EDA_of_loan_data.ipynb

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    "colab": {
      "name": "episource assignment(loan data analysis.ipynb",
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      "source": [
        "Importing necessary libraries"
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        "id": "GvgLwLV3ML_H"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.graph_objects as go\n",
        "from plotly.offline import iplot\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn import model_selection\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loan=pd.read_csv(\"loan_data.csv\")"
      ],
      "metadata": {
        "id": "hdjhyWSeNceB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loan.tail()"
      ],
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              "      <th>Application_ID</th>\n",
              "      <th>Gender</th>\n",
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              "    Application_ID  Gender Married  ... Property_Area  Income Application_Status\n",
              "506       LP002978  Female      No  ...         Rural     low                  Y\n",
              "507       LP002979    Male     Yes  ...         Rural  medium                  Y\n",
              "508       LP002983    Male     Yes  ...         Urban  medium                  Y\n",
              "509       LP002984    Male     Yes  ...         Urban  medium                  Y\n",
              "510       LP002990  Female      No  ...     Semiurban  medium                  N\n",
              "\n",
              "[5 rows x 10 columns]"
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    {
      "cell_type": "code",
      "source": [
        "loan.info()"
      ],
      "metadata": {
        "colab": {
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        "outputId": "44e2a50b-e63c-4890-bbe5-cfe2fe70f663"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 511 entries, 0 to 510\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   Application_ID      511 non-null    object\n",
            " 1   Gender              511 non-null    object\n",
            " 2   Married             511 non-null    object\n",
            " 3   Dependents          511 non-null    object\n",
            " 4   Education           511 non-null    object\n",
            " 5   Self_Employed       511 non-null    object\n",
            " 6   Credit_History      511 non-null    int64 \n",
            " 7   Property_Area       511 non-null    object\n",
            " 8   Income              511 non-null    object\n",
            " 9   Application_Status  511 non-null    object\n",
            "dtypes: int64(1), object(9)\n",
            "memory usage: 40.0+ KB\n"
          ]
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    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no null values and hence a lot of data cleaning and preprocessing time is saved.\n"
      ],
      "metadata": {
        "id": "sSJFQNxpZlt4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Find % of total applicants for each unique value of dependents."
      ],
      "metadata": {
        "id": "zusTxHLWZxGP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_dependent=loan['Dependents'].value_counts().to_frame().reset_index().rename(columns={'index':'Dependents','Dependents':'count'})\n",
        "\n",
        "\n",
        "fig = go.Figure([go.Pie(labels=df_dependent['Dependents'], values=df_dependent['count'],pull=[0,0])])\n",
        "\n",
        "fig.update_traces(hoverinfo='label+percent', textinfo='value+percent', textfont_size=12)\n",
        "\n",
        "fig.update_layout(title=\"Dependents Count \",title_x=0.5)\n",
        "fig.show()"
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              "  Dependents  count\n",
              "0          0    294\n",
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        "Total applicants for each unique value of dependents.\n",
        "\n",
        "*   57.5 % applicants have 0 dependents\n",
        "*   16.6 % applicants have 1 dependents\n",
        "\n",
        "*   17.2 % applicants have 2 dependents\n",
        "*   8.61 % applicants have 3+ dependents\n",
        "\n",
        "\n",
        "\n",
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        "\n",
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        "fig.update_layout(title=\"Self employed and loan approved \",title_x=0.5)\n",
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\"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Self employed and loan approved \", \"x\": 0.5}},\n",
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              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##46 people have be approved of loan who are self employed, while 301 people have been approved of loan who are not self employed."
      ],
      "metadata": {
        "id": "_CS-Pe_PiNPZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Find the % of applications approved for self-employed applicants.\n",
        "\n",
        "##[46/511]*100 = 9.0019%   \n",
        "[511 is the total number of applicants]"
      ],
      "metadata": {
        "id": "vO7WPRL_jXYQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " ## What is the % of rejections for married male applicants?"
      ],
      "metadata": {
        "id": "cL27KOdHostt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "married_male_applicants=loan['Married'][loan['Gender']=='Male'][loan['Application_Status']=='N'].value_counts()\n",
        "married_male_applicants"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EayWMUe1jp5I",
        "outputId": "8e0e4d5c-6bc0-48f2-dde7-402010d10591"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Yes    87\n",
              "No     43\n",
              "Name: Married, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "87 applicants are married men whose loan applicantion were rejected.\n",
        "In total their % is   [87/511]*100 = 17.025%\n"
      ],
      "metadata": {
        "id": "jLhRgbE3mqtA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "married_male=loan.groupby(['Married','Gender','Application_Status']).count()\n",
        "married_male"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "A5bKT_EenmHf",
        "outputId": "60ced063-3b3a-47d9-cad7-0d47af0c90cd"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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            ],
            "text/plain": [
              "                                   Application_ID  ...  Income\n",
              "Married Gender Application_Status                  ...        \n",
              "No      Female N                               26  ...      26\n",
              "               Y                               40  ...      40\n",
              "        Male   N                               43  ...      43\n",
              "               Y                               71  ...      71\n",
              "Yes     Female N                                8  ...       8\n",
              "               Y                               17  ...      17\n",
              "        Male   N                               87  ...      87\n",
              "               Y                              219  ...     219\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Out of 306 married males 87 were rejected.\n",
        "### Their % is [87/306]*100 = 28.431% which goes along with the data.\n",
        "Banks tend to approve the loans of married males as they tend to be settled and pay back their loans without much risk."
      ],
      "metadata": {
        "id": "CNfMzBD-n49e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " ## Which property area has the maximum approval ratio."
      ],
      "metadata": {
        "id": "xcgvUSoTo52m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "area=loan['Property_Area'][loan['Application_Status']=='Y'].value_counts()\n",
        "area"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hn92rVOUo83O",
        "outputId": "6aab20ca-81fa-4495-9a94-bce4f37dad91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Semiurban    153\n",
              "Urban        104\n",
              "Rural         90\n",
              "Name: Property_Area, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### We can see that Semiurban area has the highest loan application status apporved.\n",
        " Applicant living in semiurban places tend to seek more loans than people living in urban places.Banks also eaily approve the loan applications of semiurban people easily than in rural places due to payback risk."
      ],
      "metadata": {
        "id": "eNVvZ8KKp2Pf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Find average dependents per income group."
      ],
      "metadata": {
        "id": "SrSrUt7Eq4KO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(hue=loan.Dependents,x=loan.Income)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "oTpp7UVAq-be",
        "outputId": "9d97b5b1-69f5-4a79-ba66-5dd91f14f960"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4f2a739390>"
            ]
          },
          "metadata": {},
          "execution_count": 160
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loan.groupby(['Income','Dependents']).count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "yLSZgVLKzfq2",
        "outputId": "7c2aea41-7d32-4296-d9da-5a0b6b910897"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8f3d18dc-9a7f-4edb-870c-e02ae2c1b0d0\">\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></th>\n",
              "      <th>Application_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Application_Status</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Income</th>\n",
              "      <th>Dependents</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">high</th>\n",
              "      <th>0</th>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
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              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f3d18dc-9a7f-4edb-870c-e02ae2c1b0d0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-8f3d18dc-9a7f-4edb-870c-e02ae2c1b0d0 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8f3d18dc-9a7f-4edb-870c-e02ae2c1b0d0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                   Application_ID  Gender  ...  Property_Area  Application_Status\n",
              "Income Dependents                          ...                                   \n",
              "high   0                       21      21  ...             21                  21\n",
              "       1                       13      13  ...             13                  13\n",
              "       2                        6       6  ...              6                   6\n",
              "       3+                       5       5  ...              5                   5\n",
              "low    0                      173     173  ...            173                 173\n",
              "       1                       42      42  ...             42                  42\n",
              "       2                       41      41  ...             41                  41\n",
              "       3+                      17      17  ...             17                  17\n",
              "medium 0                      100     100  ...            100                 100\n",
              "       1                       30      30  ...             30                  30\n",
              "       2                       41      41  ...             41                  41\n",
              "       3+                      22      22  ...             22                  22\n",
              "\n",
              "[12 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Find the average number of dependents per applicant."
      ],
      "metadata": {
        "id": "7Eu_ZtvWnKJB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_dependent.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxGpsR_JnMrP",
        "outputId": "2b7e6024-27bf-4951-f8fc-ef371bb1e9e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    127.75\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create a simple predictive model to assess whether a loan application will be approved or rejected and provide the accuracy score."
      ],
      "metadata": {
        "id": "pIiOHwqawWhV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing the identifiers"
      ],
      "metadata": {
        "id": "j_suAFYjbYZw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loan.drop(['Application_ID'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "0Pp58IOQwbB8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loan.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bje_VOce9eoU",
        "outputId": "f50c2f7b-b7f2-4981-c89e-60970c19fffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender                object\n",
              "Married               object\n",
              "Dependents            object\n",
              "Education             object\n",
              "Self_Employed         object\n",
              "Credit_History         int64\n",
              "Property_Area         object\n",
              "Income                object\n",
              "Application_Status    object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Feature engineering.\n",
        "We cannot feed the model with string values.Hence we need to encode them/convert them in values suitable to be fed to the model."
      ],
      "metadata": {
        "id": "_t1C7p0sbciB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "loan[\"Gender\"] = le.fit_transform(loan[\"Gender\"])\n",
        "loan[\"Married\"] = le.fit_transform(loan[\"Married\"])\n",
        "loan[\"Dependents\"] = le.fit_transform(loan[\"Dependents\"])\n",
        "loan[\"Self_Employed\"] = le.fit_transform(loan[\"Self_Employed\"])\n",
        "loan[\"Education\"] = le.fit_transform(loan[\"Education\"])\n",
        "loan[\"Property_Area\"] = le.fit_transform(loan[\"Property_Area\"])\n",
        "loan[\"Income\"] = le.fit_transform(loan[\"Income\"])\n",
        "loan[\"Application_Status\"] = le.fit_transform(loan[\"Application_Status\"])\n",
        "loan.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XsPItw1V915o",
        "outputId": "f49aafc6-ca86-4994-97a2-070570d5fe96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <div>\n",
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              "          const element = document.querySelector('#df-70954933-0343-4995-953d-7fa500e44ca7');\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",
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              "  "
            ],
            "text/plain": [
              "   Gender  Married  Dependents  ...  Property_Area  Income  Application_Status\n",
              "0       1        0           0  ...              2       2                   1\n",
              "1       1        1           1  ...              0       2                   0\n",
              "2       1        1           0  ...              2       1                   1\n",
              "3       1        1           0  ...              2       1                   1\n",
              "4       1        0           0  ...              2       2                   1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spliting the data to train and test.\n",
        "We use most of the data to train our model and then test it using the test data.Here we used 0.2 of our data for testing."
      ],
      "metadata": {
        "id": "RabReJILb834"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=loan.drop(columns='Application_Status', axis=1)\n",
        "y=loan['Application_Status']\n",
        "x_train, x_test, y_train, y_test = model_selection.train_test_split(x,y, test_size=0.2, random_state=7)"
      ],
      "metadata": {
        "id": "PfkTFRcbB3yu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Chose logistic regression as it gives better results in such problems"
      ],
      "metadata": {
        "id": "yg4TpmU6cadx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()\n",
        "model.fit(x_train.values,y_train.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DShHA1HSFnlG",
        "outputId": "73fb25bf-4626-4829-e618-40257db90976"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can user-feed the input data."
      ],
      "metadata": {
        "id": "HHbGBTDGch1Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (1,1,0,1,0,0,1,1)\n",
        "input_= np.asarray(input_data)\n",
        "input_data_reshaped = input_.reshape(1,-1)\n",
        "prediction = model.predict(input_data_reshaped)\n",
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acfe-iB5HFPt",
        "outputId": "8cc8b6b7-bdac-41d0-f907-64c8c6e5448d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Accuracy\n",
        "We should not be overwhelmed with this accuracy as accuracy is not a good evalution metrics.Data is imbalanced hence the accuracy will change if we change the random state value."
      ],
      "metadata": {
        "id": "iRsSd6w_cxRI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(x_test.values)\n",
        "print(accuracy_score(y_test, predictions)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2DFu6xPqF010",
        "outputId": "484ebab7-c541-481e-ec23-7ee0e7eb6555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "88.3495145631068\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confusion matrix is a much better evalution metrics."
      ],
      "metadata": {
        "id": "Hd7G57r-dFeX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix \n",
        "pd.crosstab(y_test, predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "G8Bhzba6GvbN",
        "outputId": "d51a9cdb-b943-485b-ea56-a47a2138b365"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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}