Blanks_Implementing_a_loan_approval_RaghadAlmutairi.ipynb

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        "<a href=\"https://colab.research.google.com/github/RaghadAlmutairi/RaghadAlmutairi-SDAIA_Machine_Learning_Bootcamp/blob/main/Blanks_Implementing_a_loan_approval_RaghadAlmutairi.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "#Loan Approval\n",
        "\n",
        "---\n",
        "**Objective:**\n",
        "In this exercise, we will explore a dataset of Loan data with the below info. We will try later to predict the status of loan for individuals based the following information.</font>\n",
        "\n",
        "## DataSet Description\n",
        "\n",
        "- **Loan_ID:** data set unique ID <br>\n",
        "- **Gender:** individual gender<br>\n",
        "- **Married:** individual marital status<br>\n",
        "- **Dependents:** number of dependents<br>\n",
        "- **Education:** individual education status<br>\n",
        "- **Self_Employed:** individual employment status<br>\n",
        "- **ApplicantIncome:** individual income<br>\n",
        "- **CoapplicantIncome:** individual Coapplicant income<br>\n",
        "- **LoanAmount:** Loan amount in thousands<br>\n",
        "- **Loan_Amount_Term:** term of loan in months<br>\n",
        "- **Credit_History:** credit history meets guidelines<br>\n",
        "- **Property_Area:** Urban/ Semi Urban/ Rural<br>\n",
        "- **Loan_Status:** loan approved (Y/N)<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KIFhQSUBK-lS"
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      "source": [
        "# 1. Import necessary python modules (libraries)\n",
        "<br>\n",
        "We will need the following libraries:\n",
        "\n",
        "- Numpy — for scientific computing (e.g., linear algebra (vectors & matrices)).\n",
        "- Pandas — providing high-performance, easy-to-use data reading, manipulation, and analysis.\n",
        "- Matplotlib & seaborn — plotting & visualization.\n",
        "- scikit-learn — a tool for data mining and machine learning models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZwyPrVkK-lT"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4xjXdDyTK-lW"
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      "source": [
        "# 2. Read & visualize data\n",
        "To load the data to our code, we use pandas module, more specifically, the read_csv function."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will use two datasets: the **train.csv** and **test.csv** files"
      ],
      "metadata": {
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      }
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "Combine the two dataset in order to apply the same preprocessing steps in both dataframes"
      ]
    },
    {
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      "source": [
        "df_train=pd.read_csv('/content/train.csv')\n",
        "df_test=pd.read_csv('/content/test.csv')\n",
        "df=df_train.append(df_test)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
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          "text": [
            "<ipython-input-69-92f421a5eaa3>:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  df=df_train.append(df_test)\n"
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          "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": {},
          "execution_count": 69
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKcAeoOVK-ld"
      },
      "source": [
        "# 4. Exploratory Data Analysis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 1:** Use the shape function to check the dimensions of your dataframe"
      ],
      "metadata": {
        "id": "UtN7MWS1rR0V"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-DmXe7W2K-ld",
        "outputId": "5bc38ae7-b174-444a-ebfa-0620c7b48293",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(614, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVH8LAjLK-lg"
      },
      "source": [
        "##### Using the function info(), we can check:\n",
        "\n",
        "- data types (int, float, or object (e.g., string))\n",
        "- missing values\n",
        "- memory usage\n",
        "- number of rows and columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kesvYr2pK-lg",
        "outputId": "f320507c-37cb-4401-cf2a-d89465916496",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 614 entries, 0 to 100\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: 67.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 2:** Which Columns contain Missing Values?"
      ],
      "metadata": {
        "id": "KVCIbYvarZoV"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZ15pJj2K-lj"
      },
      "source": [
        "\n",
        "\n",
        "Analysis:  Gender, Dependents, Self_Employed, LoanAmount, Credit_History."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W4ebzltrK-lj"
      },
      "source": [
        "##### Using the function describe(), we can check the following  values for each numerical feature (column)\n",
        "- mean\n",
        "- standard deviation\n",
        "- maximum\n",
        "- minimum\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECiIV49DK-lk",
        "outputId": "e78573f6-3501-4714-b838-8b416b4765f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "df.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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              "\n",
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              "    <div>\n",
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              "      <th></th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
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              "      <td>592.000000</td>\n",
              "      <td>600.00000</td>\n",
              "      <td>564.000000</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>0.842199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6109.041673</td>\n",
              "      <td>2926.248369</td>\n",
              "      <td>85.587325</td>\n",
              "      <td>65.12041</td>\n",
              "      <td>0.364878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>360.00000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>1188.500000</td>\n",
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              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <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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              "    <tr>\n",
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              "      <td>41667.000000</td>\n",
              "      <td>700.000000</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 3:** Which feature is not actually numerical and yet was included in the above statistical description?"
      ],
      "metadata": {
        "id": "cBOJZ8x9ru6Y"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsmkavZQK-ln"
      },
      "source": [
        "Analysis: Loan_ID,\tGender,\tMarried,\tDependents,\tEducation,\tSelf_Employed, Property_Area,\tLoan_Status"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYPOBUMYK-ln",
        "outputId": "6d5c3a5e-11e0-4134-a708-618af6daee4d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "np.sort(df.Credit_History.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  1., nan])"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 4:** Write the code to get unique values of the Loan_Amount_Term variable and sort the values"
      ],
      "metadata": {
        "id": "CZ9f7p_jsBXU"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bOBFeq9EK-lq"
      },
      "source": [
        "Loan_Amount_Term is a variable of numerical discrete data type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdUjK1DPK-lq",
        "outputId": "c0b051c1-4ec5-46fd-ca6d-7ac8a303d0de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "np.sort(df.Loan_Amount_Term.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 12.,  36.,  60.,  84., 120., 180., 240., 300., 360., 480.,  nan])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Remove Na and create a density graph"
      ],
      "metadata": {
        "id": "vbY2IDDJqScF"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GLVciuCK-l4",
        "outputId": "d61a7017-04c6-4098-ce19-c6fee001a987",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        }
      },
      "source": [
        "plt.figure(1)\n",
        "plt.subplot(121)\n",
        "removeNA_df=df.dropna() # we need to remove Na in order to plot a density graph\n",
        "sns.distplot(removeNA_df['LoanAmount']);\n",
        "\n",
        "plt.subplot(122)\n",
        "df['LoanAmount'].plot.box(figsize=(16,5))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-75-972ad0c805d5>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(removeNA_df['LoanAmount']);\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MNk4bn1bK-l7"
      },
      "source": [
        "**Question 5:** Do you notice any outliers?\n",
        "\n",
        "Analysis: Yes, there are some maximum outliers above 300 exactly between +300 and 600 out of the range.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 6:** Draw the same plots for the ApplicantIncome Feature and check if there are any outliers."
      ],
      "metadata": {
        "id": "FQYtbfc4sbee"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KeyynE7mK-l7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "outputId": "82be42dc-fb9e-4087-a48c-c4af01f4d38f"
      },
      "source": [
        "plt.figure(1)\n",
        "plt.subplot(121)\n",
        "removeNA_df=df.dropna() # we need to remove Na in order to plot a density graph\n",
        "sns.distplot(removeNA_df['ApplicantIncome']);\n",
        "\n",
        "plt.subplot(122)\n",
        "df['ApplicantIncome'].plot.box(figsize=(16,5))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-76-5aebba1edfca>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(removeNA_df['ApplicantIncome']);\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Do you notice any outliers?**\n",
        "\n",
        "Analysis: Yes, there are some maximum outliers above 10000 exactly between 1100 and 20000 out of the range."
      ],
      "metadata": {
        "id": "KW-fMTulvtJm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Check if the gender affects the loan approval"
      ],
      "metadata": {
        "id": "1MaYv6qdqjaR"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKEkDE5bK-mI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "ad3fbbe5-e2eb-4534-8c53-2f597b200aca"
      },
      "source": [
        "Gender=pd.crosstab(df['Gender'],df['Loan_Status'], normalize='index')\n",
        "print(Gender)\n",
        "Gender.plot(kind='bar', stacked=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loan_Status         N         Y\n",
            "Gender                         \n",
            "Female       0.330357  0.669643\n",
            "Male         0.306748  0.693252\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Gender'>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 7:**\n",
        "\n",
        "a. Does gender affect loan approval?\n",
        "\n",
        "b. Suggest another method to study this relation"
      ],
      "metadata": {
        "id": "H1_LIotjssQS"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bVP8SFLlK-mK"
      },
      "source": [
        "Analysis: a: Yes, the graph shows that there is a gender gap in loan approval rates, with female borrowers having a lower approval rate than male borrowers.\n",
        " b: we can use statistical analysis to understand the relation more clearly, or we can study the relation between the loan status and another feature such as LoanAmount."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# draw a scatter plot to show correlation between age and charges\n",
        "plt.scatter(data[\"age\"], data[\"charges\"])\n",
        "plt.title(\"Cost of treatment for different ages\")\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Charges\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "hLpwLM5EOF7J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PuiYEDpYK-mm"
      },
      "source": [
        "# 5. Preprocessing\n",
        "\"Garbage in, garbage out\".\n",
        "<br>\n",
        "<br>\n",
        "Data should be preprocessed and cleaned to get rid of noisy data. Preprocessing includes:\n",
        "\n",
        "- remove unsued column(s)\n",
        "- remove grabage data\n",
        "- convert continous data into categorical\n",
        "- treat missing data\n",
        "- encode categorical data\n",
        "- normalization: normalize data ranges for all features (e.g., between 0 and 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGumTAZLK-mm"
      },
      "source": [
        "## Remove unused columns\n",
        "Here we will drop some columns we beleive they do not affect the loan approval."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aw3LlHX1K-mn"
      },
      "source": [
        "df.drop(['Loan_ID','Gender','Self_Employed','ApplicantIncome','LoanAmount','Loan_Amount_Term'], inplace=True, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBpOQ6MVK-mr"
      },
      "source": [
        "##Convert continous data into categorical"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UATLaXcwK-ms"
      },
      "source": [
        "df['Credit_History'] = df['Credit_History'].astype('O')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RpDPJgQzK-mu"
      },
      "source": [
        "## Treat missing data\n",
        "- fill missing categorical data & discrete data with mode\n",
        "- fill missing continous data with mean\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ifJSObwK-mv"
      },
      "source": [
        "df['Credit_History'].fillna(df['Credit_History'].mode()[0], inplace=True)\n",
        "df['Dependents'].fillna(df['Dependents'].mode()[0], inplace=True)\n",
        "df['Married'].fillna(df['Married'].mode()[0], inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x90s4TXqK-mx"
      },
      "source": [
        "## Check if any null value exist"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question 8:** Write the code to check for any null values"
      ],
      "metadata": {
        "id": "kesbuF-htDWq"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJi4rqkcK-mx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cb3693f-a930-4a92-f2c9-c01e62ebec57"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Married              0\n",
              "Dependents           0\n",
              "Education            0\n",
              "CoapplicantIncome    0\n",
              "Credit_History       0\n",
              "Property_Area        0\n",
              "Loan_Status          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "result: Non null values"
      ],
      "metadata": {
        "id": "UCu4mMzXw-T7"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-aMmUFxK-m0"
      },
      "source": [
        "## Encode categorical data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eP1emO7hK-m0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e6f7bd64-6411-44d5-e478-db23993f82d2"
      },
      "source": [
        "le = LabelEncoder()\n",
        "cat_data=df[['Credit_History','Dependents','Education','Married','Property_Area','Loan_Status']]\n",
        "num_data=df[['CoapplicantIncome']]\n",
        "cat_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Credit_History Dependents     Education Married Property_Area Loan_Status\n",
              "0             1.0          0      Graduate      No         Urban           Y\n",
              "1             1.0          1      Graduate     Yes         Rural           N\n",
              "2             1.0          0      Graduate     Yes         Urban           Y\n",
              "3             1.0          0  Not Graduate     Yes         Urban           Y\n",
              "4             1.0          0      Graduate      No         Urban           Y"
            ],
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uwzdy2KjK-m2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "outputId": "845fa3c3-b6ad-45eb-d7a8-a856f258e9f4"
      },
      "source": [
        "for i in cat_data:\n",
        "    cat_data[i] = le.fit_transform(cat_data[i])\n",
        "cat_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-83-699cec605dfd>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  cat_data[i] = le.fit_transform(cat_data[i])\n",
            "<ipython-input-83-699cec605dfd>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  cat_data[i] = le.fit_transform(cat_data[i])\n",
            "<ipython-input-83-699cec605dfd>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  cat_data[i] = le.fit_transform(cat_data[i])\n",
            "<ipython-input-83-699cec605dfd>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  cat_data[i] = le.fit_transform(cat_data[i])\n",
            "<ipython-input-83-699cec605dfd>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  cat_data[i] = le.fit_transform(cat_data[i])\n",
            "<ipython-input-83-699cec605dfd>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  cat_data[i] = le.fit_transform(cat_data[i])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Credit_History  Dependents  Education  Married  Property_Area  Loan_Status\n",
              "0               1           0          0        0              2            1\n",
              "1               1           1          0        1              0            0\n",
              "2               1           0          0        1              2            1\n",
              "3               1           0          1        1              2            1\n",
              "4               1           0          0        0              2            1"
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3bf25b03-f1d4-4b72-9566-3eb417fdbdb3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3bf25b03-f1d4-4b72-9566-3eb417fdbdb3 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-3bf25b03-f1d4-4b72-9566-3eb417fdbdb3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0010a444-6238-4463-9c2c-662c77b2c46f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0010a444-6238-4463-9c2c-662c77b2c46f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0010a444-6238-4463-9c2c-662c77b2c46f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T1jU9d28K-m4"
      },
      "source": [
        "## Combine both cat_data and numerical_data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Dl3xeaLK-m5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "3c2d6fb6-e948-4ba4-9fd0-44995ea7239c"
      },
      "source": [
        "df = pd.concat([cat_data, num_data], axis=1)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Credit_History  Dependents  Education  Married  Property_Area  Loan_Status  \\\n",
              "0               1           0          0        0              2            1   \n",
              "1               1           1          0        1              0            0   \n",
              "2               1           0          0        1              2            1   \n",
              "3               1           0          1        1              2            1   \n",
              "4               1           0          0        0              2            1   \n",
              "\n",
              "   CoapplicantIncome  \n",
              "0                0.0  \n",
              "1             1508.0  \n",
              "2                0.0  \n",
              "3             2358.0  \n",
              "4                0.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b63cf4b6-05dc-42be-b840-53f4f0c44ee5\" 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>Credit_History</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Married</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1508.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2358.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b63cf4b6-05dc-42be-b840-53f4f0c44ee5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b63cf4b6-05dc-42be-b840-53f4f0c44ee5 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-b63cf4b6-05dc-42be-b840-53f4f0c44ee5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5ab97f20-5cf4-44ec-a7f2-afcb6fd65e3b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5ab97f20-5cf4-44ec-a7f2-afcb6fd65e3b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5ab97f20-5cf4-44ec-a7f2-afcb6fd65e3b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjg5n5OfK-m8"
      },
      "source": [
        "## Normalization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Luz-HezAucAx"
      },
      "source": [
        "**Question 9:** Normalize data ranges for all features (e.g., between 0 and 1)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0b-_jm55K-m8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b7cc0f9-15f2-4b83-9d50-be4919e630f0"
      },
      "source": [
        "# get the max of each column\n",
        "data_max = df.max()\n",
        "data_max"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Credit_History           1.0\n",
              "Dependents               3.0\n",
              "Education                1.0\n",
              "Married                  1.0\n",
              "Property_Area            2.0\n",
              "Loan_Status              1.0\n",
              "CoapplicantIncome    41667.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# divide each column by its maximum value\n",
        "data = df.divide(data_max)\n",
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-StfNBlWxdDg",
        "outputId": "31581958-9642-45f6-858e-f6273c9e10fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Credit_History  Dependents   Education     Married  Property_Area  \\\n",
              "count      614.000000  614.000000  614.000000  614.000000     614.000000   \n",
              "mean         0.855049    0.248100    0.218241    0.653094       0.518730   \n",
              "std          0.352339    0.336541    0.413389    0.476373       0.393741   \n",
              "min          0.000000    0.000000    0.000000    0.000000       0.000000   \n",
              "25%          1.000000    0.000000    0.000000    0.000000       0.000000   \n",
              "50%          1.000000    0.000000    0.000000    1.000000       0.500000   \n",
              "75%          1.000000    0.333333    0.000000    1.000000       1.000000   \n",
              "max          1.000000    1.000000    1.000000    1.000000       1.000000   \n",
              "\n",
              "       Loan_Status  CoapplicantIncome  \n",
              "count   614.000000         614.000000  \n",
              "mean      0.687296           0.038910  \n",
              "std       0.463973           0.070229  \n",
              "min       0.000000           0.000000  \n",
              "25%       0.000000           0.000000  \n",
              "50%       1.000000           0.028524  \n",
              "75%       1.000000           0.055134  \n",
              "max       1.000000           1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-503f188a-12b2-4f63-91d4-05d010a44dfe\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Married</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.855049</td>\n",
              "      <td>0.248100</td>\n",
              "      <td>0.218241</td>\n",
              "      <td>0.653094</td>\n",
              "      <td>0.518730</td>\n",
              "      <td>0.687296</td>\n",
              "      <td>0.038910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.352339</td>\n",
              "      <td>0.336541</td>\n",
              "      <td>0.413389</td>\n",
              "      <td>0.476373</td>\n",
              "      <td>0.393741</td>\n",
              "      <td>0.463973</td>\n",
              "      <td>0.070229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.028524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.055134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-503f188a-12b2-4f63-91d4-05d010a44dfe 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-503f188a-12b2-4f63-91d4-05d010a44dfe');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-197df406-ee53-4182-9da4-d38ca3fdbc36\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-197df406-ee53-4182-9da4-d38ca3fdbc36')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-197df406-ee53-4182-9da4-d38ca3fdbc36 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rW7e-IqSK-nA"
      },
      "source": [
        "# 6. Training & Validating"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1R3upjnRVks"
      },
      "source": [
        "We split the data between training and testing.\n",
        "Then, we train a model to predict the loan status"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfUvvKFTK-nA"
      },
      "source": [
        "input_variables = ['Credit_History','Dependents','Education','Married','Property_Area','CoapplicantIncome']\n",
        "x = df[input_variables]\n",
        "y = df['Loan_Status']\n",
        "\n",
        "x_train, x_val, y_train, y_val = train_test_split(x, y, test_size =0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qIDedqKTtfN"
      },
      "source": [
        "##Creating & Fitting the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ejo6DMwoSTbV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "88a48ca8-8c46-4537-c933-0fab22be464b"
      },
      "source": [
        "model=LogisticRegression()\n",
        "model.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTy8UkrEUNLM"
      },
      "source": [
        "##Validating the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWFaCCnWUB2y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5957c50-911c-4a61-9b66-e9e027628e28"
      },
      "source": [
        "pred_val = model.predict(x_val)\n",
        "accuracy_score(y_val,pred_val)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7891891891891892"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    }
  ]
}