Core_learning_algorithms.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Core learning algorithms.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "sxObqgxLFMEP"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### **Linear Regression Algorithm**\n"
      ],
      "metadata": {
        "id": "sxObqgxLFMEP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FTZij4AvAHwu"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating datapoints"
      ],
      "metadata": {
        "id": "wDue0sbvEKK0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = [1,2,2.5,3,4,5]\n",
        "y = [1,4,7,9,15,19]"
      ],
      "metadata": {
        "id": "dJCcabi0EOVk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calling the plot function and creating line of best fit"
      ],
      "metadata": {
        "id": "lVCjwT-QEWvX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x,y,'ro')\n",
        "plt.axis([0,6,0,20])\n",
        "plt.plot(np.unique(x), np.poly1d(np.polyfit(x,y,1))(np.unique(x)))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "EImuJ04GE-Bt",
        "outputId": "a93c3257-d0c0-4536-af55-f0a6c63fce29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Setup**"
      ],
      "metadata": {
        "id": "rJvw_yboJevK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q sklearn"
      ],
      "metadata": {
        "id": "H8v-46ZgJjzK"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%tensorflow_version 2.x"
      ],
      "metadata": {
        "id": "0K_yu8ksJuKo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "from six.moves import urllib\n",
        "\n",
        "import tensorflow.compat.v2.feature_column as fc\n",
        "\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "PahuQmVSJ2I5"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the dataset"
      ],
      "metadata": {
        "id": "3_PlWAK9zGVl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')#training dataset\n",
        "dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv') #test dataset"
      ],
      "metadata": {
        "id": "Gl7WhN_MzUEO"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "I am storing the survived column to a new variable for each dataset using the .pop() method\n",
        "\n",
        " "
      ],
      "metadata": {
        "id": "r__xJ5Nw3TSQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = dftrain.pop('survived')\n",
        "y_eval = dfeval.pop('survived')\n",
        "print(dftrain.loc[0], y_train.loc[0]) # prints the data located at the 0 index of each dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQC0duKw4JT6",
        "outputId": "ac123f85-b89a-4417-89c8-819861ba5cb8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sex                          male\n",
            "age                          22.0\n",
            "n_siblings_spouses              1\n",
            "parch                           0\n",
            "fare                         7.25\n",
            "class                       Third\n",
            "deck                      unknown\n",
            "embark_town           Southampton\n",
            "alone                           n\n",
            "Name: 0, dtype: object 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the .head() function prints the first five elements of the indicated dataset"
      ],
      "metadata": {
        "id": "5t2jN3786mZ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "oy-eLJ_U68Uy",
        "outputId": "51e1af97-65e3-4492-9a13-91b3d0524d85"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sex   age  n_siblings_spouses  parch     fare  class     deck  \\\n",
              "0    male  22.0                   1      0   7.2500  Third  unknown   \n",
              "1  female  38.0                   1      0  71.2833  First        C   \n",
              "2  female  26.0                   0      0   7.9250  Third  unknown   \n",
              "3  female  35.0                   1      0  53.1000  First        C   \n",
              "4    male  28.0                   0      0   8.4583  Third  unknown   \n",
              "\n",
              "   embark_town alone  \n",
              "0  Southampton     n  \n",
              "1    Cherbourg     n  \n",
              "2  Southampton     y  \n",
              "3  Southampton     n  \n",
              "4   Queenstown     y  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-19cd1d96-73eb-434e-a7fd-0163bf18dd3d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>n_siblings_spouses</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>class</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>Third</td>\n",
              "      <td>unknown</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>First</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>Third</td>\n",
              "      <td>unknown</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>First</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>n</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>Third</td>\n",
              "      <td>unknown</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-19cd1d96-73eb-434e-a7fd-0163bf18dd3d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-19cd1d96-73eb-434e-a7fd-0163bf18dd3d button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-19cd1d96-73eb-434e-a7fd-0163bf18dd3d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ".describe() function runs statistical analysis of the dataset"
      ],
      "metadata": {
        "id": "sW_p64EE7EdA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.describe()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "nl_HdVgx7u2b",
        "outputId": "44fd95c7-d649-461a-d3ba-8ce8579bcb07"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age  n_siblings_spouses       parch        fare\n",
              "count  627.000000          627.000000  627.000000  627.000000\n",
              "mean    29.631308            0.545455    0.379585   34.385399\n",
              "std     12.511818            1.151090    0.792999   54.597730\n",
              "min      0.750000            0.000000    0.000000    0.000000\n",
              "25%     23.000000            0.000000    0.000000    7.895800\n",
              "50%     28.000000            0.000000    0.000000   15.045800\n",
              "75%     35.000000            1.000000    0.000000   31.387500\n",
              "max     80.000000            8.000000    5.000000  512.329200"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-161eb2f3-c6aa-4461-9e00-58c3abbd8fed\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>n_siblings_spouses</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>627.000000</td>\n",
              "      <td>627.000000</td>\n",
              "      <td>627.000000</td>\n",
              "      <td>627.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>29.631308</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.379585</td>\n",
              "      <td>34.385399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.511818</td>\n",
              "      <td>1.151090</td>\n",
              "      <td>0.792999</td>\n",
              "      <td>54.597730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.750000</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>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.045800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.387500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-161eb2f3-c6aa-4461-9e00-58c3abbd8fed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ".shape returns the number of rows by columns"
      ],
      "metadata": {
        "id": "m3b5yJIr7z5Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z3eDtgp17xoU",
        "outputId": "213871a7-79f4-4e22-b4f5-5790cb78706b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(627, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Correlation review"
      ],
      "metadata": {
        "id": "Vbr7E7Ku8kdT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Returning a histogram of the ages"
      ],
      "metadata": {
        "id": "n4W3xmUB9Dbe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.age.hist(bins = 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "9zkB2flD8qAw",
        "outputId": "6632076e-8904-44a5-ee5a-c26eaa768950"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a72fbf1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain.sex.value_counts().plot(kind = 'barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "s82T3ynC9QMD",
        "outputId": "67edd731-2ce7-4a49-8f6d-fef79e0926b0"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a735b7590>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD4CAYAAADo30HgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAMdUlEQVR4nO3cf4xld1nH8c8D225NS4rQhmxacChuJKRAW0tFRQKICF1DQTAhEigJoVEUNabRIpHUVLSCKJqgpCgWFQVBDAghiLTGBLF11/7Y1nah2jVSKw0SlpomVenXP+5ZmGec2XbbmXtmy+uVTPbcc+/e88x3cve959y7W2OMAMBhj5h7AAC2F2EAoBEGABphAKARBgCaHXMPsBlOOeWUsbKyMvcYAMeUffv2fWmMcera/Q+LMKysrGTv3r1zjwFwTKmqf11vv0tJADTCAEAjDAA0wgBAIwwANMIAQCMMADTCAEAjDAA0wgBAIwwANMIAQCMMADTCAEAjDAA0wgBAIwwANMIAQCMMADTCAEAjDAA0wgBAIwwANMIAQCMMADTCAECzY+4BNsP+Ow5l5ZKPzz0GrOvg5XvmHgGOijMGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCgEQYAmvsNQ1X9VFXdUlXv24oBqurSqrp4K54bgKO34wE85vVJnj/G+MJWDwPA/I4Yhqp6V5Izknyiqt6f5ElJzkxyXJJLxxgfqarXJHlJkhOT7E7y60mOT/KqJPcmOX+M8eWqel2Si6b7bkvyqjHGPWuO96Qk70xyapJ7krxujHHrJn2vADwAR7yUNMb4sST/nuS5WfzBf9UY47zp9tuq6sTpoWcm+eEkz0jyliT3jDHOTvLZJK+eHvPhMcYzxhhPT3JLkteuc8grkrxhjPGdSS5O8jsbzVZVF1XV3qra+7V7Dj2w7xaA+/VALiUd9oIkL171fsAJSZ4wbV89xrg7yd1VdSjJX0779yd52rR9ZlX9cpJHJzkpySdXP3lVnZTke5J8sKoO79650TBjjCuyCEl27to9juL7AOAIjiYMleRlY4wDbWfVd2Vxyeiw+1bdvm/VMa5M8pIxxg3T5afnrHn+RyT5yhjjrKOYCYBNdjQfV/1kkjfU9Nf5qjr7KI/1qCR3VtVxSV659s4xxleT3F5VPzI9f1XV04/yGAA8REcThsuyeNP5xqq6ebp9NH4xyTVJPpNkozeUX5nktVV1Q5Kbk1xwlMcA4CGqMY79y/M7d+0euy58x9xjwLoOXr5n7hFgXVW1b4xx7tr9/uUzAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwDNjrkH2AxPPe3k7L18z9xjADwsOGMAoBEGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCgEQYAGmEAoBEGABphAKARBgAaYQCg2TH3AJth/x2HsnLJx+ceA2CpDl6+Z0ue1xkDAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAzbYIQ1U9p6o+NvccAGyTMACwfWxaGKpqpapuraorq+pzVfW+qnp+VX2mqj5fVedNX5+tquuq6u+q6jvWeZ4Tq+o9VXXt9LgLNmtGAO7fZp8xfHuStyd58vT1o0meleTiJL+Q5NYk3zfGODvJm5P8yjrP8aYkV40xzkvy3CRvq6oT1z6oqi6qqr1Vtfdr9xza5G8D4JvXjk1+vtvHGPuTpKpuTvLpMcaoqv1JVpKcnOS9VbU7yUhy3DrP8YIkL66qi6fbJyR5QpJbVj9ojHFFkiuSZOeu3WOTvw+Ab1qbHYZ7V23ft+r2fdOxLkty9RjjpVW1kuRv1nmOSvKyMcaBTZ4NgAdg2W8+n5zkjmn7NRs85pNJ3lBVlSRVdfYS5gJgsuwwvDXJr1bVddn4bOWyLC4x3ThdjrpsWcMBkNQYx/7l+Z27do9dF75j7jEAlurg5Xse0u+vqn1jjHPX7vfvGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgEYYAGiEAYBGGABohAGARhgAaIQBgGbH3ANshqeednL2Xr5n7jEAHhacMQDQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMAjTAA0AgDAI0wANAIAwCNMADQCAMATY0x5p7hIauqu5McmHuODZyS5EtzD7GO7TpXYrYHy2wPzjfzbN82xjh17c4dW3jAZTowxjh37iHWU1V7t+Ns23WuxGwPltkeHLP9fy4lAdAIAwDNwyUMV8w9wBFs19m261yJ2R4ssz04ZlvjYfHmMwCb5+FyxgDAJhEGAJpjOgxV9cKqOlBVt1XVJdtgnoNVtb+qrq+qvdO+x1TVp6rq89Ov37qkWd5TVXdV1U2r9q07Sy389rSON1bVOTPMdmlV3TGt3fVVdf6q+944zXagqn5wi2d7fFVdXVX/VFU3V9VPT/tnXbsjzDX7ulXVCVV1bVXdMM32S9P+J1bVNdMMH6iq46f9O6fbt033r8ww25VVdfuqdTtr2r/U18J0zEdW1XVV9bHp9uzrljHGMfmV5JFJ/jnJGUmOT3JDkqfMPNPBJKes2ffWJJdM25ck+bUlzfLsJOckuen+ZklyfpJPJKkkz0xyzQyzXZrk4nUe+5TpZ7szyROnn/kjt3C2XUnOmbYfleRz0wyzrt0R5pp93abv/aRp+7gk10xr8WdJXjHtf1eSH5+2X5/kXdP2K5J8YAt/nhvNdmWSl6/z+KW+FqZj/mySP0nysen27Ot2LJ8xnJfktjHGv4wx/jvJ+5NcMPNM67kgyXun7fcmeckyDjrG+NskX36As1yQ5A/Hwt8neXRV7VrybBu5IMn7xxj3jjFuT3JbFj/7rZrtzjHGP07bdye5JclpmXntjjDXRpa2btP3/l/TzeOmr5HkeUk+NO1fu2aH1/JDSb6/qmrJs21kqa+Fqjo9yZ4kvzfdrmyDdTuWw3Bakn9bdfsLOfILZRlGkr+qqn1VddG073FjjDun7f9I8rh5RjviLNtlLX9yOn1/z6pLbrPNNp2qn53F3zK3zdqtmSvZBus2XQ65PsldST6VxRnKV8YY/7vO8b8+23T/oSSPXdZsY4zD6/aWad1+s6p2rp1tnbm3wjuS/FyS+6bbj802WLdjOQzb0bPGGOckeVGSn6iqZ6++cyzOAbfF54O30yyT303ypCRnJbkzydvnHKaqTkry50l+Zozx1dX3zbl268y1LdZtjPG1McZZSU7P4szkyXPMsZ61s1XVmUnemMWMz0jymCQ/v+y5quqHktw1xti37GPfn2M5DHckefyq26dP+2Yzxrhj+vWuJH+RxQvki4dPRadf75pvwg1nmX0txxhfnF7A9yV5d75x2WPps1XVcVn84fu+McaHp92zr916c22ndZvm+UqSq5N8dxaXYQ7/f2yrj//12ab7T07yn0uc7YXTpbkxxrg3yR9knnX73iQvrqqDWVwKf16S38o2WLdjOQz/kGT39A7+8Vm8GfPRuYapqhOr6lGHt5O8IMlN00wXTg+7MMlH5pkwOcIsH03y6ukTGc9McmjVZZOlWHMd96VZrN3h2V4xfSLjiUl2J7l2C+eoJL+f5JYxxm+sumvWtdtoru2wblV1alU9etr+liQ/kMV7IFcnefn0sLVrdngtX57kquksbFmz3boq8pXFNfzV67aU18IY441jjNPHGCtZ/Pl11RjjldkG67al77Zv9VcWnyD4XBbXM9808yxnZPEpkBuS3Hx4niyuAX46yeeT/HWSxyxpnj/N4tLC/2RxnfK1G82SxScw3jmt4/4k584w2x9Nx74xixfArlWPf9M024EkL9ri2Z6VxWWiG5NcP32dP/faHWGu2dctydOSXDfNcFOSN696TVybxRvfH0yyc9p/wnT7tun+M2aY7app3W5K8sf5xieXlvpaWDXnc/KNTyXNvm7+SwwAmmP5UhIAW0AYAGiEAYBGGABohAGARhgAaIQBgOb/AEYEJAUZdZ+FAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftrain['class'].value_counts().plot(kind = 'barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "evVWb1wT9rnq",
        "outputId": "0eee89c1-4f27-43b2-dac4-92d398219546"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2a76f65c90>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([dftrain,y_train], axis=1).groupby('sex').survived.mean().plot(kind = 'barh').set_xlabel('Survival percentage')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "v6W9D2g9-0CM",
        "outputId": "69d0d401-26f2-4e18-f267-7aad5769b525"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Survival percentage')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}