Kidney_disease_case_study.ipynb

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    "colab": {
      "name": "Kidney disease case study.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JUPUDI-VAMSI-KALYAN/Data_Science-Python-/blob/master/Kidney_disease_case_study.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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        "colab": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-85426c1d-37a3-4b73-b9a0-b16178118753\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-85426c1d-37a3-4b73-b9a0-b16178118753\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kidney.csv to kidney.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrFtNY1SU2Q3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FToBa0stU2T0",
        "colab_type": "code",
        "outputId": "f6deb938-e3b6-470d-c872-db79c1a0fc11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df=pd.read_csv(\"kidney.csv\")\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>48</th>\n",
              "      <th>80</th>\n",
              "      <th>1.020</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <th>?</th>\n",
              "      <th>normal</th>\n",
              "      <th>notpresent</th>\n",
              "      <th>notpresent.1</th>\n",
              "      <th>121</th>\n",
              "      <th>36</th>\n",
              "      <th>1.2</th>\n",
              "      <th>?.1</th>\n",
              "      <th>?.2</th>\n",
              "      <th>15.4</th>\n",
              "      <th>44</th>\n",
              "      <th>7800</th>\n",
              "      <th>5.2</th>\n",
              "      <th>yes</th>\n",
              "      <th>yes.1</th>\n",
              "      <th>no</th>\n",
              "      <th>good</th>\n",
              "      <th>no.1</th>\n",
              "      <th>no.2</th>\n",
              "      <th>ckd</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7</td>\n",
              "      <td>50</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>?</td>\n",
              "      <td>18</td>\n",
              "      <td>0.8</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>11.3</td>\n",
              "      <td>38</td>\n",
              "      <td>6000</td>\n",
              "      <td>?</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>62</td>\n",
              "      <td>80</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>423</td>\n",
              "      <td>53</td>\n",
              "      <td>1.8</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>9.6</td>\n",
              "      <td>31</td>\n",
              "      <td>7500</td>\n",
              "      <td>?</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>48</td>\n",
              "      <td>70</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>117</td>\n",
              "      <td>56</td>\n",
              "      <td>3.8</td>\n",
              "      <td>111</td>\n",
              "      <td>2.5</td>\n",
              "      <td>11.2</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>51</td>\n",
              "      <td>80</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>106</td>\n",
              "      <td>26</td>\n",
              "      <td>1.4</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>11.6</td>\n",
              "      <td>35</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>60</td>\n",
              "      <td>90</td>\n",
              "      <td>1.015</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>74</td>\n",
              "      <td>25</td>\n",
              "      <td>1.1</td>\n",
              "      <td>142</td>\n",
              "      <td>3.2</td>\n",
              "      <td>12.2</td>\n",
              "      <td>39</td>\n",
              "      <td>7800</td>\n",
              "      <td>4.4</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   48  80  1.020  1  0       ?    normal  ...  yes yes.1  no  good no.1 no.2  ckd\n",
              "0   7  50  1.020  4  0       ?    normal  ...   no    no  no  good   no   no  ckd\n",
              "1  62  80  1.010  2  3  normal    normal  ...   no   yes  no  poor   no  yes  ckd\n",
              "2  48  70  1.005  4  0  normal  abnormal  ...  yes    no  no  poor  yes  yes  ckd\n",
              "3  51  80  1.010  2  0  normal    normal  ...   no    no  no  good   no   no  ckd\n",
              "4  60  90  1.015  3  0       ?         ?  ...  yes   yes  no  good  yes   no  ckd\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09ittEbuU2WM",
        "colab_type": "code",
        "outputId": "da4a6142-344f-4653-e4db-b67413741b33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 399 entries, 0 to 398\n",
            "Data columns (total 25 columns):\n",
            "48              399 non-null object\n",
            "80              399 non-null object\n",
            "1.020           399 non-null object\n",
            "1               399 non-null object\n",
            "0               399 non-null object\n",
            "?               399 non-null object\n",
            "normal          399 non-null object\n",
            "notpresent      399 non-null object\n",
            "notpresent.1    399 non-null object\n",
            "121             399 non-null object\n",
            "36              399 non-null object\n",
            "1.2             399 non-null object\n",
            "?.1             399 non-null object\n",
            "?.2             399 non-null object\n",
            "15.4            399 non-null object\n",
            "44              399 non-null object\n",
            "7800            399 non-null object\n",
            "5.2             399 non-null object\n",
            "yes             399 non-null object\n",
            "yes.1           399 non-null object\n",
            "no              399 non-null object\n",
            "good            399 non-null object\n",
            "no.1            399 non-null object\n",
            "no.2            399 non-null object\n",
            "ckd             399 non-null object\n",
            "dtypes: object(25)\n",
            "memory usage: 78.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDsQ2oaiU2Y5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=df.drop(\"ckd\",axis=1)\n",
        "y=df[\"ckd\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFW9nKoYV0xP",
        "colab_type": "code",
        "outputId": "fea7b533-755d-42b7-fcb9-656f42f89182",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        }
      },
      "source": [
        "# Import necessary modules\n",
        "from sklearn_pandas import DataFrameMapper\n",
        "from sklearn_pandas import CategoricalImputer\n",
        "\n",
        "# Check number of nulls in each feature column\n",
        "nulls_per_column = X.isnull().sum()\n",
        "print(nulls_per_column)\n",
        "\n",
        "# Create a boolean mask for categorical columns\n",
        "categorical_feature_mask = X.dtypes == object\n",
        "\n",
        "# Get list of categorical column names\n",
        "categorical_columns = X.columns[categorical_feature_mask].tolist()\n",
        "\n",
        "# Get list of non-categorical column names\n",
        "non_categorical_columns = X.columns[~categorical_feature_mask].tolist()\n",
        "\n",
        "# Apply numeric imputer\n",
        "numeric_imputation_mapper = DataFrameMapper(\n",
        "                                            [([numeric_feature],Imputer(strategy=\"median\")) for numeric_feature in non_categorical_columns],\n",
        "                                            input_df=True,\n",
        "                                            df_out=True\n",
        "                                           )\n",
        "\n",
        "# Apply categorical imputer\n",
        "categorical_imputation_mapper = DataFrameMapper(\n",
        "                                                [(category_feature, CategoricalImputer()) for category_feature in categorical_columns],\n",
        "                                                input_df=True,\n",
        "                                                df_out=True\n",
        "                                               )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "48              0\n",
            "80              0\n",
            "1.020           0\n",
            "1               0\n",
            "0               0\n",
            "?               0\n",
            "normal          0\n",
            "notpresent      0\n",
            "notpresent.1    0\n",
            "121             0\n",
            "36              0\n",
            "1.2             0\n",
            "?.1             0\n",
            "?.2             0\n",
            "15.4            0\n",
            "44              0\n",
            "7800            0\n",
            "5.2             0\n",
            "yes             0\n",
            "yes.1           0\n",
            "no              0\n",
            "good            0\n",
            "no.1            0\n",
            "no.2            0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Um_mYCXxV0u4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.pipeline import FeatureUnion\n",
        "\n",
        "# Combine the numeric and categorical transformations\n",
        "numeric_categorical_union = FeatureUnion([\n",
        "                                          (\"num_mapper\", numeric_imputation_mapper),\n",
        "                                          (\"cat_mapper\", categorical_imputation_mapper)\n",
        "                                         ])\n",
        "Dictifier = X.to_dict(\"record\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dDW-Yl9WV0sF",
        "colab_type": "code",
        "outputId": "0156fb1a-4b42-4a91-9537-60079885eac1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "# Create full pipeline\n",
        "from sklearn.pipeline import Pipeline\n",
        "pipeline = Pipeline([\n",
        "                     (\"featureunion\", numeric_categorical_union),\n",
        "                     (\"dictifier\", Dictifier(X.to_dict())),\n",
        "                     (\"vectorizer\", DictVectorizer(sort=False)),\n",
        "                     (\"clf\", xgb.XGBClassifier(max_depth=3))\n",
        "                    ])\n",
        "\n",
        "# Perform cross-validation\n",
        "cross_val_scores = cross_val_score(pipeline, X, y, scoring=\"roc_auc\", cv=3)\n",
        "\n",
        "# Print avg. AUC\n",
        "print(\"3-fold AUC: \", np.mean(cross_val_scores))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-c00efbe07009>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m pipeline = Pipeline([\n\u001b[1;32m      3\u001b[0m                      \u001b[0;34m(\u001b[0m\u001b[0;34m\"featureunion\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_categorical_union\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m                      \u001b[0;34m(\u001b[0m\u001b[0;34m\"dictifier\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDictifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m                      \u001b[0;34m(\u001b[0m\u001b[0;34m\"vectorizer\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDictVectorizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m                      \u001b[0;34m(\u001b[0m\u001b[0;34m\"clf\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mXGBClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'list' object is not callable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsT_tdiUV0pZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7KjbMqOV0mO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6c5J1S1V0iK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}