loanprediction.ipynb

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              "      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",
              "609  LP002978  Female      No          0      Graduate            No   \n",
              "610  LP002979    Male     Yes         3+      Graduate            No   \n",
              "611  LP002983    Male     Yes          1      Graduate            No   \n",
              "612  LP002984    Male     Yes          2      Graduate            No   \n",
              "613  LP002990  Female      No          0      Graduate           Yes   \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",
              "609             2900                0.0        71.0             360.0   \n",
              "610             4106                0.0        40.0             180.0   \n",
              "611             8072              240.0       253.0             360.0   \n",
              "612             7583                0.0       187.0             360.0   \n",
              "613             4583                0.0       133.0             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  \n",
              "..              ...           ...         ...  \n",
              "609             1.0         Rural           Y  \n",
              "610             1.0         Rural           Y  \n",
              "611             1.0         Urban           Y  \n",
              "612             1.0         Urban           Y  \n",
              "613             0.0     Semiurban           N  \n",
              "\n",
              "[614 rows x 13 columns]"
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        document.querySelector('#id_2acd0fd9-25ca-4b9a-96d2-0e712f7fca9f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 614,\n  \"fields\": [\n    {\n      \"column\": \"Loan_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 614,\n        \"samples\": [\n          \"LP002139\",\n          \"LP002223\",\n          \"LP001570\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Married\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1\",\n          \"3+\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Graduate\",\n          \"Graduate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Self_Employed\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ApplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6109,\n        \"min\": 150,\n        \"max\": 81000,\n        \"num_unique_values\": 505,\n        \"samples\": [\n          8333,\n          4342\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CoapplicantIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2926.2483692241885,\n        \"min\": 0.0,\n        \"max\": 41667.0,\n        \"num_unique_values\": 287,\n        \"samples\": [\n          1840.0,\n          2042.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LoanAmount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85.58732523570545,\n        \"min\": 9.0,\n        \"max\": 700.0,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          100.0,\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Amount_Term\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.12040985461256,\n        \"min\": 12.0,\n        \"max\": 480.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          84.0,\n          120.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Credit_History\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3648783192364048,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property_Area\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Urban\",\n          \"Rural\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loan_Status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"Y\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 664
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/Loan_prediction(1).csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "ISnuXYR1wRyc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lyWgzts4wWp1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "id": "Jk80k_k6wUer"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "anVR2mV8wdZ9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "WsG97P4MwX6D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gender'].unique()"
      ],
      "metadata": {
        "id": "DiRkwH2HwhmZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a=df['Gender'].mode()\n",
        "a"
      ],
      "metadata": {
        "id": "2Txz0arzxRBL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gender']=df['Gender'].map({'Male':1,'Female':2,'nan':a})\n"
      ],
      "metadata": {
        "id": "4bMYBoOFwoEn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "-svgTlgnyDMj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gender'].unique()"
      ],
      "metadata": {
        "id": "OXQ4L74LyFsm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mILS-CUOybSN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " #df['Gender']=df.fillna(df['Gender'].mean(),inplace=True)\n"
      ],
      "metadata": {
        "id": "o9KqTGzdyeDu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "MNoKgAFPziFp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "fzHFhdx-x-Ep"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Married'].unique()"
      ],
      "metadata": {
        "id": "cNXOJJQlz-n0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "b=df['Married'].mode()\n",
        "b"
      ],
      "metadata": {
        "id": "jj8A2ldV0kVu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['Married']=df['Married'].map({'No':1,'Yes':2})"
      ],
      "metadata": {
        "id": "K40PRGXt0d1A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Dependents'].unique()"
      ],
      "metadata": {
        "id": "SdhWoT6z1eIH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Dependents']=df['Dependents'].map({'0':0,'1':1,'2':2,'3+':4})"
      ],
      "metadata": {
        "id": "F6wlwOkn1kmG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Self_Employed'].unique()"
      ],
      "metadata": {
        "id": "Fd7WDU1n12_3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Self_Employed']=df['Self_Employed'].map({'No':0,'Yes':1})"
      ],
      "metadata": {
        "id": "RDb8NwZR1_53"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Credit_History'].unique(\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "l5onRfam2JH_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df.fillna(df.mode().iloc[0], inplace=True)"
      ],
      "metadata": {
        "id": "_-6lH9sB22gk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "BixUSCTyIOC6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "JcrmshTs25a5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "cc0HcajN3Bnu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "ouk10D0EBbbF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('Loan_ID',axis=1)"
      ],
      "metadata": {
        "id": "sdZSThwPBe1t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df.cor()"
      ],
      "metadata": {
        "id": "KM082Wdm3nRA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " #cor=df.corr()\n",
        " #cor"
      ],
      "metadata": {
        "id": "lVPpVHgo34Ou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "p4IcDTbgCYNq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop('Loan_Status',axis=1)\n",
        "X"
      ],
      "metadata": {
        "id": "6oCKeHVaCngZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop(['Loan_ID','Loan_Status'],axis=1)\n",
        "X"
      ],
      "metadata": {
        "id": "qn_FWmHaDFU7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Loan_Status']=df['Loan_Status'].map({'N':0,'Y':1})"
      ],
      "metadata": {
        "id": "WAg3MoAID5pQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "-EtwEwJnEB9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=df['Loan_Status']\n",
        "y"
      ],
      "metadata": {
        "id": "dz4F3Y4QDbEU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "71CvJ4H4DhzZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "-InxEfCsDNnH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X['Education']=X['Education'].map({'Graduate':1,'NotGraduate':0})"
      ],
      "metadata": {
        "id": "UmAZRoT4Ejyf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X['Property_Area']=X['Property_Area'].map({'Rural\t':0,'Urban':2,'Semiurban':1})"
      ],
      "metadata": {
        "id": "NAFFv7aiE1bb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.35,random_state=30)"
      ],
      "metadata": {
        "id": "I00PorCD4OQU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "SUbkF1tSFPGy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "id": "VPHHZoQDFS67"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.fillna(X.mode().iloc[0], inplace=True)"
      ],
      "metadata": {
        "id": "Y0XTnDSIFsoA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.isnull().sum()"
      ],
      "metadata": {
        "id": "JjfJDSyxFgw2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "reg=LogisticRegression()\n",
        "reg"
      ],
      "metadata": {
        "id": "tJxn44Fs4RCI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.isnull().sum()"
      ],
      "metadata": {
        "id": "EuhdzJhUGCXd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "id": "Bxfl-OF4GKTJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head(10)"
      ],
      "metadata": {
        "id": "mIQ6GRIYHLTs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.35,random_state=30)\n"
      ],
      "metadata": {
        "id": "0Owbm4EkGP97"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "reg=LogisticRegression()\n",
        "reg"
      ],
      "metadata": {
        "id": "FPIX_GqMGTC9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.fillna(X.mode().iloc[0],inplace=True)"
      ],
      "metadata": {
        "id": "thj8hDcRIZSs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.isnull().sum()"
      ],
      "metadata": {
        "id": "izCn4GWtIyEV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "id": "0TDkYS4xIsYf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "DDGWFEqaGVv1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "id": "Xj6JXdPSGZuG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ypre=reg.predict(X_test)\n",
        "ypre"
      ],
      "metadata": {
        "id": "mtk_Au4JGcip"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "con=confusion_matrix(y_test,ypre)\n",
        "con\n"
      ],
      "metadata": {
        "id": "6hLQytXcGiSx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "ac=accuracy_score(y_test,ypre)\n",
        "ac"
      ],
      "metadata": {
        "id": "JUfXDY7eGlEw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new=[[1,\t2.0,\t1.0,\t1.0,\t0.0\t,12841,\t10968.0,\t349.0,\t360.0,\t1.0\t,1.0]]\n",
        "ypre=reg.predict(new)\n",
        "ypre"
      ],
      "metadata": {
        "id": "nvm-NVlMGtqB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}