Machine_Learning_with_python_(Classification) (1).ipynb

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      "source": [
        "# **Classification**"
      ],
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      "source": [
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import NullFormatter\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.ticker as ticker\n",
        "from sklearn import preprocessing\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Dataset"
      ],
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            "--2024-01-20 09:46:59--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv\n",
            "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
            "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 23101 (23K) [text/csv]\n",
            "Saving to: ‘loan_train.csv’\n",
            "\n",
            "loan_train.csv      100%[===================>]  22.56K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2024-01-20 09:46:59 (1.30 MB/s) - ‘loan_train.csv’ saved [23101/23101]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv"
      ]
    },
    {
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              "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
              "0             0           0     PAIDOFF       1000     30       9/8/2016   \n",
              "1             2           2     PAIDOFF       1000     30       9/8/2016   \n",
              "2             3           3     PAIDOFF       1000     15       9/8/2016   \n",
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              "7             9           9     PAIDOFF        800     15      9/10/2016   \n",
              "8            10          10     PAIDOFF        300      7      9/10/2016   \n",
              "9            11          11     PAIDOFF       1000     15      9/10/2016   \n",
              "\n",
              "    due_date  age             education  Gender  \n",
              "0  10/7/2016   45  High School or Below    male  \n",
              "1  10/7/2016   33              Bechalor  female  \n",
              "2  9/22/2016   27               college    male  \n",
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              "6  10/8/2016   28               college    male  \n",
              "7  9/24/2016   26               college    male  \n",
              "8  9/16/2016   29               college    male  \n",
              "9  10/9/2016   39  High School or Below    male  "
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      "source": [
        "df = pd.read_csv('loan_train.csv')\n",
        "df.head(10)"
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              "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
              "0             0           0     PAIDOFF       1000     30     2016-09-08   \n",
              "1             2           2     PAIDOFF       1000     30     2016-09-08   \n",
              "2             3           3     PAIDOFF       1000     15     2016-09-08   \n",
              "3             4           4     PAIDOFF       1000     30     2016-09-09   \n",
              "4             6           6     PAIDOFF       1000     30     2016-09-09   \n",
              "\n",
              "    due_date  age             education  Gender  \n",
              "0 2016-10-07   45  High School or Below    male  \n",
              "1 2016-10-07   33              Bechalor  female  \n",
              "2 2016-09-22   27               college    male  \n",
              "3 2016-10-08   28               college  female  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df['due_date'] = pd.to_datetime(df['due_date'])\n",
        "df['effective_date'] = pd.to_datetime(df['effective_date'])\n",
        "df.head()"
      ]
    },
    {
      "source": [
        "# Data visualization and pre-processing\n",
        "\n"
      ],
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "AwkaXfsRBsxs"
      }
    },
    {
      "execution_count": 6,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnG8js1ZBsxs",
        "outputId": "a70f17d3-93bb-41f5-8d0a-086956536411"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PAIDOFF       260\n",
              "COLLECTION     86\n",
              "Name: loan_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df['loan_status'].value_counts()"
      ]
    },
    {
      "source": [
        "\n",
        "*   260 people have paid off the loan on time\n",
        "*   86 have gone into collection\n",
        "\n",
        "\n"
      ],
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "FY9n1okGBsxs"
      }
    },
    {
      "execution_count": 7,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "5YtACrIMBsxs",
        "outputId": "2100731a-a08b-4cd3-84a3-e4d87fe315cb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\n",
        "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
        "g.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n",
        "\n",
        "g.axes[-1].legend()\n",
        "plt.show()"
      ]
    },
    {
      "execution_count": 8,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "73Y7GjgpBsxt",
        "outputId": "17574bbf-8bc5-4fd3-e1c4-7c3e40ffe84d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "bins = np.linspace(df.age.min(), df.age.max(), 10)\n",
        "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
        "g.map(plt.hist, 'age', bins=bins, ec=\"k\")\n",
        "\n",
        "g.axes[-1].legend()\n",
        "plt.show()"
      ]
    },
    {
      "source": [
        "# Pre-processing:  Feature selection/extraction"
      ],
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "lgHOtUOnBsxt"
      }
    },
    {
      "execution_count": 9,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "9Zk4b_KOBsxt",
        "outputId": "5f8fad81-b511-4afb-db0a-2d7a6e49b54a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['dayofweek'] = df['effective_date'].dt.dayofweek\n",
        "bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\n",
        "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
        "g.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\n",
        "g.axes[-1].legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "source": [
        "people who get the loan at the end of the week dont pay it off"
      ],
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "QRHYsbbhBsxt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " use Feature binarization to set a threshold values less then day 4"
      ],
      "metadata": {
        "id": "Mpfvf0__Ds9P"
      }
    },
    {
      "execution_count": 10,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LSe7t80SBsxt",
        "outputId": "b24d8113-544a-4e35-961d-c96977c9a9c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
              "0             0           0     PAIDOFF       1000     30     2016-09-08   \n",
              "1             2           2     PAIDOFF       1000     30     2016-09-08   \n",
              "2             3           3     PAIDOFF       1000     15     2016-09-08   \n",
              "3             4           4     PAIDOFF       1000     30     2016-09-09   \n",
              "4             6           6     PAIDOFF       1000     30     2016-09-09   \n",
              "\n",
              "    due_date  age             education  Gender  dayofweek  weekend  \n",
              "0 2016-10-07   45  High School or Below    male          3        0  \n",
              "1 2016-10-07   33              Bechalor  female          3        0  \n",
              "2 2016-09-22   27               college    male          3        0  \n",
              "3 2016-10-08   28               college  female          4        1  \n",
              "4 2016-10-08   29               college    male          4        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ed7c59fe-ce01-4f5a-9ef9-5a40e7a4174a\" class=\"colab-df-container\">\n",
              "    <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>Unnamed: 0.1</th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>effective_date</th>\n",
              "      <th>due_date</th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>weekend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-10-07</td>\n",
              "      <td>45</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>male</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-10-07</td>\n",
              "      <td>33</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-09-22</td>\n",
              "      <td>27</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "      <td>college</td>\n",
              "      <td>male</td>\n",
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              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      --bg-color: #E8F0FE;\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
        "df.head()"
      ]
    },
    {
      "execution_count": 11,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OIv088XSBsxu",
        "outputId": "8b92f472-a494-4511-e413-75ffdca2faf5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender  loan_status\n",
              "female  PAIDOFF        0.865385\n",
              "        COLLECTION     0.134615\n",
              "male    PAIDOFF        0.731293\n",
              "        COLLECTION     0.268707\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
      ]
    },
    {
      "source": [
        "\n",
        "\n",
        "*   86 % of female pay there loans\n",
        "*   73 % of males pay there loan\n",
        "\n",
        "\n"
      ],
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "6PIV5a8wBsxu"
      }
    },
    {
      "source": [
        "convert male to 0 and female to 1:\n"
      ],
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "d2KXqUDNBsxu"
      }
    },
    {
      "execution_count": 12,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xACna9udBsxu",
        "outputId": "0d2af5fa-cd37-4c96-f88a-f4bb83c6c3e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
              "0             0           0     PAIDOFF       1000     30     2016-09-08   \n",
              "1             2           2     PAIDOFF       1000     30     2016-09-08   \n",
              "2             3           3     PAIDOFF       1000     15     2016-09-08   \n",
              "3             4           4     PAIDOFF       1000     30     2016-09-09   \n",
              "4             6           6     PAIDOFF       1000     30     2016-09-09   \n",
              "\n",
              "    due_date  age             education  Gender  dayofweek  weekend  \n",
              "0 2016-10-07   45  High School or Below       0          3        0  \n",
              "1 2016-10-07   33              Bechalor       1          3        0  \n",
              "2 2016-09-22   27               college       0          3        0  \n",
              "3 2016-10-08   28               college       1          4        1  \n",
              "4 2016-10-08   29               college       0          4        1  "
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-68ff5047-c413-4d6d-aa17-5ae2c826ccc7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Education"
      ],
      "metadata": {
        "id": "o98EplbTEYSu"
      }
    },
    {
      "execution_count": 13,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WcYGu4CjBsxu",
        "outputId": "19e6aea9-0da5-45ee-f0dd-2de0ffbc361d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "education             loan_status\n",
              "Bechalor              PAIDOFF        0.750000\n",
              "                      COLLECTION     0.250000\n",
              "High School or Below  PAIDOFF        0.741722\n",
              "                      COLLECTION     0.258278\n",
              "Master or Above       COLLECTION     0.500000\n",
              "                      PAIDOFF        0.500000\n",
              "college               PAIDOFF        0.765101\n",
              "                      COLLECTION     0.234899\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
      ]
    },
    {
      "execution_count": 14,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FZo2VmC_Bsxu",
        "outputId": "9a18a98a-6455-4a4e-c1d6-6dfdf7c3ab39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Principal  terms  age  Gender             education\n",
              "0       1000     30   45       0  High School or Below\n",
              "1       1000     30   33       1              Bechalor\n",
              "2       1000     15   27       0               college\n",
              "3       1000     30   28       1               college\n",
              "4       1000     30   29       0               college"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4fbc9352-1519-4754-8d5d-a10c791fd76f\" class=\"colab-df-container\">\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4fbc9352-1519-4754-8d5d-a10c791fd76f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4fbc9352-1519-4754-8d5d-a10c791fd76f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-8e361ec9-bd23-41a0-94b0-7be37aa2dc0b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8e361ec9-bd23-41a0-94b0-7be37aa2dc0b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8e361ec9-bd23-41a0-94b0-7be37aa2dc0b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df[['Principal','terms','age','Gender','education']].head()"
      ]
    },
    {
      "execution_count": 15,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AaAvrJmoBsxv",
        "outputId": "b2e2528d-3693-4a92-e9a6-d7ceee2dc8c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
              "0       1000     30   45       0        0         0                     1   \n",
              "1       1000     30   33       1        0         1                     0   \n",
              "2       1000     15   27       0        0         0                     0   \n",
              "3       1000     30   28       1        1         0                     0   \n",
              "4       1000     30   29       0        1         0                     0   \n",
              "\n",
              "   college  \n",
              "0        0  \n",
              "1        0  \n",
              "2        1  \n",
              "3        1  \n",
              "4        1  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>weekend</th>\n",
              "      <th>Bechalor</th>\n",
              "      <th>High School or Below</th>\n",
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              "      <th>0</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000</td>\n",
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              "      <th>4</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>29</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0fb1d713-8c38-4591-afce-559b11fd6a51')\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0fb1d713-8c38-4591-afce-559b11fd6a51 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0fb1d713-8c38-4591-afce-559b11fd6a51');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "  </div>\n",
              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "\n",
              "  @keyframes spin {\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1e43f8f8-7cbf-4f53-b0e1-e68f4943fd86 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "Feature = df[['Principal','terms','age','Gender','weekend']]\n",
        "Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n",
        "Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
        "Feature.head()\n"
      ]
    },
    {
      "execution_count": 16,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tXcdSgLQBsx9",
        "outputId": "0a546a4c-665f-46bd-d694-59b6afbfd4f7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
              "0       1000     30   45       0        0         0                     1   \n",
              "1       1000     30   33       1        0         1                     0   \n",
              "2       1000     15   27       0        0         0                     0   \n",
              "3       1000     30   28       1        1         0                     0   \n",
              "4       1000     30   29       0        1         0                     0   \n",
              "\n",
              "   college  \n",
              "0        0  \n",
              "1        0  \n",
              "2        1  \n",
              "3        1  \n",
              "4        1  "
            ],
            "text/html": [
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3f3048c5-eb8c-425f-81c5-8c1c55ae86ed');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ee9a26a2-c9c8-41e4-9a22-9c78a3a6cae3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "X = Feature\n",
        "X[0:5]"
      ]
    },
    {
      "execution_count": 17,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1uDYe7S5Bsx9",
        "outputId": "0de74dae-eecb-4a4a-d05e-113c1d7beed2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0.1  Unnamed: 0  loan_status  Principal  terms effective_date  \\\n",
              "0             0           0            0       1000     30     2016-09-08   \n",
              "1             2           2            0       1000     30     2016-09-08   \n",
              "2             3           3            0       1000     15     2016-09-08   \n",
              "3             4           4            0       1000     30     2016-09-09   \n",
              "4             6           6            0       1000     30     2016-09-09   \n",
              "\n",
              "    due_date  age             education  Gender  dayofweek  weekend  \n",
              "0 2016-10-07   45  High School or Below       0          3        0  \n",
              "1 2016-10-07   33              Bechalor       1          3        0  \n",
              "2 2016-09-22   27               college       0          3        0  \n",
              "3 2016-10-08   28               college       1          4        1  \n",
              "4 2016-10-08   29               college       0          4        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f6ee7d1-23cd-43ab-85f5-ab03eea7693f\" class=\"colab-df-container\">\n",
              "    <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>Unnamed: 0.1</th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>effective_date</th>\n",
              "      <th>due_date</th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>weekend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-10-07</td>\n",
              "      <td>45</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-10-07</td>\n",
              "      <td>33</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-09-22</td>\n",
              "      <td>27</td>\n",
              "      <td>college</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016-10-08</td>\n",
              "      <td>28</td>\n",
              "      <td>college</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016-10-08</td>\n",
              "      <td>29</td>\n",
              "      <td>college</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f6ee7d1-23cd-43ab-85f5-ab03eea7693f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3f6ee7d1-23cd-43ab-85f5-ab03eea7693f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3f6ee7d1-23cd-43ab-85f5-ab03eea7693f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d0453fa7-fb7e-44ca-8dad-c8af665444cf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d0453fa7-fb7e-44ca-8dad-c8af665444cf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d0453fa7-fb7e-44ca-8dad-c8af665444cf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df['loan_status'].replace(to_replace=['PAIDOFF','COLLECTION'], value=[0,1],inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "execution_count": 18,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmBbU3YFBsx-",
        "outputId": "4aa5101c-026b-4f67-ca46-f6498aa56cb6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "y = df['loan_status'].values\n",
        "y[0:20]"
      ]
    },
    {
      "execution_count": 19,
      "cell_type": "code",
      "metadata": {
        "id": "hR3df2EYBsx-"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.40, random_state=5)"
      ]
    },
    {
      "execution_count": 20,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAAORMCKBsx-",
        "outputId": "d86d7b41-aca9-4799-971c-56c00e1c8ee8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set: (207, 8) (207,)\n",
            "Test set: (139, 8) (139,)\n"
          ]
        }
      ],
      "source": [
        "print ('Train set:', X_train.shape,  y_train.shape)\n",
        "print ('Test set:', X_test.shape,  y_test.shape)"
      ]
    },
    {
      "execution_count": 21,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "SqmXIMSQBsx-",
        "outputId": "a1dec617-beb6-402e-b3b5-0cc1c361afcc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(207, 8)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(207,)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(139, 8)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(139,)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "display(X_train.shape, y_train.shape)\n",
        "display(X_test.shape, y_test.shape)"
      ]
    },
    {
      "source": [
        "## Normalize Data"
      ],
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "qJXIi6SXBsx-"
      }
    },
    {
      "execution_count": 22,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xmxPrTWrBsx-",
        "outputId": "ac5f7a00-9ce0-469e-be61-0b098758e618"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.52533066, -0.97207061,  0.53495582, -0.45109685,  0.85993942,\n",
              "         2.76134025, -0.86846836, -0.91206272],\n",
              "       [ 0.52533066,  0.91317564, -0.47326137,  2.21681883,  0.85993942,\n",
              "        -0.36214298, -0.86846836,  1.09641582],\n",
              "       [ 0.52533066,  0.91317564, -0.13718897, -0.45109685,  0.85993942,\n",
              "        -0.36214298,  1.15145243, -0.91206272],\n",
              "       [ 0.52533066,  0.91317564, -0.64129757, -0.45109685,  0.85993942,\n",
              "        -0.36214298, -0.86846836,  1.09641582],\n",
              "       [ 0.52533066, -0.97207061, -0.47326137,  2.21681883, -1.16287262,\n",
              "        -0.36214298, -0.86846836,  1.09641582]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# (technically should be done after train test split )\n",
        "X_train = preprocessing.StandardScaler().fit(X_train).transform(X_train)\n",
        "X_test = preprocessing.StandardScaler().fit(X_test).transform(X_test)\n",
        "X_train[0:5]"
      ]
    },
    {
      "source": [
        "# Classification"
      ],
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "A7DL6M7YBsx-"
      }
    },
    {
      "source": [
        "# K Nearest Neighbor(KNN)\n"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "Tb_xb2UUBsx-"
      }
    },
    {
      "execution_count": 23,
      "cell_type": "code",
      "metadata": {
        "id": "yKdsVijYBsx_"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "execution_count": 24,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSVDwQ0FBsx_",
        "outputId": "ebe7e457-b716-4ff9-a426-5f698e7877c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.65467626, 0.68345324, 0.6618705 , 0.71223022, 0.69064748,\n",
              "       0.69064748, 0.65467626, 0.70503597, 0.64028777, 0.69784173,\n",
              "       0.69784173, 0.69784173, 0.69064748, 0.69784173, 0.65467626,\n",
              "       0.69064748, 0.6618705 , 0.67625899, 0.67625899, 0.67625899,\n",
              "       0.70503597, 0.70503597, 0.71942446, 0.74100719, 0.74820144])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# We will be checking for value of K from 0 to 25 so\n",
        "n = 25\n",
        "accuracy = np.zeros(n)\n",
        "for i in range(1,n+1):\n",
        "    clf = KNeighborsClassifier(n_neighbors = i).fit(X_train, y_train)\n",
        "    y_test_predicted = clf.predict(X_test)\n",
        "    accuracy[i-1] = (accuracy_score(y_test, y_test_predicted))\n",
        "accuracy"
      ]
    },
    {
      "execution_count": 25,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "igDTq5URBsx_",
        "outputId": "b2f7980d-99d7-4e6a-88aa-674e83eba00b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum Accuracy Got is - \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0\n",
              "24  0.748201"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c33d5b7-9be1-48cd-aff6-3e96511fac62\" class=\"colab-df-container\">\n",
              "    <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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.748201</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c33d5b7-9be1-48cd-aff6-3e96511fac62')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8c33d5b7-9be1-48cd-aff6-3e96511fac62 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8c33d5b7-9be1-48cd-aff6-3e96511fac62');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "plt.plot(range(1,n+1),accuracy,'g')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Number of Neighbors (K)')\n",
        "plt.show()\n",
        "\n",
        "accuracy = pd.DataFrame(accuracy)\n",
        "print(\"Maximum Accuracy Got is - \" )\n",
        "accuracy.sort_values(by = 0, ascending = False)[0:1]"
      ]
    },
    {
      "execution_count": 26,
      "cell_type": "code",
      "metadata": {
        "id": "c4fk3eSABsx_"
      },
      "outputs": [],
      "source": [
        "clf_KNN = KNeighborsClassifier(n_neighbors = 24).fit(X_train, y_train)\n",
        "# y_test_pred_KNN = clf_KNN.predict(X_test)"
      ]
    },
    {
      "source": [
        "# Decision Tree"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "uPsKfK8UBsx_"
      }
    },
    {
      "execution_count": 27,
      "cell_type": "code",
      "metadata": {
        "id": "ziD_2FckBsx_"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ]
    },
    {
      "execution_count": 28,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BQNMPLfXBsx_",
        "outputId": "f2ff423e-cbe2-44b7-cfe8-3e2314a0526f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy using criterion as gini -  0.7482014388489209\n",
            "Accuracy using criterion as entropy -  0.6474820143884892\n"
          ]
        }
      ],
      "source": [
        "clf2 = DecisionTreeClassifier(criterion = 'gini').fit(X_train, y_train)\n",
        "y_test_pred_KNN = clf.predict(X_test)\n",
        "print(\"Accuracy using criterion as gini - \", accuracy_score(y_test, y_test_pred_KNN))\n",
        "clf3 = DecisionTreeClassifier(criterion = 'entropy').fit(X_train, y_train)\n",
        "y_test_pred_KNN = clf2.predict(X_test)\n",
        "print(\"Accuracy using criterion as entropy - \", accuracy_score(y_test, y_test_pred_KNN))"
      ]
    },
    {
      "execution_count": 29,
      "cell_type": "code",
      "metadata": {
        "id": "j4qg3joIBsx_"
      },
      "outputs": [],
      "source": [
        "# using criterion as gini\n",
        "clf_DT = DecisionTreeClassifier(criterion = 'gini').fit(X_train, y_train)"
      ]
    },
    {
      "source": [
        "# Support Vector Machine"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "lQA0NWmrBsx_"
      }
    },
    {
      "execution_count": 30,
      "cell_type": "code",
      "metadata": {
        "id": "3jjZsgEJBsx_"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC"
      ]
    },
    {
      "execution_count": 31,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KBAHHqNvBsx_",
        "outputId": "662f1014-509a-486d-ad59-a6dff5dabfeb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy using polynomial kernel -  0.6474820143884892\n",
            "accuracy using Radial Basis function kernel -  0.6402877697841727\n"
          ]
        }
      ],
      "source": [
        "clf4 = SVC(kernel = 'poly').fit(X_train, y_train)\n",
        "print(\"accuracy using polynomial kernel - \", accuracy_score(y_test, clf2.predict(X_test)))\n",
        "clf5 = SVC(kernel = 'rbf').fit(X_train, y_train)\n",
        "print(\"accuracy using Radial Basis function kernel - \", accuracy_score(y_test, clf3.predict(X_test)))"
      ]
    },
    {
      "execution_count": 32,
      "cell_type": "code",
      "metadata": {
        "id": "dA-YaQakBsyA"
      },
      "outputs": [],
      "source": [
        "# using linear for kernel in our SVM model\n",
        "clf_SVM = SVC(kernel = 'poly', random_state = 4).fit(X_train, y_train)"
      ]
    },
    {
      "source": [
        "# Logistic Regression"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "jpyj1n_NBsyA"
      }
    },
    {
      "execution_count": 33,
      "cell_type": "code",
      "metadata": {
        "id": "_Qu2Tvl-BsyA"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ]
    },
    {
      "execution_count": 34,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "E-TCB5CkBsyA",
        "outputId": "8ec0254d-320f-4d40-da8b-ad88ad77bb55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(warm_start=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(warm_start=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(warm_start=True)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "clf_LR = LogisticRegression(solver='lbfgs', warm_start = True)\n",
        "clf_LR.fit(X_train, y_train)\n",
        "#print(\"accuracy score - \", accuracy_score(y_test, clf_LR.predict(X_test)))"
      ]
    },
    {
      "source": [
        "# Model Evaluation"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "z99pIsh0BsyA"
      }
    },
    {
      "execution_count": 35,
      "cell_type": "code",
      "metadata": {
        "id": "Z2648izQBsyA"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import jaccard_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import log_loss\n"
      ]
    },
    {
      "execution_count": 36,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBWRCIWLBsyA",
        "outputId": "3719ad83-fa36-4231-bd83-a2c28a4648c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-01-20 09:47:04--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\n",
            "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
            "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3642 (3.6K) [text/csv]\n",
            "Saving to: ‘loan_test.csv’\n",
            "\n",
            "loan_test.csv       100%[===================>]   3.56K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-01-20 09:47:04 (1.68 GB/s) - ‘loan_test.csv’ saved [3642/3642]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
      ]
    },
    {
      "execution_count": 37,
      "cell_type": "code",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "x4DcZ1V4BsyA",
        "outputId": "1c94fe80-0514-477e-e9c7-7eea63784a2d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
              "0             1           1     PAIDOFF       1000     30       9/8/2016   \n",
              "1             5           5     PAIDOFF        300      7       9/9/2016   \n",
              "2            21          21     PAIDOFF       1000     30      9/10/2016   \n",
              "3            24          24     PAIDOFF       1000     30      9/10/2016   \n",
              "4            35          35     PAIDOFF        800     15      9/11/2016   \n",
              "\n",
              "    due_date  age             education  Gender  \n",
              "0  10/7/2016   50              Bechalor  female  \n",
              "1  9/15/2016   35       Master or Above    male  \n",
              "2  10/9/2016   43  High School or Below  female  \n",
              "3  10/9/2016   26               college    male  \n",
              "4  9/25/2016   29              Bechalor    male  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e3bc545-90ca-4c24-a990-16a2d869d54c\" class=\"colab-df-container\">\n",
              "    <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>Unnamed: 0.1</th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>effective_date</th>\n",
              "      <th>due_date</th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>50</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>300</td>\n",
              "      <td>7</td>\n",
              "      <td>9/9/2016</td>\n",
              "      <td>9/15/2016</td>\n",
              "      <td>35</td>\n",
              "      <td>Master or Above</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/10/2016</td>\n",
              "      <td>10/9/2016</td>\n",
              "      <td>43</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/10/2016</td>\n",
              "      <td>10/9/2016</td>\n",
              "      <td>26</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35</td>\n",
              "      <td>35</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>800</td>\n",
              "      <td>15</td>\n",
              "      <td>9/11/2016</td>\n",
              "      <td>9/25/2016</td>\n",
              "      <td>29</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e3bc545-90ca-4c24-a990-16a2d869d54c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2e3bc545-90ca-4c24-a990-16a2d869d54c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2e3bc545-90ca-4c24-a990-16a2d869d54c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dc681a44-d021-4057-b40b-f79c6efaf8fd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dc681a44-d021-4057-b40b-f79c6efaf8fd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-dc681a44-d021-4057-b40b-f79c6efaf8fd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "test_df = pd.read_csv('loan_test.csv')\n",
        "test_df.head()"
      ]
    },
    {
      "execution_count": 38,
      "cell_type": "code",
      "metadata": {
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        "id": "W6vTQsk8BsyA",
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              "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
              "0             1           1     PAIDOFF       1000     30     2016-09-08   \n",
              "1             5           5     PAIDOFF        300      7     2016-09-09   \n",
              "2            21          21     PAIDOFF       1000     30     2016-09-10   \n",
              "3            24          24     PAIDOFF       1000     30     2016-09-10   \n",
              "4            35          35     PAIDOFF        800     15     2016-09-11   \n",
              "\n",
              "    due_date  age             education  Gender  \n",
              "0 2016-10-07   50              Bechalor  female  \n",
              "1 2016-09-15   35       Master or Above    male  \n",
              "2 2016-10-09   43  High School or Below  female  \n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "# conversion to datetime object\n",
        "test_df['due_date'] = pd.to_datetime(test_df['due_date'])\n",
        "test_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\n",
        "test_df.head()"
      ]
    },
    {
      "execution_count": 39,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1XWplOp3BsyA",
        "outputId": "bacc9ee2-2d05-4d45-f083-0ae259b8b4e3"
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              "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
              "0             1           1     PAIDOFF       1000     30     2016-09-08   \n",
              "1             5           5     PAIDOFF        300      7     2016-09-09   \n",
              "2            21          21     PAIDOFF       1000     30     2016-09-10   \n",
              "3            24          24     PAIDOFF       1000     30     2016-09-10   \n",
              "4            35          35     PAIDOFF        800     15     2016-09-11   \n",
              "\n",
              "    due_date  age             education  Gender  dayofweek  weekend  \n",
              "0 2016-10-07   50              Bechalor  female          3        0  \n",
              "1 2016-09-15   35       Master or Above    male          4        1  \n",
              "2 2016-10-09   43  High School or Below  female          5        1  \n",
              "3 2016-10-09   26               college    male          5        1  \n",
              "4 2016-09-25   29              Bechalor    male          6        1  "
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              "      <td>2016-10-09</td>\n",
              "      <td>43</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>female</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-10</td>\n",
              "      <td>2016-10-09</td>\n",
              "      <td>26</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35</td>\n",
              "      <td>35</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>800</td>\n",
              "      <td>15</td>\n",
              "      <td>2016-09-11</td>\n",
              "      <td>2016-09-25</td>\n",
              "      <td>29</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>male</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-98354e51-5f5b-4222-bbf6-2bd0dbe890b0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-98354e51-5f5b-4222-bbf6-2bd0dbe890b0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      --bg-color: #E8F0FE;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-003dfb46-f44f-44b7-af31-6b18be98968e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "# creating weekend column\n",
        "test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\n",
        "test_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
        "test_df.head()"
      ]
    },
    {
      "execution_count": 40,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SFfpsYNzBsyB",
        "outputId": "673304e4-5c74-4266-91e5-7d46e61d06a5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0.1  Unnamed: 0 loan_status  Principal  terms effective_date  \\\n",
              "0             1           1     PAIDOFF       1000     30     2016-09-08   \n",
              "1             5           5     PAIDOFF        300      7     2016-09-09   \n",
              "2            21          21     PAIDOFF       1000     30     2016-09-10   \n",
              "3            24          24     PAIDOFF       1000     30     2016-09-10   \n",
              "4            35          35     PAIDOFF        800     15     2016-09-11   \n",
              "\n",
              "    due_date  age             education  Gender  dayofweek  weekend  \n",
              "0 2016-10-07   50              Bechalor       1          3        0  \n",
              "1 2016-09-15   35       Master or Above       0          4        1  \n",
              "2 2016-10-09   43  High School or Below       1          5        1  \n",
              "3 2016-10-09   26               college       0          5        1  \n",
              "4 2016-09-25   29              Bechalor       0          6        1  "
            ],
            "text/html": [
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              "      <td>2016-09-10</td>\n",
              "      <td>2016-10-09</td>\n",
              "      <td>26</td>\n",
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              "      <td>1</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-baad93ff-9353-43a1-8c5f-cb68c3f7bf18 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "# replacing male and female string with 0 and 1\n",
        "test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
        "test_df.head()"
      ]
    },
    {
      "execution_count": 41,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5eywzjRbBsyB",
        "outputId": "5d1af9a8-cf23-4528-dde3-a68c31fa71f7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
              "0       1000     30   50       1        0         1                     0   \n",
              "1        300      7   35       0        1         0                     0   \n",
              "2       1000     30   43       1        1         0                     1   \n",
              "3       1000     30   26       0        1         0                     0   \n",
              "4        800     15   29       0        1         1                     0   \n",
              "\n",
              "   college  \n",
              "0        0  \n",
              "1        0  \n",
              "2        0  \n",
              "3        1  \n",
              "4        0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6a75f53-5080-42cf-82aa-a5b3d6139436\" class=\"colab-df-container\">\n",
              "    <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>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>weekend</th>\n",
              "      <th>Bechalor</th>\n",
              "      <th>High School or Below</th>\n",
              "      <th>college</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>300</td>\n",
              "      <td>7</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>800</td>\n",
              "      <td>15</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6a75f53-5080-42cf-82aa-a5b3d6139436')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b6a75f53-5080-42cf-82aa-a5b3d6139436 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b6a75f53-5080-42cf-82aa-a5b3d6139436');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-44f85e8e-6c20-4ded-8457-99e81cd15af7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-44f85e8e-6c20-4ded-8457-99e81cd15af7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-44f85e8e-6c20-4ded-8457-99e81cd15af7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# creaing dummies and dropping one column\n",
        "\n",
        "X_t = test_df[['Principal','terms','age','Gender','weekend']]\n",
        "X_t = pd.concat([X_t,pd.get_dummies(test_df['education'])], axis=1)\n",
        "X_t.drop(['Master or Above'], axis = 1,inplace=True)\n",
        "X_t.head()"
      ]
    },
    {
      "execution_count": 42,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nh4aAqbXBsyB",
        "outputId": "702e75fb-3f29-4f93-8801-0ccd7515ee24"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.49362588,  0.92844966,  3.05981865,  1.97714211, -1.30384048,\n",
              "         2.39791576, -0.79772404, -0.86135677],\n",
              "       [-3.56269116, -1.70427745,  0.53336288, -0.50578054,  0.76696499,\n",
              "        -0.41702883, -0.79772404, -0.86135677],\n",
              "       [ 0.49362588,  0.92844966,  1.88080596,  1.97714211,  0.76696499,\n",
              "        -0.41702883,  1.25356634, -0.86135677],\n",
              "       [ 0.49362588,  0.92844966, -0.98251057, -0.50578054,  0.76696499,\n",
              "        -0.41702883, -0.79772404,  1.16095912],\n",
              "       [-0.66532184, -0.78854628, -0.47721942, -0.50578054,  0.76696499,\n",
              "         2.39791576, -0.79772404, -0.86135677]])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "# Feature Scaling\n",
        "X_t = preprocessing.StandardScaler().fit(X_t).transform(X_t)\n",
        "X_t[0:5]"
      ]
    },
    {
      "execution_count": 43,
      "cell_type": "code",
      "metadata": {
        "id": "01LZwCkkBsyB"
      },
      "outputs": [],
      "source": [
        "test_df['loan_status'].replace(to_replace=['PAIDOFF','COLLECTION'], value=[0,1],inplace=True)"
      ]
    },
    {
      "execution_count": 44,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ue-1ij3KBsyB",
        "outputId": "f830e1a7-946c-48fb-d886-f1fc8b4ccf36"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "y_t = test_df['loan_status'].values\n",
        "y_t[0:20]"
      ]
    },
    {
      "execution_count": 45,
      "cell_type": "code",
      "metadata": {
        "id": "FUOv0KagBsyB"
      },
      "outputs": [],
      "source": [
        "# np arrays to store intermediate result\n",
        "Jaccard = np.full(4, np.nan)\n",
        "F1_score = np.full(4, np.nan)\n",
        "LogLoss = np.full(4, np.nan)\n",
        "Algorithm = np.array(4)\n",
        "Algorithm = [\"KNN\", \"Decision Tree\", \"SVM\", \"LogisticRegression\"]"
      ]
    },
    {
      "execution_count": 46,
      "cell_type": "code",
      "metadata": {
        "id": "-P-_NCN8BsyB"
      },
      "outputs": [],
      "source": [
        "Jaccard[0] = jaccard_score(y_t, clf_KNN.predict(X_t))\n",
        "Jaccard[1] = jaccard_score(y_t, clf_DT.predict(X_t))\n",
        "Jaccard[2] = jaccard_score(y_t, clf_SVM.predict(X_t))\n",
        "Jaccard[3] = jaccard_score(y_t, clf_LR.predict(X_t))"
      ]
    },
    {
      "execution_count": 47,
      "cell_type": "code",
      "metadata": {
        "id": "b0Qulo9jBsyB"
      },
      "outputs": [],
      "source": [
        "F1_score[0] = f1_score(y_t, clf_KNN.predict(X_t))\n",
        "F1_score[1] = f1_score(y_t, clf_DT.predict(X_t))\n",
        "F1_score[2] = f1_score(y_t, clf_SVM.predict(X_t))\n",
        "F1_score[3] = f1_score(y_t, clf_LR.predict(X_t))"
      ]
    },
    {
      "execution_count": 48,
      "cell_type": "code",
      "metadata": {
        "id": "Iq6bOnBkBsyB"
      },
      "outputs": [],
      "source": [
        "LogLoss[3] = log_loss(y_t, clf_LR.predict_proba(X_t))"
      ]
    },
    {
      "execution_count": 49,
      "cell_type": "code",
      "metadata": {
        "id": "aEpGwObPBsyB"
      },
      "outputs": [],
      "source": [
        "Report_data = pd.DataFrame({\"Jaccard\":Jaccard, \"F1-score\":F1_score, \"LogLoss\":LogLoss}, index=Algorithm)"
      ]
    },
    {
      "source": [
        "#  Report"
      ],
      "cell_type": "markdown",
      "metadata": {
        "id": "BsL-zgGZBsyB"
      }
    },
    {
      "execution_count": 50,
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "4mSwEO-8BsyC",
        "outputId": "a3429000-b5f9-440f-e288-e65f35831d6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Jaccard  F1-score   LogLoss\n",
              "KNN                 0.133333  0.235294       NaN\n",
              "Decision Tree       0.176471  0.300000       NaN\n",
              "SVM                 0.214286  0.352941       NaN\n",
              "LogisticRegression  0.142857  0.250000  0.470967"
            ],
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              "      <th>F1-score</th>\n",
              "      <th>LogLoss</th>\n",
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              "  <tbody>\n",
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              "      <th>KNN</th>\n",
              "      <td>0.133333</td>\n",
              "      <td>0.235294</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "      <th>Decision Tree</th>\n",
              "      <td>0.176471</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "      <th>SVM</th>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.352941</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>0.470967</td>\n",
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              "        document.querySelector('#df-406c4a15-2f1f-482d-b491-4600ccb7b4aa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-406c4a15-2f1f-482d-b491-4600ccb7b4aa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f5eeb38f-4afb-404f-be48-515f64bc6994 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "Report_data"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.5",
      "name": "python3",
      "language": "python"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "version": "3.5.5",
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      "codemirror_mode": {
        "version": 3,
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      }
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4
}