ComputeTask3.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv('/content/haberman.csv')\n",
        "dataset.columns=['patient_age','operation_year','positive_auxillary_nodes','survival_status']\n",
        "dataset"
      ],
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              "     patient_age  operation_year  positive_auxillary_nodes  survival_status\n",
              "0             30              62                         3                1\n",
              "1             30              65                         0                1\n",
              "2             31              59                         2                1\n",
              "3             31              65                         4                1\n",
              "4             33              58                        10                1\n",
              "..           ...             ...                       ...              ...\n",
              "300           75              62                         1                1\n",
              "301           76              67                         0                1\n",
              "302           77              65                         3                1\n",
              "303           78              65                         1                2\n",
              "304           83              58                         2                2\n",
              "\n",
              "[305 rows x 4 columns]"
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              "      <th></th>\n",
              "      <th>patient_age</th>\n",
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              "      <th>302</th>\n",
              "      <td>77</td>\n",
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      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
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      "execution_count": 126,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "patient_age                 0\n",
              "operation_year              0\n",
              "positive_auxillary_nodes    0\n",
              "survival_status             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr=dataset.corr()\n",
        "sns.heatmap(corr)"
      ],
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        "id": "lgHvXrgHxPun",
        "outputId": "19191478-6a0f-4942-db48-25e7981d1de6"
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      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3bebc32a90>"
            ]
          },
          "metadata": {},
          "execution_count": 127
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This graph tells us how patient age and positive auxillary nodes directly correlate to the survival status of a person"
      ],
      "metadata": {
        "id": "YmbAp84_ZJft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(30,18))\n",
        "sns.barplot(x='patient_age',y='positive_auxillary_nodes',hue='survival_status',data=dataset,ci=None,palette='mako')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        },
        "id": "_2N-9UHJx1qf",
        "outputId": "1337e10c-b4ab-4291-a00a-aa05174c7118"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3bebc15c90>"
            ]
          },
          "metadata": {},
          "execution_count": 128
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x1296 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This graph tells number of people who survived or didn't survive in different operation years"
      ],
      "metadata": {
        "id": "nSbADLTJasVL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='operation_year',hue='survival_status',data=dataset,palette='mako')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "duvdnmDizbMD",
        "outputId": "ed5bfc40-f201-41c3-a6e4-0e9410b3fdb1"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3beb9af290>"
            ]
          },
          "metadata": {},
          "execution_count": 129
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_survived=dataset[dataset['survival_status']==2] #splitting into survived or not survived\n",
        "df_not_survived=dataset[dataset['survival_status']==1]"
      ],
      "metadata": {
        "id": "ytQy8XycCSoI"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grp=df_survived.groupby(['patient_age','positive_auxillary_nodes']).size().reset_index(name=\"Count\").sort_values(\"Count\", ascending = False)"
      ],
      "metadata": {
        "id": "ndVSA9430qK9"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(grp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgGHFKZG1hdw",
        "outputId": "b3512f2c-f4f7-4b66-e38e-15e5caff9a1b"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    patient_age  positive_auxillary_nodes  Count\n",
            "32           52                         3      2\n",
            "24           48                        11      2\n",
            "4            41                         0      2\n",
            "8            43                         0      2\n",
            "21           47                         0      2\n",
            "..          ...                       ...    ...\n",
            "27           50                         0      1\n",
            "26           49                        10      1\n",
            "25           49                         0      1\n",
            "23           48                         7      1\n",
            "75           83                         2      1\n",
            "\n",
            "[76 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grp2=df_not_survived.groupby(['patient_age','positive_auxillary_nodes']).size().reset_index(name=\"Count\").sort_values(\"Count\", ascending = False)\n",
        "print(grp2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4m6n034TCyjN",
        "outputId": "b10f9003-ca0d-4ff6-eeee-4bc0ad4b1e16"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     patient_age  positive_auxillary_nodes  Count\n",
            "89            57                         0      7\n",
            "68            52                         0      6\n",
            "104           61                         0      5\n",
            "108           63                         0      5\n",
            "29            41                         0      5\n",
            "..           ...                       ...    ...\n",
            "45            45                         1      1\n",
            "43            44                        16      1\n",
            "42            44                         1      1\n",
            "40            43                        14      1\n",
            "131           77                         3      1\n",
            "\n",
            "[132 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l1=dataset['operation_year'].unique()\n",
        "l1.sort()\n",
        "l2=[]\n",
        "l3=[]\n",
        "l4=[]\n",
        "for i in l1:\n",
        "  count=0\n",
        "  count1=0\n",
        "  for j in range(len(dataset['operation_year'])):\n",
        "    if(i==dataset['operation_year'][j] and dataset['survival_status'][j]==2):\n",
        "      count=count+1\n",
        "      count1=count1+1\n",
        "    elif(i==dataset['operation_year'][j]):\n",
        "      count1=count1+1\n",
        "  l2.append(count)\n",
        "  l4.append(count1)\n",
        "for i in range(len(l1)):\n",
        "  l3.append(l2[i]/l4[i])"
      ],
      "metadata": {
        "id": "1RAXV9hEn5-K"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This graph tells us the survival ratios in different operation years"
      ],
      "metadata": {
        "id": "khL2qTQjoRmY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "sns.barplot(x=l1,y=l3,palette='mako')\n",
        "plt.xlabel('operation year')\n",
        "plt.ylabel('survived ratio')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "4WgUfzH4IjEF",
        "outputId": "053cbf89-e683-4d41-9f2b-7d72a03fe487"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'survived ratio')"
            ]
          },
          "metadata": {},
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l1=dataset['patient_age'].unique()\n",
        "l1.sort()\n",
        "l2=[]\n",
        "l3=[]\n",
        "l4=[]\n",
        "for i in l1:\n",
        "  count=0\n",
        "  count1=0\n",
        "  for j in range(len(dataset['patient_age'])):\n",
        "    if(i==dataset['patient_age'][j] and dataset['survival_status'][j]==2):\n",
        "      count=count+1\n",
        "      count1=count1+1\n",
        "    elif(i==dataset['patient_age'][j]):\n",
        "      count1=count1+1\n",
        "  l2.append(count)\n",
        "  l4.append(count1)\n",
        "for i in range(len(l1)):\n",
        "  l3.append(l2[i]/l4[i])\n"
      ],
      "metadata": {
        "id": "8L42TOg9LE4c"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This graph shows us the ratio of patients who survived in each age group"
      ],
      "metadata": {
        "id": "7Txar5iAtYTS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(25,15))\n",
        "sns.barplot(x=l1,y=l3,palette='mako')\n",
        "plt.xlabel('patient age')\n",
        "plt.ylabel('survived ratio')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "DIMNElgTMtoq",
        "outputId": "dc64fc1e-9445-40e5-8b5b-5119f502b430"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'survived ratio')"
            ]
          },
          "metadata": {},
          "execution_count": 137
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}