Logistic_Regression_trial.ipynb

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        "import pandas as pd"
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        "titanic = pd.read_csv(\"/content/train.csv\")"
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      "outputs": []
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              "(891, 12)"
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        "titanic.head()"
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
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              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
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              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
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              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
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              "\n",
              "            Parch        Fare  \n",
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              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
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              "      <th>25%</th>\n",
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              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>max</th>\n",
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              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 82
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    {
      "cell_type": "code",
      "source": [
        "titanic.info()"
      ],
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        },
        "id": "3eZK2UF4hxYs",
        "outputId": "e73b57cf-e135-497c-84d9-55c5a5f3f116"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ports = pd.get_dummies(titanic.Embarked , prefix='Embarked')\n",
        "ports.head()"
      ],
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        "colab": {
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      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Embarked_C  Embarked_Q  Embarked_S\n",
              "0           0           0           1\n",
              "1           1           0           0\n",
              "2           0           0           1\n",
              "3           0           0           1\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic = titanic.join(ports)\n",
        "titanic.drop(['Embarked'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "W_RONEFoh7RJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titanic.Sex = titanic.Sex.map({'male':0, 'female':1})"
      ],
      "metadata": {
        "id": "uJ1qV1loh8pL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = titanic.Survived.copy()"
      ],
      "metadata": {
        "id": "39w36xi-iDZm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = titanic.drop(['Survived'], axis=1)"
      ],
      "metadata": {
        "id": "BlqJwS5iiGbj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.drop(['Cabin'], axis=1, inplace=True) "
      ],
      "metadata": {
        "id": "ZkvzebtMiJc5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.drop(['Ticket'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "T34gm22OiNFj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.drop(['Name'], axis=1, inplace=True) \n",
        "X.drop(['PassengerId'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "y77b4KMLiPyt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E4ewH-o2iSPf",
        "outputId": "7c066bc2-6f26-4317-b74e-e958179db865"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Pclass      891 non-null    int64  \n",
            " 1   Sex         891 non-null    int64  \n",
            " 2   Age         714 non-null    float64\n",
            " 3   SibSp       891 non-null    int64  \n",
            " 4   Parch       891 non-null    int64  \n",
            " 5   Fare        891 non-null    float64\n",
            " 6   Embarked_C  891 non-null    uint8  \n",
            " 7   Embarked_Q  891 non-null    uint8  \n",
            " 8   Embarked_S  891 non-null    uint8  \n",
            "dtypes: float64(2), int64(4), uint8(3)\n",
            "memory usage: 44.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6CutLDjSiVje",
        "outputId": "8ddbcb4a-a626-46d2-b0fc-554dfffaf2a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.Age.fillna(X.Age.mean(), inplace=True)"
      ],
      "metadata": {
        "id": "dVwVXeBriZ-c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.isnull().values.any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KigSgYPiidMW",
        "outputId": "1b828c6f-0b87-4cf0-c7ad-61c3942bfa2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=7)"
      ],
      "metadata": {
        "id": "fTULlDyzig86"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def simple_heuristic(titanicDF):\n",
        "    predictions = []\n",
        "    for passenger_index, passenger in titanicDF.iterrows():\n",
        "        if passenger['Sex'] == 1:\n",
        "            predictions.append(1) \n",
        "        elif passenger['Age'] < 18 and passenger['Pclass'] == 1:\n",
        "            predictions.append(1) \n",
        "        else:\n",
        "            predictions.append(0)\n",
        "    return predictions"
      ],
      "metadata": {
        "id": "L4PdxAMliqIS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "simplePredictions = simple_heuristic(X_valid)\n",
        "correct = sum(simplePredictions == y_valid)\n",
        "print (\"Baseline: \", correct/len(y_valid))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EBBjeLwMi2V7",
        "outputId": "6c64798c-997c-4be9-9f30-c4fffcae1193"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baseline:  0.7318435754189944\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "dy2I_17ci522"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ng7HEDaui6HP",
        "outputId": "10de0874-a183-4169-a813-617695b0f098"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0RncgNoIjGJb",
        "outputId": "c736c5b2-720c-4d5d-c8c5-94642f311ee3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8089887640449438"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(X_valid, y_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4pZzM_9jJ8I",
        "outputId": "85dd8025-57f7-4916-8e43-9d2c636b6c82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7541899441340782"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_ "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKbsn5wJjNn_",
        "outputId": "079d1602-cd60-4868-96e1-42c48155a582"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.71944631])"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1k711U4GjQvL",
        "outputId": "096ca46a-ee5a-41d0-f678-720c4d9c4f22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.00656628e+00,  2.79420788e+00, -4.29609132e-02,\n",
              "        -3.69319374e-01,  2.62931584e-03,  1.00226337e-03,\n",
              "         8.29580724e-01,  3.85123945e-01,  2.46503796e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "S49mZOxXjUoF",
        "outputId": "66958b49-8236-4e6d-b389-d38e6bf806d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             PassengerId  Survived    Pclass       Sex       Age     SibSp  \\\n",
              "PassengerId     1.000000 -0.005007 -0.035144 -0.042939  0.036847 -0.057527   \n",
              "Survived       -0.005007  1.000000 -0.338481  0.543351 -0.077221 -0.035322   \n",
              "Pclass         -0.035144 -0.338481  1.000000 -0.131900 -0.369226  0.083081   \n",
              "Sex            -0.042939  0.543351 -0.131900  1.000000 -0.093254  0.114631   \n",
              "Age             0.036847 -0.077221 -0.369226 -0.093254  1.000000 -0.308247   \n",
              "SibSp          -0.057527 -0.035322  0.083081  0.114631 -0.308247  1.000000   \n",
              "Parch          -0.001652  0.081629  0.018443  0.245489 -0.189119  0.414838   \n",
              "Fare            0.012658  0.257307 -0.549500  0.182333  0.096067  0.159651   \n",
              "Embarked_C     -0.001205  0.168240 -0.243292  0.082853  0.036261 -0.059528   \n",
              "Embarked_Q     -0.033606  0.003650  0.221009  0.074115 -0.022405 -0.026354   \n",
              "Embarked_S      0.022148 -0.155660  0.081720 -0.125722 -0.032523  0.070941   \n",
              "\n",
              "                Parch      Fare  Embarked_C  Embarked_Q  Embarked_S  \n",
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          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    }
  ]
}