EDA_InsuranceClaimPrediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/Wishy-S/LearningML/blob/master/EDA_InsuranceClaimPrediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
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        "id": "-EcWLiJUbkvJ"
      },
      "source": [
        "df=pd.read_csv(\"/content/insurance2.csv\")\n",
        "df1=pd.read_csv(\"/content/insurance3r2.csv\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
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        "outputId": "a3b1b315-6057-4a11-9fa1-b78277b351e4"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1338, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6GL23EfIbkvM"
      },
      "source": [
        "# **EDA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:52.265883Z",
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        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "5NSgfGIWbkvP",
        "outputId": "b49c7aef-35f0-46de-e4d9-351f9cf162e6"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>steps</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "      <th>insuranceclaim</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>39.207025</td>\n",
              "      <td>0.505232</td>\n",
              "      <td>30.663397</td>\n",
              "      <td>5328.623318</td>\n",
              "      <td>1.094918</td>\n",
              "      <td>0.204783</td>\n",
              "      <td>1.515695</td>\n",
              "      <td>13270.422265</td>\n",
              "      <td>0.585202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.049960</td>\n",
              "      <td>0.500160</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>2453.643236</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>0.403694</td>\n",
              "      <td>1.104885</td>\n",
              "      <td>12110.011237</td>\n",
              "      <td>0.492871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1121.873900</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>26.296250</td>\n",
              "      <td>3008.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4740.287150</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>4007.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9382.033000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>8004.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16639.912515</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>53.130000</td>\n",
              "      <td>10010.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>63770.428010</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age          sex  ...       charges  insuranceclaim\n",
              "count  1338.000000  1338.000000  ...   1338.000000     1338.000000\n",
              "mean     39.207025     0.505232  ...  13270.422265        0.585202\n",
              "std      14.049960     0.500160  ...  12110.011237        0.492871\n",
              "min      18.000000     0.000000  ...   1121.873900        0.000000\n",
              "25%      27.000000     0.000000  ...   4740.287150        0.000000\n",
              "50%      39.000000     1.000000  ...   9382.033000        1.000000\n",
              "75%      51.000000     1.000000  ...  16639.912515        1.000000\n",
              "max      64.000000     1.000000  ...  63770.428010        1.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GSNuL-QdbkvQ"
      },
      "source": [
        "###### Checking for missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:52.316877Z",
          "iopub.execute_input": "2021-10-02T04:56:52.317366Z",
          "iopub.status.idle": "2021-10-02T04:56:52.326672Z",
          "shell.execute_reply.started": "2021-10-02T04:56:52.317314Z",
          "shell.execute_reply": "2021-10-02T04:56:52.325488Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-p9tczmVbkvS",
        "outputId": "c134a89a-28a8-4576-bf6a-e109f62c1735"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age               0\n",
              "sex               0\n",
              "bmi               0\n",
              "steps             0\n",
              "children          0\n",
              "smoker            0\n",
              "region            0\n",
              "charges           0\n",
              "insuranceclaim    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ca3pUaEIbkvS"
      },
      "source": [
        "### checking number of unique values, there is any cardinality in categorical features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:52.328985Z",
          "iopub.execute_input": "2021-10-02T04:56:52.329642Z",
          "iopub.status.idle": "2021-10-02T04:56:52.342674Z",
          "shell.execute_reply.started": "2021-10-02T04:56:52.329594Z",
          "shell.execute_reply": "2021-10-02T04:56:52.341418Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JgBGXxdbkvU",
        "outputId": "326911df-025f-4857-cda6-75f38e344287"
      },
      "source": [
        "L=['sex','region','children','smoker']\n",
        "for i in L:\n",
        "    print(\"{} has {} unique values\".format(i,df[i].unique()))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sex has [0 1] unique values\n",
            "region has [3 2 1 0] unique values\n",
            "children has [0 1 3 2 5 4] unique values\n",
            "smoker has [1 0] unique values\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MY18tnsVbkvW"
      },
      "source": [
        "##### Since cardinality is fine so checking with data distribution of continous variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:52.344429Z",
          "iopub.execute_input": "2021-10-02T04:56:52.345160Z",
          "iopub.status.idle": "2021-10-02T04:56:52.601342Z",
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        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "DC-xIrCabkvX",
        "outputId": "ed671ffd-ebf9-4b64-a42d-d6914c0cba27"
      },
      "source": [
        "sns.distplot(df[\"bmi\"])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd545fee50>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "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",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:52.797073Z",
          "iopub.execute_input": "2021-10-02T04:56:52.797370Z",
          "iopub.status.idle": "2021-10-02T04:56:53.024382Z",
          "shell.execute_reply.started": "2021-10-02T04:56:52.797339Z",
          "shell.execute_reply": "2021-10-02T04:56:53.023514Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "bsslTm8Pbkva",
        "outputId": "561f91f3-9569-4ba9-8ce7-3a4f53ca0b9c"
      },
      "source": [
        "sns.distplot(df[\"charges\"])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd50c06c90>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "69pq5pILbkvb"
      },
      "source": [
        "#### Since, charges is right skewed so we will do some normalization technique to make it a gaussian distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:53.025761Z",
          "iopub.execute_input": "2021-10-02T04:56:53.026078Z",
          "iopub.status.idle": "2021-10-02T04:56:53.197917Z",
          "shell.execute_reply.started": "2021-10-02T04:56:53.026046Z",
          "shell.execute_reply": "2021-10-02T04:56:53.196636Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "r98ne0Kkbkvb",
        "outputId": "904306f6-104a-4c76-b810-2db3e2971955"
      },
      "source": [
        "sns.distplot(df['steps'])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd50b5a510>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "tXStojuFi-mN"
      },
      "source": [
        "### **Checking the Correlation of attributes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "u6laLHQ6i9aE",
        "outputId": "7c038930-29f5-4bcd-f316-04bb4f2d85f1"
      },
      "source": [
        "#Plotting correlation\n",
        "corrmat=df.corr()\n",
        "top_corr_features=corrmat.index\n",
        "plt.figure(figsize=(14,8))\n",
        "#To plot heatmap\n",
        "import seaborn as sns\n",
        "g=sns.heatmap(df[top_corr_features].corr(),annot=True,cmap=\"RdYlGn\")"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQrC_NEXbkvb"
      },
      "source": [
        "### **Checking if dataset is imbalanced**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:53.199627Z",
          "iopub.execute_input": "2021-10-02T04:56:53.200089Z",
          "iopub.status.idle": "2021-10-02T04:56:53.408269Z",
          "shell.execute_reply.started": "2021-10-02T04:56:53.200040Z",
          "shell.execute_reply": "2021-10-02T04:56:53.407232Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "eSma8drVbkvd",
        "outputId": "5bf34932-e4ff-4dfd-99f8-966af9dcb47f"
      },
      "source": [
        "sns.countplot(x='insuranceclaim',data=df)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd50ae7950>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "9nFkvuZUbkve"
      },
      "source": [
        "#### Checking for Outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:53.924314Z",
          "iopub.execute_input": "2021-10-02T04:56:53.924622Z",
          "iopub.status.idle": "2021-10-02T04:56:54.044978Z",
          "shell.execute_reply.started": "2021-10-02T04:56:53.924589Z",
          "shell.execute_reply": "2021-10-02T04:56:54.044092Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "0FMqlNqcbkvf",
        "outputId": "c7f313a5-935a-4526-ad82-b2b835a61134"
      },
      "source": [
        "sns.boxplot(y=df['bmi'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd480d2550>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AzFD8AJCZlA9iwdvQ0NCQxsfHy47RFk79dxgcHCw5SXvo6+vTwMBA2THQAIofaNKyZcvKjgA0heLHgrBHB3S+lA9b/77to7YPTpt2p+0J22PFa2Oq7QMAZpfyj7s/lLRhlulbImJ18dqWcPsAgFkkK/6I+IWkl1N9PwCgOWWczvkZ2/uLQ0GXlLB9AMhaq4v/bknvlbRa0hFJ35xrQdubbdds1+r1eqvyAcDbXkuLPyJeiogTEXFS0nclrTnLssMRUY2IaqVSaV1IAHiba2nx214xbfhRSQfnWhYAkEay8/ht/6uk6yRdZvsFSV+WdJ3t1ZJC0mFJt6TaPgBgdo6IsjPMy3Zd0vNl5wBmcZmk35QdApjDuyNixrHyjih+oF3ZrkVEtewcwEJwd04AyAzFDwCZofiBczNcdgBgoTjGDwCZYY8fADJD8QNAZih+AMgMxQ8AmaH4ASAz/w9Dxh3JMMvT/QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.046429Z",
          "iopub.execute_input": "2021-10-02T04:56:54.046750Z",
          "iopub.status.idle": "2021-10-02T04:56:54.161384Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.046705Z",
          "shell.execute_reply": "2021-10-02T04:56:54.160068Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "6q4PRWf-bkvg",
        "outputId": "e079d12f-9922-4abe-e29f-3da83b8836d4"
      },
      "source": [
        "sns.boxplot(y=df['age'])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd48067110>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "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",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.162932Z",
          "iopub.execute_input": "2021-10-02T04:56:54.163266Z",
          "iopub.status.idle": "2021-10-02T04:56:54.285449Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.163233Z",
          "shell.execute_reply": "2021-10-02T04:56:54.284321Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "EFQzp25Ebkvh",
        "outputId": "9f023be4-cd3d-44ce-ea19-bdaba3bc720c"
      },
      "source": [
        "sns.boxplot(y=df['charges'])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd47fcca50>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "Mg2s_tRWbkvh"
      },
      "source": [
        "## Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.287091Z",
          "iopub.execute_input": "2021-10-02T04:56:54.287501Z",
          "iopub.status.idle": "2021-10-02T04:56:54.293679Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.287463Z",
          "shell.execute_reply": "2021-10-02T04:56:54.292367Z"
        },
        "trusted": true,
        "id": "yIgkaeqlbkvi"
      },
      "source": [
        "lower_limit=df['bmi'].mean()- 3* df['bmi'].std()\n",
        "upper_limit=df['bmi'].mean()+ 3* df['bmi'].std()"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.295326Z",
          "iopub.execute_input": "2021-10-02T04:56:54.295799Z",
          "iopub.status.idle": "2021-10-02T04:56:54.310658Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.295748Z",
          "shell.execute_reply": "2021-10-02T04:56:54.309640Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fsy980Fbkvi",
        "outputId": "ee266dfd-7907-4891-a34c-1b9e058bb802"
      },
      "source": [
        "print(upper_limit), print(lower_limit)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "48.95795759602359\n",
            "12.368836125949485\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.312428Z",
          "iopub.execute_input": "2021-10-02T04:56:54.312789Z",
          "iopub.status.idle": "2021-10-02T04:56:54.339953Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.312705Z",
          "shell.execute_reply": "2021-10-02T04:56:54.338662Z"
        },
        "trusted": true,
        "id": "kvbm_jgjbkvi"
      },
      "source": [
        "df.loc[df['bmi']>=48,'bmi']=48"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.343077Z",
          "iopub.execute_input": "2021-10-02T04:56:54.343414Z",
          "iopub.status.idle": "2021-10-02T04:56:54.459678Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.343377Z",
          "shell.execute_reply": "2021-10-02T04:56:54.458935Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "NS-31yWybkvj",
        "outputId": "b913485e-0bb1-4fba-cf1f-54f144c6ddea"
      },
      "source": [
        "sns.boxplot(y=df['bmi'])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd47fc3f50>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "Ql93tbbRbkvj"
      },
      "source": [
        "#### Bmi was having a gaussian distribution and was having outliers in order to handle that we replaced the values after the 3rd standard mean with the upper limit as calculated in order to overcome the impcat of outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.461128Z",
          "iopub.execute_input": "2021-10-02T04:56:54.461609Z",
          "iopub.status.idle": "2021-10-02T04:56:54.470355Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.461576Z",
          "shell.execute_reply": "2021-10-02T04:56:54.469469Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v2aROssobkvk",
        "outputId": "9fae35fa-ea54-462a-c332-606d11e1672d"
      },
      "source": [
        "IQR=df.charges.quantile(0.75)-df.charges.quantile(0.25)\n",
        "IQR"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11899.625365"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.471971Z",
          "iopub.execute_input": "2021-10-02T04:56:54.472667Z",
          "iopub.status.idle": "2021-10-02T04:56:54.488230Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.472619Z",
          "shell.execute_reply": "2021-10-02T04:56:54.487206Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SEJ9vpd2bkvl",
        "outputId": "728a2800-f61f-409f-a19d-78f98cf960a8"
      },
      "source": [
        "df['charges'].describe()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     1338.000000\n",
              "mean     13270.422265\n",
              "std      12110.011237\n",
              "min       1121.873900\n",
              "25%       4740.287150\n",
              "50%       9382.033000\n",
              "75%      16639.912515\n",
              "max      63770.428010\n",
              "Name: charges, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.490065Z",
          "iopub.execute_input": "2021-10-02T04:56:54.490771Z",
          "iopub.status.idle": "2021-10-02T04:56:54.502336Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.490700Z",
          "shell.execute_reply": "2021-10-02T04:56:54.500863Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Rd-xeXLbkvl",
        "outputId": "62facd20-83cd-4cbf-8d9b-0a6642c7bebe"
      },
      "source": [
        "lower_limit=df['charges'].quantile(0.25)-(IQR*3)\n",
        "upper_limit=df['charges'].quantile(0.75)+(IQR*3)\n",
        "print(lower_limit), print(upper_limit)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-30958.588945\n",
            "52338.78861\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.503786Z",
          "iopub.execute_input": "2021-10-02T04:56:54.504100Z",
          "iopub.status.idle": "2021-10-02T04:56:54.510751Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.504070Z",
          "shell.execute_reply": "2021-10-02T04:56:54.509968Z"
        },
        "trusted": true,
        "id": "6Wnz6X_gbkvm"
      },
      "source": [
        "df.loc[df['charges']>=52338,'charges']=52338"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.512429Z",
          "iopub.execute_input": "2021-10-02T04:56:54.512895Z",
          "iopub.status.idle": "2021-10-02T04:56:54.724683Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.512848Z",
          "shell.execute_reply": "2021-10-02T04:56:54.723679Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "xnPNo49Abkvn",
        "outputId": "b5d0867b-0d84-44d3-92ee-77748f87986b"
      },
      "source": [
        "sns.distplot(df['charges'])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdd47f3b250>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "vmOObB2vbkvn"
      },
      "source": [
        "#### 'Charges' was having a skewed00000 distribution and was having outliers in order to handle that we replaced the values after 523338 with the upper limit as calculated in order to overcome the impact of outliers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDHTdAICbkvo"
      },
      "source": [
        "# Model Built"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T05:01:29.257544Z",
          "iopub.execute_input": "2021-10-02T05:01:29.257956Z",
          "iopub.status.idle": "2021-10-02T05:01:29.292000Z",
          "shell.execute_reply.started": "2021-10-02T05:01:29.257904Z",
          "shell.execute_reply": "2021-10-02T05:01:29.291139Z"
        },
        "trusted": true,
        "id": "9dr13F7pbkvo"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score,roc_auc_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3jYI6XCbkvp"
      },
      "source": [
        "## **Using Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jriyf2Lbh2l1"
      },
      "source": [
        "N = df.shape[1];\n",
        "X_train,x_test,Y_train,y_test=train_test_split(df.iloc[:, :N-1],df.iloc[:,-1],test_size=0.20)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T04:56:54.960169Z",
          "iopub.execute_input": "2021-10-02T04:56:54.960502Z",
          "iopub.status.idle": "2021-10-02T04:56:55.035417Z",
          "shell.execute_reply.started": "2021-10-02T04:56:54.960471Z",
          "shell.execute_reply": "2021-10-02T04:56:55.034518Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WSDJl22bkvq",
        "outputId": "571f0e6c-f431-4a6a-b91f-0688e9ccf470"
      },
      "source": [
        "classifier=LogisticRegression()\n",
        "classifier.fit(X_train,Y_train)\n",
        "y_pred=classifier.predict(x_test)\n",
        "\n",
        "\n",
        "accuracy = accuracy_score(y_test,y_pred)\n",
        "classreport = classification_report(y_test,y_pred)\n",
        "confusnMatrix = confusion_matrix(y_test,y_pred)\n",
        "print('The accuracy: {}'.format(accuracy))\n",
        "print('The Classification Report:\\n {}'.format(classreport))\n",
        "print('The Confusion Matrix:\\n {}'.format(confusnMatrix))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy: 0.746268656716418\n",
            "The Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.57      0.66       115\n",
            "           1       0.73      0.88      0.80       153\n",
            "\n",
            "    accuracy                           0.75       268\n",
            "   macro avg       0.75      0.72      0.73       268\n",
            "weighted avg       0.75      0.75      0.74       268\n",
            "\n",
            "The Confusion Matrix:\n",
            " [[ 66  49]\n",
            " [ 19 134]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oHcUaZI1bkvq"
      },
      "source": [
        "## **Using Random Forest Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NZEvEjopArD"
      },
      "source": [
        "dff = df.copy()\n",
        "dff = dff.drop(\"region\",axis=1)\n",
        "dff.head()\n",
        "cl = dff.shape[1]\n",
        "X_train,x_test,Y_train,y_test=train_test_split(df.iloc[:, :cl-1],df.iloc[:,-1],test_size=0.25)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-10-02T05:02:08.609008Z",
          "iopub.execute_input": "2021-10-02T05:02:08.609396Z",
          "iopub.status.idle": "2021-10-02T05:02:08.930846Z",
          "shell.execute_reply.started": "2021-10-02T05:02:08.609364Z",
          "shell.execute_reply": "2021-10-02T05:02:08.929243Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKZT6DVUbkvr",
        "outputId": "242fe3f9-7523-4403-c8a3-f2ccf86bf41f"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn import metrics\n",
        "\n",
        "random_clf = RandomForestClassifier(n_estimators=50, min_samples_split=5, min_samples_leaf=5, max_features='sqrt', max_depth=25, criterion='entropy')\n",
        "random_clf.fit(X_train,Y_train)\n",
        "random_clf_predict = random_clf.predict(x_test)\n",
        "\n",
        "random_clf_report = classification_report(y_test,random_clf_predict)\n",
        "print(random_clf_report)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, random_clf_predict))\n",
        "confusnMatrix = confusion_matrix(y_test,random_clf_predict)\n",
        "print(confusnMatrix)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.97      0.96       133\n",
            "           1       0.98      0.97      0.98       202\n",
            "\n",
            "    accuracy                           0.97       335\n",
            "   macro avg       0.97      0.97      0.97       335\n",
            "weighted avg       0.97      0.97      0.97       335\n",
            "\n",
            "Accuracy: 0.9701492537313433\n",
            "[[129   4]\n",
            " [  6 196]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "D5MK1YNINtsJ",
        "outputId": "d1d99671-9d9d-48f7-f6e7-b770e2eb39fb"
      },
      "source": [
        "from sklearn.metrics import roc_curve, roc_auc_score,auc\n",
        "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test,random_clf_predict)\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
        "plt.plot(false_positive_rate,true_positive_rate, color='red',label = 'AUC = %0.2f' % roc_auc)\n",
        "plt.legend(loc = 'lower right')\n",
        "plt.plot([0, 1], [0, 1],linestyle='--')\n",
        "plt.axis('tight')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'False Positive Rate')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANvLU9Ntey43"
      },
      "source": [
        "## **Using Support Vector Machine Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iQx_N6U8kts9",
        "outputId": "bc1b060a-660c-4b6c-ac47-2f44c379c7b6"
      },
      "source": [
        "dff = df.copy()\n",
        "dff.head()"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "      <th>insuranceclaim</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>16884.92400</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1725.55230</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4449.46200</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>21984.47061</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3866.85520</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex     bmi  children  smoker  region      charges  insuranceclaim\n",
              "0   19    0  27.900         0       1       3  16884.92400               1\n",
              "1   18    1  33.770         1       0       2   1725.55230               1\n",
              "2   28    1  33.000         3       0       2   4449.46200               0\n",
              "3   33    1  22.705         0       0       1  21984.47061               0\n",
              "4   32    1  28.880         0       0       1   3866.85520               1"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KNWxcX7Lhwdx"
      },
      "source": [
        "cl = df2.shape[1]\n",
        "X_train,x_test,Y_train,y_test=train_test_split(df2.iloc[:, :cl-1],df2.iloc[:,-1],test_size=0.20)"
      ],
      "execution_count": 268,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jekR3rv6PWww"
      },
      "source": [
        "svm_clf = SVC(kernel=\"poly\",degree=2)\n",
        "svm_clf.fit(X_train,Y_train)\n",
        "y_predict_svm = svm_clf.predict(x_test)\n",
        "svm_clf_report = classification_report(y_test,y_predict_svm)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_predict_svm))\n",
        "print(svm_clf_report)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pPZSX_PWTmNo",
        "outputId": "050fd1d3-ebec-42e1-be27-84b5f8324f1e"
      },
      "source": [
        "df2 = df1.copy()\n",
        "df2.head()"
      ],
      "execution_count": 389,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>steps</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "      <th>insuranceclaim</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>27.900</td>\n",
              "      <td>3009</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>16884.92400</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>33.770</td>\n",
              "      <td>3008</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1725.55230</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3009</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4449.46200</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>22.705</td>\n",
              "      <td>10009</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>21984.47061</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>28.880</td>\n",
              "      <td>8010</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3866.85520</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex     bmi  steps  ...  smoker  region      charges  insuranceclaim\n",
              "0   19    0  27.900   3009  ...       1       3  16884.92400               1\n",
              "1   18    1  33.770   3008  ...       0       2   1725.55230               1\n",
              "2   28    1  33.000   3009  ...       0       2   4449.46200               0\n",
              "3   33    1  22.705  10009  ...       0       1  21984.47061               0\n",
              "4   32    1  28.880   8010  ...       0       1   3866.85520               1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 389
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NbDPdRFdagxg",
        "outputId": "b6e51d21-dc54-4d56-e508-3302617e4e64"
      },
      "source": [
        "print(df2.loc[df2['insuranceclaim']==1].count())\n",
        "print(df2.loc[df2['insuranceclaim']==0].count())\n",
        "783-555"
      ],
      "execution_count": 364,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age               783\n",
            "sex               783\n",
            "bmi               783\n",
            "steps             783\n",
            "children          783\n",
            "smoker            783\n",
            "region            783\n",
            "charges           783\n",
            "insuranceclaim    783\n",
            "dtype: int64\n",
            "age               555\n",
            "sex               555\n",
            "bmi               555\n",
            "steps             555\n",
            "children          555\n",
            "smoker            555\n",
            "region            555\n",
            "charges           555\n",
            "insuranceclaim    555\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "228"
            ]
          },
          "metadata": {},
          "execution_count": 364
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Za8BeaYDq1IB",
        "outputId": "697cd132-3e01-4e6b-a759-b209618ad42c"
      },
      "source": [
        "svm_clf = SVC(kernel=\"linear\")\n",
        "svm_clf.fit(X_train,Y_train)\n",
        "y_predict_svm = svm_clf.predict(x_test)\n",
        "svm_clf_report = classification_report(y_test,y_predict_svm)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test,y_predict_svm))\n",
        "print(svm_clf_report)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9044776119402985\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.87      0.88       133\n",
            "           1       0.92      0.93      0.92       202\n",
            "\n",
            "    accuracy                           0.90       335\n",
            "   macro avg       0.90      0.90      0.90       335\n",
            "weighted avg       0.90      0.90      0.90       335\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "8l-j77WSaNNR",
        "outputId": "930c0c00-ddc1-4f3b-dd19-09e8ed772c4f"
      },
      "source": [
        "x_test=pd.DataFrame\n",
        "y_test=pd.DataFrame\n",
        "k = 228\n",
        "N = df2.shape[1];\n",
        "for index, d in df2.iterrows():\n",
        "  if d['insuranceclaim'] == 1:\n",
        "    x_test.append(df2.iloc[])\n",
        "    y_test.append(1)\n",
        "    df2.drop(index,inplace=True)\n",
        "    k-=1\n",
        "\n",
        "  #print(i)\n",
        "  if k==0:\n",
        "    break"
      ],
      "execution_count": 395,
      "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-395-3bf23c2ac8bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\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;32mif\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'insuranceclaim'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mx_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\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      8\u001b[0m     \u001b[0my_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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: append() missing 1 required positional argument: 'other'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lcjna1xJkWaH"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}