ColabProject/Loan_Application_Prediction_End_to_End_Project.ipynb

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      "name": "Loan Application Prediction - End-to-End Project.ipynb",
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/M-MSilva/Predict-The-Status-of-Loan_End-to-End-Project/blob/master/ColabProject/Loan_Application_Prediction_End_to_End_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sff6-kjtlCEb"
      },
      "source": [
        "# Loan Application Prediction - End-to-End Project "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eUCr6UlQgVRI"
      },
      "source": [
        "In this project, we aim to categorize customers who may or may not receive loans, based on information about the customer's conditions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHU_wwt5Xteb"
      },
      "source": [
        "## 1 - Requirements"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First we must import some libraries that we will use throughout the code."
      ],
      "metadata": {
        "id": "bVwEbf8ghf_p"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRioq7OwVKrx"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split, StratifiedShuffleSplit\n",
        "\n",
        "import matplotlib.pyplot as plt #for the graphics\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "import matplotlib as mpl\n",
        "\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set_theme()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOGzEnntXx7I"
      },
      "source": [
        "## 2 - Get the data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We then collect data from the website: https://www.kaggle.com/vipin20/loan-application-data"
      ],
      "metadata": {
        "id": "vpTYVvh8AU70"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('https://raw.githubusercontent.com/M-MSilva/Predict-The-Status-of-Loan-End-to-End-Project/master/Dataset/LOAN.csv',  on_bad_lines='skip',decimal='.', thousands=',',encoding='utf-8')\n",
        "\n"
      ],
      "metadata": {
        "id": "z7ZgRxo6iYmO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TgWevs4XZhyQ"
      },
      "source": [
        "## 3 - Initial Data Insights"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now to better understand the dataframe we must show its first rows."
      ],
      "metadata": {
        "id": "T9_VDEJ_hsg7"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 287
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        "id": "XrYRMoRoZ785",
        "outputId": "2f5d4453-7bc8-4854-cccc-0561328b2e9b"
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      "source": [
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
              "0  LP001002   Male      No          0      Graduate            No   \n",
              "1  LP001003   Male     Yes          1      Graduate            No   \n",
              "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
              "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
              "4  LP001008   Male      No          0      Graduate            No   \n",
              "\n",
              "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "0             5849                0.0         NaN             360.0   \n",
              "1             4583             1508.0       128.0             360.0   \n",
              "2             3000                0.0        66.0             360.0   \n",
              "3             2583             2358.0       120.0             360.0   \n",
              "4             6000                0.0       141.0             360.0   \n",
              "\n",
              "   Credit_History Property_Area Loan_Status  \n",
              "0             1.0         Urban           Y  \n",
              "1             1.0         Rural           N  \n",
              "2             1.0         Urban           Y  \n",
              "3             1.0         Urban           Y  \n",
              "4             1.0         Urban           Y  "
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          "metadata": {},
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      ]
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      "cell_type": "markdown",
      "metadata": {
        "id": "vVPPe7q1aLQJ"
      },
      "source": [
        "In the dataset, each row characterizes an applicant who has 13 attributes. Of these attributes, many are easy to understand, so I have outlined a few:\n",
        "\n",
        "* ApplicantIncome: Applicant's Total Income;\n",
        "* CoapplicantIncome: Income from someone close, which will possibly help the\n",
        "individual pay off the debt;\n",
        "* Loan_Amount_Term: Installments ex: 360 = 12 months;\n",
        "* Credit_History = 1 if the individual has a good credit history, 0 otherwise;\n",
        "* Loan_Status = means whether the loan will be approved or not."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "uFUCo_Ct69Kw",
        "outputId": "f3378fd4-5d56-4caf-fee6-7151fdfbd1af"
      },
      "source": [
        "dataset.info()#to have a summary of the data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 614 entries, 0 to 613\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Loan_ID            614 non-null    object \n",
            " 1   Gender             601 non-null    object \n",
            " 2   Married            611 non-null    object \n",
            " 3   Dependents         599 non-null    object \n",
            " 4   Education          614 non-null    object \n",
            " 5   Self_Employed      582 non-null    object \n",
            " 6   ApplicantIncome    614 non-null    int64  \n",
            " 7   CoapplicantIncome  614 non-null    float64\n",
            " 8   LoanAmount         592 non-null    float64\n",
            " 9   Loan_Amount_Term   600 non-null    float64\n",
            " 10  Credit_History     564 non-null    float64\n",
            " 11  Property_Area      614 non-null    object \n",
            " 12  Loan_Status        614 non-null    object \n",
            "dtypes: float64(4), int64(1), object(8)\n",
            "memory usage: 62.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
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        "id": "peQgMPseYxg-",
        "outputId": "65cab90c-5029-447a-d0e5-331087451764"
      },
      "source": [
        "#statistical information\n",
        "dataset.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
              "count       614.000000         614.000000  592.000000         600.00000   \n",
              "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
              "std        6109.041673        2926.248369   85.587325          65.12041   \n",
              "min         150.000000           0.000000    9.000000          12.00000   \n",
              "25%        2877.500000           0.000000  100.000000         360.00000   \n",
              "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
              "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
              "max       81000.000000       41667.000000  700.000000         480.00000   \n",
              "\n",
              "       Credit_History  \n",
              "count      564.000000  \n",
              "mean         0.842199  \n",
              "std          0.364878  \n",
              "min          0.000000  \n",
              "25%          1.000000  \n",
              "50%          1.000000  \n",
              "75%          1.000000  \n",
              "max          1.000000  "
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              "      <th></th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
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              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
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              "      <td>600.00000</td>\n",
              "      <td>564.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5403.459283</td>\n",
              "      <td>1621.245798</td>\n",
              "      <td>146.412162</td>\n",
              "      <td>342.00000</td>\n",
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              "      <th>std</th>\n",
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              "      <td>65.12041</td>\n",
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              "      <th>min</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.00000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>2877.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3812.500000</td>\n",
              "      <td>1188.500000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5795.000000</td>\n",
              "      <td>2297.250000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>max</th>\n",
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              "      <td>41667.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>480.00000</td>\n",
              "      <td>1.000000</td>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44ce3cfe-f8fb-4b4f-8827-f1aca23ebc28')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-44ce3cfe-f8fb-4b4f-8827-f1aca23ebc28 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-44ce3cfe-f8fb-4b4f-8827-f1aca23ebc28');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 207
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5 - Understanding the data"
      ],
      "metadata": {
        "id": "hpZRDHt0r58V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's try to understand the data better and make some assumptions, from graphs extracted from the dataframe data."
      ],
      "metadata": {
        "id": "ClG_3Z5dhJA2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cmap = mpl.cm.Dark2\n",
        "\n",
        "listA = ['ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Amount_Term']\n",
        "fig=plt.figure(figsize=(12, 8))\n",
        "\n",
        "\n",
        "for i, x in enumerate(listA):\n",
        "  ax=fig.add_subplot(2,2,i+1)\n",
        "  dataset[x].hist(bins=50,ax=ax,color=cmap(i/ float((i + 1))))\n",
        "  if(x == 'Loan_Amount_Term'):\n",
        "    ax.set_xlabel('Days')\n",
        "  else:\n",
        "    ax.set_xlabel('₹')\n",
        "  ax.set_ylabel('Frequency Density')\n",
        "  ax.set_title(x)\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "MGwGnCI3iiwQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "outputId": "debde144-b652-482f-bcb5-1d967c61636f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the graphs above we notice that the highest occurrences for Applicant's Income, Coapplicant's Income, Loan Amount, Term of Loan Amount are respectively ₹ 200,  ₹ 100, ₹ 120,  370 days approximately. The important thing to ask is, are people who have a higher income, or who apply for a higher loan amount, more likely to receive the loan ?"
      ],
      "metadata": {
        "id": "JBTaJ0FngRZK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Also, in our data data an interesting thing is that we have a lot of categorical data, so we'll see what we can get from that."
      ],
      "metadata": {
        "id": "hlBALAfB78IN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cmap2 = mpl.cm.tab20\n",
        "\n",
        "listFirst = ['Property_Area','Married','Dependents','Education','Self_Employed','Gender']\n",
        "\n",
        "fig=plt.figure(figsize=(10,8))\n",
        "\n",
        "for i, col in enumerate(listFirst):\n",
        "  ax=fig.add_subplot(3,3,i+1)\n",
        "  plt.xlabel('Number of Occurrences')\n",
        "  plt.ylabel('Categories')\n",
        "  valuec = dataset[col].value_counts().plot(kind='barh',title=col,color=cmap2(i/ float((i + 5))),grid=True,ax=ax)\n",
        "  plt.subplots_adjust(left=0.125,\n",
        "                    bottom=0.1, \n",
        "                    right=1.4, \n",
        "                    top=1.35, \n",
        "                    wspace=0.4, \n",
        "                    hspace=0.35)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "TnZAlBNUwW2S",
        "outputId": "36c63a9f-f1d9-4d05-eec6-44b535bc714b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In these data we have, for example 233 people living in Semi-Urban areas, 202 people living in Urban areas and 179 cities living in Rural areas. In this way, another important issue comes into play. People who live in what type of area are most likely to receive a loan? This question and the previous one we will investigate in this project.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9hahtHpL09kJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6 - Stratified Sampling:"
      ],
      "metadata": {
        "id": "N_ViPR45CwiG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To perform the stratified population, we take subpopulations of a population and use the slicing method to subdivide the data. Stratification must be done, because if our dataset has a lot of data of a single type, our model may notice some wrong trend. But first we need to know which is the highest income and the lowest income:\n"
      ],
      "metadata": {
        "id": "6IK9WEKhpS0w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Maximum : ₹ ', max(dataset['ApplicantIncome']))\n",
        "print('Minimum : ₹ ', min(dataset['ApplicantIncome']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvO8dRbKW8p-",
        "outputId": "ff17689c-9d94-4ecb-dab2-4c9fb6ecea69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum : ₹  81000\n",
            "Minimum : ₹  150\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we change some null data"
      ],
      "metadata": {
        "id": "LO2hhAdX6aQv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "dataset['Credit_History'].fillna(dataset['Credit_History'].median(),inplace = True)\n",
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGt1V41A6XN-",
        "outputId": "19ce1031-00f8-4e82-f2e3-b714cf1ad985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               0\n",
              "Gender               13\n",
              "Married               3\n",
              "Dependents           15\n",
              "Education             0\n",
              "Self_Employed        32\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount           22\n",
              "Loan_Amount_Term     14\n",
              "Credit_History        0\n",
              "Property_Area         0\n",
              "Loan_Status           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use the cut method and plot its content."
      ],
      "metadata": {
        "id": "ZWQKheE7lUHh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"hist_cat\"] = pd.cut(dataset[\"Credit_History\"],\n",
        "                               bins=[-1,0.5,1.5],\n",
        "                               labels=[0,1])\n",
        "\n",
        "plt.xlabel('Categories of Income')\n",
        "plt.ylabel('Frequency Density')\n",
        "plt.title('Category Histograms For each Income')\n",
        "dataset['hist_cat'].hist(color='tomato')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ZfAs2VCwpRQ6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "970a1508-610c-4376-e4d3-c28c56f3d0f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After breaking down the populations, we actually performed the stratification."
      ],
      "metadata": {
        "id": "wRwDytYXls1k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.replace({'Loan_Status':{'Y': 1, 'N':0}},inplace=True)# I need to do this\n",
        "\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "\n",
        "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
        "for train_index, test_index in split.split(dataset, dataset[\"hist_cat\"]):\n",
        "    strat_train_set = dataset.iloc[train_index]\n",
        "    strat_test_set = dataset.iloc[test_index]"
      ],
      "metadata": {
        "id": "tKPzLZm3lQZa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To check the consistency of our stratification we make the comparison:"
      ],
      "metadata": {
        "id": "YIuK-2aN52k1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(strat_train_set['hist_cat'].value_counts()/len(strat_train_set))"
      ],
      "metadata": {
        "id": "ktFEksDZdBLk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f58bed2b-d43e-48d1-a79a-ba6c9c72328f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    0.855397\n",
            "0    0.144603\n",
            "Name: hist_cat, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset['hist_cat'].value_counts()/len(dataset))"
      ],
      "metadata": {
        "id": "1ayMoH-F5SYx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b76eb49-a0b1-4cba-8f54-fee5503f2ab2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    0.855049\n",
            "0    0.144951\n",
            "Name: hist_cat, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "and we noticed a consistency in the results."
      ],
      "metadata": {
        "id": "aCBqpradCb1V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7 - Returning the data to the original state"
      ],
      "metadata": {
        "id": "7xbZ0Nfe98TF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "\n",
        "strat_train_set.drop('hist_cat',axis=1, inplace=True)\n",
        "strat_test_set.drop('hist_cat',axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "bfUyIsMo-UDF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8 - Data Visualization"
      ],
      "metadata": {
        "id": "R9qKXWFQHu8I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To better interpret the concepts and notice patterns, let's visualize the data, which will also help us understand the possible decisions of our algorithm."
      ],
      "metadata": {
        "id": "qHTwpYmBC9zn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = strat_train_set.copy()\n",
        "\n",
        "listaB = ['Gender','Married','Dependents','Education','Self_Employed','Property_Area','Credit_History','Loan_Amount_Term']\n",
        "\n",
        "fig=plt.figure(figsize=(20, 14))\n",
        "\n",
        "for i, col in enumerate(listaB):\n",
        "  ax=fig.add_subplot(4,4,i+1)\n",
        "  sns.countplot(x=col,hue='Loan_Status', data=dataset, ax=ax)\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "qoMcFGC5MV5S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "197b9e5d-509a-4b0d-c5d3-d14e4d347008"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1008 with 8 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graph above is self-explanatory, it seems to indicate that married, childless, college-educated, non-self-employed men who live in semi-urban areas, who have a good credit history and who make 360-day payments are supposedly more likely to receive loans."
      ],
      "metadata": {
        "id": "NT1AgfiM-aE5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above we find a supposed answer to one of our questions raised in section 5, the other question asked outside whether there is any relationship between the applicant's income and the possibility of receiving loans. To speculate this question, we are going to plot a pairplot plot, which relates two variables simultaneously, in addition, we are going to distinguish in this data those who receive loans and those who do not.\n"
      ],
      "metadata": {
        "id": "Jq0VStFsYyMG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selected_columns = dataset[['ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Status']]\n",
        "\n",
        "DATA2 = selected_columns.copy()\n",
        "\n",
        "sns.pairplot(data=DATA2, hue=\"Loan_Status\",height=3)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "eYivHFY-HfQy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "outputId": "e934e79b-9ed9-4ae9-82df-53b75273ff70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 730.05x648 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the graph above, it appears that for smaller amounts of applicant income, the co-applicant and loan amount are those that are usually approved for loan. Despite this, we cannot claim anything, as it may be that people with higher incomes do not take out loans, or our data lack information about this type of population. Maybe later we'll say something."
      ],
      "metadata": {
        "id": "Hu6015tKrJMi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since the graph above didn't make any good statements, let's now plot a heatmap graph by re-correlating two data points and using a third ApplicantIncome value to tell us the magnitude of the Applicant Income for each bi-value. For example, in the chart below we can see the Applicant Income of women and men and whether they are married or not."
      ],
      "metadata": {
        "id": "wGHTrW5QXWbX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "List1 = ['Gender','Dependents','Loan_Amount_Term','Self_Employed']\n",
        "List2 = ['Married','Education','Credit_History','Property_Area']\n",
        "\n",
        "num = [0,1,2,3]\n",
        "\n",
        "fig=plt.figure(figsize=(12, 10))\n",
        "for (z,j,k) in zip(num,List1 ,List2):                   \n",
        "  infos = dataset.pivot_table(index=j,columns=k,values=\"ApplicantIncome\")\n",
        "  ax=fig.add_subplot(2,2,z+1)\n",
        "  sns.heatmap(infos, annot=True, fmt='.2f', linewidths=.5, ax=ax,cmap=plt.get_cmap(\"magma\"))"
      ],
      "metadata": {
        "id": "ZlQIkJTub2F9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "outputId": "0e0a80a4-73ba-4318-ad58-a965e2785f45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To make the assumptions, let's look at some information from Applicant Income:"
      ],
      "metadata": {
        "id": "v17YS6S7HTi0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['ApplicantIncome'].describe()"
      ],
      "metadata": {
        "id": "po-bgBgaHeAj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9ed72d3-b27f-46ab-ffa3-f9a14275f29d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      491.000000\n",
              "mean      5439.002037\n",
              "std       6307.141778\n",
              "min        210.000000\n",
              "25%       2816.000000\n",
              "50%       3813.000000\n",
              "75%       5818.500000\n",
              "max      81000.000000\n",
              "Name: ApplicantIncome, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on this information and the graph above, we note that some married men, people without children and graduates, individuals who request a lot of tuition with a good track record and people who are not self-employed, who live in semi-urban areas have incomes below the average ~ ₹ 5255.00 are more likely to receive a loan. From our first graph in section 7, we understand that these groups surveyed here would likely receive credit, as these groups fall into positive credit status. While these groups of people with below-average incomes indicate a likely certainty of receiving credit, the only certainty we can have will be with the implementation of the machine learning model, but these insights are precious."
      ],
      "metadata": {
        "id": "oEVm7Ry7Rz8k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9 - Exploratory Data Analysis and Feature Engineering"
      ],
      "metadata": {
        "id": "pnNkWudxeef-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.1 - Finding and Understanding Outliers"
      ],
      "metadata": {
        "id": "tmdi4ZEz1KSv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "At this point we will see if our numerical data have outliers, for this we will initially use the boxplot."
      ],
      "metadata": {
        "id": "UmBQApNjOfQJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datasetNewCopy = dataset.copy()\n",
        "dataset_numTest = datasetNewCopy.select_dtypes(include=[np.number]).drop([\"Credit_History\",\"Loan_Status\"], axis=1)\n",
        "\n",
        "\n",
        "fig=plt.figure(figsize=(16, 12))\n",
        "\n",
        "for i,x in enumerate(dataset_numTest):\n",
        "  ax=fig.add_subplot(2,2,i+1)\n",
        "  sns.boxplot(x=datasetNewCopy[\"Loan_Status\"], y=dataset_numTest[x],ax=ax)\n",
        "  if(x=='CoapplicantIncome' or x=='ApplicantIncome'):\n",
        "    ax.set_ylim(-500, 30000)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "DPn6paVv1t0D",
        "outputId": "9c442bdc-a0a1-4bd2-da8a-63ef4d52535a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see in the graph above we have several outliers, in all numerical data these outliers are outside the median of (ApplicantIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term), with the respective values: (₹ 3813.00, ₹ 1125.00, 128.00, 360.00 ). As we have some outliers, we will deal with some of this data later. Certain statistical information coincides with the following visa:"
      ],
      "metadata": {
        "id": "BkNxF6hLPEcz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_numTest.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "BDr6GiuaSux3",
        "outputId": "201ab698-9b40-4b26-f578-5f462cb41af0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term\n",
              "count       491.000000         491.000000  470.000000        481.000000\n",
              "mean       5439.002037        1610.158697  146.919149        340.665281\n",
              "std        6307.141778        2822.873486   85.384106         67.452871\n",
              "min         210.000000           0.000000    9.000000         12.000000\n",
              "25%        2816.000000           0.000000  100.000000        360.000000\n",
              "50%        3813.000000        1125.000000  128.000000        360.000000\n",
              "75%        5818.500000        2304.000000  169.500000        360.000000\n",
              "max       81000.000000       41667.000000  700.000000        480.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c7cfc456-86b4-4679-9069-ebf6df13cc7d\">\n",
              "    <div 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>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>491.000000</td>\n",
              "      <td>491.000000</td>\n",
              "      <td>470.000000</td>\n",
              "      <td>481.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5439.002037</td>\n",
              "      <td>1610.158697</td>\n",
              "      <td>146.919149</td>\n",
              "      <td>340.665281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6307.141778</td>\n",
              "      <td>2822.873486</td>\n",
              "      <td>85.384106</td>\n",
              "      <td>67.452871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>210.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2816.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>360.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3813.000000</td>\n",
              "      <td>1125.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>360.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5818.500000</td>\n",
              "      <td>2304.000000</td>\n",
              "      <td>169.500000</td>\n",
              "      <td>360.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>81000.000000</td>\n",
              "      <td>41667.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>480.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7cfc456-86b4-4679-9069-ebf6df13cc7d')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c7cfc456-86b4-4679-9069-ebf6df13cc7d 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-c7cfc456-86b4-4679-9069-ebf6df13cc7d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.2 kernel density with violin graphics"
      ],
      "metadata": {
        "id": "FfzSIbiKGBak"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Previously we applied the box-plot to visualize the outliers, now we can understand these outliers again using the violinplot, but with this graph we can also understand the kernel density. Through kernel density we smooth the data with the initiative to understand the probability density. Therefore, extensive regions symbolize a higher probability that the observations can represent a certain value, while narrower regions characterize a lower probability.\n"
      ],
      "metadata": {
        "id": "i4EDHYml5u2f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "datasetNewCopy = dataset.copy()\n",
        "\n",
        "#plot\n",
        "fig=plt.figure(figsize=(12, 8))\n",
        "for i,x in enumerate(dataset_numTest):\n",
        "  ax=fig.add_subplot(2,2,i+1)\n",
        "  sns.violinplot(x=datasetNewCopy[\"Loan_Status\"], y=dataset_numTest[x],ax=ax)\n",
        "  if(x=='CoapplicantIncome' or x=='ApplicantIncome'):\n",
        "    ax.set_ylim(-5000, 20000)   \n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "yOLRuDvvGCBK",
        "outputId": "c96277d9-9604-4d99-df0d-6cfa2d0fa402"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this way, in this graph we can better contemplate data that were not well represented before. Furthermore, we also understand that the highest probabilities that (ApplicantIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term) can assume are respectively ~ (₹ 4000, ₹ 100, 100, 380)."
      ],
      "metadata": {
        "id": "RoXoSR40F6ME"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.3 - Creating New Data"
      ],
      "metadata": {
        "id": "fkhgyghPzPyi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will now make resource combinations to create new data."
      ],
      "metadata": {
        "id": "9I6YUtA4Wcfl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def feature_engineering(data):\n",
        "  data['Total_Income'] = data['ApplicantIncome'] + data['CoapplicantIncome']\n",
        "  data['Loan_Term_per_T_Income'] = data['Loan_Amount_Term']/data['CoapplicantIncome'] #installments per couple or helpers\n",
        "  data['LoanA_per_Total_Income'] = data['LoanAmount']/data['CoapplicantIncome'] #Loan Amount by Total Income\n",
        "\n",
        "  return data"
      ],
      "metadata": {
        "id": "GZ-DpwTN3ZDJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.4 Relationship Between The Features"
      ],
      "metadata": {
        "id": "n3RfSZgcLCxz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's now concern ourselves with how variables are associated with each other in our dataset. There are a few ways to find the relationship between variables, but this search becomes more complex when dealing with nominal categorical variables and continuous numeric variables. Initially we will see the relationship between nominal and continuous numeric variables, soon after we will see the relationships between nominal categorical variables."
      ],
      "metadata": {
        "id": "hJ4uI7tA5X0k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 9.4.1 - ANOVA test"
      ],
      "metadata": {
        "id": "HTi2a7P4x9S7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Without getting too much into the math, the ANOVA test relies on an analysis of variance to determine whether there is a discrepancy between means in any data set. Basically the higher an F-statistic value the feature will be more relevant to our model. In addition, in this test we also find a p-value, the smaller it is, the more unlikely that the mean values are similar. That is, a valuable resource will have p close to zero, as it will help to distinguish between negative and positive classes.\n"
      ],
      "metadata": {
        "id": "eDMS8xZ8L1nz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import f_classif\n",
        "\n",
        "\n",
        "dataset2 = dataset.copy()#we take a copy to preserve the data\n",
        "\n",
        "\n",
        "#we do a treatment that will not be applied later\n",
        "dataset2['LoanAmount'].fillna(dataset2['LoanAmount'].median(),inplace = True)\n",
        "dataset2['Loan_Amount_Term'].fillna(dataset2['Loan_Amount_Term'].median(),inplace = True)\n",
        "dataset2 = dataset2.replace(to_replace='3+', value=4)\n",
        "dataset2.replace({'Credit_History':{1: 'Yes',0:'No'}},inplace=True)\n",
        "\n",
        "\n",
        "#we take only the numerical data removing the loan status\n",
        "dataset_numTest = dataset2.select_dtypes(include=[np.number]).drop(\"Loan_Status\", axis=1)\n",
        "\n",
        "#feature engineering\n",
        "dataset_numTest = feature_engineering(dataset_numTest)\n",
        "\n",
        "# treatment \n",
        "dataset_numTest['Loan_Term_per_T_Income'].replace([np.inf, -np.inf], dataset_numTest['Loan_Term_per_T_Income'].median(), inplace=True)\n",
        "dataset_numTest['LoanA_per_Total_Income'].replace([np.inf, -np.inf], dataset_numTest['LoanA_per_Total_Income'].median(), inplace=True)\n",
        "\n",
        "#ANOVA test\n",
        "[f_stat, f_p_value] = f_classif(dataset_numTest, dataset2['Loan_Status'])\n",
        "\n",
        "f_test_df = pd.DataFrame({'Features':list(dataset_numTest),\n",
        "                          'F statistic':f_stat,\n",
        "                          'p value':f_p_value})\n",
        "f_test_df.sort_values('p value')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "zsRgfG9vSyvP",
        "outputId": "ba13990f-650f-487b-c28f-fd3f8e74cabc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Features  F statistic   p value\n",
              "1       CoapplicantIncome     1.549319  0.213832\n",
              "5  Loan_Term_per_T_Income     0.600086  0.438920\n",
              "6  LoanA_per_Total_Income     0.477514  0.489878\n",
              "2              LoanAmount     0.346245  0.556518\n",
              "4            Total_Income     0.100976  0.750797\n",
              "0         ApplicantIncome     0.049354  0.824284\n",
              "3        Loan_Amount_Term     0.020770  0.885465"
            ],
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              "      <th></th>\n",
              "      <th>Features</th>\n",
              "      <th>F statistic</th>\n",
              "      <th>p value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CoapplicantIncome</td>\n",
              "      <td>1.549319</td>\n",
              "      <td>0.213832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Loan_Term_per_T_Income</td>\n",
              "      <td>0.600086</td>\n",
              "      <td>0.438920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LoanA_per_Total_Income</td>\n",
              "      <td>0.477514</td>\n",
              "      <td>0.489878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LoanAmount</td>\n",
              "      <td>0.346245</td>\n",
              "      <td>0.556518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Total_Income</td>\n",
              "      <td>0.100976</td>\n",
              "      <td>0.750797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ApplicantIncome</td>\n",
              "      <td>0.049354</td>\n",
              "      <td>0.824284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Loan_Amount_Term</td>\n",
              "      <td>0.020770</td>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our result indicates that the above characteristics are not relevant to the model, as we need a larger F statistic and a smaller p, but although the requesting F Income Co statistic is small, this variable has more influence on the credit situation than the Total Income and the Applicant Income. Despite all this, we are not going to implement the model with this new data, as we have a lot of values that need to be handled as seen above, and this data is not very relevant to the loan status.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "gQ96KijpVLiX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also use a selection of sklearn features with SelectKBest:"
      ],
      "metadata": {
        "id": "DDjAuEd1K-Kw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import f_classif\n",
        "\n",
        "#feature engineering\n",
        "dataset2 = feature_engineering(dataset2)\n",
        "\n",
        "Sb = SelectKBest(score_func=f_classif, k='all')\n",
        "Sb.fit(dataset_numTest, dataset2['Loan_Status'])\n",
        "\n",
        "# what are scores for the features\n",
        "for i in range(len(Sb.scores_)):\n",
        "\tprint('Feature %d: %f' % (i, Sb.scores_[i]))\n",
        " "
      ],
      "metadata": {
        "id": "6FAZcwaoyAkZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e33f6c36-0861-4d7b-be2c-688b326fe0a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature 0: 0.049354\n",
            "Feature 1: 1.549319\n",
            "Feature 2: 0.346245\n",
            "Feature 3: 0.020770\n",
            "Feature 4: 0.100976\n",
            "Feature 5: 0.600086\n",
            "Feature 6: 0.477514\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "and we obtain similar results.\n"
      ],
      "metadata": {
        "id": "E1_cbIHJWbpU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 9.4.2 - Cramér’s V"
      ],
      "metadata": {
        "id": "KhjImM5TyDHf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As reported, to understand the relationship between categorical variables we must use another method, for this we will use the Cramér V method. In this case, the closer to 1 the more related are two variables, otherwise 0."
      ],
      "metadata": {
        "id": "g-vM5BsvW-rR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "from math import sqrt\n",
        "from scipy.stats import chi2_contingency \n",
        "\n",
        "#Basic Treatment\n",
        "categorical = dataset2.copy()\n",
        "categorical.replace({'Loan_Status':{1: 'Yes',0:'No'}},inplace=True)\n",
        "categorical = categorical.select_dtypes(include=['object']).drop(\"Loan_ID\", axis=1)#take the categorical data\n",
        "\n",
        "#cramers_v function\n",
        "def cramers_v(D1, D2):\n",
        "    data = pd.crosstab(D1, D2).values\n",
        "    chi_2 = stats.chi2_contingency(data)[0]\n",
        "    phi_2 = chi_2 / (data.sum())\n",
        "    r, k = data.shape\n",
        "    return np.sqrt(phi_2 / min((k-1), (r-1)))\n",
        "\n",
        "\n",
        "#we perform the cramer V method on all categorical data\n",
        "rows= []\n",
        "for D1 in categorical:\n",
        "  col = []\n",
        "  for D2 in categorical :\n",
        "    cramers =cramers_v(categorical[D1], categorical[D2])\n",
        "    col.append(round(cramers,2))   \n",
        "  rows.append(col)\n",
        "  \n",
        "  \n",
        "results = np.asarray(rows)\n",
        "Matrix_corr = pd.DataFrame(results, columns = categorical.columns, index =categorical.columns)\n"
      ],
      "metadata": {
        "id": "V7EcJtZgDlzO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.5 - Correlation Matrix"
      ],
      "metadata": {
        "id": "fh9iJXEBOvJh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see through the correlation matrix all the relationships between the categorical variables."
      ],
      "metadata": {
        "id": "QKVicUT_b8GM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Matrix_corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "lgDVFAx8Rpf6",
        "outputId": "78a0f839-27c3-4eaa-ee76-f580b2abab74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Gender  Married  Dependents  Education  Self_Employed  \\\n",
              "Gender            0.99     0.40        0.17       0.05           0.01   \n",
              "Married           0.40     1.00        0.35       0.00           0.00   \n",
              "Dependents        0.17     0.35        1.00       0.07           0.12   \n",
              "Education         0.05     0.00        0.07       0.99           0.02   \n",
              "Self_Employed     0.01     0.00        0.12       0.02           0.99   \n",
              "Credit_History    0.01     0.00        0.07       0.10           0.00   \n",
              "Property_Area     0.14     0.05        0.09       0.10           0.03   \n",
              "Loan_Status       0.02     0.05        0.09       0.11           0.00   \n",
              "\n",
              "                Credit_History  Property_Area  Loan_Status  \n",
              "Gender                    0.01           0.14         0.02  \n",
              "Married                   0.00           0.05         0.05  \n",
              "Dependents                0.07           0.09         0.09  \n",
              "Education                 0.10           0.10         0.11  \n",
              "Self_Employed             0.00           0.03         0.00  \n",
              "Credit_History            0.99           0.06         0.54  \n",
              "Property_Area             0.06           1.00         0.16  \n",
              "Loan_Status               0.54           0.16         1.00  "
            ],
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              "\n",
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              "      <th>Gender</th>\n",
              "      <td>0.99</td>\n",
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              "      <td>0.17</td>\n",
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              "      <td>0.11</td>\n",
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              "    <tr>\n",
              "      <th>Self_Employed</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>Credit_History</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>0.06</td>\n",
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              "    <tr>\n",
              "      <th>Property_Area</th>\n",
              "      <td>0.14</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loan_Status</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.54</td>\n",
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              "    }\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",
              "    [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-314245ed-ce22-4fb1-8e66-833771286f08 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-314245ed-ce22-4fb1-8e66-833771286f08');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The correlation coefficient close to 1 reveals a strong relationship between the data, if the correlation coefficient is close to 0 it expresses a very low relationship. For example, loan status has a moderate association with credit history."
      ],
      "metadata": {
        "id": "4BsYSRBxQ5oU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Furthermore, we can look at the data in the form of a heat map."
      ],
      "metadata": {
        "id": "0qkaap3gfb52"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mask = np.triu(np.ones_like(Matrix_corr))\n",
        "\n",
        "f,ax = plt.subplots(figsize=(11,9))\n",
        "\n",
        "sns.heatmap(Matrix_corr,mask=mask,cmap=plt.get_cmap(\"cividis\"),center=0,square=True,linewidths=.5,cbar_kws={\"shrink\": .5})\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "5xCZQnnjfceg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "outputId": "94853e08-4492-4902-b4eb-db461f56782d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The conclusions seem to be the same, and we note the strong relationship between credit history and loan status."
      ],
      "metadata": {
        "id": "ELUqZm1WuTzL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.6 Prepare the Data"
      ],
      "metadata": {
        "id": "xLMp91SpuX68"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = strat_train_set.drop(\"Loan_Status\", axis=1) # drop labels for training set\n",
        "dataset_labels = strat_train_set[\"Loan_Status\"].copy()\n",
        "\n",
        "#basic treatment\n",
        "dataset.replace(to_replace='3+', value=4, inplace=True)\n",
        "dataset['Dependents'] = pd.to_numeric(dataset['Dependents'],downcast='integer')\n",
        "\n",
        "#split data into categorical and numeric\n",
        "dataset_num = dataset.select_dtypes(include=[np.number])\n",
        "cat = dataset.select_dtypes(include=['object']).drop('Loan_ID',axis=1)"
      ],
      "metadata": {
        "id": "fItKjmGGvVvG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9.7 Transformations by Pipelines"
      ],
      "metadata": {
        "id": "rJombbcGfVgy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As seen so far, our data has many imperfections, to remedy some of these limitations, we employ a pepilines transformation that sequentially employs several transformations and a final estimator.\n",
        "In our pipeline:\n",
        "\n",
        "\n",
        "1.  First, we fill in data that is missing from the database, with the median of the column;\n",
        "2.   We apply RobustScaler(), to remove out-of-curve points and scale data between 25% of the sample and 75% of the sample;\n",
        "3. Finally, we use the StandardScaler, which makes the features have similar weights, normalizing them through the elimination of the mean and scaling to the range of units.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "hc4Gf3LJav4L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "        ('imputer', SimpleImputer(strategy='median')),\n",
        "        ('rbscaler',RobustScaler()),\n",
        "        ('std_scaler', StandardScaler()),\n",
        "        \n",
        "    ])\n",
        "\n",
        "dataset_num_tr = num_pipeline.fit_transform(dataset_num)"
      ],
      "metadata": {
        "id": "_fEVugLxue3B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Later, we joined our numerical and categorical data through the ColumnTransformer and used the OneHotEncoder method, to transform words into numbers, as the models do not understand words, only numbers.\n",
        "We turn names into numbers such as labels that the pc understands.\n"
      ],
      "metadata": {
        "id": "HZGI8LyaiJnJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "\n",
        "num_attribs = list(dataset_num)\n",
        "cat_attribs = list(cat)\n",
        "\n",
        "full_pipeline = ColumnTransformer([\n",
        "        (\"num\", num_pipeline, num_attribs),\n",
        "        (\"cat\", OneHotEncoder(), cat_attribs)\n",
        "    ])\n",
        "\n",
        "\n",
        "dataset_before = full_pipeline.fit_transform(dataset)"
      ],
      "metadata": {
        "id": "-U9OKopsv0ko"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we apply the SMOTE() method, which oversamples the minority class and balances the distribution of classes, despite not providing additional content to the model."
      ],
      "metadata": {
        "id": "eeaO-xdijyon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "\n",
        "dataset_prepared, dataset_labels = SMOTE().fit_resample(dataset_before, dataset_labels)"
      ],
      "metadata": {
        "id": "E3JptqfGoGzL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10 - Choose and Train The Model"
      ],
      "metadata": {
        "id": "wO3CagVMFfSI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's train some models and try to find the best one, as soon as we find the best model I'll give more explanations about it. I'll start with the model Kernelized Support Vector Machines can be used for both regression and quasification. I will use the evaluation metric called ROC curve and then explain more about it"
      ],
      "metadata": {
        "id": "LDx3TGqHJKQu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import roc_curve,auc\n",
        "\n",
        "#model\n",
        "modelOne = SVC(kernel='sigmoid',max_iter=1e4,random_state=42).fit(dataset_prepared,dataset_labels)\n",
        "modelOne.fit(dataset_prepared, dataset_labels)\n",
        "\n",
        "#we create an estimate that uses a cross-validation in one by one  of the input data points\n",
        "y_scores = cross_val_predict(modelOne, dataset_prepared, dataset_labels, cv=3,\n",
        "                             method=\"decision_function\")\n",
        "\n",
        "#we calculate the ROC\n",
        "fpr, tpr, thresholds = roc_curve(dataset_labels, y_scores)\n",
        "\n",
        "#and the area under the curve \n",
        "roc_auc_lr = auc(fpr, tpr)\n",
        "\n",
        "print(\"Area\",roc_auc_lr) #the higher the value the better"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HB4m9ZYyKG7X",
        "outputId": "6dc6187d-b097-4c93-a102-3a3002b0e1f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Area 0.6756694367497691\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's try the logistic regression model,"
      ],
      "metadata": {
        "id": "5uoI2ZEWM_z9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "modelTwo = LogisticRegression(random_state=42).fit(dataset_prepared, dataset_labels)\n",
        "modelTwo.fit(dataset_prepared, dataset_labels)\n",
        "\n",
        "#we do the same as before\n",
        "y_scores = cross_val_predict(modelTwo , dataset_prepared, dataset_labels, cv=3,\n",
        "                             method=\"decision_function\")\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(dataset_labels, y_scores)\n",
        "\n",
        "roc_auc_lr = auc(fpr, tpr)\n",
        "\n",
        "print(\"Area\",roc_auc_lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z0gLPLtJM_R2",
        "outputId": "a4e50abd-bbec-4200-caab-a94a0627eb2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Area 0.7593276563729011\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will also test Random Forest Classifier."
      ],
      "metadata": {
        "id": "a-Z-_lrJNYsc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "modelThree = RandomForestClassifier(random_state = 42)\n",
        "modelThree.fit(dataset_prepared, dataset_labels)\n",
        "\n",
        "\n",
        "#we use the predict_proba method\n",
        "y_scores_proba = cross_val_predict(modelThree, dataset_prepared, dataset_labels, cv=3,\n",
        "                             method=\"predict_proba\")\n",
        "\n",
        "y_scores = y_scores_proba[:,1]\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(dataset_labels, y_scores)\n",
        "\n",
        "roc_auc_lr = auc(fpr, tpr)\n",
        "\n",
        "print(\"Area\",roc_auc_lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rAI0VsRUNYIg",
        "outputId": "6467d5e2-cfdc-4a5c-ca15-98c719226371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Area 0.8963569987346534\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The latter is unfeasible, as it overfits. We can see this by the following assessment:"
      ],
      "metadata": {
        "id": "FSuslLZdN5vB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy of  RandomForestClassifier classifier on training set: {:.2f}'\n",
        "     .format(modelThree.score(dataset_prepared, dataset_labels)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMBkPFlIaqB0",
        "outputId": "410acdbb-3e77-4284-c676-1d830c328766"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of  RandomForestClassifier classifier on training set: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will try to test the k-nearest neighbor mode."
      ],
      "metadata": {
        "id": "TYU_YbE4a2I7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "clf = KNeighborsClassifier(n_neighbors = 15)\n",
        "\n",
        "clf.fit(dataset_prepared, dataset_labels)\n",
        "\n",
        "#we use the predict_proba method\n",
        "y_scores_proba = cross_val_predict(clf, dataset_prepared, dataset_labels, cv=3,\n",
        "                             method=\"predict_proba\")\n",
        "\n",
        "y_scores = y_scores_proba[:,1]\n",
        "\n",
        "fpr, tpr, thresholds = roc_curve(dataset_labels, y_scores)\n",
        "\n",
        "roc_auc_lr = auc(fpr, tpr)\n",
        "\n",
        "print(\"Area\",roc_auc_lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T30DTOrLOkmh",
        "outputId": "8530ede0-3bb2-4fbf-a92e-fb7d53b80614"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Area 0.813134810710988\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It seems that the k-nearest neighbors algorithm is the best, so let's explain it. In the k-nearest neighbor model, objects are classified according to the plural vote of their neighbors, where each object is assigned the most similar class among its k-nearest neighbors. "
      ],
      "metadata": {
        "id": "qF4IhHyh0tCs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our algorithm is already qualified, however, we will search for several hyperparameters to be tested in our model using cross validation. For this purpose we will use the grid search."
      ],
      "metadata": {
        "id": "lPgRqE9sO6I9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "grid_params = {\n",
        "    'n_neighbors' : [3,5,7,9,11,13,15],\n",
        "    'weights' : ['uniform','distance'],\n",
        "    'metric' : ['minkowski','euclidean','manhattan'],\n",
        "    'algorithm' : ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
        "}\n",
        "\n",
        "gs = GridSearchCV(\n",
        "    KNeighborsClassifier(),\n",
        "    grid_params,\n",
        "    verbose = 1,\n",
        "    cv=3,\n",
        "    n_jobs = -1,\n",
        "    scoring='roc_auc'\n",
        ")\n",
        "\n",
        "clf = gs.fit(dataset_prepared, dataset_labels)\n",
        "\n",
        "print('score: {:.3f}'\n",
        "     .format(clf.score(dataset_prepared, dataset_labels)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7szAoWxzOu96",
        "outputId": "fddabbb7-5103-43e0-a559-e601390fdf62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 168 candidates, totalling 504 fits\n",
            "score: 1.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We found the best parameters:"
      ],
      "metadata": {
        "id": "Y5nYJRv2ZcXy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Best parameters: {clf.best_params_}')\n",
        "print(f'Best score: {clf.best_score_}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VvWS5sAdO9Jy",
        "outputId": "df8c2ada-870d-4fe0-eff5-492cb2ee16cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters: {'algorithm': 'auto', 'metric': 'minkowski', 'n_neighbors': 5, 'weights': 'distance'}\n",
            "Best score: 0.8930953113778598\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Although our model is good, it is overfitting , which is basically learning very well but only the training data, i.e. it's like our model is biased in the training data, and when it comes across new data, it won't behave well then I will apply n_neighbors = 15."
      ],
      "metadata": {
        "id": "in_4PWo0EIyg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = KNeighborsClassifier(n_neighbors = 15)\n",
        "\n",
        "clf.fit(dataset_prepared, dataset_labels)"
      ],
      "metadata": {
        "id": "_n_TcT-7EIBh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc576cc3-94cf-4ae2-bfae-20ce2da4e683"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=15)"
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For a quick evaluation of the model, we used the precision and recall, the recal is the fraction of all positive instances that the classifier correctly identified as being positive. The precision is the fraction of correctly performed positive predictions."
      ],
      "metadata": {
        "id": "vnB8N3nLgRQS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "\n",
        "\n",
        "dataset_predictions = clf.predict(dataset_prepared)\n",
        "\n",
        "precision = precision_score(dataset_labels, dataset_predictions)\n",
        "\n",
        "recall = recall_score(dataset_labels, dataset_predictions)\n",
        "\n",
        "print(\"Recall\", recall, \"precision\", precision)"
      ],
      "metadata": {
        "id": "KdeIfKaqJnBL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a4c71aa-de81-4181-8c38-ef4f33e16978"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall 0.8625730994152047 precision 0.7603092783505154\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our model is good, because the closer to one is better."
      ],
      "metadata": {
        "id": "3iCjUhMZL-vW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 11 - Evaluation method in TRAINING SET"
      ],
      "metadata": {
        "id": "1G1wn2zmN0bM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will evaluate our model by applying the roc_curve curve and the auc value. The ROC curve describes the true positive rate by the false positive rate. In our case, we aim to have a high rate of true positives, as we would like to know the individuals who should receive the loan. The AUC value ranges from 0 to 1, and the closer to 1, the better are our estimates.\n"
      ],
      "metadata": {
        "id": "kthSalzri4dP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To have a ROC curve we need something to use as a reference in our decision threshold, what we use is the score, which if it is greater than the threshold it conceives the instance in the positive class, otherwise not. To get the scores of the instances, we use the cross_val_predict method."
      ],
      "metadata": {
        "id": "t7gDv2Q8lfk0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#we will do the same evaluation again, however, now we will make a graph to visualize better\n",
        "from sklearn.model_selection import cross_val_predict \n",
        "\n",
        "y_scores_proba = cross_val_predict(clf, dataset_prepared, dataset_labels, cv=3,\n",
        "                             method=\"predict_proba\")\n",
        "\n",
        "y_scores = y_scores_proba[:,1]"
      ],
      "metadata": {
        "id": "sL7UEfrKQumW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Having the scores in hand, we check recall and accuracy."
      ],
      "metadata": {
        "id": "MRuMfVwpoBcF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve,auc\n",
        "\n",
        "pr, rec, thresholds = roc_curve(dataset_labels, y_scores)"
      ],
      "metadata": {
        "id": "IKzhMawXkVwc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally we plot the graph:"
      ],
      "metadata": {
        "id": "UuzFpfLhsGCM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc_lr = auc(pr, rec)\n",
        "\n",
        "fig, ax = plt.subplots(1, figsize= (8,6))\n",
        "ax.set_aspect('equal') \n",
        "plt.xlim([-0.01, 1.00])\n",
        "plt.ylim([-0.01, 1.01])\n",
        "\n",
        "\n",
        "#plots the roc curve, with its coordinates \n",
        "plt.plot(pr, rec, lw=3, label='ROC curve (area = {:0.2f})'.format(roc_auc_lr))\n",
        "\n",
        "plt.xlabel('False Positive Rate', fontsize=16)\n",
        "plt.ylabel('True Positive Rate', fontsize=16)\n",
        "plt.title('ROC curve (1-of-10 digits classifier)', fontsize=16)\n",
        "plt.legend(loc='lower right', fontsize=13)\n",
        "\n",
        "#plots a list of values ​​that goes from zero to one, and how x = y is plotted a 45 degree line\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')#plot the stippling\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "vmyjq_-ZlGtO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "6998b9f5-5a9b-473d-86c3-86fa990c2f35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 12 - Implementation in Test Set"
      ],
      "metadata": {
        "id": "TqHY9u4uP5mR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, we do the same procedure as before, only now in our test set."
      ],
      "metadata": {
        "id": "Zhc5h8jKsM-P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "#testing division\n",
        "X_test = strat_test_set.drop(\"Loan_Status\", axis=1)\n",
        "y_test = strat_test_set[\"Loan_Status\"].copy()\n",
        "\n",
        "#preprocessing\n",
        "X_test.replace(to_replace='3+', value=4, inplace=True)\n",
        "X_test['Dependents'] = pd.to_numeric(X_test['Dependents'],downcast='integer')\n",
        "\n",
        "#I applied the transformation\n",
        "X_test_prepared = full_pipeline.transform(X_test)\n",
        "\n",
        "#I made the prediction\n",
        "final_predictions = clf.predict(X_test_prepared)\n",
        "\n",
        "\n",
        "#evaluation\n",
        "precision = precision_score(y_test,final_predictions)\n",
        "\n",
        "recall = recall_score(y_test, final_predictions)\n",
        "\n",
        "print(\"Recall\", recall, \"precision\", precision)"
      ],
      "metadata": {
        "id": "FwihpFFlP8UC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eebc43d7-1dd2-48c4-f157-4bd2482ac8fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall 0.6875 precision 0.7142857142857143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "\n",
        "y_scores_proba = cross_val_predict(clf, X_test_prepared, y_test, cv=3,\n",
        "                             method=\"predict_proba\")\n",
        "\n",
        "\n",
        "y_scores = y_scores_proba[:,1]"
      ],
      "metadata": {
        "id": "G4ow-TwuPZZZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve,auc\n",
        "\n",
        "pr, rec, thresholds = roc_curve(y_test, y_scores)"
      ],
      "metadata": {
        "id": "G93i0GenPZZa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc_lr = auc(pr, rec)\n",
        "\n",
        "fig, ax = plt.subplots(1, figsize= (8,6))\n",
        "ax.set_aspect('equal') \n",
        "plt.xlim([-0.01, 1.00])\n",
        "plt.ylim([-0.01, 1.01])\n",
        "\n",
        "\n",
        "#plots the roc curve, with its coordinates\n",
        "plt.plot(pr, rec, lw=3, label='ROC curve (area = {:0.2f})'.format(roc_auc_lr))\n",
        "\n",
        "plt.xlabel('False Positive Rate', fontsize=16)\n",
        "plt.ylabel('True Positive Rate', fontsize=16)\n",
        "plt.title('ROC curve (1-of-10 digits classifier)', fontsize=16)\n",
        "plt.legend(loc='lower right', fontsize=13)\n",
        "\n",
        "#plots a list of values ​​that goes from zero to one, and how x = y is plotted a 45 degree line\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--') #plot the stippling\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "5F38m85IPZZb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "2d0e0531-f45b-4110-e915-c9839fbd1f09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our model is great."
      ],
      "metadata": {
        "id": "-pVXqWU2t_8v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 13 - To Deploy"
      ],
      "metadata": {
        "id": "o8I97JdS-tR3"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgL6wblpQUtX"
      },
      "source": [
        "### 13.1 - Saving the trained model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's save the model, to deploy it later."
      ],
      "metadata": {
        "id": "E5Amj7syIh4s"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nn60MdxByjgz"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use a dump method, which takes the clf model as an input and writes our classifier in binary to the file \"wb\"."
      ],
      "metadata": {
        "id": "Slc2-acB4yrv"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWzPQs4mQZN_"
      },
      "source": [
        "filename = 'trained_model.sav'\n",
        "pickle.dump(clf, open(filename, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "loading the saved model"
      ],
      "metadata": {
        "id": "eMYfagAuRY21"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wk1T2sMcQ6_U"
      },
      "source": [
        "loaded_model = pickle.load(open('trained_model.sav', 'rb'))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}