Logistic_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Logistic Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/smlra-kjsce/Cyber-ML-DL-101/blob/master/Logistic_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpfV-EvZPiP0",
        "colab_type": "text"
      },
      "source": [
        "# Logistic Regression\n",
        "\n",
        "####While we used linear regression to deal with continuous data, that helped in predicting a future value on the basis of past data, logistic regression is different. Logsitic regression is used when the dependent variable is dichotomous (binary). For example: positive-negative, 0-1, pass-fail, benign-malignant, etc. It is assumed that the data for such dichotomous nature would be independent and that there's no correlation of data between two classes.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avQGnJNbTvNN",
        "colab_type": "text"
      },
      "source": [
        "Many will think, why not linear regression? That's because linear data plotted on graph may look something like this:\n",
        "\n",
        "\n",
        "<figure>\n",
        "<center>\n",
        "<img src='https://drive.google.com/uc?id=192bLivyYTXRLQaJsG8h-8jphieelQ-HX'/>\n",
        "  </center>\n",
        "  <center><figcaption><b>Linear Regression to fit dichotomous data</b></figcaption></center>\n",
        "</figure>\n",
        "\n",
        "But what if we are faced with data having outlier points? \n",
        "\n",
        "<figure>\n",
        "<center>\n",
        "<img src='https://drive.google.com/uc?id=1Bk_tgAS1gMjEKqh6CVo6iZ3vNYzLf6Hx'/>\n",
        "  </center>\n",
        "  <center><figcaption><b>Linear Regression to fit dichotomous data with outliers</b></figcaption></center>\n",
        "</figure>\n",
        "\n",
        "It is clearly visible that the line shifts just because of one outlier. This increases confusion of the model. \n",
        "\n",
        "Hence, Logistic regression is used."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lPX6x1cRX81E",
        "colab_type": "text"
      },
      "source": [
        "In logistic regression, we use logistic functions that are used to plot probabilistic models. \n",
        "\n",
        "Sigmoid function is a logistic function that's used in logistic regression. This is how it looks: \n",
        "\n",
        "\n",
        "<figure>\n",
        "<center>\n",
        "<img src='https://drive.google.com/uc?id=1ro0WaW1kszK3InLQNVoKAoFD9K370REO'/>\n",
        "  </center>\n",
        "  <center><figcaption><b>Sigmoid function</b></figcaption></center>\n",
        "</figure>\n",
        "\n",
        "### Let's plot this graph using Python."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0ZJewoXXrKQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import warnings\n",
        "\n",
        "warnings.simplefilter('ignore')\n",
        "\n",
        "def sigmoid(x):\n",
        "    a = []\n",
        "    for item in x:\n",
        "        a.append(1/(1+math.exp(-item)))\n",
        "    return a"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v1PijRvKaf2E",
        "colab_type": "code",
        "outputId": "4213cfe1-17ab-4622-e920-f0e267891e3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "x = np.arange(-10., 10., 0.2)\n",
        "sig = sigmoid(x)\n",
        "plt.plot(x,sig)\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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xaPvgP74FRpS20rR7E03ArhS210Bqbm7u176QacrVd2HNFtZckMFsnji67O4B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\n4i4ikoP+P7xoo1i73lcEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ItbgATl7tE0G",
        "colab_type": "text"
      },
      "source": [
        "### As an example, we will start working on the famous Titanic Dataset hosted on Kaggle. This example will also help us understand some data pre processing and how to draw inference and make next steps.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oykQKWNotUX1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib import style\n",
        "\n",
        "# Algorithms\n",
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import LogisticRegression\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BHDjA76uPFa",
        "colab_type": "code",
        "outputId": "314974ac-1b09-4d28-af20-b36c51cddc1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKbFxzy8tX_q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df = pd.read_csv(\"/content/drive/My Drive/ML-DL101 Datasets/test_titanic.csv\")\n",
        "train_df = pd.read_csv(\"/content/drive/My Drive/ML-DL101 Datasets/train_titanic.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7Pb5nNeu-8Q",
        "colab_type": "code",
        "outputId": "9b75e656-1665-4e47-b26d-aabf41ed3a8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "train_df.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",
            "PassengerId    891 non-null int64\n",
            "Survived       891 non-null int64\n",
            "Pclass         891 non-null int64\n",
            "Name           891 non-null object\n",
            "Sex            891 non-null object\n",
            "Age            714 non-null float64\n",
            "SibSp          891 non-null int64\n",
            "Parch          891 non-null int64\n",
            "Ticket         891 non-null object\n",
            "Fare           891 non-null float64\n",
            "Cabin          204 non-null object\n",
            "Embarked       889 non-null object\n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0UBHi6ivEUe",
        "colab_type": "code",
        "outputId": "023943f5-8143-4266-b033-f452daf1dd59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "train_df.describe()"
      ],
      "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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EW2l3yuIvJ2q",
        "colab_type": "code",
        "outputId": "b658bfb4-84c5-438b-a45e-49339b1dda5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train_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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7h7PKBWuwTAR",
        "colab_type": "text"
      },
      "source": [
        "The head of the data gives us an indication of various parameters that need to be converted into numeric form for prediction. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LsdW4ojwigB",
        "colab_type": "code",
        "outputId": "00a0ec08-1dee-4815-eaa1-93c3b6ae3ee4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "total = train_df.isnull().sum().sort_values(ascending=False)\n",
        "percent_1 = train_df.isnull().sum()/train_df.isnull().count()*100\n",
        "percent_2 = (round(percent_1, 1)).sort_values(ascending=False)\n",
        "missing_data = pd.concat([total, percent_2], axis=1, keys=['Total', '% missing'])\n",
        "missing_data.head(5)"
      ],
      "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>Total</th>\n",
              "      <th>% missing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>687</td>\n",
              "      <td>77.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>177</td>\n",
              "      <td>19.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Total  % missing\n",
              "Cabin       687       77.1\n",
              "Age         177       19.9\n",
              "Embarked      2        0.2\n",
              "Fare          0        0.0\n",
              "Ticket        0        0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHm7kQY8w7Zs",
        "colab_type": "text"
      },
      "source": [
        "From above data, we can make following inferences:\n",
        "\n",
        "1. Around 77% of cabin data is missing. This % is huge. So, we can't afford to drop data row-wise. It's better to delete this column entirely since majority values are missing anyways.\n",
        "\n",
        "2. Embarked value can be easily filled.\n",
        "\n",
        "3. With some common sense, we can eliminate variables like PassengerId, Name and Ticket as we don't expect them to have much corelation with survival chance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vr0zZm8mPZFu",
        "colab_type": "code",
        "outputId": "285cee9b-07a5-4ecc-92c6-6762e4afa491",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "survived = 'survived'\n",
        "not_survived = 'not survived'\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(10, 4))\n",
        "women = train_df[train_df['Sex']=='female']\n",
        "men = train_df[train_df['Sex']=='male']\n",
        "ax = sns.distplot(women[women['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[0], kde =False)\n",
        "ax = sns.distplot(women[women['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[0], kde =False)\n",
        "ax.legend()\n",
        "ax.set_title('Female')\n",
        "ax = sns.distplot(men[men['Survived']==1].Age.dropna(), bins=18, label = survived, ax = axes[1], kde = False)\n",
        "ax = sns.distplot(men[men['Survived']==0].Age.dropna(), bins=40, label = not_survived, ax = axes[1], kde = False)\n",
        "ax.legend()\n",
        "_ = ax.set_title('Male')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TQLB9vQ+PBk6KiBXAjVQO0X+rxTVtemdEZq4BbqUSPFu571YBqzJzQfX+zVSa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\nYUqSJKmAYUqSJKmAYUqSJKnA/wdhpYOuQMTQigAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFJX-UZD1jNe",
        "colab_type": "code",
        "outputId": "f2a875ed-b7ff-485c-b884-21b8a1977c4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "sns.barplot(x='Pclass', y='Survived', data=train_df)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f98e6d519b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yO8kWgCQ/nuQk8Brg/UmO9JVHkrSwPn99RFUdAA7MGts1cP8QM5uVJEnLwIrY0SxJWhqW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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKYfbR-N1vsd",
        "colab_type": "code",
        "outputId": "fc9457a6-25f2-437b-cd6e-b3f20a10bdda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#Combining SibSp and Parch to show if someone is alone or not\n",
        "data = [train_df, test_df]\n",
        "for dataset in data:\n",
        "    dataset['relatives'] = dataset['SibSp'] + dataset['Parch']\n",
        "    dataset.loc[dataset['relatives'] > 0, 'not_alone'] = 0\n",
        "    dataset.loc[dataset['relatives'] == 0, 'not_alone'] = 1\n",
        "    dataset['not_alone'] = dataset['not_alone'].astype(int)\n",
        "train_df['not_alone'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    537\n",
              "0    354\n",
              "Name: not_alone, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GiygRG_X2GXR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = train_df.drop(['PassengerId','Cabin'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_iMmf8W2wQp",
        "colab_type": "code",
        "outputId": "0c233087-5040-49f0-d7ca-eae26e787e88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data = [train_df, test_df]\n",
        "\n",
        "for dataset in data:\n",
        "    mean = train_df[\"Age\"].mean()\n",
        "    std = test_df[\"Age\"].std()\n",
        "    is_null = dataset[\"Age\"].isnull().sum()\n",
        "    # compute random numbers between the mean, std and is_null\n",
        "    rand_age = np.random.randint(mean - std, mean + std, size = is_null)\n",
        "    # fill NaN values in Age column with random values generated\n",
        "    age_slice = dataset[\"Age\"].copy()\n",
        "    age_slice[np.isnan(age_slice)] = rand_age\n",
        "    dataset[\"Age\"] = age_slice\n",
        "    dataset[\"Age\"] = train_df[\"Age\"].astype(int)\n",
        "train_df[\"Age\"].isnull().sum()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oiazbi23F3i",
        "colab_type": "code",
        "outputId": "e7ef4238-7ad0-4077-dba9-817377088d47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "train_df['Embarked'].describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     889\n",
              "unique      3\n",
              "top         S\n",
              "freq      644\n",
              "Name: Embarked, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91r6YkKR3NJU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "common_value = 'S'\n",
        "data = [train_df, test_df]\n",
        "\n",
        "for dataset in data:\n",
        "    dataset['Embarked'] = dataset['Embarked'].fillna(common_value)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2feOofoi3P3K",
        "colab_type": "code",
        "outputId": "f3a91856-21c0-405c-8215-9ae05afa7057",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "train_df.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",
            "Survived     891 non-null int64\n",
            "Pclass       891 non-null int64\n",
            "Name         891 non-null object\n",
            "Sex          891 non-null object\n",
            "Age          891 non-null int64\n",
            "SibSp        891 non-null int64\n",
            "Parch        891 non-null int64\n",
            "Ticket       891 non-null object\n",
            "Fare         891 non-null float64\n",
            "Embarked     891 non-null object\n",
            "relatives    891 non-null int64\n",
            "not_alone    891 non-null int64\n",
            "dtypes: float64(1), int64(7), object(4)\n",
            "memory usage: 83.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWEXlXZc3UQf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = train_df.drop(['Ticket','Name'], axis=1)\n",
        "test_df = test_df.drop(['Ticket','Name'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZhOp69G38gC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "genders = {\"male\": 0, \"female\": 1}\n",
        "data = [train_df, test_df]\n",
        "\n",
        "for dataset in data:\n",
        "    dataset['Sex'] = dataset['Sex'].map(genders)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USyX_tSg4C9N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ports = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
        "data = [train_df, test_df]\n",
        "\n",
        "for dataset in data:\n",
        "    dataset['Embarked'] = dataset['Embarked'].map(ports)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApXleNFj4Df1",
        "colab_type": "code",
        "outputId": "4a146c54-e5e6-450e-f7e2-d87283fd13f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "data = [train_df, test_df]\n",
        "for dataset in data:\n",
        "    dataset['Age'] = dataset['Age'].astype(int)\n",
        "    dataset.loc[ dataset['Age'] <= 11, 'Age'] = 0\n",
        "    dataset.loc[(dataset['Age'] > 11) & (dataset['Age'] <= 18), 'Age'] = 1\n",
        "    dataset.loc[(dataset['Age'] > 18) & (dataset['Age'] <= 22), 'Age'] = 2\n",
        "    dataset.loc[(dataset['Age'] > 22) & (dataset['Age'] <= 27), 'Age'] = 3\n",
        "    dataset.loc[(dataset['Age'] > 27) & (dataset['Age'] <= 33), 'Age'] = 4\n",
        "    dataset.loc[(dataset['Age'] > 33) & (dataset['Age'] <= 40), 'Age'] = 5\n",
        "    dataset.loc[(dataset['Age'] > 40) & (dataset['Age'] <= 66), 'Age'] = 6\n",
        "    dataset.loc[ dataset['Age'] > 66, 'Age'] = 6\n",
        "\n",
        "# let's see how it's distributed \n",
        "train_df['Age'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4    170\n",
              "6    154\n",
              "5    146\n",
              "3    134\n",
              "2    122\n",
              "1     97\n",
              "0     68\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8sr8fSz4rbt",
        "colab_type": "code",
        "outputId": "9b42d801-183e-447b-d601-e47057aae644",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "train_df.head(10)"
      ],
      "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>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>relatives</th>\n",
              "      <th>not_alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  ...  Fare  Embarked  relatives  not_alone\n",
              "0         0       3    0    2  ...     7         0          1          0\n",
              "1         1       1    1    5  ...    71         1          1          0\n",
              "2         1       3    1    3  ...     7         0          0          1\n",
              "3         1       1    1    5  ...    53         0          1          0\n",
              "4         0       3    0    5  ...     8         0          0          1\n",
              "5         0       3    0    4  ...     8         2          0          1\n",
              "6         0       1    0    6  ...    51         0          0          1\n",
              "7         0       3    0    0  ...    21         0          4          0\n",
              "8         1       3    1    3  ...    11         0          2          0\n",
              "9         1       2    1    1  ...    30         1          1          0\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jSkVyUP34t77",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = train_df.drop([\"Survived\",\"Fare\"], axis=1)\n",
        "Y_train = train_df[\"Survived\"]\n",
        "X_test  = test_df.drop([\"PassengerId\",\"Cabin\",\"Fare\"], axis=1).copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WzFHuTHj5u2g",
        "colab_type": "code",
        "outputId": "83b6c27d-db12-4f08-e5a8-5e4cd3eec328",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "X_train.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",
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              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>relatives</th>\n",
              "      <th>not_alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>3</td>\n",
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              "      <td>5</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Sex  Age  SibSp  Parch  Embarked  relatives  not_alone\n",
              "0       3    0    2      1      0         0          1          0\n",
              "1       1    1    5      1      0         1          1          0\n",
              "2       3    1    3      0      0         0          0          1\n",
              "3       1    1    5      1      0         0          1          0\n",
              "4       3    0    5      0      0         0          0          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pOckEeq50PC",
        "colab_type": "code",
        "outputId": "ce603a97-d8dd-4f34-9ab1-4c5f98649ecc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "X_test.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>relatives</th>\n",
              "      <th>not_alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Sex  Age  SibSp  Parch  Embarked  relatives  not_alone\n",
              "0       3    0    2      0      0         2          0          1\n",
              "1       3    1    5      1      0         0          1          0\n",
              "2       2    0    3      0      0         2          0          1\n",
              "3       3    0    5      0      0         0          0          1\n",
              "4       3    1    5      1      1         0          2          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfCIAPAh5Opm",
        "colab_type": "code",
        "outputId": "08176d66-5f01-4548-9e68-5afea28e07a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "logreg = LogisticRegression()\n",
        "logreg.fit(X_train, Y_train)\n",
        "\n",
        "Y_pred = logreg.predict(X_test)\n",
        "\n",
        "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
        "print(\"Accuracy via logistic regression: \",acc_log)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-b2b2f196a47b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlogreg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mlogreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mY_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'LogisticRegression' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEt0at6c6Vbc",
        "colab_type": "code",
        "outputId": "2e2f2dff-50dd-4723-9fca-f8b4f5c07f99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(logreg, X_train, Y_train, cv=10, scoring = \"accuracy\")\n",
        "print(\"Scores:\", scores)\n",
        "print(\"Mean:\", scores.mean())\n",
        "print(\"Standard Deviation:\", scores.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [0.78888889 0.76666667 0.7752809  0.84269663 0.80898876 0.79775281\n",
            " 0.79775281 0.7752809  0.82022472 0.80681818]\n",
            "Mean: 0.7980351265463625\n",
            "Standard Deviation: 0.021880254046102804\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnKHfSxW7ZpK",
        "colab_type": "text"
      },
      "source": [
        "This shows us that the model accuracy using Logistic regression is 80.58% with an error of 2.18%."
      ]
    }
  ]
}