPH6azs5OkpCQWLlwY6pZEfhId2YiI\niHG6ZiMiIsYpbERExDiFjYiIGKewERER4xQ2IiJinMJGRESM+z+VxPUD8p2H1gAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q8msNqYYgRpx",
        "colab_type": "text"
      },
      "source": [
        "## 2. Feature inspection and filtering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ly9iVEcmgRpz",
        "colab_type": "text"
      },
      "source": [
        "### Correlation plots\n",
        "As a starter, let us generate some linear correlation plots just to have a quick look at how a feature is linearly correlated to the next and perhaps start gaining some insights from here. At this juncture, I will use the seaborn statistical visualisation package to plot a heatmap of the correlation values. Conveniently, Pandas dataframes come with the corr() method inbuilt, which calculates the Pearson correlation. Also as convenient is Seaborn's way of invoking a correlation plot. Just literally the word \"heatmap\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U4TCPZQFgRpz",
        "colab_type": "text"
      },
      "source": [
        "#### Correlation of float features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oeMfrcQPgRp0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_float = train.select_dtypes(include=['float64'])\n",
        "train_int = train.select_dtypes(include=['int64'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OnljsYEgRp2",
        "colab_type": "code",
        "outputId": "72190c7c-97b1-4fee-9c9f-84e6e9822026",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        }
      },
      "source": [
        "colormap = plt.cm.magma\n",
        "plt.figure(figsize=(16, 12))\n",
        "plt.title(\"Paerson correlation of continuous features\", y=1.05, size=15)\n",
        "sns.heatmap(train_float.corr(), linewidths=0.1, vmax=1.0, square=True,\n",
        "           cmap=colormap)\n",
        "plt.show()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NGzZUXFycoqOjVb58eef6Rx55xCPBAAAAANiP6QIiOztbVapU0Z49\newqsp4AAAABAqVRKhzA5DMNwyxMo3n77bY0aNcodhwIAAABs7+q4WKsjqOzrq+74OU13IFz57LPP\nii0gyvqFuutUd6Wr106qXeXSPQH9L5mvK6RKlNUxLHXq/A7l5e+wOoalynhFSZIux1n/H10rVXhr\nlfx8a1gdw1LXsn9SFf+mVsew1PmsVPn4BFodw1I5OWclieuQc5Zr8H/fhbsJk6hvk5saGQAAAABs\nzG0FhMPhcNehAAAAANiU24Yw0YEAAABAqcIQptsTHR3trkMBAAAAsCnTBcScOXOUlZWlnJwcDRs2\nTBEREfr444+d2+Pi4jwSEAAAALAjw7D+ZQXTBcTu3bvl7++vHTt2qEaNGvr888+1bNkyT2YDAAAA\nYDOmC4jc3FxJ0vbt2xUdHa2AgACPhQIAAABgT6YLiE6dOqlHjx46cuSIIiMjlZGRIT8/P09mAwAA\nAGzLyLf+ZQXTd2EaM2aMKlWqpL1792r8+PFq0KCBkpKSPJkNAAAAgM2Y7kDEx8crLS1Nw4cPV0xM\njE6fPq0ZM2Z4MhsAAABgX/k2eFnAdAfi6NGj2rRpk3M5IiJCPXv29EgoAAAAAPZkugPRqFEjHThw\nwLl88OBBNWnSxCOhAAAAANiT6Q7E4cOHNWjQIIWEhEiSTp06pVq1aql3796SpJSUFM8kBAAAAGzI\nqknMVjNdQCxdutSTOQAAAADcBUwXEDVq1PBkDgAAAAB3AdMFBAAAAID/MgyrE1jD9CRqAAAAAKAD\nAQAAAJREvsPqBJagAwEAAADANAoIAAAAAKYxhAkAAAAogdL6HAg6EAAAAABMowMBAAAAlIBhMIka\nAAAAAIpFAQEAAADANIYwAQAAACXAJGoAAAAAcIEOBAAAAFACdCAAAAAAwAUKCAAAAACmuSwg/vWv\nf+n3v/+9OnbsqISEBF24cMG5rX///h4NBwAAANiVYTgsf1nBZQGRmJioMWPGKCUlRQ8++KCGDBmi\nEydOSJJyc3M9HhAAAACAfbicRH358mV16NBBkvTUU0+pcePGGjFihObMmSOHo3Q+fQ8AAAAw8kvn\nz8Km7sJ06dIlBQQESJIiIiK0cOFCPffccwWGMwEAAAC497kcwjRy5Eh99913BdY1aNBAK1asULdu\n3TwWDAAAAID9uOxA9O7du8j1ISEhevXVV90eCAAAALgbGIbVCaxxW7dxTUhIcFcOAAAAAHcBlx2I\nzMzMItcbhqGdO3e6PRAAAABwN7DqNqpWc1lAREZGKiQkRMYNPRqHwyHDMJSRkeHRcAAAAADsxWUB\nERoaqhUrVigkJKTQto4dO3okFAAAAAB7cllADBs2TBcvXiyygBgxYoRHQgEAAAB2V1qfA+FyEnVM\nTIwaNGhQ5LbY2Fjnn3fv3u2+VAAAAABs6bbuwnSjefPmuetQAAAAAGzK1JOozTBK641wAQAAUCqV\n1h9/3daBcDhK5xgwAAAAoDRxWwcCAAAAKE1K63MgTHUg8vPztW/fvmL3qVGjhlsCAQAAALAvUwWE\nl5eXpk+fXuw+ixcvdksgAAAAAPZleg5EZGSkNm/ezGRpAAAAQFJ+vsPylxVMz4H44IMPtHz5cnl7\ne8vX11eGYcjhcLgc2gQAAADg3mG6gNi/f78ncwAAAAB3lbthYM6uXbs0Y8YM5efna8CAAXr66acL\n7bNp0yYtXrxYDodDDRo00GuvvVbsMW/pLkwXLlzQDz/8oGvXrjnXtW7d+lYOAQAAAOAOyMvL0/Tp\n07V8+XIFBQWpf//+6ty5s+rWrevcJy0tTUuWLNH777+vSpUq6dy5cy6Pa7qAWLt2rZKTk3XmzBk1\naNBABw8eVPPmzZWcnFyyTwQAAADAY1JTU1WzZk2FhoZKknr16qVt27YVKCA+/PBDxcTEqFKlSpKk\natWquTyu6UnUycnJWrdunUJCQrRq1Spt2LBBFStWvNXPAQAAANwTDMNh+as46enpCg4Odi4HBQUp\nPT29wD5paWn6/vvvNWjQIP3+97/Xrl27XH5u0x0IX19f+fn5SZKys7NVp04dff/992bfDgAAAMDN\n1qxZozVr1jiXBw4cqIEDB5p+f15enn744QetWrVKZ86c0dChQ5WSklJso8B0AREcHKyLFy+qa9eu\neuKJJ1SxYkWFhISYDgcAAADcS+zwJOriCoagoCCdOXPGuZyenq6goKBC+zRr1kw+Pj4KDQ3Vgw8+\nqLS0NDVt2vSm5zRdQCQlJUmSxo4dq7Zt2+rSpUtq37692bcDAAAAuIPCw8OVlpamkydPKigoSBs3\nbix0h6WuXbtq48aNeuyxx5SRkaG0tDTnnImbMV1AHDhwQHXr1pW/v7/atGmjrKwsffPNN2rWrFnJ\nPhEAAAAAj/H29tbLL7+sESNGKC8vT4899pjq1aunBQsWqEmTJurSpYvat2+v3bt3q2fPnipTpowm\nTpyoKlWqFH9cswESExO1YcMG53L58uULrQMAAABKi3wbDGFypWPHjurYsWOBdc8//7zzzw6HQy+9\n9JJeeukl08c0fRem60+edr7Ry0u5ubmmTwQAAADg7me6gAgNDVVycrJycnKUk5OjlStXuhwfBQAA\nANyrjHyH5S8rmC4gpk2bpv3796tDhw7q2LGjUlNT9corr3gyGwAAAACbMT0Holq1anr99ddvuv3t\nt9/WqFGj3BIKAAAAgD05DMMw3HGgfv36MaEaAAAApcb3PZ+xOoJqbXrzjp/TdAfCFVd1SPmytdx1\nqrvSz1e/1yNVJlkdw1Jbzs9Wzao9rY5hqR8yNulyXKzVMSxV4a1VkqS8/B3WBrFYGa8oORw+Vsew\nlGHkaE/HF6yOYam2O+fJxyfQ6hiWysk5K0lch5yzXIP/+y7A/kzPgXDlxjs0AQAAALg33bEOBAAA\nAHAvuRueA+EJbutAREdHu+tQAAAAAGzKdAExZ84cZWVlKScnR8OGDVNERIQ+/vhj5/a4uDiPBAQA\nAADsyDAclr+sYLqA2L17t/z9/bVjxw7VqFFDn3/+uZYtW+bJbAAAAABsxnQBkZubK0navn27oqOj\nFRAQ4LFQAAAAAOzJ9CTqTp06qUePHvLz89O0adOUkZEhPz8/T2YDAAAAbKu0TqI2XUCMGTNGlSpV\n0t69ezV+/Hg1aNBASUlJnswGAAAAwGZMD2GKj49XWlqahg8frpiYGJ0+fVozZszwZDYAAADAtqye\nQG3VJGrTHYijR49q06ZNzuWIiAj17Fm6nyoMAAAAlDamOxCNGjXSgQMHnMsHDx5UkyZNPBIKAAAA\ngD2Z7kAcPnxYgwYNUkhIiCTp1KlTqlWrlnr37i1JSklJ8UxCAAAAwIbyrQ5gEdMFxNKlSz2ZAwAA\nAMBdwHQBUaNGDU/mAAAAAO4qVk1itprpORAAAAAAQAEBAAAAwDTTQ5gAAAAA/FdpfRI1HQgAAAAA\nptGBAAAAAEqASdQAAAAA4AIFBAAAAADTGMIEAAAAlEC+YXUCa9CBAAAAAGAaHQgAAACgBJhEDQAA\nAAAuUEAAAAAAMI0hTAAAAEAJ5IshTAAAAABQLAoIAAAAAKaVaAhTZmamKleu7O4sAAAAwF3D4DkQ\nRdu7d6969OihXr166eDBg3riiSfUv39/dezYUfv3778TGQEAAADYhMsOxKxZs/TGG2/o559/1tNP\nP62kpCS1atVKhw8f1iuvvKIPPvjgTuQEAAAAbCW/lD4HwmUBkZubq/r160uSqlatqlatWkmSGjdu\nrGvXrnk2HQAAAABbcTmEKT8/3/nnCRMmFNiWk5Pj/kQAAAAAbMtlB+L555/XlStXVK5cOXXt2tW5\n/sSJE+rTp49HwwEAAAB2VVqfA+GygOjSpUuR6x944AGNHDnS7YEAAAAA2NdtPQciISHBXTkAAACA\nu4phWP+ygssORGZmZpHrDcPQzp073R4IAAAAgH25LCAiIyMVEhIi44YSx+FwyDAMZWRkeDQcAAAA\nAHtxWUCEhoZqxYoVCgkJKbStY8eOHgkFAAAA2F1pfQ6EyzkQw4YN08WLF4vcNmLECLcHAgAAAGBf\nLjsQMTExN90WGxvr/PPu3bv18MMPuycVAAAAYHNGKb2N623dhelG8+bNc9ehAAAAANiU2woIw6r7\nSAEAAAC4Y1wOYTLL4SidLRwAAACUTvml9PfnbutAAAAAALj3mSog8vPztW/fvmL3qVGjhlsCAQAA\nAHeDfMNh+csKpgoILy8vTZ8+vdh9Fi9e7JZAAAAAAOzL9BCmyMhIbd68mcnSAAAAQClmehL1Bx98\noOXLl8vb21u+vr4yDEMOh8Pl0CYAAADgXlRanwNhuoDYv3+/J3MAAAAAuAvc0m1cL1y4oB9++EHX\nrl1zrmvdurXbQwEAAAB2V1pv42q6gFi7dq2Sk5N15swZNWjQQAcPHlTz5s2VnJzsyXwAAAAAbMT0\nJOrk5GStW7dOISEhWrVqlTZs2KCKFSt6MhsAAAAAmzHdgfD19ZWfn58kKTs7W3Xq1NH333/vsWAA\nAACAnTGJ2oXg4GBdvHhRXbt21RNPPKGKFSsqJCTEk9kAAAAA2IzpAiIpKUmSNHbsWLVt21aXLl1S\n+/btPRYMAAAAgP2YngNx4MABZWVlSZLatGmjtm3b6ptvvvFYMAAAAMDO8g3rX1YwXUAkJiaqQoUK\nzuXy5csrMTHRE5kAAAAA2JTpIUzXnzx9nZeXl3Jzcz0SCgAAALC7fKN0TqI23YEIDQ1VcnKycnJy\nlJOTo5UrVyo0NNST2QAAAADYjOkCYtq0adq/f786dOigjh07KjU1Va+88oonswEAAACwGdNDmKpV\nq6bXX3/9ptvffvttjRo1yi2hAAAAALuzaA6z5RyGYbjls/fr108bNmxwx6EAAAAA2/ss4g9WR1D0\nVzPv+DlNdyBccVWH+PnWcNep7krXsn+Sr0+Q1TEslZ2TzjXISeffQvZPkiSHw8fiJNYyjBzl5e+w\nOoalynhFycvha3UMS+Ub2fL2rmZ1DEvl5p6TJPn4BFqcxFo5OWe5BjlnrY5wy5hEfZtuvEMTAAAA\ngHuT2woIN42EAgAAAGBjbhvCFB0d7a5DAQAAALaXb3UAi5juQMyZM0dZWVnKycnRsGHDFBERoY8/\n/ti5PS4uziMBAQAAANiH6QJi9+7d8vf3144dO1SjRg19/vnnWrZsmSezAQAAALZlGA7LX1YwXUDk\n5uZKkrZv367o6GgFBAR4LBQAAAAAezJdQHTq1Ek9evTQkSNHFBkZqYyMDPn5+XkyGwAAAACbMT2J\nesyYMapUqZL27t2r8ePHq0GDBkpKSvJkNgAAAMC2mETtQnx8vNLS0jR8+HDFxMTo9OnTmjFjhiez\nAQAAALAZ0x2Io0ePatOmTc7liIgI9ezZ0yOhAAAAALvLL6WPQTPdgWjUqJEOHDjgXD548KCaNGni\nkVAAAAAA7Ml0B+Lw4cMaNGiQQkJCJEmnTp1SrVq11Lt3b0lSSkqKZxICAAAAsA3TBcTSpUs9mQMA\nAAC4qxiy5jkMVjNdQNSoUcOTOQAAAADcBUzPgQAAAAAA0x0IAAAAAP/FXZgAAAAAwAU6EAAAAEAJ\nlNZJ1HQgAAAAAJhGAQEAAADANIYwAQAAACXAJGoAAAAAcIEOBAAAAFACdCAAAAAAwAUKCAAAAACm\nMYQJAAAAKAGeAwEAAAAALtCBAAAAAEqASdQAAAAA4EKJCoht27a5OwcAAACAu4DLIUxbtmwpsGwY\nhqZPn668vDxJ0iOPPOKZZAAAAICN5VsdwCIuC4hx48apXbt2qlq1qnPdzz//rO3bt0uigAAAAABK\nE5cFxPvvv6/XXntN4eHhGjJkiCRpz549mjVrlsfDAQAAAHZlGNzGtUhNmzbV8uXLlZOTo9jYWKWm\npsrhKJ0XCwAAACjtTN3G1cvLS8OGDVN0dLRmzpzp6UwAAAAAbOqWngMRFBSkBQsWeCoLAAAAcNco\nrZOob+s5EAkJCe7KAQAAAOAu4LIDkZmZWeR6wzC0c+dOtwcCAAAA7gal9UnULguIyMhIhYSEyDD+\ne4UcDocMw1BGRoZHwwEAAACwF5cFRGhoqFasWKGQkJBC2zp27OiRUAAAAADsyeUciGHDhunixYtF\nbhsxYoTbAwEAAAB3A8MGLyu4LCBiYmLUoEGDIrfFxsY6/7x79273pQIAAABgS7d1F6YbzZs3z12H\nAgAAAGBTt/QciOLcOMkaAAAAuNflGw6rI1jCbR0Ih6N0XkAAAACgNHFbBwIAAAAoTUrr+BtTHYj8\n/Hzt27ev2H1q1KjhlkAAAAAA3GPXrl3q3r27unXrpiVLltx0v82bN6t+/fo6dOiQy2OaKiC8vLw0\nffr0YvdZvHixmUMBAAAAuAPy8vI0ffp0LV26VBs3btQnn3yiY8eOFdovKytLycnJatasmanjmp4D\nERkZqc2bNzNZGgAAAJCUb1j/Kk5qaqpq1qyp0NBQ+fr6qlevXtq2bVuh/RYsWKCRI0fKz8/P1Oc2\nPQfigw8+0PLly+Xt7S1fX18ZhiGHw+FyaBMAAACAOy89PV3BwcHO5aCgIKWmphbY5/Dhwzpz5oyi\noqK0bNkyU8c1XUDs37/f7K4A8P/bu/voqqo7jeNPXkwUIaGik5iY6UJAA0JLbYEwC5KCIJdgQFEa\nhEFepAQrDCO+BJkRA4gtAUtlSG1WodbA2FJiIwIaRCI6RXG1lJARKKIDiEIAhbxZTO7N3fOH5RYq\n5B6Se3LOJd8P6651z33Z5zmbm5df9t7nAABw2fM7HUDS2rVrtXbt2sB2dna2srOzLb3X7/frJz/5\niX784x9f0j4v6SxM1dXVOnz4sOrr6wOP9enT55J2CAAAACA0mioYEhISVFlZGdg+fvy4EhISAttf\nfPGFPvjgA913332SpJMnT+qBBx7Qc889p169el10n5YLiHXr1qmoqEiVlZVKTU3V7t271bt3bxUV\nFVltAgAAAEAr6dWrlw4dOqQjR44oISFBmzZt0jPPPBN4vkOHDnrvvfcC2xMmTNBjjz3WZPEgXcIi\n6qKiIhUXFyspKUmrV69WSUmJ4uLimnEoAAAAQPgzxvlbU6KjozVv3jxNnTpVmZmZGj58uLp166Zn\nn332gouprbI8AhETExNYmd3Q0KAuXbro4MGDzd4xAAAAAHtlZGQoIyPjvMdmzZp1wdeuXr3aUpuW\nC4jExETV1NRoyJAhmjx5suLi4pSUlGT17QAAAMBlxa8IpyM4wnIBUVBQIEmaOXOm+vXrp9raWg0c\nONC2YAAAAADcx/IaiPLyctXV1UmS+vbtq379+mnfvn22BQMAAADgPpYLiLy8PF199dWB7Xbt2ikv\nL8+OTAAAAIDrOb2AOtgiartYLiDOXnk68MbISPl8PltCAQAAAHAnywVESkqKioqK5PV65fV69cIL\nLyglJcXObAAAAIBr+WfUtUQAACAASURBVF1wc4LlAmL+/PnatWuX0tPTlZGRoYqKCi1cuNDObAAA\nAABcxvJZmDp16qRly5Zd9PnCwkLl5OSEJBQAAAAAd7JcQARTWlraZAFR3/BpqHYVthq8x52O4Dj6\ngK+Fs4zxOh3BcVGR33c6guP8psHpCI7z+T53OoIreL0nnY7gOPog/PgdWsTsNMtTmIIxTi0DBwAA\nANBqQjYCce4Zmi7kiiuuC9WuwpLXe1LXdOjtdAxHnaot11VXftPpGI468+VhfaP9t5yO4ajTdRWS\npPcyHnE4ibP6vbVUkRExTsdwlN80qNG/zekYjoqK/D4/H//2V3f64aSiozs5HcNR4Tga11b/fM4I\nBAAAAADLQlZAeDyeUDUFAAAAwKUsFxD5+fmqq6uT1+vVxIkTlZaWpvXr1weenz59ui0BAQAAADfy\nG+dvTrBcQGzfvl3t27fXtm3blJycrC1btmjVqlV2ZgMAAADgMpYLCJ/PJ0l688035fF41KFDB9tC\nAQAAAHAnywXEoEGDNHz4cO3du1f9+/fXqVOnFBsba2c2AAAAwLWMcf7mBMuncZ0xY4bi4+O1c+dO\nzZ49W6mpqSooKLAzGwAAAACXsTwCkZubq0OHDmnSpEkaP368jh07pkWLFtmZDQAAAHAtvwtuTrA8\nAnHgwAG9+uqrge20tDRlZmbaEgoAAACAO1kegejRo4fKy8sD27t371bPnj1tCQUAAADAnSyPQOzZ\ns0djx45VUlKSJOno0aPq3LmzsrKyJEkbNmywJyEAAADgQk5dh8FplguIlStX2pkDAAAAQBiwXEAk\nJyfbmQMAAAAIK210AML6GggAAAAAoIAAAAAAYJnlKUwAAAAA/q6tLqJmBAIAAACAZYxAAAAAAM1g\nFOF0BEcwAgEAAADAMgoIAAAAAJYxhQkAAABoBhZRAwAAAEAQjEAAAAAAzcAIBAAAAAAEQQEBAAAA\nwDKmMAEAAADN0EZnMDECAQAAAMA6CggAAAAAljGFCQAAAGgGzsJ0CaqqqkKdAwAAAEAYCFpA/Pzn\nPw/c//DDDzVs2DCNHj1agwcP1u7du20NBwAAALiVccE/JwQtILZs2RK4n5+fr7lz56qsrEw/+9nP\n9OMf/9jWcAAAAADc5ZKmMJ04cUIZGRmSpG9961v68ssvbQkFAAAAwJ2CLqI+cuSIpk+fLkmqrKzU\nmTNndNVVV0mSfD6fvekAAAAAl2qri6iDFhDnroGQJL/fL0n67LPPdO+999qTCgAAAIArBS0g+vbt\ne8HHr732Wo0fPz7kgQAAAIBw0EYHIFp2IbknnngiVDkAAAAAhIGgIxAXu+aDMUZvvfVWyAMBAAAA\ncK+gBUT//v2VlJQkY/4+SBMRESFjjE6dOmVrOAAAAMCtWER9ESkpKfr1r3+tpKSkrz139pSuAAAA\nANqGoGsgJk6cqJqamgs+N3Xq1JAHAgAAAMKBMc7fnBC0gBg/frxSU1Mv+NyECRMC97dv3x66VAAA\nAABcqUVnYTrX0qVLQ9UUAAAAAJcKugbCKuPUGAoAAADgAL/TARwSshGIiIiIUDUFAAAAwKVCNgIB\nAAAAtCVt9TSulkYg/H6//vznPzf5muTk5JAEAgAAAOBelgqIyMhILViwoMnXrFixIiSBAAAAALiX\n5TUQ/fv31+bNm1ksDQAAAMj5a0A49Wu55TUQv/3tb/X8888rOjpaMTExMsYoIiIi6NQmAAAAAJcP\nywXErl277MwBAAAAhJW2ehrXSzoLU3V1tQ4fPqz6+vrAY3369Al5KAAAAADuZLmAWLdunYqKilRZ\nWanU1FTt3r1bvXv3VlFRkZ35AAAAALiI5UXURUVFKi4uVlJSklavXq2SkhLFxcXZmQ0AAABwLacX\nUDu1iNpyARETE6PY2FhJUkNDg7p06aKDBw/aFgwAAACA+1iewpSYmKiamhoNGTJEkydPVlxcnJKS\nkuzMBgAAAMBlLBcQBQUFkqSZM2eqX79+qq2t1cCBA20LBgAAALhZWz0Lk+UpTOXl5aqrq5Mk9e3b\nV/369dO+fftsCwYAAADAfSwXEHl5ebr66qsD2+3atVNeXp4dmQAAAADXM8Y4fnOC5QLi7JWnA2+M\njJTP57MlFAAAAAB3slxApKSkqKioSF6vV16vVy+88IJSUlLszAYAAADAZSwXEPPnz9euXbuUnp6u\njIwMVVRUaOHChXZmAwAAAFzLb5y/OcHyWZg6deqkZcuWXfT5wsJC5eTkhCQUAAAAAHeKMCFafXHX\nXXeppKQkFE0BAAAArvfD5PlOR9AvP32y1fdpeQQimGB1SHR0p1DtKiz5fJ/riiuuczqGo7zek3wO\n+BzI6z0pSfQDXw98Peirz0Gjf5vTMRwVFfl9SfyewNfD338+wP0sr4EI5twzNAEAAAC4PLXaCAQA\nAABwOXFqEbPTQjYC4fF4QtUUAAAAAJeyXEDk5+errq5OXq9XEydOVFpamtavXx94fvr06bYEBAAA\nANzI6VO4OjUCYrmA2L59u9q3b69t27YpOTlZW7Zs0apVq+zMBgAAAMBlLBcQPp9PkvTmm2/K4/Go\nQ4cOtoUCAAAA4E6WC4hBgwZp+PDh2rt3r/r3769Tp04pNjbWzmwAAACAaxkX/HOC5bMwzZgxQ/Hx\n8dq5c6dmz56t1NRUFRQU2JkNAAAAgMtYHoHIzc3VoUOHNGnSJI0fP17Hjh3TokWL7MwGAAAAuJbT\nC6idWkRteQTiwIEDevXVVwPbaWlpyszMtCUUAAAAAHeyPALRo0cPlZeXB7Z3796tnj172hIKAAAA\ngDtZHoHYs2ePxo4dq6SkJEnS0aNH1blzZ2VlZUmSNmzYYE9CAAAAwIVMG70SteUCYuXKlXbmAAAA\nABAGLBcQycnJduYAAAAAworfodOoOs3yGggAAAAAoIAAAAAAYJnlKUwAAAAA/q6tLqJmBAIAAACA\nZRQQAAAAACxjChMAAADQDH6nAziEEQgAAAAAljECAQAAADSDaaOrqBmBAAAAAGAZBQQAAAAAy5jC\nBAAAADSDv23OYGIEAgAAAIB1jEAAAAAAzeBX2xyCYAQCAAAAgGUUEAAAAAAsa1EB8dFHH4UqBwAA\nABBWjHH+5oQWFRD3339/qHIAAAAACANBF1E/9dRTF3zcGKOampqQBwIAAADCQVtdRB20gHjppZc0\nZ84cxcTEfO25jRs32hIKAAAAgDsFLSB69eqlbt266dZbb/3ac//1X/9lSygAAAAA7hS0gFi+fLli\nY2Mv+FxZWVnIAwEAAADhwKlFzE4LWkB07NgxcL+qquprjwEAAABoO4IWEEePHtWSJUv07rvvKi4u\nTsYY1dXVKS0tTQ8//LBuuOGG1sgJAAAAuAqLqC/ioYce0sSJE7V06VJFRUVJkhobG1VaWqrZs2fr\nd7/7ne0hAQAAALhD0OtAnD59WpmZmYHiQZKioqI0YsSIwJQmAAAAAG1D0BGIW265RXl5ebrrrruU\nmJgoSaqsrFRJSYm6d+9ue0AAAADAjfxtdBV10AJi8eLFKi4u1vLly3XixAlJUkJCggYNGqQxY8bY\nHhAAAACAewQtIGJiYjRu3DiNGzeuydcVFhYqJycnZMEAAAAAtMzbb7+tRYsWye/3a8yYMZo2bdp5\nzz///PNat26doqKidM011+jpp59WcnJyk20GXQNhVWlpaaiaAgAAAFzPuOBfUxobG7VgwQKtXLlS\nmzZt0saNG/Xhhx+e95ru3bvrpZde0oYNGzRs2DAtWbIk6HGHrIAwbXQOGAAAAOBGFRUV+uY3v6mU\nlBTFxMRoxIgR2rp163mvSUtL01VXXSVJ6t27tyorK4O2G7ICIiIiIlRNAQAAAK7nd8GtKcePHw+c\nBEn6ah3z8ePHL/r64uJipaenBz3uoGsgrGIEAgAAAGhda9eu1dq1awPb2dnZys7OvuR21q9fr/ff\nf19r1qwJ+tqQFRAejydUTQEAAACwoKmCISEh4bwpScePH1dCQsLXXvfOO+/oF7/4hdasWaOYmJig\n+7Q8hSk3N1c1NTWB7erqaj3++OOB7enTp1ttCgAAAAh7fhnHb03p1auXDh06pCNHjqihoUGbNm3S\n4MGDz3vN3r17NW/ePD333HPq1KmTpeO2PAKxf/9+xcXFBbbj4+O1b98+q28HAAAA0Iqio6M1b948\nTZ06VY2Njbr77rvVrVs3Pfvss+rZs6duu+025efn669//atmzZolSbr++uv1i1/8oul2rQbw+/2q\nrq5WfHy8JKmqqkqNjY0tOCQAAAAgfIXDGuCMjAxlZGSc99jZYkGSfv3rX19ym5YLiClTpig7O1se\nj0fGGG3evJlpSwAAAEAbY7mAuPPOO9WzZ0/t2LFDkrRixQp17drVtmAAAAAA3CdoAVFVVRW4f+21\n1+qOO+4477mOHTvakwwAAABwsWCLmC9XQQuI0aNHKyIiIjDH6+wF44wxioiI+NrV7AAAAABcvoIW\nEGVlZa2RAwAAAAgrjEBYUF1drcOHD6u+vj7wWJ8+fUIeCgAAAIA7WS4g1q1bp6KiIlVWVio1NVW7\nd+9W7969VVRUZGc+AAAAAC5i+UrURUVFKi4uVlJSklavXq2SkpLzLiwHAAAAtCXOX4fa78hxWy4g\nYmJiFBsbK0lqaGhQly5ddPDgQduCAQAAAHAfy1OYEhMTVVNToyFDhmjy5MmKi4tTUlKSndkAAAAA\n12IRdRAFBQWSpJkzZ6pfv36qra3VwIEDbQsGAAAAwH0sT2EqLy9XXV2dJKlv377q16+f9u3bZ1sw\nAAAAAO5juYDIy8vT1VdfHdhu166d8vLy7MgEAAAAuJ7zS6idmUJleQrT2StPnxUZGSmfz2d5Rz7f\n55eW7DLk9Z50OoLj+BzwOTiLfuDrQeJzIElRkd93OoIr8PXA1wPCh+UCIiUlRUVFRbr33nslSS++\n+KJSUlIs7+iKK6679HSXEa/3JH1AH9AH+vsPSPqBzwJ98FUfREd3cjqGo84WDo3+bc4GcVhU5Pf5\negjDAsrv0GlUnWZ5CtP8+fO1a9cupaenKyMjQxUVFVq4cKGd2QAAAAC4jOURiE6dOmnZsmUXfb6w\nsFA5OTkhCQUAAADAnSyPQARTWloaqqYAAAAA1zMRfsdvTghZAWFM27yQBgAAANCWhKyAOPcMTQAA\nAAAuT5bXQATDCAQAAADaEqeuw+C0kI1AeDyeUDUFAAAAwKUsFxD5+fmqq6uT1+vVxIkTlZaWpvXr\n1weenz59ui0BAQAAADfyu+CfEywXENu3b1f79u21bds2JScna8uWLVq1apWd2QAAAAC4jOUCwufz\nSZLefPNNeTwedejQwbZQAAAAANzJcgExaNAgDR8+XHv37lX//v116tQpxcbG2pkNAAAAcC0jv+M3\nJ1g+C9OMGTMUHx+vnTt3avbs2UpNTVVBQYGd2QAAAAC4jOUCIjc3V+3bt9ekSZMkSRs3btSiRYv0\n7LPP2pUNAAAAcC2/Q1eCdprlAuLAgQN69dVXA9tpaWnKzMy0JRQAAAAAd7K8BqJHjx4qLy8PbO/e\nvVs9e/a0JRQAAAAAd7I8ArFnzx6NHTtWSUlJkqSjR4+qc+fOysrKkiRt2LDBnoQAAACACzl1HQan\nWS4gVq5caWcOAAAAAGHAcgGRnJxsZw4AAAAgrLTVEQjLayAAAAAAgAICAAAAgGWWpzABAAAA+Dun\nrgTtNEYgAAAAAFjGCAQAAADQDH41Oh3BEYxAAAAAALCMAgIAAACAZUxhAgAAAJqBRdQAAAAAEAQj\nEAAAAEAz+CMYgQAAAACAJlkuILxe79ceO3XqVEjDAAAAAHC3oAXEjh07lJ6ergEDBmjKlCn65JNP\nAs/df//9toYDAAAA3MqvRsdvTghaQCxZskSrVq3Se++9px/84AeaMmWKysvLJUnGGNsDAgAAAHCP\noIuovV6vunXrJknyeDzq0qWLZsyYoUcffVQRERG2BwQAAADgHkELiOjoaJ08eVLXXXedJKlbt256\n4YUXlJOTo48//tj2gAAAAIAbcR2Ii3jkkUf0+eefn/dYYmKiVq9erWnTptkWDAAAAID7BB2B+Jd/\n+ZfA/aqqKklSx44dFRcXpwceeMC+ZAAAAICL+Y0zi5idFrSAOHr0qJYsWaJ3331XcXFxMsaorq5O\naWlpevjhh3XDDTe0Rk4AAAAALhC0gHjooYc0ceJELV26VFFRUZKkxsZGlZaWavbs2frd735ne0gA\nAAAA7hB0DcTp06eVmZkZKB4kKSoqSiNGjAhMaQIAAADaGiO/4zcnBB2BuOWWW5SXl6e77rpLiYmJ\nkqTKykqVlJSoe/futgcEAAAA4B5BC4jFixeruLhYy5cv14kTJyRJCQkJGjRokMaMGWN7QAAAAMCN\njENXgnZa0AIiJiZG48aN07hx45p8XWFhoXJyckIWDAAAAID7BF0DYVVpaWmomgIAAADgUkFHIKwy\nxoSqKQAAAMD1/FyJumUiIiJC1RQAAAAAl2IEAgAAAGgGp06j6rSQjUB4PJ5QNQUAAADApSwXELm5\nuaqpqQlsV1dX6/HHHw9sT58+PbTJAAAAALiO5SlM+/fvV1xcXGA7Pj5e+/btsyUUAAAA4HbGtM3r\nQFgegfD7/aqurg5sV1VVqbGxbXYaAAAA0FZZHoGYMmWKsrOz5fF4ZIzR5s2bmbYEAACANqutnsbV\ncgFx5513qmfPntqxY4ckacWKFeratattwQAAAAC4T9ACoqqqKnD/2muv1R133HHecx07drQnGQAA\nAADXCVpAjB49WhEREYHrPJy9YJwxRhEREdq6dau9CQEAAAAXMmqb64GDFhBlZWWtkQMAAABAGLik\nK1FXV1fr8OHDqq+vDzzWp0+fkIcCAAAA3M4YFlE3ad26dSoqKlJlZaVSU1O1e/du9e7dW0VFRXbm\nAwAAAOAilq8DUVRUpOLiYiUlJWn16tUqKSk578JyAAAAAC5/lkcgYmJiFBsbK0lqaGhQly5ddPDg\nQduCAQAAAG7GdSCCSExMVE1NjYYMGaLJkycrLi5OSUlJdmYDAAAA4DKWC4iCggJJ0syZM9WvXz/V\n1tZq4MCBtgUDAAAA4D6WC4jy8nJ17dpV7du3V9++fVVXV6d9+/bp29/+tp35AAAAAFcypm1eB8Ly\nIuq8vDxdffXVge127dopLy/PjkwAAAAAXMryCMTZK0+fFRkZKZ/PZ3lHXu/JS0t2GaIP6AOJPjiL\nfqAPJPpAkny+z52O4ApRkd93OoLj+HoIP4ZF1E1LSUlRUVGR7r33XknSiy++qJSUFOs7iu506eku\nIz7f57riiuucjuEor/ckfUAfBH5A0g98Frzek/xs4GcD3xP+xus9qUb/NqdjOIoiMnxYnsI0f/58\n7dq1S+np6crIyFBFRYUWLlxoZzYAAAAALmN5BKJTp05atmzZRZ8vLCxUTk5OSEIBAAAAbmdM25zC\nZHkEIpjS0tJQNQUAAADApSyPQARjjAlVUwAAAIDrGXEa1xY59wxNAAAAAC5PISsgGIEAAAAALn8h\nm8Lk8XhC1RQAAADgeiyiDiI/P191dXXyer2aOHGi0tLStH79+sDz06dPtyUgAAAAAPewXEBs375d\n7du317Zt25ScnKwtW7Zo1apVdmYDAAAAXMsYv+M3J1guIHw+nyTpzTfflMfjUYcOHWwLBQAAAMCd\nLBcQgwYN0vDhw7V37171799fp06dUmxsrJ3ZAAAAALiM5UXUM2bMUHx8vHbu3KnZs2crNTVVBQUF\ndmYDAAAAXMsvFlE3KTc3V4cOHdKkSZM0fvx4HTt2TIsWLbIzGwAAAACXsTwCceDAAb366quB7bS0\nNGVmZtoSCgAAAHA7TuMaRI8ePVReXh7Y3r17t3r27GlLKAAAAADuZHkEYs+ePRo7dqySkpIkSUeP\nHlXnzp2VlZUlSdqwYYM9CQEAAAC4huUCYuXKlXbmAAAAAMKKMY1OR3CE5QIiOTnZzhwAAAAAwoDl\nNRAAAAAAYHkEAgAAAMDfGa4DAQAAAABNYwQCAAAAaAauAwEAAAAAQVBAAAAAALCMKUwAAABAMzCF\nCQAAAACCYAQCAAAAaAZO4woAAAAAQVBAAAAAALCMKUwAAABAM7CIugl+v19+/1cd1NDQoD179qiq\nqsrWYAAAAADcJ2gB8cYbb2jAgAFKT0/XG2+8ofHjxys/P18jR45UWVlZa2QEAAAAXMcYv+M3JwSd\nwrRixQqtX79eX375pUaNGqXi4mLdeOON+vTTTzVz5kwNHjy4NXICAAAAcAFLayCuu+46SVJSUpJu\nvPFGSVJycrKMMfYlAwAAAOA6lgoIv9+vyMhIPf3004HHGhsb5fV6bQsGAAAAuBuLqC9owYIFgULh\nW9/6VuDxY8eO6Yc//KF9yQAAAAC4TtARiHOLhrNnXurYsaNuuOEG3XDDDfYlAwAAAFysrZ7GNWgB\ncfToUS1ZskTvvvuu4uLiZIxRXV2d0tLS9PDDD1NEAAAAAC719ttva9GiRfL7/RozZoymTZt23vMN\nDQ167LHHtGfPHnXs2FHLli0L+vt90ClMDz30kIYOHart27fr9ddf15YtW/SHP/xBQ4YM0ezZs1t2\nRAAAAABs0djYqAULFmjlypXatGmTNm7cqA8//PC816xbt05xcXHasmWLJk2apKVLlwZtN2gBcfr0\naWVmZioqKirwWFRUlEaMGMHF5AAAANBmGfkdvzWloqJC3/zmN5WSkqKYmBiNGDFCW7duPe81ZWVl\nuuuuuyRJw4YN07vvvhv0TKtBpzDdcsstysvL01133aXExERJUmVlpUpKStS9e/dgbwcAAABgk7Vr\n12rt2rWB7ezsbGVnZ0uSjh8/Hvj9XZISEhJUUVFx3vuPHz+u66+/XpIUHR2tDh066PTp07rmmmsu\nus+gBcTixYtVXFys5cuX68SJE4GdDxo0SGPGjLmEwwMAAAAuH25YRH1uwdBaghYQMTExGjdunMaN\nG9fk6woLC5WTkxOyYAAAAACaLyEhQZWVlYHt48ePKyEh4WuvOXbsmBITE+Xz+VRbW6tvfOMbTbYb\ndA2EVaWlpaFqCgAAAEAL9erVS4cOHdKRI0fU0NCgTZs2afDgwee9ZvDgwSopKZEkbd68WWlpaYqI\niGiyXUtXorYi2GILAAAA4PLS6HSAJkVHR2vevHmaOnWqGhsbdffdd6tbt2569tln1bNnT9122226\n55579Oijj2ro0KGKj4/XsmXLgrcbqoDBKhUAAAAArSsjI0MZGRnnPTZr1qzA/djYWC1fvvyS2gzZ\nFCZGIAAAAIDLX8hGIDweT6iaAgAAAFzPDWdhcoLlEYjc3FzV1NQEtqurq/X4448HtqdPnx7aZAAA\nAABcx/IIxP79+xUXFxfYjo+P1759+2wJBQAAALgfIxBN8vv9qq6uDmxXVVWpsdHdK88BAAAAhJbl\nEYgpU6YoOztbHo9Hxhht3ryZaUsAAABAG2O5gLjzzjvVs2dP7dixQ5K0YsUKde3a1bZgAAAAgKu1\n0UXUQQuIqqqqwP1rr71Wd9xxx3nPdezY0Z5kAAAAAFwnaAExevRoRUREBK7zcPaCccYYRUREaOvW\nrfYmBAAAAFzIqG1eBy1oAVFWVtYaOQAAAACEgUu6kFx1dbUOHz6s+vr6wGN9+vQJeSgAAAAA7mS5\ngFi3bp2KiopUWVmp1NRU7d69W71791ZRUZGd+QAAAACXapuLqC1fB6KoqEjFxcVKSkrS6tWrVVJS\nct6F5QAAAABc/iyPQMTExCg2NlaS1NDQoC5duujgwYO2BQMAAABczbCIukmJiYmqqanRkCFDNHny\nZMXFxSkpKcnObAAAAABcxnIBUVBQIEmaOXOm+vXrp9raWg0cONC2YAAAAADcx/IaiPLyctXV1UmS\n+vbtq379+mnfvn22BQMAAADczLjgnxMijLE2eevOO+9USUlJ4EJyfr9fd999t0pKSmwNCAAAALhR\nRMQVTkeQMd5W36flKUxnrzx9VmRkpHw+ny2hAAAAALdz4pd3N7A8hSklJUVFRUXyer3yer164YUX\nlJKSYmc2AAAAAC5jeQrT559/rqeeeko7duxQRESE+vfvr7lz56pTp052ZwQAAADgEpYLiGAKCwuV\nk5MTiqYAAAAAuJTlKUzBlJaWhqopAAAAAC4VsgIiRAMZAAAAAFwsZAXEuWdoAgAAAHB5YgQCAAAA\ngGUhKyA8Hk+omnKN999/X1lZWRo6dKieeuqpQJH02muvacSIEUpNTdX//u//OpzSXhfrg8WLF8vj\n8SgrK0sPPvigampqHE5qr4v1w89+9jNlZWVp1KhRmjJlio4fP+5wUnusWbNGQ4cO1c0336xTp04F\nHn/llVeUlZWlrKwsjR07Vn/5y18cTGm/i/XDG2+8EfgcjB49Wn/6058cTGmvi/XBWRUVFerRo8dl\nvS7uYn3w3nvv6bvf/a5GjRqlUaNGacWKFQ6mvHSDBw++4P9pUxoaGvTv//7vGjp0qMaMGaNPPvlE\nknT69GlNmDBB3/nOd7RgwQI74toilH2wfft2jR49WllZWRo9erTeffddOyLbIpT9UFFREfiaGDly\npLZs2WJHZLQ2Y9HixYtNbW2taWhoMPfdd5/p16+fefnll62+vdV4vd6QtXX33XebXbt2Gb/fb+6/\n/36zbds2Y4wxH374ofnoo4/Mv/7rv5qKioqQ7S9UWqMP/ud//iewn/z8fJOfnx+yfYZKa/RDbW1t\n4DUvvPCCeeKJJ0K2Tyf4fL4LPr5nzx5z5MgRM2jQIPP5558HHt+5c6epqqoyxhizbds2c88997RK\nTrtdaj/U1dUZv99vjDFm3759ZtiwYa2S006X2gdn3zNhwgQzdepU89prr7VGTFtdah/s2LHDTJs2\nrbXihdyF/k+DWbNmTeD73saNG82sWbOMMcZ88cUX5o9//KN58cUXzfz580Oe1S6h7IM9e/aYyspK\nY4wx+/fvNwMGFFUfeQAACiNJREFUDAhtWBuFsh/++te/Bn4eHz9+3KSlpYX05zOcYflK1Nu3b9dj\njz2mLVu2KDk5WStWrND48eM1atSoFhcxn3zyiaZOnapbbrlFe/fuVbdu3bR48WIVFBSorKxMUVFR\nGjBggHJzcy/4/jlz5igmJkb79u3TrbfeqlmzZmnhwoU6cOCAfD6fZsyYoSFDhujMmTOaM2eODhw4\noM6dO+vEiROaN2+eevXq9bU2T5w4obq6OvXu3VuSdOedd2rr1q3KyMhQly5dWnzM4d4HAwYMCLyu\nd+/eIftrY7j1Q/v27QOvO3PmTEjWArW0Dz777DM9+eSTOnLkiCQpLy9Pt956q370ox+psrJS9fX1\nuu+++5SdnS1J+s53vqPs7Gy98847mjdvnr73ve99rc0ePXpccF+33npr4H7v3r1VWVnZ0sMPCKd+\nuPrqqwP3Q/U5kMKrDyRp9erVGjZsWEhHZsOtD0KptY/9XC+//LJWrVqliIgI3XzzzVqyZMkF91FW\nVqYZM2ZIkoYNG6YFCxbIGKN27drpe9/7nj7++OM22wfnfk66deum+vp6NTQ0KCYmpk31w1VXXRV4\nTX19PWtmLxdWK43MzExjjDGPP/64eeutt4wxxmRlZYWkijly5Ii56aabzJ/+9CdjjDFz5swxhYWF\n5vbbbw/8Va+6uvqi78/NzTXTpk0L/LXomWeeCYyOVFdXm9tvv9188cUXZuXKlYHqeP/+/aZ79+4X\nHUGoqKgwEydODGz/8Y9//NpflkI5AhGufWCMMTk5OSEbjQrHfvjpT39q0tPTzYgRIy75LzYX0tI+\nmDVrlnn++eeNMV/9BbWmpsYYY8zp06eNMcacOXPGjBgxwpw6dcoYY8xNN91kNm3aZClbU3+VWrly\npZk7d66ldqwIt354/fXXzbBhw0yfPn3Mn//8Z+sH2oRw6oPKykozfvx409jYaHJzc0M2AhFOfbBj\nxw7Tt29fk5WVZe6//37zwQcfXNrB/oPWPvazx/PBBx+Y22+/PXBsZ19/ISNGjDDHjh0LbN92223n\n9clLL73UohGIy6EPjDHmtddeO+/nyKUK934oLy83mZmZpnfv3ub1119vThfAZSyvgRg0aJCGDx+u\nvXv3qn///jp16pRiY2NDVshcf/31+u53vytJGjlypHbu3KnY2FjNnTtXr7/+uq688som3+/xeBQV\nFSVJ+sMf/qBf/vKXGjVqlCZMmKD6+nodO3ZMO3fuVGZmpiTppptu0s033xyy/KEQjn3w3HPPKSoq\nSiNHjmxRO+cKt3546KGH9NZbbykrK0tr1qxpdjvnakkf7NixQ+PGjZMkRUVFqUOHDpK++uvwyJEj\n9YMf/EDHjh3T4cOHA68ZNmxYi/Lu2LFDxcXFeuSRR1rUzj8Kp34YOnSoSktLVVBQoGeffbbZ7fyj\ncOmDRYsW6ZFHHlFkZMiW1gWESx/ccsstKisr0yuvvKIJEybowQcfbFY752rNYz/3fR6PR9dcc40k\nqWPHji0+jpYI9z44cOCAli5d2uK1IOHcD9/+9re1adMmFRcXq7CwUPX19c1qB+5h+Tv9jBkzNHr0\naCUmJmr27Nn6zW9+o4KCgpAF+cchrejoaBUXF8vj8ejNN9/U1KlTm3z/uUNkkrR8+XKtX79e69ev\n17Zt2y552lFCQsJ50zEqKyuVkJBwSW1cqnDrg9///vfatm2bli5dGtIhyXDrh7OysrL0+uuvX1Lb\nF9PSPvhH7733nt555x2tXbtWr7zyinr06BH4Bh4bGxsouJrjL3/5i/7zP/9TP//5z/WNb3yj2e1c\nSDj1w1l9+vTRkSNHLnkB4sWESx+8//77mj17tgYPHqzNmzdr/vz5euONN5rV1j8Klz5o3759YDpb\nRkaGfD5fiz8HrXnszZGQkKBjx45Jknw+n2pra8P6+0BzNNUHlZWVmjFjhhYvXqx//ud/bvY+pPDu\nh7O6dOmidu3a6YMPPmj2fuAOlguI3NxcHTp0SJMmTdL48eN17NgxLVq0KGRBjh49ql27dkmSNm7c\nqO7du6u2tlYZGRmaO3eu9u/fb7mtAQMGaM2aNYEz5ezdu1fSV/O1X3vtNUnShx9+2OQH+J/+6Z/U\nvn17lZeXyxijl19+WbfddltzD8+ScOqDt99+WytXrtRzzz33tV/YWyqc+uHQoUOB123dulU33njj\nJR3rxbSkD/r3768XX3xRktTY2Kja2lrV1tYqPj5eV111lT766COVl5eHLOfMmTOVn5+vzp07h6TN\nf2w/HPrh8OHDgc/Ynj171NDQELJfosKlD8rKygK3YcOG6cknn9SQIUNC0na49MHJkycDn4OKigr5\n/f4Wfw6cOPa0tDSVlpbq9OnTkqSqqqqL7mPw4MEqKSmRJG3evFlpaWkhn+Mern1QU1OjadOm6eGH\nHw6MHLREuPbDkSNH5PP5JEmffvqp/u///k/JycnN6wS4huVF1AcOHNCrr74a2E5LSwtMAQmFzp07\n67//+781d+5cde3aVf/2b/+mnJycQDU8Z84cy2396Ec/0tNPP62RI0fK7/frhhtuUGFhocaNG6c5\nc+YoMzNTN954o7p27RoYxruQJ598Uo8//ri+/PJLpaenKz09XZK0ZcsWLVy4UKdOnVJOTo66d++u\nVatWtawDFF59sHDhQjU0NGjy5MmSvhqeDNWp+sKpH5555hkdPHhQERERSk5O1vz581t28H/Tkj74\nj//4Dz3xxBN66aWXFBkZqby8PKWnp+u3v/2thg8frs6dOwcWhFtVVFSklStX6rPPPtPIkSOVkZGh\nRYsWqaCgQFVVVYHjjoqK0u9///vmH/g/CJd+2Lx5s9avX6/o6GhdeeWVWrZsWch+iQqXPrBTuPTB\n5s2b9Zvf/EZRUVG68sor9dOf/rTFnwMnjr1bt26aPn26JkyYoMjISPXo0UM/+clPLriPe+65R48+\n+qiGDh2q+Ph4LVu2LPDc4MGDVVdXJ6/XqzfeeEO/+tWv1LVr1zbTB2vWrNHHH3+sgoKCwIyNX/3q\nV+rUqdMl90E498POnTv1y1/+UtHR0YF9n50ShTBmdbHEww8/bHbt2hXYLi8vN48++mhIFmIcOXLE\njBgxIiRtNcXn85kvv/zSGGPM4cOHzaBBg0x9fb3t+7WCPvgK/dB6feB29AN9YEzb7oO2fOxn0Qdf\noR/gNpZHIPbs2aOxY8cqKSlJ0ldDaZ07d1ZWVpYkacOGDfZUOCF05swZ3XffffL5fDLG6Mknn2zW\n6dTCGX3wFfoBAACgeSKM+duEzSA+/fTTJp9vjflszz333NeuN+DxePTAAw+0qN0xY8aooaHhvMfy\n8/Ndd5YmiT44i36wpw8efPDBwNVDz3rkkUc0cODAZrdpN/qBPpDadh/Y9f2wtffREvTBV+gHtBbL\nBQQAAAAAhP6E3QAAAAAuWxQQAAAAACyjgAAAAABgGQUEAAAAAMsoIAAAAABY9v/3CqkKGRQsKwAA\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbSGdx5tgRp5",
        "colab_type": "text"
      },
      "source": [
        "From the correlation plot, we can see that the majority of the features display zero or no correlation to one another. This is quite an interesting observation that will warrant our further investigation later down. For now, the paired features that display a positive linear correlation are listed as follows:\n",
        "\n",
        "(ps_reg_01, ps_reg_03)\n",
        "\n",
        "(ps_reg_02, ps_reg_03)\n",
        "\n",
        "(ps_car_12, ps_car_13)\n",
        "\n",
        "(ps_car_13, ps_car_15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "709RXpmngRp5",
        "colab_type": "text"
      },
      "source": [
        "### Correlation of integer features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqDWFok31rGE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "55089604-a331-414a-b041-0dad7395c895"
      },
      "source": [
        "# Your code here\n",
        "# Hint: correlation of train_int\n",
        "train_int = train_int.drop([\"id\", \"target\"], axis=1)\n",
        "colormap = plt.cm.CMRmap_r\n",
        "plt.figure(figsize=(21,16))\n",
        "plt.title('Pearson correlation of categorical features', y=1.05, size=15)\n",
        "sns.heatmap(train_int.corr(),linewidths=0.1,vmax=1.0, square=True, cmap=colormap, annot=False)\n",
        "plt.show()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LGJcjLeYOsDDLFzqyBAM+S/qKKTnX/eaULnx7\nluGcG15+X5JMy3LttBjOsTnckiTXDuO9oLYtfXKdecp4zq3PSJJc/99K41lfe1Ou/5pnPGfZXyRJ\nfn5+hrP6+/tNO96STPseuAoCjec8OXCrni/W9PGmGwxnBe+5oA+ygwzn3JI/sHqZacfpWePHSZJs\nj/eq3XGd4ZyQnZ+ZcpykgWPl2mM1nGPb1CPXT6aZUJFke7RLHz55veGcmwu6TTkXSAPng/dzjH8P\nZu3q1YePm3Ocbn62y7zfnYnHyVVs/Hdn29gtV77xvxVJsmV/Jtd2E64JW/tMyTEzi5qubY6ZWdR0\nbXPMzPLVmoDxoLMEAAAAAIApgqWDzcFRBAAAAAAAGILOEgAAAAAAgCHoLAEAAAAAABiCOUsAAAAA\nAJgimLPEHBNyFCsqKrRv375x7ZOamqpjx45d8fU//vGPWr16teLj47Vjxw59viJyc3OzHnzwQa1e\nvVqPPvqoLly4cMWM119/Xdu3bx/1tYceemhc9QIAAAAAgMlpQjpLUlJStGbNGlMzt23bpmeeeUa1\ntbU6efKk3nnnHUnS9773PT3xxBPav3+/7r77bv30pz/1Kv/VV181s1wAAAAAAOCjvLoNp62tTenp\n6bLb7WpqatKCBQu0e/dulZSUqK6uThaLRcuWLdPmzZtH3b+oqEjTpk1TWlqaUlNTFRkZqfr6enV2\ndiovL0/R0dHq6elRbm6umpubNXfuXPX09Fyxnvfff18XLlzQ4sWLJUlr1qzR22+/rbi4OJ08eVJL\nly6VJP3TP/2T0tLS9Nhjj10xy+VyKTU1Ve3t7UpKStLGjRslSUuWLFFjY6Pq6+tVXFysmTNnqrW1\nVXa7XQUFBfLz8/PmUAIAAAAAYBpuwzGH10fxxIkTWrdunaqrqzV9+nSVlZXp4MGDqqqq0v79+5WZ\nmelxltvtltPplMPhUHFxsaSBW3WsVquqq6uVlZWl48ePX3H/9vZ2hYaGDj4PDQ1Ve3u7JGnBggV6\n++23JUk1NTVyuVxj1nLs2DHt2bNHv/nNb1RTUzPqrT9NTU1yOBw6cOCA2tra1NDQ4PFnBQAAAAAA\nvs3rzhKbzaaoqChJUlJSkhoaGhQUFCSHw6Ha2lpZrVaPs+Lj4yVJdrtdZ86ckSQdOXJESUlJkqSF\nCxcqPDzcqzrz8vL0i1/8QmvXrtWnn36qwMDAMbe/4447NHPmTFmtVsXHx4/aERIZGanQ0FD5+/tr\n4cKFgzUDAAAAAIDJz+vVcC6/7SQgIEBOp1O/+93vVFNTo1deeUUvv/yyR1mfd2D4+/vL7XaPu5aQ\nkBCdO3du8Pm5c+cUEhIiSZo3b55+/vOfSxoYDfPb3/52zKzLP9dot9cM7XCxWCxe1QwAAAAAgNm4\nDcccXh/Fs2fPqrGxUZJUWVmpiIgIdXZ2Ki4uTg6HQy0tLYYKW7p0qSorKyVJra2tY+bNmjVLN9xw\ng/7whz+ov79f+/bt01133SVJ+uijjyRJfX19Ki0tveqqNu+9954++eQT9fT06K233tJtt91m6HMA\nAAAAAIDJxeuRJXPmzFF5ebkcDofmz5+vTZs2KSMjQxcvXpQk5eTkGCosJSVFubm5SkxM1Lx582S3\n28fc/umnn1Zubq56enoUGxur2NhYSQMdOb/4xS8kDdzuc999942ZExkZqaysrMEJXhctWmTocwAA\nAAAAgMnF686SgIAAFRQUDGtzOp0e7ZuVlTX4uKysbPBxcHCw6urqJElWq1WFhYUe17No0aLBkShD\nPfzww3r44Yc9yli7dq3Wrl076mufj6KJiYlRTEzMYPvWrVs9rhEAAAAAAPg+rztLAAAAAACAb/Fn\nzhJTeNVZEhYWNuoojsuVlpaqpqZmWFtCQsK4lhW+XHJysnp7e4e15efnj2u1nHfffXfEqJiwsDCV\nlJR4XRcAAAAAAJga/Pr7+/snuggAAAAAAGDcP4X/50SXMC7vtXg2bca1xm0414BruznDoGxb+0zJ\nsm3tkyS5to1cFnncWdv6za3JpCzXTovxHMfAktDnthrPCt3uNu14S5JrhwnHaUvfqEtjj9fn/a0f\nb7rBcFbwngvq/LcvG86Z8fOBVbA+efRLhrO+9JNP9H/vjDecM/fdg5IkV/51hrNs2Z+ZliOZdy5o\ndxivSZJCdn5m3vlplzmXOVvOJVM+X8jOz3Ruizk1he645FPnJ8m8Y27LuWTKeU4aONf50vVO+tt1\nyhdrMusabGZNJl3vpvxxoqZrlmXW91Iy77vps8fJB88Ffy9YOtgcHEUAAAAAAIAh6CwBAAAAAAAY\ngs4SAAAAAACAIZizBAAAAACAKYI5S8zBUQQAAAAAABhiQjpLKioqtG/fvnHtk5qaqmPHjl3x9cLC\nQsXFxWnJkiXD2nt7e/XYY48pPj5eycnJamtru2LG66+/ru3bt4/62kMPPTSuegEAAAAAwOQ0IZ0l\nKSkpWrNmjamZy5cv1969e0e07927VzfeeKMOHjyof/3Xf1VBQYFX+a+++qrREgEAAAAA+EL5yX9S\n/fgqr+YsaWtrU3p6uux2u5qamrRgwQLt3r1bJSUlqqurk8Vi0bJly7R58+ZR9y8qKtK0adOUlpam\n1NRURUZGqr6+Xp2dncrLy1N0dLR6enqUm5ur5uZmzZ07Vz09PWPWtHjx4lHb6+rqtHHjRknSypUr\ntX37dvX398vPz2/U7V0ul1JTU9Xe3q6kpKTBfZcsWaLGxkbV19eruLhYM2fOVGtrq+x2uwoKCq6Y\nBwAAAAAAJhevJ3g9ceKE8vLyFBUVpdzcXJWVlengwYOqqamRn5+fOjo6PM5yu91yOp06fPiwiouL\n9dJLL6miokJWq1XV1dVqbm7W2rVrvaqzvb1dNptNkhQQEKAZM2bo/PnzCg4OHnX7Y8eOaf/+/br+\n+ut1//33Ky4uTosWLRq2TVNTk6qqqjRr1iylpKSooaFB0dHRXtUHAAAAAAB8i9djXmw2m6KioiRJ\nSUlJamhoUFBQkBwOh2pra2W1Wj3Oio+PlyTZ7XadOXNGknTkyBElJSVJkhYuXKjw8HBvSx2XO+64\nQzNnzpTValV8fLwaGhpGbBMZGanQ0FD5+/tr4cKFgzUDAAAAADCRJvq2mqlyG47XlV1+20lAQICc\nTqcSEhJ06NAhpaene5wVGBg4UIy/v9xut7cljSokJEQul0uSdOnSJXV2dmrmzJlX3P7yzzXa7TWf\n1ytJFovF9JoBAAAAAMDE8bqz5OzZs2psbJQkVVZWKiIiQp2dnYqLi5PD4VBLS4uhwpYuXarKykpJ\nUmtrq9d5K1as0K9//WtJ0ptvvqnbb799zPlF3nvvPX3yySfq6enRW2+9pdtuu82r9wUAAAAAAJOT\n150lc+bMUXl5uRITE9XR0aHk5GRlZGRo9erVWrdunXJycgwVlpKSoq6uLiUmJmrPnj2y2+1jbp+f\nn6/Y2Fh1d3crNjZWRUVFkqT7779fn3zyieLj4/Xiiy/qySefHDMnMjJSWVlZSkpK0sqVK0fMVwIA\nAAAAAKY2ryd4DQgIGLEMr9Pp9GjfrKyswcdlZWWDj4ODg1VXVydJslqtKiws9Lie7OxsZWdnj2gP\nCgrSnj17PMpYu3btFSeS/XwUTUxMjGJiYgbbt27d6nGNAAAAAAB8kfx9eB6QyYSjCAAAAAAAMIRX\nI0vCwsIG5xMZS2lpqWpqaoa1JSQkKDMz05u3lSQlJyert7d3WFt+fv64Vst59913R4yKCQsLU0lJ\nidd1AQAAAACAqcHr23A8kZmZaahjZDR79+41nHHnnXfqzjvvNKEaAAAAAAB8hy8vxzuZ+PX39/dP\ndBEAAAAAAMC4FeG/mugSxqWu5b6JLmFUX+jIEgxw7TLnMNtyLsm13XgvoW1rnyTJtcOErC195tZk\nUpZZn02Szm21GM4K3e42LUeSXDuNZ9kcbn286QbDOcF7LkjSmEtye6q/v1/nYpcZzgl9578kSe6E\neYazLDV/0bqIA4ZzfvGneyRJruemGc6yfadLruLrjeds7JZk3t/d+Y0zDOdI0sziTrm2Gf8+2bb1\nm/LZpIHP9+GTxo/5zQXdpnw2aeDzvZ8TaDhn1q5eU4+TGdc8W84lU87j0sC5/NwW4zWF7jDnGiz9\n7Tj54HfctGswNV2zHDOzqOna5piZRU2e5wDjwfgcAAAAAACAIRhZAgAAAADAFMGcJebgKAIAAAAA\nAAxBZwkAAAAAAMAQ3IYDAAAAAMAUwW045rjmR7GiokL79u0b1z6pqak6duzYFV8vLCxUXFyclixZ\nMqz9yJEj+uY3v6l/+Id/UE1NzZjvUV9fr4yMjFFf27Bhgzo6OsZVMwAAAAAAmJyueWdJSkqK1qxZ\nY2rm8uXLtXfv3hHtNptN3//+9/WNb3zDUP4LL7ygG2+80VAGAAAAAACYHMZ9G05bW5vS09Nlt9vV\n1NSkBQsWaPfu3SopKVFdXZ0sFouWLVumzZs3j7p/UVGRpk2bprS0NKWmpioyMlL19fXq7OxUXl6e\noqOj1dPTo9zcXDU3N2vu3Lnq6ekZs6bFixeP2h4WFiZJ8vf3rE/owoULeuSRR3Tq1CnFxMRo27Zt\n8vf314oVK+R0OtXV1aUNGzYoKipKjY2NCgkJ0XPPPSer1epRPgAAAAAAXyRuwzGHV0fxxIkTWrdu\nnaqrqzV9+nSVlZXp4MGDqqqq0v79+5WZmelxltvtltPplMPhUHFxsaSBW3WsVquqq6uVlZWl48eP\ne1PmuB09elRPPfWUDhw4oNOnT6u2tnbENqdOndL69etVVVWlGTNm6M0337wmtQEAAAAAgGvDq84S\nm82mqKgoSVJSUpIaGhoUFBQkh8Oh2tracY20iI+PlyTZ7Xa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HDuSp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\n77Tb/sEHH3Dp0iWOHDnCv/zLv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+XxeOTxeBQaGqrg4GDV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\n",
            "text/plain": [
              "<Figure size 1512x1152 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvwK9POHgRp_",
        "colab_type": "text"
      },
      "source": [
        "#### Which features have negatively correlation?\n",
        "Negatively correlated features : ps_ind_06_bin, ps_ind_07_bin, ps_ind_08_bin, ps_ind_09_bin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4b3psrLqgRqA",
        "colab_type": "text"
      },
      "source": [
        "### Binary features inspection\n",
        "Another aspect of the data that we may want to inspect would be the coulmns that only contain binary valeus, i.e where values take on only either of the two values 1 or 0. Proceeding, we store all columns that contain these binary values and then generate a vertical plotly bar plot of these binary values as follows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZbB4qARgRqA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bin_col = list(filter(lambda x: '_bin' in x, train.columns))\n",
        "bin_value_counts = train[bin_col].apply(pd.value_counts)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1w4Vhj5gRqD",
        "colab_type": "code",
        "outputId": "3176d839-fabe-4f91-84f1-183145d114a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "bin_value_counts"
      ],
      "execution_count": 76,
      "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>ps_ind_06_bin</th>\n",
              "      <th>ps_ind_07_bin</th>\n",
              "      <th>ps_ind_08_bin</th>\n",
              "      <th>ps_ind_09_bin</th>\n",
              "      <th>ps_ind_10_bin</th>\n",
              "      <th>ps_ind_11_bin</th>\n",
              "      <th>ps_ind_12_bin</th>\n",
              "      <th>ps_ind_13_bin</th>\n",
              "      <th>ps_ind_16_bin</th>\n",
              "      <th>ps_ind_17_bin</th>\n",
              "      <th>ps_ind_18_bin</th>\n",
              "      <th>ps_calc_15_bin</th>\n",
              "      <th>ps_calc_16_bin</th>\n",
              "      <th>ps_calc_17_bin</th>\n",
              "      <th>ps_calc_18_bin</th>\n",
              "      <th>ps_calc_19_bin</th>\n",
              "      <th>ps_calc_20_bin</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>360852</td>\n",
              "      <td>442223</td>\n",
              "      <td>497644</td>\n",
              "      <td>484917</td>\n",
              "      <td>594990</td>\n",
              "      <td>594205</td>\n",
              "      <td>589594</td>\n",
              "      <td>594648</td>\n",
              "      <td>201882</td>\n",
              "      <td>523143</td>\n",
              "      <td>503879</td>\n",
              "      <td>522342</td>\n",
              "      <td>221514</td>\n",
              "      <td>265356</td>\n",
              "      <td>424278</td>\n",
              "      <td>387469</td>\n",
              "      <td>503955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>234360</td>\n",
              "      <td>152989</td>\n",
              "      <td>97568</td>\n",
              "      <td>110295</td>\n",
              "      <td>222</td>\n",
              "      <td>1007</td>\n",
              "      <td>5618</td>\n",
              "      <td>564</td>\n",
              "      <td>393330</td>\n",
              "      <td>72069</td>\n",
              "      <td>91333</td>\n",
              "      <td>72870</td>\n",
              "      <td>373698</td>\n",
              "      <td>329856</td>\n",
              "      <td>170934</td>\n",
              "      <td>207743</td>\n",
              "      <td>91257</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ps_ind_06_bin  ps_ind_07_bin  ...  ps_calc_19_bin  ps_calc_20_bin\n",
              "0         360852         442223  ...          387469          503955\n",
              "1         234360         152989  ...          207743           91257\n",
              "\n",
              "[2 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sDhDPSagRqF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# hint pandas plot.bar with stacked=True\n",
        "# Your code here"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKQZ4B0_gRqH",
        "colab_type": "text"
      },
      "source": [
        "Those columns ps_ind_10_bin, ps_ind_11_bin, ps_ind_12_bin, ps_ind_13_bin which are completely dominated by zeros. They do not contain much information."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s3qeUbergRqI",
        "colab_type": "text"
      },
      "source": [
        "## 3. Preprocess Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myDDZ1EQgRqK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import train_test_split \n",
        "# Your code here"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLRjifsAgRqN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define X (features), y (target)\n",
        "X = # Your code here\n",
        "y = # Your code here"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eyNl4qRMgRqX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split data into train and test set\n",
        "X_train, X_test, y_train, y_test = # Your code here"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lt7PDCL_gRqd",
        "colab_type": "text"
      },
      "source": [
        "#### Randomly Under-Sampling the Training Dataset¶\n",
        "**Note:** Validate ratio between 2 class after Under-Sampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4a9hJJLAgRqg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Your code here"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhqCb0vegRqk",
        "colab_type": "text"
      },
      "source": [
        "#### Randomly Over-Sampling the Training Dataset\n",
        "**Note:** Validate ratio between 2 class after Over-Sampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHUwcfB8gRql",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Your code here"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfvzDBkPgRqo",
        "colab_type": "text"
      },
      "source": [
        "## 4. Modeling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjRnOzt1gRqp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Spot check with LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, BernoulliNB, GaussianNB\n",
        "# Import libraries and and create model\n",
        "# Your code here\n",
        "lr = #Your code here\n",
        "dtc = #Your code here\n",
        "rfc = #Your code here\n",
        "bnb = #Your code here\n",
        "gnb = #Your code here\n",
        "\n",
        "models = [lr, dtc, rfc, gnb, bnb]\n",
        "models_name = [\"Logistic Regression\", \"Decision Tree\", \"Random Forest\", \"Bernoulli NB\", \"Gaussian NB\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6GDUljOogRqs",
        "colab_type": "text"
      },
      "source": [
        "## 5. Evaluation Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSqT07kngRqt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import confusion_matrix, classification_report\n",
        "# Your code here\n",
        "\n",
        "# We create an utils function, that take a trained model as argument and print out confusion matrix\n",
        "# classification report base on X and y\n",
        "def evaluate_model(estimator, X, y):\n",
        "    # Your code here\n",
        "    return 0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LinyBzoEgRqv",
        "colab_type": "text"
      },
      "source": [
        "### Evaluate with Origin dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwEhDyWMgRqw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for idx, model in enumerate(models):\n",
        "    print(\"Model: {}\".format(models_name[idx]))\n",
        "    # Your code here\n",
        "    # Fit your model with Origin dataset\n",
        "    \n",
        "    # Evaluate model with X_test, y_test\n",
        "    print(\"=======================================\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_8BYL-UgRqy",
        "colab_type": "text"
      },
      "source": [
        "### Evaluate with *Undersampled* dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1wn71IGgRqz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for idx, model in enumerate(models):\n",
        "    print(\"Model: {}\".format(models_name[idx]))\n",
        "    # Your code here\n",
        "    # Fit your model with Undersampled dataset\n",
        "    \n",
        "    # Evaluate model with X_test, y_test\n",
        "    print(\"=======================================\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4XqXyO58gRq2",
        "colab_type": "text"
      },
      "source": [
        "### Evaluate with *Oversampled* dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7n0SbP7igRq2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for idx, model in enumerate(models):\n",
        "    print(\"Model: {}\".format(models_name[idx]))\n",
        "    # Your code here\n",
        "    # Fit your model with Oversampled dataset\n",
        "    \n",
        "    # Evaluate model with X_test, y_test\n",
        "    print(\"=======================================\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOih60UAgRq5",
        "colab_type": "text"
      },
      "source": [
        "### Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EAP4RcvAgRq6",
        "colab_type": "text"
      },
      "source": [
        "Which model has lowest/highest accuracy?\n",
        "Which model has lowest/highest recall?\n",
        "Which model has lowest/highest f1?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxApCytWgRq6",
        "colab_type": "text"
      },
      "source": [
        "## 6. GridsearchCV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBqEdOiCgRq7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Using gridsearchcv, random forest model and this param grid to find the best combination of parameters \n",
        "# Hint: example\n",
        "# https://stackoverflow.com/questions/30102973/how-to-get-best-estimator-on-gridsearchcv-random-forest-classifier-scikit\n",
        "\n",
        "param_grid = {\n",
        "    'bootstrap': [True],\n",
        "    'max_depth': [80, 90, 100, 110],\n",
        "    'max_features': [2, 3],\n",
        "    'min_samples_leaf': [3, 4, 5],\n",
        "    'min_samples_split': [8, 10, 12],\n",
        "    'n_estimators': [100, 200, 300, 1000]\n",
        "}\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gj48ERPMgRq9",
        "colab_type": "text"
      },
      "source": [
        "#### which is the best combination of parameter"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Q5lvx_HgRq-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Your code here"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QEmiJsuGgRrA",
        "colab_type": "text"
      },
      "source": [
        "# Done! Great Job."
      ]
    }
  ]
}