Titanic_naive_bayes.ipynb

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      "source": [
        "import pandas as pd\n",
        "data=pd.read_csv('/content/titanic.csv')"
      ]
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        "data.head()"
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              "   PassengerId                                               Name  Pclass  \\\n",
              "0            1                            Braund, Mr. Owen Harris       3   \n",
              "1            2  Cumings, Mrs. John Bradley (Florence Briggs Th...       1   \n",
              "2            3                             Heikkinen, Miss. Laina       3   \n",
              "3            4       Futrelle, Mrs. Jacques Heath (Lily May Peel)       1   \n",
              "4            5                           Allen, Mr. William Henry       3   \n",
              "\n",
              "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \\\n",
              "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S   \n",
              "1  female  38.0      1      0          PC 17599  71.2833   C85        C   \n",
              "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S   \n",
              "3  female  35.0      1      0            113803  53.1000  C123        S   \n",
              "4    male  35.0      0      0            373450   8.0500   NaN        S   \n",
              "\n",
              "   Survived  \n",
              "0         0  \n",
              "1         1  \n",
              "2         1  \n",
              "3         1  \n",
              "4         0  "
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        "data.drop(['PassengerId','Name','SibSp','Parch','Ticket','Cabin','Embarked'],axis='columns',inplace=True)"
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              "   Pclass     Sex   Age     Fare  Survived\n",
              "0       3    male  22.0   7.2500         0\n",
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              "2       3  female  26.0   7.9250         1\n",
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          "data": {
            "text/plain": [
              "Pclass      0\n",
              "Sex         0\n",
              "Age       177\n",
              "Fare        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
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      "execution_count": 19,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "Pclass    False\n",
              "Sex       False\n",
              "Age        True\n",
              "Fare      False\n",
              "dtype: bool"
            ]
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          "metadata": {},
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        "inp.Age=inp.Age.fillna(inp.Age.mean())"
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        "id": "AyZPWZp0WDz7",
        "outputId": "2e9210f3-535b-4a11-8218-ea5eb48d6867"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pclass  Sex        Age     Fare\n",
              "0       3    1  22.000000   7.2500\n",
              "1       1    0  38.000000  71.2833\n",
              "2       3    0  26.000000   7.9250\n",
              "3       1    0  35.000000  53.1000\n",
              "4       3    1  35.000000   8.0500\n",
              "5       3    1  29.699118   8.4583\n",
              "6       1    1  54.000000  51.8625\n",
              "7       3    1   2.000000  21.0750\n",
              "8       3    0  27.000000  11.1333\n",
              "9       2    0  14.000000  30.0708"
            ],
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              "      <td>2.000000</td>\n",
              "      <td>21.0750</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>3</td>\n",
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              "      <th>9</th>\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a7153c3c-42d6-4cb8-829b-c2b5f76400fc');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n"
      ],
      "metadata": {
        "id": "DpriR26UWG7E"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(inp,target,test_size=0.15,stratify=target)"
      ],
      "metadata": {
        "id": "66zl8a5xWd9n"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPN3-CHMWqTg",
        "outputId": "d7aaa0c2-5c85-4c8e-9270-fb545869a849"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(757, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB"
      ],
      "metadata": {
        "id": "fc9rsz7JW1KR"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model= GaussianNB()"
      ],
      "metadata": {
        "id": "pop0quwzXAiQ"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WxMR3vmZXDPc",
        "outputId": "5e0bcff6-fa20-4b21-aad1-0426145a4a4d"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mzSJvGw6XHe6",
        "outputId": "5c407fe6-c6e1-4f44-af2a-3a15b4bc8a36"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.835820895522388"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSU2lwJ_XREI",
        "outputId": "690fbb9d-14d9-4bb7-949c-e04f5edb16ba"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "323    1\n",
              "881    0\n",
              "445    1\n",
              "231    0\n",
              "629    0\n",
              "35     0\n",
              "754    1\n",
              "123    1\n",
              "632    1\n",
              "612    1\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(x_test[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z61_qFWDX4K-",
        "outputId": "f59ec83a-9ca9-4764-fe74-c1554796a8fa"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 0, 0, 1, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "7bQkhz8dX-RU"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1=LogisticRegression()"
      ],
      "metadata": {
        "id": "Tl3PT8YqYm34"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6g3oD6dmYrhK",
        "outputId": "917b9d40-cd0e-46aa-bd66-755961c6fae5"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1.predict(x_test[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNk6BhNPYvXg",
        "outputId": "5ff15559-a146-4042-b014-e2978ad61074"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 0, 0, 1, 1, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1.score(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "thdUWvfzY0D_",
        "outputId": "43b446fa-9788-4901-b1f3-a8bad656354f"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7985074626865671"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3k3CusFY5Q8",
        "outputId": "7871d66a-4d83-4b08-835e-6719822fe115"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "323    1\n",
              "881    0\n",
              "445    1\n",
              "231    0\n",
              "629    0\n",
              "35     0\n",
              "754    1\n",
              "123    1\n",
              "632    1\n",
              "612    1\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5pbQYa2WZF7O"
      },
      "execution_count": 86,
      "outputs": []
    }
  ]
}