Titanic_Dataset_Random_Forest.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Titanic Dataset-Random Forest.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "1vLUrr0wD8Ez",
        "outputId": "65f4ef7c-af54-4d55-b6d5-db5a406c6401",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHBPh7x9Elk4"
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiPg8CLKESjZ",
        "outputId": "4ed5d827-0ea4-4e39-b600-d7716cf5d514",
        "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": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 76
        }
      },
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-aa0a25bb-c09a-4662-837a-b895deb714ac\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-aa0a25bb-c09a-4662-837a-b895deb714ac\">\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 train.csv to train.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bs4uvzrKGEXT",
        "outputId": "4e16da22-95c4-40ca-851f-f72db18e9403",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "User uploaded file \"train.csv\" with length 61194 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41tUlI72GKrk",
        "outputId": "48704f35-b28e-4e31-f7a8-5e3a204d22e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "uploaded"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'train.csv': b'PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked\\r\\n1,0,3,\"Braund, Mr. Owen Harris\",male,22,1,0,A/5 21171,7.25,,S\\r\\n2,1,1,\"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\",female,38,1,0,PC 17599,71.2833,C85,C\\r\\n3,1,3,\"Heikkinen, Miss. Laina\",female,26,0,0,STON/O2. 3101282,7.925,,S\\r\\n4,1,1,\"Futrelle, Mrs. Jacques Heath (Lily May Peel)\",female,35,1,0,113803,53.1,C123,S\\r\\n5,0,3,\"Allen, Mr. William Henry\",male,35,0,0,373450,8.05,,S\\r\\n6,0,3,\"Moran, Mr. James\",male,,0,0,330877,8.4583,,Q\\r\\n7,0,1,\"McCarthy, Mr. Timothy J\",male,54,0,0,17463,51.8625,E46,S\\r\\n8,0,3,\"Palsson, Master. Gosta Leonard\",male,2,3,1,349909,21.075,,S\\r\\n9,1,3,\"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\",female,27,0,2,347742,11.1333,,S\\r\\n10,1,2,\"Nasser, Mrs. Nicholas (Adele Achem)\",female,14,1,0,237736,30.0708,,C\\r\\n11,1,3,\"Sandstrom, Miss. 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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3H0TfC4HGQuA"
      },
      "source": [
        "import io\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JdTYGNPbGYDr",
        "outputId": "7579e494-e278-4310-a848-b6c2ba2732cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train= pd.read_csv(io.StringIO(uploaded['train.csv'].decode('utf-8')))\n",
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uS8BYzaWIECV",
        "outputId": "7081e53a-f93a-4859-e905-dfcf9886c444",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efc3395b400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yICF1PxsJQro"
      },
      "source": [
        "sns.set_style('whitegrid')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T7PpAMHBJZNC",
        "outputId": "24f50486-87ce-4d08-cfca-a70d7b9dcec1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.countplot(x='Survived',data=train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efc2fe5ea58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGHbOBP-KNkt",
        "outputId": "94e884d9-5f9c-4e4e-a2e5-dbd498e71eb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.countplot(x='Survived',hue='Sex',data=train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efc2f99f160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zi6ZKJ9rKXma",
        "outputId": "9b1d33bb-b462-4011-c173-3cab2a7de8f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.countplot(x='Survived',hue='Pclass',data=train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efc2f901240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snGRTWkvKkje",
        "outputId": "88ad3a41-95b0-43f9-9e1c-db9b617f4025",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.distplot(train['Age'].dropna(),kde=False,bins=30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efc2f880278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pw-UHJ1XKkoA",
        "outputId": "36038da0-0e4b-42f0-f17e-b9b4bbedcee8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "train.info"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of      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",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25bdafvWMDAD",
        "outputId": "bdd9c3f6-1e0f-435d-a7c1-0a0c9659c69a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "train['Fare'].hist(bins=40,figsize=(10,4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efc2f7bd198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tM3CkOE7VAAAAq+OIFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEAABhCsAIAADCEYAUAAGAIwQoAAMAQghUAAIAhMYPVk08+qcLCQu3YsSM69vLLL+uuu+5SaWmpSktL1d3dHX3t+PHjcrlcKioqUm9vb3yqBgAAsKCYj7QpLy/Xb3/7W/3pT3+6bLyyslJ79uy5bOzcuXNqbW1Va2ur/H6/3G632traZLfbzVYNAABgQTGPWN15551KT0+f0pt1dnaquLhYycnJysnJ0Y033qj+/v5ZFwkAAJAIZvwQ5jfeeEMej0dr165VbW2t0tPT5ff7tW7duugyDodDfr8/5nuFQiF5vd6ZljJl+fn5cd+GpDmZS6IbGxtjP1kEvbAOemEd9MI6Eq0XMwpW9913nx5++GHZbDa9+OKLOnLkiJ5//vkZF5GSkjJnoWcuLKS5xIvX62U/WQS9sA56YR30wjqs2IurBb0Z/Srwuuuuk91u15IlS1RRUaEvvvhC0qUjVD6fL7qc3++Xw+GYySYAAAASzoyCVSAQiP7d0dGhvLw8SZLT6VRra6vGx8c1ODiogYEB3X777WYqBQAAsLiYpwJramp05swZjYyMaNOmTXr00Ud15swZffnll5KkG264QYcOHZIk5eXl6Z577tH27dtlt9t14MABfhEIAAAWjZjB6ujRoz8Zq6iouOLye/fu1d69e2dXFQAAQALizuuGjU2E52QdAABgPTO+3QJ+3jW/sOum2tZprTNwpDhO1QAAgLnEESsAAABDCFYAAACGEKwAAAAMIVgBAAAYQrACAAAwhGAFAABgCMEKAADAEIIVAACAIQQrAAAAQwhWAAAAhhCsAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjBCgAAwBCCFQAAgCExg9WTTz6pwsJC7dixIzo2Ojoqt9utrVu3yu1268KFC5KkSCSi5557Ti6XSyUlJTp79mz8KgcAALCYmMGqvLxcr7zyymVjjY2NKiwsVHt7uwoLC9XY2ChJ6unp0cDAgNrb2/Xss8/q4MGDcSkaAADAimIGqzvvvFPp6emXjXV2dqqsrEySVFZWpo6OjsvGbTab1q9fr2AwqEAgEIeyAQAArCdpJisNDw8rKytLkrRy5UoNDw9Lkvx+v7Kzs6PLZWdny+/3R5e9klAoJK/XO5NSpiU/Pz/u25ipuZi/lYyNjS26OVsVvbAOemEd9MI6Eq0XMwpWP2az2WSz2Wb1HikpKZYOPXNhsc3f6/UuujlbFb2wDnphHfTCOqzYi6sFvRn9KjAzMzN6ii8QCCgjI0OS5HA45PP5osv5fD45HI6ZbAIAACDhzChYOZ1OeTweSZLH49GWLVsuG49EIurr61NqamrM04AAAAALRcxTgTU1NTpz5oxGRka0adMmPfroo3rwwQdVXV2t5uZmrVq1Sg0NDZKkzZs3q7u7Wy6XS0uXLlVdXV3cJwAAAGAVMYPV0aNHf3a8qanpJ2M2m03PPPPM7KsCAABIQNx5HQAAwBCCFQAAgCEEKwAAAEMIVgAAAIYQrAAAAAwhWAEAABhCsAIAADCEYAUAAGAIwQoAAMAQghUAAIAhBCsAAABDCFYAAACGEKwAAAAMIVgBAAAYQrACAAAwhGAFAABgCMEKAADAkKTZrOx0OrVs2TItWbJEdrtdb7/9tkZHR/XYY4/pv//9r2644QY1NDQoPT3dVL0AAACWNesjVk1NTWppadHbb78tSWpsbFRhYaHa29tVWFioxsbGWRcJAACQCIyfCuzs7FRZWZkkqaysTB0dHaY3AQAAYEmzDlZ79uxReXm5Tpw4IUkaHh5WVlaWJGnlypUaHh6e7SYAAAASwqyusXrrrbfkcDg0PDwst9ut3Nzcy1632Wyy2Wwx3ycUCsnr9c6mlCnJz8+P+zZmai7mbyVjY2OLbs5WRS+sg15YB72wjkTrxayClcPhkCRlZmbK5XKpv79fmZmZCgQCysrKUiAQUEZGRsz3SUlJsXTomQuLbf5er3fRzdmq6IV10AvroBfWYcVeXC3ozfhU4Pfff6+LFy9G//7www+Vl5cnp9Mpj8cjSfJ4PNqyZctMNwEAAJBQZnzEanh4WPv27ZMkhcNh7dixQ5s2bdJtt92m6upqNTc3a9WqVWpoaDBWLAAAgJXNOFjl5OTonXfe+cn4ihUr1NTUNKuiAAAAEhF3Xk9AYxPhOVkHAABMz6wuXsf8uOYXdt1U2zqtdQaOFMepGgAA8AOOWAEAABhCsAIAADCEYAUAAGAIwcoCrHph+XTrsuo8AACYK1y8bgHTvRh9ri5Et2pdAABYFUesAAAADCFYLRKcpgMAIP44FbhIcO8rAADijyNWAAAAhhCsAAAADCFYAQAAGEKwAgAAMIRgBQAAYAjBCgAAwBCCFebVTO6vxT25AABWxX2sYMzYRFjX/MI+pWXz8/MlWff+WtOZy2zWAQAsLHELVj09PTp8+LD+97//qaKiQg8++GC8NgWLWEghyapzAQBYW1yCVTgc1qFDh/Taa6/J4XBo165dcjqduvnmm+OxOeCqCEnxZ9UjfFatC8DCFZdg1d/frxtvvFE5OTmSpOLiYnV2dhKsYARffPG1kI7wWbUuAFMzNhGOXjoynXXm8zsiLsHK7/crOzs7+m+Hw6H+/v54bAqL0HS/LOfqi3K6/zHP93/8V2LlMGLVfQYgPqz8eXQltkgkEjH9pv/4xz/U29urw4cPS5I8Ho/6+/t14MCBn12+r69PKSkppssAAAAwLhQKaf369T/7WlyOWDkcDvl8vui//X6/HA7HFZe/UnEAAACJJC73sbrttts0MDCgwcFBjY+Pq7W1VU6nMx6bAgAAsIy4HLFKSkrSgQMH9Pvf/17hcFj33nuv8vLy4rEpAAAAy4jLNVYAAACLEY+0AQAAMIRgBQAAYMiiCFY9PT0qKiqSy+VSY2PjfJez4D355JMqLCzUjh07omOjo6Nyu93aunWr3G63Lly4IEmKRCJ67rnn5HK5VFJSorNnz85X2QvS0NCQHnjgAW3fvl3FxcVqamqSRD/mQygU0q5du/Sb3/xGxcXFeumllyRJg4ODqqiokMvlUnV1tcbHxyVJ4+Pjqq6ulsvlUkVFhc6fPz+f5S9I4XBYZWVleuihhyTRi/nidDpVUlKi0tJSlZeXS0rsz6gFH6x+eLzOK6+8otbWVr377rs6d+7cfJe1oJWXl+uVV165bKyxsVGFhYVqb29XYWFhNOD29PRoYGBA7e3tevbZZ3Xw4MF5qHjhstvtqq2t1XvvvacTJ07ozTff1Llz5+jHPEhOTlZTU5PeeecdeTwe9fb2qq+vT/X19aqsrNTp06eVlpam5uZmSdLJkyeVlpam06dPq7KyUvX19fM8g4Xn9ddf1+rVq6P/phfzp6mpSS0tLXr77bclJfZ3xoIPVj9+vE5ycnL08TqInzvvvFPp6emXjXV2dqqsrEySVFZWpo6OjsvGbTab1q9fr2AwqEAgMOc1L1RZWVlas2aNJGn58uXKzc2V3++nH/PAZrNp2bJlkqTJyUlNTk7KZrPpk08+UVFRkSRp586d0c+nrq4u7dy5U5JUVFSkjz/+WPzWyByfz6cPPvhAu3btknTpSAi9sI5E/oxa8MHq5x6v4/f757GixWl4eFhZWVmSpJUrV2p4eFjST/uTnZ1Nf+Lk/Pnz8nq9WrduHf2YJ+FwWKWlpdq4caM2btyonJwcpaWlKSnp0p1vfry//X6/rr/+ekmXbmGTmpqqkZGReat9oamrq9P+/fu1ZMmlr8GRkRF6MY/27Nmj8vJynThxQlJif2fE5T5WwNXYbDbZbLb5LmNR+e6771RVVaWnnnpKy5cvv+w1+jF37Ha7WlpaFAwGtW/fPn399dfzXdKi9P777ysjI0Nr167Vp59+Ot/lLHpvvfWWHA6HhoeH5Xa7lZube9nrifYZteCD1XQfr4P4yMzMVCAQUFZWlgKBgDIyMiT9tD8+n4/+GDYxMaGqqiqVlJRo69atkujHfEtLS1NBQYH6+voUDAY1OTmppKSky/a3w+HQ0NCQsrOzNTk5qW+//VYrVqyY58oXhs8//1xdXV3q6elRKBTSxYsXdfjwYXoxT37Yz5mZmXK5XOrv70/oz6gFfyqQx+tYg9PplMfjkXTpodxbtmy5bDwSiaivr0+pqanRw7+YvUgkoqefflq5ublyu93Rcfox97755hsFg0FJ0tjYmD766COtXr1aBQUFamtrkySdOnUq+vnkdDp16tQpSVJbW5s2bNiQUP/XbmWPP/64enp61NXVpaNHj2rDhg164YUX6MU8+P7773Xx4sXo3x9++KHy8vIS+jNqUdx5vbu7W3V1ddHH6+zdu3e+S1rQampqdObMGY2MjCgzM1OPPvqofv3rX6u6ulpDQ0NatWqVGhoadO211yoSiejQoUPq7e3V0qVLVVdXp9tuu22+p7Bg/POf/9T999+vW265JXotSU1NjW6//Xb6Mce+/PJL1dbWKhwOKxKJaNu2bXrkkUc0ODioxx57TBcuXFB+fr7q6+uVnJysUCik/fv3y+v1Kj09XceOHVNOTs58T2PB+fTTT/Xqq6/q+PHj9GIeDA4Oat++fZIuXYO4Y8cO7d27VyMjIwn7GbUoghUAAMBcWPCnAgEAAOYKwQoAAMAQghUAAIAhBCsAAABDCFYAAACGEKwAAAAMIVgBAAAYQrACAAAw5P8BW9l2ToBYyvcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNKOZTWGMDIg",
        "outputId": "1350fc59-fdf9-4947-8092-bfb1ab13d912",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        }
      },
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "sns.boxplot(x='Pclass',y='Age',data=train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efc2f99f748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GjhzZ7esej0cej6fH50lMTFRmZqZVMfE1Xq9XknjPEdMY53ADxrk9DlZ0LT3UmJaWJklKTk5Wbm6uNm/erOTkZAWDQUlSMBjkTAsAAOAals14hcNhdXZ2atCgQQqHw1q3bp1uvPFG5eTkqKKiQiUlJaqoqNDYsWOtitBNaWmpGhoajOyrr6uvr5f0v2fD4MAyMjJ4nwAAh8yy4hUKhXTTTTdJkjo6OnThhRfq3HPP1UknnaRbbrlFS5cu1dFHH61FixZZFaGbhoYGvf1/3lWnlxm2nng6vhwWb/7PNpuTOFtcuMXuCACAPsay4pWenq5ly5Z94/EhQ4aovLzcqt0eVKfXpz0nXGjLvhF7Bry73O4IAIA+hns1AgAAGELxAgAAMITiBQAAYAjFCwAAwBCKFwAAgCEULwAAAEMoXgAAAIZQvAAAAAyheAEAABhC8QIAADCE4gUAAGAIxQsAAMAQihcAAIAhFC8AAABDKF4AAACGULwAAAAMoXgBAAAYQvECAAAwhOIFAABgCMULAADAEIoXAACAIRQvAAAAQyheAAAAhlC8AAAADKF4AQAAGELxAgAAMITiBQAAYAjFCwAAwBCKFwAAgCEULwAAAEN6LF7Nzc268847de2110qSGhoa9Mwzz1geDAAAINb0WLxmz56ts88+W8FgUJI0YsQIPf7445YHAwAAiDU9Fq/t27dr/Pjxiov78lvj4+O7/hsAAACHrscG5fV6tX37dnk8HknSpk2bdOSRR1oeDAAAINbE9/QNs2fP1g033KCPPvpIl156qbZv364//vGPh7yDjo4OTZkyRWlpaXr44Ye1detWzZo1S62trcrKytLChQvVv3//w3oRAAAAfUGPxSsrK0t///vftWXLFkWjUX3/+99XQkLCIe/g8ccf17HHHqtdu3ZJku677z5dddVVmjBhgu655x4tXbpUl1122Xd/BQAAAH1Ej4caa2tr9eKLL2rLli1qbGzUSy+9pA0bNigUCvX45Nu2bdOaNWs0depUSVI0GtWrr76qcePGSZImT56surq6w3wJAAAAfUOPM15Lly7Vpk2bNGbMGEnSxo0blZWVpY8//lg33nijCgsLD/izv/71r3Xbbbdp9+7dkr5cqJ+UlKT4+C93O2zYMDU1NfUYsq2tTYFA4JBe0IGEw+HD+nlgf8Lh8GGPTdhj32cCvz/EMsa58/RYvDo6OlRVVaWUlBRJX17X64477tDTTz+tn/zkJwcsXi+99JJ8Pp9OPPFEvfbaa4cVMjExUZmZmYf1HF6vV9LOw3oO4Ou8Xu9hj03Y48vPBPH7Q0xjnNvjYEW3x+L12WefdZUuSUpOTtZnn32mo446qmvman/eeustvfjii1q7dq3a2tq0a9cuLViwQDt37lR7e7vi4+O1bds2paWlfcuXAwAA0Df1WLxOP/10XXfddcrPz5ckrVy5UqeffrrC4fBBLytx66236tZbb5Ukvfbaa/rrX/+q+++/X36/XytXrtSECRP0/PPPKycnp5deCgAAgLP1uLj+F7/4hS6++GIFAgEFAgGdeOKJ8ng88nq9+tvf/vatd3jbbbfpscceU25urlpbWzVt2rTvFBwAAKCv6XHGy+PxKD09XZs2bdLKlSs1fPjwrrMSD9WYMWO6Fuenp6dr6dKl3y0tAABAH3bA4rVlyxatWLFCy5cv15AhQzR+/HhFo9HvNMsFAACAgxSvgoICnXrqqXr44Yd1zDHHSJKWLFliKhcAAEDMOeAarz/96U9KTU3VlVdeqbvuuksbNmxQNBo1mQ0AACCmHHDG64ILLtAFF1ygcDisuro6lZeXq6WlRb/4xS+Um5urs88+22ROAACAPq/Hsxq9Xq8mTpyohx56SC+//LJOOOEEPfLIIyayAQAAxJQez2r8T4MHD9b06dM1ffp0q/IAAADErG9VvPqylpYWxYVDGvDucrujIEbEhUNqaelvdwwAQB/S46FGAAAA9A7XzHj5fD5t2b5Xe0640O4oiBED3l0un89ndwwAQB/CjBcAAIAhFC8AAABDXHOoEXCD0tJSNTQ02B2jT6ivr5ck+f1+m5M4X0ZGBu8T0EsoXkAMaWho0Pv/9y3916AOu6M4XlLUI0na0/i6zUmc7aNd/eyOAMQUihcQY/5rUIfuOnWX3TEQI+a/McjuCEBMYY0XAACAIRQvAAAAQyheAAAAhlC8AAAADKF4AQAAGELxAgAAMITiBQAAYAjFCwAAwBCKFwAAgCEULwAAAEMoXgAAAIZQvAAAAAyheAEAABhC8QIAADAk3u4AAAB8G6WlpWpoaLA7Rp9QX18vSfL7/TYncb6MjAwj7xPFCwDQpzQ0NOjt//e2dJTdSfqAr45rvf3J2/bmcLpWc7uieAEA+p6jpM4fd9qdAjEibo25lVes8QIAADCE4gUAAGAIxQsAAMAQihcAAIAhli2ub2tr0+WXX669e/eqo6ND48aNk9/v19atWzVr1iy1trYqKytLCxcuVP/+/a2KAQAA4BiWzXj1799f5eXlWrZsmSoqKvTKK69o06ZNuu+++3TVVVdp1apVSkpK0tKlS62KAAAA4CiWFS+Px6OBAwdKktrb29Xe3i6Px6NXX31V48aNkyRNnjxZdXV1VkUAAABwFEuv49XR0aGLL75YH330kS677DKlp6crKSlJ8fFf7nbYsGFqamrq8Xna2toUCAQOK0s4HD6snwf2JxwOH/bY7E3hcJiFm+h1ThznQG8zNc4tLV79+vVTZWWldu7cqZtuukkffPDBd3qexMREZWZmHlYWr9craedhPQfwdV6v97DHZm/yer3aY3cIxBwnjnNttzsFYk1vjvODFTgjfxwnJSVpzJgx2rRpk3bu3Kn29nZJ0rZt25SWlmYiAgAAgO0sm/FqaWlRfHy8kpKStGfPHq1fv14//elPNWbMGK1cuVITJkzQ888/r5ycHKsiAK7T0tKiz7/op/lvDLI7CmLEh1/0U2pLi90xgJhhWfEKBoOaPXu2Ojo6FI1GlZ+fr/PPP18ZGRmaOXOmFi1apMzMTE2bNs2qCAAAAI5iWfE6/vjjVVFR8Y3H09PTuYQEYBGfzyfvzv/RXafusjsKYsT8NwZpgM9ndwwgZnACFAAAgCEULwAAAEMoXgAAAIZQvAAAAAyheAEAABhC8QIAADDE0lsGOU1cuEUD3l1udwzH80T+LUmKJhxhcxJniwu3SBpmdwwAQB/imuKVkZFhd4Q+o76+XpJ03LGUioMbxrgCAHwrrilefr/f7gh9xr73qrS01OYkAADEFtZ4AQAAGELxAgAAMITiBQAAYAjFCwAAwBCKFwAAgCEULwAAAEMoXgAAAIZQvAAAAAyheAEAABhC8QIAADDENbcMAgDEhpaWFqlVilvD3AF6SavUckSLkV0xagEAAAxhxgsA0Kf4fD59+O8P1fnjTrujIEbErYmTz+czsy8jewEAAAAzXkCs+WhXP81/Y5DdMRxvx16PJGlw/6jNSZzto139NMruEEAMoXgBMSQjI8PuCH3G1vp6SVLaiONsTuJso8S4AnoTxQuIIX6/3+4Ifca+96q0tNTmJADchDVeAAAAhlC8AAAADKF4AQAAGELxAgAAMITiBQAAYAjFCwAAwBCKFwAAgCEULwAAAEMsu4DqZ599pttvv12hUEgej0eXXHKJioqK1NraqpkzZ+qTTz7R8OHDtWjRIg0ePNiqGAAAAI5h2YxXv379NHv2bFVVVempp57SP//5TzU0NKisrEzZ2dmqra1Vdna2ysrKrIoAAADgKJYVr6FDhyorK0uSNGjQII0cOVJNTU2qq6tTYWGhJKmwsFCrV6+2KgIAAICjGLlX48cff6xAIKDRo0crFApp6NChkqTU1FSFQqEef76trU2BQMDqmPhKOByWJN5zxDTGed+173cH9KZwOGzk88Dy4rV79275/X7deeedGjRoULeveTweeTyeHp8jMTFRmZmZVkXE13i9XkniPUdMY5z3XV6vV9pudwrEGq/X22ufBwcrcJae1RiJROT3+zVx4kTl5eVJkpKTkxUMBiVJwWBQPp/PyggAAACOYVnxikajmjt3rkaOHKni4uKux3NyclRRUSFJqqio0NixY62KAAAA4CiWHWp88803VVlZqVGjRmnSpEmSpFmzZqmkpES33HKLli5dqqOPPlqLFi2yKgIAAICjWFa8Tj31VL333nv7/Vp5eblVuwUAAHAsrlwPAABgCMULAADAECPX8QIAoFe1SnFrmDvo0Z6v/j3A1hTO1yppuJldUbwAAH1KRkaG3RH6jPr6eknSccOPszmJww03N64oXgCAPsXv99sdoc/Y916VlpbanAT7ME8LAABgCMULAADAEIoXAACAIRQvAAAAQyheAAAAhlC8AAAADKF4AQAAGELxAgAAMITiBQAAYAjFCwAAwBCKFwAAgCEULwAAAEMoXgAAAIZQvAAAAAyheAEAABhC8QIAADCE4gUAAGAIxQsAAMAQihcAAIAhFC8AAABDKF4AAACGULwAAAAMoXgBAAAYQvECAAAwhOIFAABgCMULAADAEIoXAACAIRQvAAAAQyheAAAAhlC8AAAADLGseM2ZM0fZ2dm68MILux5rbW1VcXGx8vLyVFxcrB07dli1ewAAAMexrHhdfPHF+stf/tLtsbKyMmVnZ6u2tlbZ2dkqKyuzavcAAACOY1nxOu200zR48OBuj9XV1amwsFCSVFhYqNWrV1u1ewAAAMeJN7mzUCikoUOHSpJSU1MVCoUO6efa2toUCASsjIb/EA6HJYn3HDGNcQ43YJw7j9Hi9Z88Ho88Hs8hfW9iYqIyMzMtToR9vF6vJPGeI6YxzuEGjHN7HKzoGj2rMTk5WcFgUJIUDAbl8/lM7h4AAMBWRotXTk6OKioqJEkVFRUaO3asyd0DAADYyrLiNWvWLF166aXasmWLzj33XD3zzDMqKSnRunXrlJeXp/Xr16ukpMSq3QMAADiOZWu8fv/73+/38fLycqt2CQAA4GhcuR4AAMAQihcAAIAhFC8AAABDKF4AAACGULwAAAAMoXgBAAAYQvECAAAwhOIFAABgCMULAADAEIoXAACAIRQvAAAAQyheAAAAhlC8AAAADKF4AQAAGELxAgAAMITiBQAAYAjFCwAAwBCKFwAAgCEULwAAAEPi7Q4AIPbV1NSoqqrK7hjd1NfXS5L8fr/NSbobP3688vPz7Y4BwCLMeAFwJa/Xq927d2vnzp12RwHgIsx4AbBcfn6+42Zxxo8fL0kKBoNasmSJvWEAuAYzXgBcZ+PGjdq1a5ckadeuXXrzzTdtTgTALSheAFznl7/8Zbftu+++254gAFyH4gXAdfbNdh1oGwCsQvEC4DqDBg066DYAWIXiBcB1vn6o8Ve/+pU9QQC4DsULgOscddRR3bYHDx5sUxIAbkPxAuA68+fP77Y9b948m5IAcBuKFwDXaWxsPOg2AFiF4gXAdUaMGHHQbQCwCsULgOtceeWV3baLi4ttSgLAbbhlkM24efCh4+bB6C0PPfRQt+0HH3xQ559/vk1pALgJxQvfkJycbHcEwFLBYLDbdlNTk01JALiNLcVr7dq1WrBggTo7OzVt2jSVlJTYEcMRnHjzYAAAYA3jxaujo0Pz5s3TY489prS0NE2dOlU5OTnKyMgwHQWAS3m9XoXD4W7bwOFi6cihc/PSEeOL6zdv3qxjjjlG6enp6t+/vyZMmKC6ujrTMQC42Nev27VgwQKbkgDWSk5OZvmIwxif8WpqatKwYcO6ttPS0rR58+aD/kxbW5sCgYDV0QC4xJFHHqnExES1tbUpMTFRXq+XzxgctmOOOUY33HCD3TH6DLf+P9cnFtcnJiYqMzPT7hgAYsiCBQt0++2369577+XzBUCvOlipNF680tLStG3btq7tpqYmpaWlmY4BwOVOP/10rVmzxu4YAFzG+Bqvk046SY2Njdq6dav27t2rFStWKCcnx3QMAAAA44zPeMXHx+uee+7Rtddeq46ODk2ZMkXHHXec6RgAAADG2bLG67zzztN5551nx64BAABsw70aAQAADKF4AQAAGELxAgAAMITiBQAAYAjFCwAAwBCKFwAAgCEULwAAAEMoXgAAAGjRRlMAAAT5SURBVIZQvAAAAAyheAEAABhiyy2Dvq22tjYFAgG7YwAAAPSora3tgF/zRKPRqMEsAAAArsWhRgAAAEMoXgAAAIZQvAAAAAyheAEAABhC8QIAADCE4gUAAGBIn7iOF8yZM2eO1qxZo+TkZC1fvtzuOECv++yzz3T77bcrFArJ4/HokksuUVFRkd2xgF7V1tamyy+/XHv37lVHR4fGjRsnv99vdyyI63jha15//XV5vV7dcccdFC/EpGAwqM8//1xZWVnatWuXpkyZogceeEAZGRl2RwN6TTQaVTgc1sCBAxWJRHTZZZdp7ty5OuWUU+yO5nocakQ3p512mgYPHmx3DMAyQ4cOVVZWliRp0KBBGjlypJqammxOBfQuj8ejgQMHSpLa29vV3t4uj8djcypIFC8ALvbxxx8rEAho9OjRdkcBel1HR4cmTZqkM888U2eeeSbj3CEoXgBcaffu3fL7/brzzjs1aNAgu+MAva5fv36qrKzUyy+/rM2bN+v999+3OxJE8QLgQpFIRH6/XxMnTlReXp7dcQBLJSUlacyYMXrllVfsjgJRvAC4TDQa1dy5czVy5EgVFxfbHQewREtLi3bu3ClJ2rNnj9avX6+RI0fanAoSZzXia2bNmqWNGzdq+/btSk5O1owZMzRt2jS7YwG95o033tDll1+uUaNGKS7uy789Z82apfPOO8/mZEDv+de//qXZs2ero6ND0WhU+fn5+tnPfmZ3LIjiBQAAYAyHGgEAAAyheAEAABhC8QIAADCE4gUAAGAIxQsAAMCQeLsDAMDhyMzM1KhRo9TR0aGRI0fqt7/9rY444oj9fu/ixYvl9Xp1zTXXGE4JAF9ixgtAnzZgwABVVlZq+fLlSkhI0JNPPml3JAA4IGa8AMSMU089Ve+9954kqaKiQo8++qg8Ho9+8IMf6He/+12373366af11FNPKRKJ6JhjjtHChQt1xBFHqLq6Wg888IDi4uJ05JFH6h//+Ifq6+s1Z84cRSIRdXZ2avHixRoxYoQNrxBAX0fxAhAT2tvbtXbtWp1zzjmqr6/Xn//8Zz3xxBPy+XxqbW39xvfn5ubqkksukST94Q9/0NKlS3XFFVfowQcf1KOPPqq0tLSuW648+eSTuvLKK3XRRRdp79696uzsNPraAMQOiheAPm3Pnj2aNGmSpC9nvKZOnaqnnnpK+fn58vl8kqSjjjrqGz9XX1+vRYsW6YsvvtDu3bt19tlnS5J++MMfavbs2SooKFBubq4k6ZRTTtFDDz2kbdu2KS8vj9kuAN8ZxQtAn7Zvjde3NXv2bD344IM6/vjj9dxzz2njxo2SpHnz5umdd97RmjVrNGXKFD377LOaOHGiRo8erTVr1qikpET//d//rezs7N5+KQBcgMX1AGLOGWecoZqaGm3fvl2S9nuocffu3UpNTVUkEtELL7zQ9fhHH32k0aNH6+abb9aQIUO0bds2bd26Venp6bryyis1duzYrnVkAPBtMeMFIOYcd9xxuv7663XFFVcoLi5OJ5xwgu69995u33PzzTdr2rRp8vl8Gj16tHbv3i1JWrhwoT788ENFo1GdccYZOv744/XII4+osrJS8fHxSklJ0XXXXWfHywIQAzzRaDRqdwgAAAA34FAjAACAIRQvAAAAQyheAAAAhlC8AAAADKF4AQAAGELxAgAAMITiBQAAYMj/Bz1vewNrv1nAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9azuniZpbkU"
      },
      "source": [
        "IMPUTATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OskebxqUMDP8"
      },
      "source": [
        "def impute_age(cols):\n",
        "  Age=cols[0]\n",
        "  Pclass = cols[1]\n",
        "  if pd.isnull(Age):\n",
        "    if Pclass==1:\n",
        "      return 37\n",
        "    elif Pclass==2:\n",
        "      return 29\n",
        "    else:\n",
        "      return 24\n",
        "  else:\n",
        "    return Age"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfLQ3aJJMDWf"
      },
      "source": [
        "train['Age']= train[['Age','Pclass']].apply(impute_age,axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRS83WmCMDdU",
        "outputId": "359d86c2-99ac-4854-db4f-898fe3e80cfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efc2f65efd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPU-BtE5MDis"
      },
      "source": [
        "train.drop('Cabin',axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wzj0uo_XQ4du",
        "outputId": "32b2067c-46cb-45c8-b570-d74767572298",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "   PassengerId  Survived  Pclass  ...            Ticket     Fare  Embarked\n",
              "0            1         0       3  ...         A/5 21171   7.2500         S\n",
              "1            2         1       1  ...          PC 17599  71.2833         C\n",
              "2            3         1       3  ...  STON/O2. 3101282   7.9250         S\n",
              "3            4         1       1  ...            113803  53.1000         S\n",
              "4            5         0       3  ...            373450   8.0500         S\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJfxx1bKQ4hb"
      },
      "source": [
        "sex=pd.get_dummies(train['Sex'],drop_first=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0gBlpnpQ4mn"
      },
      "source": [
        "embark=pd.get_dummies(train['Embarked'],drop_first=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Op7ZaoimQ4tf"
      },
      "source": [
        "train = pd.concat([train,sex,embark],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MK3guvs4azPh",
        "outputId": "f074f977-78f7-4af5-c3b5-eae8eadc8c4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
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      "source": [
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      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "   PassengerId  Survived  Pclass  ... male  Q  S\n",
              "0            1         0       3  ...    1  0  1\n",
              "1            2         1       1  ...    0  0  0\n",
              "2            3         1       3  ...    0  0  1\n",
              "3            4         1       1  ...    0  0  1\n",
              "4            5         0       3  ...    1  0  1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
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          },
          "execution_count": 26
        }
      ]
    },
    {
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        "id": "kCp32rbgazVw"
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      "source": [
        "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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        "id": "SQaULsaIazcz",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
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      "source": [
        "train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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            "text/plain": [
              "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
              "0            1         0       3  22.0      1      0   7.2500     1  0  1\n",
              "1            2         1       1  38.0      1      0  71.2833     0  0  0\n",
              "2            3         1       3  26.0      0      0   7.9250     0  0  1\n",
              "3            4         1       1  35.0      1      0  53.1000     0  0  1\n",
              "4            5         0       3  35.0      0      0   8.0500     1  0  1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8Wrlk3DazkX"
      },
      "source": [
        "train.drop(['PassengerId'],axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
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        "id": "jPzBtvL-azLF",
        "outputId": "722e89b1-00be-44ea-d0b7-dc2c3d36427f",
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      "source": [
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      "outputs": [
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          "data": {
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            ],
            "text/plain": [
              "   Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
              "0         0       3  22.0      1      0   7.2500     1  0  1\n",
              "1         1       1  38.0      1      0  71.2833     0  0  0\n",
              "2         1       3  26.0      0      0   7.9250     0  0  1\n",
              "3         1       1  35.0      1      0  53.1000     0  0  1\n",
              "4         0       3  35.0      0      0   8.0500     1  0  1"
            ]
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          "metadata": {
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          },
          "execution_count": 30
        }
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        "id": "u9UKiLS6cdsh",
        "outputId": "009be5d0-2366-4963-85c4-6da4c8af1334",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efc2f5b0240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dihl3z4ucdwR"
      },
      "source": [
        "X= train.drop('Survived',axis=1)\n",
        "Y= train['Survived']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhxGy82Ecd0F"
      },
      "source": [
        "from sklearn.model_selection  import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siHErd8tcd82",
        "outputId": "7aceafd6-99af-49ae-a157-59cc67cfb6cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=101)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(Y_train.shape)\n",
        "print(Y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(712, 8)\n",
            "(179, 8)\n",
            "(712,)\n",
            "(179,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xA4UaCiMceAR"
      },
      "source": [
        "from sklearn.ensemble import  RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1K3t9q8cd6H"
      },
      "source": [
        "rfc= RandomForestClassifier(n_estimators=200)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZZMXSKScd4N",
        "outputId": "aa68a066-dddc-448e-8a16-45a0e49fb0fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        }
      },
      "source": [
        "rfc.fit(X_train,Y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PnIV_aPIgdTK"
      },
      "source": [
        "\n",
        "rfc_pred = rfc.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBUktwWPgmVc",
        "outputId": "a4192bdc-91d0-47ed-f3a8-38b901f4787a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        }
      },
      "source": [
        "rfc_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
              "       0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,\n",
              "       1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
              "       0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0,\n",
              "       0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
              "       1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
              "       0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
              "       1, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBgCnhWRgpp8"
      },
      "source": [
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdfsMUA3gwjq"
      },
      "source": [
        "accuracy=rfc.score(X_test,Y_test)\n",
        "accuracytrain=rfc.score(X_train,Y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf2KxKDOg3Q5",
        "outputId": "a1ad02ca-8dd4-440c-b741-8c6395b243db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "print(accuracy*100)\n",
        "print(accuracytrain*100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "81.56424581005587\n",
            "98.59550561797754\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}