Logistic_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Logistic Regression.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "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/GopiMindgloer/Machine_Learning/blob/master/Logistic_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "f3Vo0hsi6tKC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#install dependencies\n",
        "!pip install -q kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZlwDtIQh8pQr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#import dependencies\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import math\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Clm8f78w8zSC",
        "colab_type": "code",
        "outputId": "1ff67083-22ed-4913-b76d-6a45240ba8e2",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "cell_type": "code",
      "source": [
        "#connect with kaggle \n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-29aa62df-a704-4508-bee1-2d359609d0cc\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-29aa62df-a704-4508-bee1-2d359609d0cc\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uSeZJZpLAy1P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "593abd3d-b948-406e-d1e3-fc037b31a224"
      },
      "cell_type": "code",
      "source": [
        "#check if the file is uploaded\n",
        "ls -lha kaggle.json\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 68 Jan 23 11:01 kaggle.json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4GAe5ZkO87SA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#create a directory to put the json file and give it necessary permissions\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uHH7E6ZTB9W2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "32b64f55-95c6-4b5c-e181-797eabfebc15"
      },
      "cell_type": "code",
      "source": [
        "#download the dataset directly from kaggle\n",
        "!kaggle competitions download -c titanic"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading train.csv to /content\n",
            "\r  0% 0.00/59.8k [00:00<?, ?B/s]\n",
            "100% 59.8k/59.8k [00:00<00:00, 22.3MB/s]\n",
            "Downloading test.csv to /content\n",
            "  0% 0.00/28.0k [00:00<?, ?B/s]\n",
            "100% 28.0k/28.0k [00:00<00:00, 8.38MB/s]\n",
            "Downloading gender_submission.csv to /content\n",
            "  0% 0.00/3.18k [00:00<?, ?B/s]\n",
            "100% 3.18k/3.18k [00:00<00:00, 3.17MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qZW6VJ3oCPut",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bb186085-83f0-4ad1-f089-b0078f497ef7"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender_submission.csv  kaggle.json  sample_data  test.csv  train.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-aI55B_XEJIq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('train.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8YcdxiGgJ8ZX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3974425f-29e0-457f-fd20-192e92fbf769"
      },
      "cell_type": "code",
      "source": [
        "print(\"number of datapoints = \" +str(len(dataset.index)))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of datapoints = 891\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3OWgre8rK_yf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Analyzing the data"
      ]
    },
    {
      "metadata": {
        "id": "l4ThP4guLCk0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "563a5753-7ef8-4ed5-bd3b-7143cc328d0f"
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(x = \"Survived\", data = dataset)\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6j6uBy4HfjIifA24FjpbPmPgMMP8M35o0YYaB2lpmfhF4L7C+/HdPRHzyNId9uzz2WeDt\nEXFxRFwCHM/M52rOPdb2q4FfL3sZOyl6AO8HfhZ4qjz2APCfk/dOpfF5mUhtLSJmZubLwBPAExHx\nNYrxgtp5WqaPOuyNmuXNFH/9d1GMP4xWb/sQsDoz+0fVcg3FPFEjOib2bqQzZ89AbSsirgeejohZ\nNc0XA/uA14B5Zds145xmM3BT+drc4PangI+WNbwtIr4UEXOAfweuKNvnAY1cspImhWGgtpWZ24F1\nwM6IeDIivknxONA7KXoH6yNiG/Djcc7xAkUvYmDkG0kNbF8DvB4RTwPPAK+Ws3FuAt4VEbuAz1MM\nUEtN4aylkiR7BpIkw0CShGEgScIwkCRhGEiSMAwkSRgGkiTg/wAecLEbAUG5EQAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cSBHHotYLZjs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "9f5cd876-7ea2-4e07-9442-16ca7074f37e"
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(x = \"Survived\", hue = \"Sex\", data = dataset)\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ShGUgScIykCRhGUiSsAwkSVgGkiQsA0kSloEkCctAkgSUitx5RIwGvg/Mysw5EbEvcCvQ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "C69bY2CLL06K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "998bc692-3ab1-49ea-a012-269caa969d8a"
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(x = \"Survived\", hue = \"Pclass\", data = dataset)\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "XNmOG6AxMcM0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "486093dd-feb1-4621-b2de-8d963d551e16"
      },
      "cell_type": "code",
      "source": [
        "dataset[\"Age\"].plot.hist()\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kCctAkoRlIEnCMpAkYRlIkrAMJElYBpIkLANJEpaBJAnLQJKEZSBJwjKQJFHDNZAj4jRg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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "eMCrHswdNgnU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Data Wrangling"
      ]
    },
    {
      "metadata": {
        "id": "qok7pxn1Mwx0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "253c099e-3e71-47f1-fc71-1890371768f8"
      },
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "_8XRJnUMOOLb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "b91d7782-8460-4925-fda0-9d287766f08e"
      },
      "cell_type": "code",
      "source": [
        "sns.heatmap(dataset.isnull(), yticklabels = False )\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7I+J/Kn69cWBuRPy5wpibRMSaquLN8BrjEbGugjhzgEcUo4CVk7QVMBERKwcR32xY+kqU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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QSEmHV8jOckS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset.dropna(inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "leFMmG4vYvxv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "5906c8ab-006d-45c1-ca36-91767e55df23"
      },
      "cell_type": "code",
      "source": [
        "sns.heatmap(dataset.isnull(), yticklabels = False )\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bZtY/7asvDx8pMeKltm10uaWA0S6TmztL49SO+wtJIwNKXY7YzEYQQ1dfXok0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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6biFO7zSQL49",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "9d8e8088-cd93-44bb-c36b-58abef28a71b"
      },
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Ticket         0\n",
              "Fare           0\n",
              "Cabin          0\n",
              "Embarked       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "zunF0IlvQZGi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can also use imputation to fill in missing data."
      ]
    },
    {
      "metadata": {
        "id": "EzIrbryPQV9G",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sex = pd.get_dummies(dataset[\"Sex\"],drop_first = True)\n",
        "embark = pd.get_dummies(dataset[\"Embarked\"],drop_first = True)\n",
        "pclass = pd.get_dummies(dataset[\"Pclass\"],drop_first = True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kDRkp-VoTApj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset = pd.concat([dataset,sex,embark,pclass],axis= 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9S8F4oB6UMcH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "1dba7479-80c3-4193-8102-cc5ac71d458f"
      },
      "cell_type": "code",
      "source": [
        "dataset.head(5)"
      ],
      "execution_count": 39,
      "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",
              "      <th>male</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>PP 9549</td>\n",
              "      <td>16.7000</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113783</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>C103</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass  \\\n",
              "1             2         1       1   \n",
              "3             4         1       1   \n",
              "6             7         0       1   \n",
              "10           11         1       3   \n",
              "11           12         1       1   \n",
              "\n",
              "                                                 Name     Sex   Age  SibSp  \\\n",
              "1   Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "3        Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "6                             McCarthy, Mr. Timothy J    male  54.0      0   \n",
              "10                    Sandstrom, Miss. Marguerite Rut  female   4.0      1   \n",
              "11                           Bonnell, Miss. Elizabeth  female  58.0      0   \n",
              "\n",
              "    Parch    Ticket     Fare Cabin Embarked  male  Q  S  2  3  \n",
              "1       0  PC 17599  71.2833   C85        C     0  0  0  0  0  \n",
              "3       0    113803  53.1000  C123        S     0  0  1  0  0  \n",
              "6       0     17463  51.8625   E46        S     1  0  1  0  0  \n",
              "10      1   PP 9549  16.7000    G6        S     0  0  1  0  1  \n",
              "11      0    113783  26.5500  C103        S     0  0  1  0  0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "Jm1HZXT2VWwA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset.drop(['Name','PassengerId','Ticket','Fare','Cabin','Embarked','Sex'],axis = 1,inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7uLLkReDnoCT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset.drop(['Pclass'],axis = 1,inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X7jr1vhrXcc1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "4f67868c-f041-43f5-83bd-d1d4ecf0de7f"
      },
      "cell_type": "code",
      "source": [
        "dataset.head(5)"
      ],
      "execution_count": 89,
      "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>male</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Survived   Age  SibSp  Parch  male  Q  S  2  3\n",
              "1          1  38.0      1      0     0  0  0  0  0\n",
              "3          1  35.0      1      0     0  0  1  0  0\n",
              "6          0  54.0      0      0     1  0  1  0  0\n",
              "10         1   4.0      1      1     0  0  1  0  1\n",
              "11         1  58.0      0      0     0  0  1  0  0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "metadata": {
        "id": "pY-cB2FLW4dg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Train Data"
      ]
    },
    {
      "metadata": {
        "id": "WhUL4cAxW32H",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = dataset.drop(\"Survived\",axis = 1)\n",
        "y = dataset[\"Survived\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kcsXw_lUn4Ca",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train = x\n",
        "y_train = y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_zx2ezNOZkgp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_test = pd.read_csv(\"test.csv\")\n",
        "y_test = pd.read_csv(\"gender_submission.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Zvwe7NsipEvm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset2 = pd.merge(X_test,y_test,on = 'PassengerId')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MIQbUly_cKDX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sex = pd.get_dummies(X_test[\"Sex\"],drop_first = True)\n",
        "pclass = pd.get_dummies(X_test[\"Pclass\"],drop_first = True)\n",
        "embark = pd.get_dummies(X_test[\"Embarked\"],drop_first = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hO3qusm5dLjz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset2.dropna(inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1Rg8OaBZdxT0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset2 = pd.concat([dataset2,sex,pclass,embark],axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ezYae43Okaqz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset2.drop(['PassengerId','Pclass','Name','Ticket','Fare','Embarked','Cabin'],axis = 1,inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "abk0fo9BlagD",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset2.dropna(inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LDna6x0pk_Qe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset2.drop(['Sex'],axis = 1,inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "v6zp0JyPqikv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_test = dataset2.drop(['Survived'],axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hINe6oSdq5f6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_test = dataset2['Survived']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3EZgjXXLat4b",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ikVbjRmoaz7n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oSONbtWubMoo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "ca4a4717-c837-43e1-ec9b-96853fb0750b"
      },
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
              "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
              "          tol=0.0001, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "metadata": {
        "id": "9P_mKpfYbTry",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predictions = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QnYUBzOWnUJt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "V9EU50GwoL1w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "b1cffc21-862c-4829-c0c2-5e707ae377e2"
      },
      "cell_type": "code",
      "source": [
        "classification_report(y_test,predictions)"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n         0.0       1.00      0.77      0.87        43\\n         1.0       0.81      1.00      0.90        44\\n\\n   micro avg       0.89      0.89      0.89        87\\n   macro avg       0.91      0.88      0.88        87\\nweighted avg       0.91      0.89      0.88        87\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "metadata": {
        "id": "1gD0WLLXovMg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qHYcslZesM9d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "2f722339-76fa-4042-c6d4-354ef9212630"
      },
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[33, 10],\n",
              "       [ 0, 44]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "metadata": {
        "id": "J13R2TIWsRUB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uiXwEHrKsl56",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "267bfd27-14c5-4198-dd79-bd779d03fdd9"
      },
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,predictions)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8850574712643678"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "metadata": {
        "id": "El6wnF9Ysp1b",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}