Untitled0.ipynb

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      "name": "Untitled0.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Sjankatti/SANTOSH/blob/master/Untitled0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "epTweOmYOHzD",
        "colab_type": "code",
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      },
      "source": [
        "### CSV - comma separated value file "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozhSnN3gPd_K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = 'https://raw.githubusercontent.com/umangkejriwal1122/Machine-Learning/master/Data%20Sets/titanic.csv'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82pdqDPdQHaz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path1 = 'titani.csv'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DAnsWYR_QgOH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_MvSWTGSn4O",
        "colab_type": "code",
        "outputId": "37aaafa1-0bd9-41ba-8e23-c253fc4786b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "titanic_data = pd.read_csv(path)\n",
        "titanic_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
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              "      <td>1</td>\n",
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              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
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              "      <td>3</td>\n",
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              "      <td>Heikkinen, Miss. Laina</td>\n",
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              "      <td>1</td>\n",
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              "      <td>S</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 8
        }
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(titanic_data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6Qtxa2-VByf",
        "colab_type": "code",
        "outputId": "bf7b4753-9410-4608-a38f-cb91b64aa80f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "list1 = [100,90,80,70,75]\n",
        "index_d1 = [\"Maths\",\"Science\",\"English\",\"Hindi\",\"SST\"]\n",
        "s1 = pd.Series(list1,index=index_d1)\n",
        "s1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Maths      100\n",
              "Science     90\n",
              "English     80\n",
              "Hindi       70\n",
              "SST         75\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
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      },
      "source": [
        "list2 = [90,90,100,80,75]\n",
        "index_d1 = [\"Maths\",\"Maths\",\"English\",\"Hindi\",\"SST\"]\n",
        "s2 = pd.Series(list1,index=index_d1)\n",
        "s2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Maths      100\n",
              "Maths       90\n",
              "English     80\n",
              "Hindi       70\n",
              "SST         75\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
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      "metadata": {
        "id": "UZe3ds9BWPhH",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "s1+s2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Maths      200\n",
              "Maths      180\n",
              "English    160\n",
              "Hindi      140\n",
              "SST        150\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9tktgqQWS7J",
        "colab_type": "code",
        "outputId": "c39b6291-0400-4bbf-9f9f-d5bcee67e7f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "titanic_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>Heikkinen, Miss. Laina</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
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              "      <th>4</th>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "titanic_data.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4j-22S6XQq6",
        "colab_type": "code",
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        "colab": {
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        }
      },
      "source": [
        "type(titanic_data['Name'].head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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      },
      "source": [
        "titanic_data[['Name', 'Pclass','Age']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>Braund, Mr. Owen Harris</td>\n",
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              "      <td>22.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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              "      <th>2</th>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
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              "      <td>26.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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            "text/plain": [
              "                                                Name  Pclass   Age\n",
              "0                            Braund, Mr. Owen Harris       3  22.0\n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...       1  38.0\n",
              "2                             Heikkinen, Miss. Laina       3  26.0\n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)       1  35.0\n",
              "4                           Allen, Mr. William Henry       3  35.0"
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        "outputId": "fc45b3cd-b807-4f44-f62b-6d09def6ce13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "sns.countplot(titanic_data['Survived'])\n",
        "sns.countplot(titanic_data['Pclass'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCFFrm7SZvPO",
        "colab_type": "code",
        "outputId": "a8a0a1f4-0bbe-4714-d27e-df59ccb4ae7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "#sns.countplot(titanic_data['Survived'])\n",
        "sns.countplot(titanic_data['Pclass'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GII6fyCzab7A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#sns.countplot(titanic_data['Survived'])\n",
        "sns.countplot(titanic_data['Pclass'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4RdykZma19A",
        "colab_type": "code",
        "outputId": "42bfc010-d95b-47af-8c03-bd405c2cad8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "### Plot for Count of Survived Data\n",
        "sns.countplot(titanic_data['Survived'],hue=titanic_data['Sex'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9X3B_86Va21U",
        "colab_type": "code",
        "outputId": "0042cf1e-702a-4224-c566-b7be2bb5c1ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "### Plot for Count of Survived Data\n",
        "sns.countplot(titanic_data['Survived'],hue=titanic_data['Pclass'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwOWEJoPbLlz",
        "colab_type": "code",
        "outputId": "a816fa5b-9861-45b2-b8b4-24c6148743f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "titanic_data['Age'].plot.hist()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f793e4a5208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rb8eJodMbYEL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "titanic_data.drop(['PassengerId','Name','Fare','Cabin','Embarked'],axis=1,inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SECBq3v6cSKy",
        "colab_type": "code",
        "outputId": "7c9406e4-9f92-4216-b8b0-115de61e3e08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "titanic_data.head()"
      ],
      "execution_count": 0,
      "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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex   Age  SibSp  Parch            Ticket\n",
              "0         0       3    male  22.0      1      0         A/5 21171\n",
              "1         1       1  female  38.0      1      0          PC 17599\n",
              "2         1       3  female  26.0      0      0  STON/O2. 3101282\n",
              "3         1       1  female  35.0      1      0            113803\n",
              "4         0       3    male  35.0      0      0            373450"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FREF6gkWdzjC",
        "colab_type": "code",
        "outputId": "6787442d-0333-4385-f7d7-66cca2084666",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "titanic_data.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived      0\n",
              "Pclass        0\n",
              "Sex           0\n",
              "Age         177\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Ticket        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teTdetesd7gS",
        "colab_type": "code",
        "outputId": "1069226a-950d-4441-ea95-22ba17f9f313",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_age = titanic_data['Age'].mean()\n",
        "mean_age = np.round(mean_age,2)\n",
        "mean_age"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THsC27BKe2kD",
        "colab_type": "code",
        "outputId": "2c648ca8-33a6-458b-82e9-4d9ab3a484be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "titanic_data['Age'].fillna(mean_age,inplace = True)\n",
        "titanic_data.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived    0\n",
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Ticket      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNVRlvQDfghO",
        "colab_type": "code",
        "outputId": "e7a8326e-c7b6-48b2-aefa-cfbfe0ba7983",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "sex=pd.get_dummies(titanic_data['Sex'],drop_first=True)\n",
        "sex.head()"
      ],
      "execution_count": 0,
      "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>male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   male\n",
              "0     1\n",
              "1     0\n",
              "2     0\n",
              "3     0\n",
              "4     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6wdegAUg43U",
        "colab_type": "code",
        "outputId": "e426f8c4-8a85-49bf-d73d-213e61f6b16d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "pclass=pd.get_dummies(titanic_data['Pclass'],drop_first=True)\n",
        "pclass.head()"
      ],
      "execution_count": 0,
      "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>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   2  3\n",
              "0  0  1\n",
              "1  0  0\n",
              "2  0  1\n",
              "3  0  0\n",
              "4  0  1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0xHv1FNh7ui",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "titanic_data = pd.concat([titanic_data,sex,pclass],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyuScSmqinIx",
        "colab_type": "code",
        "outputId": "b49cef67-1043-4598-8e85-6c8e2370b9b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "titanic_data.drop(['Sex','Pclass'],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-51-4b34cb16a176>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtitanic_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Sex'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Pclass'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\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[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3995\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3996\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3997\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   3998\u001b[0m         )\n\u001b[1;32m   3999\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3934\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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   3935\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3936\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   3937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3938\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3968\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3969\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3970\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   3971\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\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   3972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   5015\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\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   5016\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5017\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{labels[mask]} not found in axis\"\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   5018\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5019\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['Sex' 'Pclass'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGfZM3iojDeH",
        "colab_type": "code",
        "outputId": "57790f49-d56e-4100-b775-2740d5a11f81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "titanic_data.head()"
      ],
      "execution_count": 0,
      "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>Survived</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>male</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived   Age  SibSp  Parch            Ticket  male  2  3\n",
              "0         0  22.0      1      0         A/5 21171     1  0  1\n",
              "1         1  38.0      1      0          PC 17599     0  0  0\n",
              "2         1  26.0      0      0  STON/O2. 3101282     0  0  1\n",
              "3         1  35.0      1      0            113803     0  0  0\n",
              "4         0  35.0      0      0            373450     1  0  1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wuw6X_S9jRb9",
        "colab_type": "code",
        "outputId": "3ba8e365-5d9c-4292-daac-f5679eababbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "titanic_data.rename(columns={'male':\"sex\",2:\"pclass_2\",3:'plcass_3'},inplace=True)\n",
        "titanic_data"
      ],
      "execution_count": 0,
      "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>Survived</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>sex</th>\n",
              "      <th>pclass_2</th>\n",
              "      <th>plcass_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>29.7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived   Age  SibSp  Parch            Ticket  sex  pclass_2  plcass_3\n",
              "0           0  22.0      1      0         A/5 21171    1         0         1\n",
              "1           1  38.0      1      0          PC 17599    0         0         0\n",
              "2           1  26.0      0      0  STON/O2. 3101282    0         0         1\n",
              "3           1  35.0      1      0            113803    0         0         0\n",
              "4           0  35.0      0      0            373450    1         0         1\n",
              "..        ...   ...    ...    ...               ...  ...       ...       ...\n",
              "886         0  27.0      0      0            211536    1         1         0\n",
              "887         1  19.0      0      0            112053    0         0         0\n",
              "888         0  29.7      1      2        W./C. 6607    0         0         1\n",
              "889         1  26.0      0      0            111369    1         0         0\n",
              "890         0  32.0      0      0            370376    1         0         1\n",
              "\n",
              "[891 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WxyZvhTkCAH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import plotly.express as px"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNiJFfRL0v49",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = 'https://raw.githubusercontent.com/umangkejriwal1122/Machine-Learning/master/Data%20Sets/titanic.csv'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HyPVxqft0y6C",
        "colab_type": "code",
        "outputId": "2b5bdcf0-efae-4a12-9a25-829bcfddd139",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = pd.read_csv(path)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1rVbPGV03HI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = 'https://raw.githubusercontent.com/umangkejriwal1122/Machine-Learning/master/Data%20Sets/covid_19_clean_complete.csv'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9TcsWYhM1Kdg",
        "colab_type": "code",
        "outputId": "89546cd6-7db1-4985-bb88-0f0a22eaab59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = pd.read_csv(path)\n",
        "df.tail()"
      ],
      "execution_count": 0,
      "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>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24885</th>\n",
              "      <td>Saint Pierre and Miquelon</td>\n",
              "      <td>France</td>\n",
              "      <td>46.885200</td>\n",
              "      <td>-56.315900</td>\n",
              "      <td>4/25/20</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24886</th>\n",
              "      <td>NaN</td>\n",
              "      <td>South Sudan</td>\n",
              "      <td>6.877000</td>\n",
              "      <td>31.307000</td>\n",
              "      <td>4/25/20</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24887</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Western Sahara</td>\n",
              "      <td>24.215500</td>\n",
              "      <td>-12.885800</td>\n",
              "      <td>4/25/20</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24888</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Sao Tome and Principe</td>\n",
              "      <td>0.186360</td>\n",
              "      <td>6.613081</td>\n",
              "      <td>4/25/20</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24889</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>15.552727</td>\n",
              "      <td>48.516388</td>\n",
              "      <td>4/25/20</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Province/State         Country/Region  ...  Deaths  Recovered\n",
              "24885  Saint Pierre and Miquelon                 France  ...       0          0\n",
              "24886                        NaN            South Sudan  ...       0          0\n",
              "24887                        NaN         Western Sahara  ...       0          5\n",
              "24888                        NaN  Sao Tome and Principe  ...       0          0\n",
              "24889                        NaN                  Yemen  ...       0          0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8F5_A05t1NnP",
        "colab_type": "code",
        "outputId": "4f6b4ae8-d14e-4636-c939-1a4a8f5ee859",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 24890 entries, 0 to 24889\n",
            "Data columns (total 8 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Province/State  7600 non-null   object \n",
            " 1   Country/Region  24890 non-null  object \n",
            " 2   Lat             24890 non-null  float64\n",
            " 3   Long            24890 non-null  float64\n",
            " 4   Date            24890 non-null  object \n",
            " 5   Confirmed       24890 non-null  int64  \n",
            " 6   Deaths          24890 non-null  int64  \n",
            " 7   Recovered       24890 non-null  int64  \n",
            "dtypes: float64(2), int64(3), object(3)\n",
            "memory usage: 1.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfAsst3h1oda",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}