titanic.ipynb

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    "colab": {
      "name": "titanic.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPPsEvx72l392dZ8PcagYi8",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/giacomoaccursi/DataIntensiveExercise/blob/main/titanic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fJshsS-zI1G"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOMbe004wmyy"
      },
      "source": [
        "import os\n",
        "from urllib.request import urlretrieve\n",
        "def download(file, url): \n",
        "  if not os.path.isfile(file): \n",
        "    urlretrieve(url, file)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ia4YLMyYyWj9"
      },
      "source": [
        "download(\"titanic_train.csv\", \"https://raw.githubusercontent.com/dsindy/kaggle-titanic/master/data/train.csv\")\n",
        "download(\"titanic_test.csv\", \"https://raw.githubusercontent.com/dsindy/kaggle-titanic/master/data/test.csv\")"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECeSEsb103xr"
      },
      "source": [
        "- pclass = classe del ticket\n",
        "- sex = sesso della persona\n",
        "- age = anno di età\n",
        "- sibsp = compagni o sposi a bordo\n",
        "- parch = parenti o figli a bordo\n",
        "- ticket = codice del biglietto\n",
        "- fare = tariffa del biglietto\n",
        "- cabin = numero della cabina\n",
        "- embarked = porto di imbarcazione (C = cherbourg, Q = queenstown, S = southampton. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZFOb4RizAH-"
      },
      "source": [
        "train = pd.read_csv(\"titanic_train.csv\")\n",
        "test = pd.read_csv(\"titanic_test.csv\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "InZI5ZrdzRjr",
        "outputId": "029cd15c-d8d6-4397-830d-bc0592d9af94"
      },
      "source": [
        "train"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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              "      <td>Q</td>\n",
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              "<p>891 rows × 12 columns</p>\n",
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            "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",
              "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]"
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          "metadata": {
            "tags": []
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        }
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    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 419
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        "id": "Yyk44a51CCY1",
        "outputId": "2a6af89c-644b-4c91-940a-2c84e1aac5db"
      },
      "source": [
        "test"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
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              "      <td>C</td>\n",
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            "text/plain": [
              "     PassengerId  Pclass  ... Cabin Embarked\n",
              "0            892       3  ...   NaN        Q\n",
              "1            893       3  ...   NaN        S\n",
              "2            894       2  ...   NaN        Q\n",
              "3            895       3  ...   NaN        S\n",
              "4            896       3  ...   NaN        S\n",
              "..           ...     ...  ...   ...      ...\n",
              "413         1305       3  ...   NaN        S\n",
              "414         1306       1  ...  C105        C\n",
              "415         1307       3  ...   NaN        S\n",
              "416         1308       3  ...   NaN        S\n",
              "417         1309       3  ...   NaN        C\n",
              "\n",
              "[418 rows x 11 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hq9WNv1OdjxF"
      },
      "source": [
        "# Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "WX44ZTa4z9kw",
        "outputId": "be15dcd1-e418-450f-ea3f-4222d48931e3"
      },
      "source": [
        "train[\"Survived\"].value_counts().plot.pie(); "
      ],
      "execution_count": 12,
      "outputs": [
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "hKLbL6tf19Te",
        "outputId": "319157f3-dbed-45ad-b0ce-126526ec41d4"
      },
      "source": [
        "train.describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oRpzBxkT2VID",
        "outputId": "69778059-201e-40d6-ebd8-00d9ccf273a5"
      },
      "source": [
        "train.isnull().sum()"
      ],
      "execution_count": 14,
      "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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8NPOkPh49Fk"
      },
      "source": [
        "female = train[train[\"Sex\"] == \"female\"]"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcUeFFdrGW5z"
      },
      "source": [
        "male = train[train[\"Sex\"] == \"male\"]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "BBvfrOBJIHvc",
        "outputId": "1f9b3522-d9a2-4bad-82e6-ea14c0d843c7"
      },
      "source": [
        "fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize=(10, 4))\n",
        "ax = sns.histplot(female[female[\"Survived\"] == 1][\"Age\"].dropna(), bins = 18, label = \"Survived\", ax = axes[0], kde = False, color=\"red\")\n",
        "ax = sns.histplot(female[female[\"Survived\"] == 0][\"Age\"].dropna(), bins = 40, label = \"Not Survived\", ax = axes[0], kde = False, color=\"blue\")\n",
        "ax.legend()\n",
        "\n",
        "ax = sns.histplot(male[male[\"Survived\"] == 1][\"Age\"].dropna(), bins = 18, label = \"Survived\", ax = axes[1], kde = False, color=\"red\")\n",
        "ax = sns.histplot(male[male[\"Survived\"] == 0][\"Age\"].dropna(), bins = 40, label = \"Not Survived\", ax = axes[1], kde = False, color=\"blue\")\n",
        "ax.legend()\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb143fc4190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "pEAUP3C6R-AU",
        "outputId": "e49d793a-92db-4777-e564-bc2b0c0b4119"
      },
      "source": [
        "\n",
        "train.groupby(\"Pclass\")[\"Survived\"].mean().plot.bar();"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+DDhWVX8KfAY4rz4MSj9YPEPXeaX3BecfAP4hyVPAV4Hr5i37XeAQc9E+xFzgAT7Se9MzwOeAe4EbgF9O8iRzn/b3h8v+S0iL8E1RSWqEl1wkqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIa8X8V+6HwKQ2RHQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvXT55npWCBM"
      },
      "source": [
        "Si nota come le persone della classe 1 abbiamo più chance di sopravvivere rispetto alle altre classi, soprattutto della terza."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j27ylk5DZB_J",
        "outputId": "d37766d9-b375-49a7-963a-7035bc20173c"
      },
      "source": [
        "train.groupby(\"Sex\")[\"Survived\"].value_counts()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sex     Survived\n",
              "female  1           233\n",
              "        0            81\n",
              "male    0           468\n",
              "        1           109\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "5DiLS42wTBmZ",
        "outputId": "8e61bc0a-5944-4285-cf6f-2b0dab2e0e5f"
      },
      "source": [
        "train.groupby(\"Sex\")[\"Survived\"].mean().plot.bar();"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "-fxmxC6CZE7b"
      },
      "source": [
        "Le donne hanno una probabilità molto maggiore di salvarsi rispetto agli uomini"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rD5s4pqWZHl5",
        "outputId": "2af6dc4e-ea6d-4138-8f2e-ded2f00a8105"
      },
      "source": [
        "train[\"Fare\"].describe()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    891.000000\n",
              "mean      32.204208\n",
              "std       49.693429\n",
              "min        0.000000\n",
              "25%        7.910400\n",
              "50%       14.454200\n",
              "75%       31.000000\n",
              "max      512.329200\n",
              "Name: Fare, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jxHz8MgZ5ky",
        "outputId": "799c0b7c-76e3-40bc-8efe-ee3938c721c5"
      },
      "source": [
        "pd.qcut(train[\"Fare\"], 4).value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7.91, 14.454]     224\n",
              "(-0.001, 7.91]     223\n",
              "(31.0, 512.329]    222\n",
              "(14.454, 31.0]     222\n",
              "Name: Fare, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gGoaW7zFdxnD"
      },
      "source": [
        "# Data processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQ6hzczpma3x"
      },
      "source": [
        "Elimino passengerId poichè non influisce sulle probabilità di sopravvivenza"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tn49HIZNeJl5"
      },
      "source": [
        "train.drop(\"PassengerId\", axis=1, inplace=True)\n",
        "test.drop(\"PassengerId\", axis=1, inplace=True)\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QKlP8F0_mnW6"
      },
      "source": [
        "ci sono 177 campi Nan nell'età. li sostituisco con valori casuali fra mean-std e mean+std"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7vbWzuSdwix",
        "outputId": "b24e774c-3a26-49fe-a4c0-9458bd5a1d79"
      },
      "source": [
        "for dataset in [train, test]: \n",
        "  mean = train[\"Age\"].mean()\n",
        "  std = train[\"Age\"].std()\n",
        "  isnull = dataset[\"Age\"].isnull().sum()\n",
        "  random_ages = np.random.randint(mean - std, mean + std, size=isnull)\n",
        "  dataset[\"Age\"][dataset[\"Age\"].isnull()] = random_ages"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_8VCChqm4bU"
      },
      "source": [
        "ci sono due valori mancanti nella colonna \"Embarked\". Li sostituisco con quello presente più volte"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJzvKw80lxb1"
      },
      "source": [
        "for dataset in [train, test]:\n",
        "  dataset[\"Embarked\"].fillna(dataset[\"Embarked\"].describe()[\"top\"], inplace=True)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3pKkDxgEn6p3"
      },
      "source": [
        "elimino la colonna delle cabine per scarsità di dati disponibili"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDUycw5QoCzC"
      },
      "source": [
        "train.drop(\"Cabin\", axis = 1, inplace = True)\n",
        "test.drop(\"Cabin\", axis = 1, inplace = True)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Vmn4Fwup1QR"
      },
      "source": [
        "Guardando il dataset ci si accorge che abbiamo Fare che è float e 4 features categoriche: name, sex, ticket, e embarked. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rd-8_ybqHIx"
      },
      "source": [
        "for dataset in [train, test]:\n",
        "  dataset.fillna(0, inplace=True)\n",
        "  dataset[\"Fare\"] = dataset[\"Fare\"].astype(int)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Qtl_OpkrczB",
        "outputId": "39bd9975-035c-4d35-a035-834cc76f041f"
      },
      "source": [
        "train[\"Fare\"]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       7\n",
              "1      71\n",
              "2       7\n",
              "3      53\n",
              "4       8\n",
              "       ..\n",
              "886    13\n",
              "887    30\n",
              "888    23\n",
              "889    30\n",
              "890     7\n",
              "Name: Fare, Length: 891, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WOU6txwrjwP"
      },
      "source": [
        "\n",
        "titles = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
        "for dataset in [train , test]:\n",
        "  dataset[\"Title\"] = dataset[\"Name\"].str.extract(\"([A-Za-z]+)\\.\", expand = False)\n",
        "  dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
        "  dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
        "  dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
        "  dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
        "  dataset[\"Title\"] = dataset[\"Title\"].map(titles)\n",
        "  dataset[\"Title\"].fillna(0, inplace=True)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6QO7_JLnmGZ2"
      },
      "source": [
        "train.drop(\"Name\",axis=1, inplace = True)\n",
        "test.drop(\"Name\", axis = 1, inplace=True)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yn1O-z0PujtM"
      },
      "source": [
        "gender = {\"male\" : 0, \"female\" : 1}\n",
        "for dataset in [train, test]:\n",
        "  dataset[\"Sex\"] = dataset[\"Sex\"].map(gender)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3eJa84N_vSin",
        "outputId": "3980f1b1-afec-469b-a3da-8a3d76db3565"
      },
      "source": [
        "train[\"Ticket\"].describe()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count        891\n",
              "unique       681\n",
              "top       347082\n",
              "freq           7\n",
              "Name: Ticket, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpEJdImDnGQH"
      },
      "source": [
        "Ticket ha 681 valori differenti, conviene quindi eliminarlo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIAXxfoznFDJ"
      },
      "source": [
        "for dataset in [train, test]: \n",
        "  dataset.drop(\"Ticket\", axis=1, inplace=True)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tZFvSzdfvXnu",
        "outputId": "e31db173-debf-4446-db33-ae4f9ca7936c"
      },
      "source": [
        "train[\"Embarked\"].unique()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['S', 'C', 'Q'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yk7WdbJnazA",
        "outputId": "9aafda2f-1037-420f-e49f-8278501f6686"
      },
      "source": [
        "ports = {\n",
        "    port[1] : port[0] for port in enumerate(train[\"Embarked\"].unique(), start = 1) \n",
        "}\n",
        "ports"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 2, 'Q': 3, 'S': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kA8UPcJpn4HM"
      },
      "source": [
        "for dataset in [train, test]: \n",
        "  dataset[\"Embarked\"] = dataset[\"Embarked\"].map(ports)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbNUaKMeoEY2"
      },
      "source": [
        "for dataset in [train, test]: \n",
        "  dataset[\"Age\"] = dataset[\"Age\"].astype(int)\n",
        "  "
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "VhJB3jPmp_Hf",
        "outputId": "3e1ed70c-2ffe-40f2-c7c1-4cffe46ee1d1"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  Title\n",
              "0         0       3    0   22      1      0     7         1      1\n",
              "1         1       1    1   38      1      0    71         2      3\n",
              "2         1       3    1   26      0      0     7         1      2\n",
              "3         1       1    1   35      1      0    53         1      3\n",
              "4         0       3    0   35      0      0     8         1      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6aD2MdNDtWFY"
      },
      "source": [
        "\n",
        "for dataset in [train, test]:\n",
        "    dataset['Age'] = dataset['Age'].astype(int)\n",
        "    dataset.loc[ dataset['Age'] <= 11, 'Age'] = 0\n",
        "    dataset.loc[(dataset['Age'] > 11) & (dataset['Age'] <= 18), 'Age'] = 1\n",
        "    dataset.loc[(dataset['Age'] > 18) & (dataset['Age'] <= 22), 'Age'] = 2\n",
        "    dataset.loc[(dataset['Age'] > 22) & (dataset['Age'] <= 27), 'Age'] = 3\n",
        "    dataset.loc[(dataset['Age'] > 27) & (dataset['Age'] <= 33), 'Age'] = 4\n",
        "    dataset.loc[(dataset['Age'] > 33) & (dataset['Age'] <= 40), 'Age'] = 5\n",
        "    dataset.loc[(dataset['Age'] > 40) & (dataset['Age'] <= 66), 'Age'] = 6\n",
        "    dataset.loc[ dataset['Age'] > 66, 'Age'] = 6\n",
        "\n"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYQtiVCWuNGG",
        "outputId": "ae41ff31-9dc8-402f-f4e6-ddefb42556e4"
      },
      "source": [
        "train['Age'].value_counts()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6    167\n",
              "4    159\n",
              "5    147\n",
              "3    139\n",
              "2    119\n",
              "1     92\n",
              "0     68\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RfT3ivZ3vnaG"
      },
      "source": [
        "for dataset in [train, test]:\n",
        "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
        "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
        "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
        "    dataset.loc[(dataset['Fare'] > 31) & (dataset['Fare'] <= 99), 'Fare']   = 3\n",
        "    dataset.loc[(dataset['Fare'] > 99) & (dataset['Fare'] <= 250), 'Fare']   = 4\n",
        "    dataset.loc[ dataset['Fare'] > 250, 'Fare'] = 5\n",
        "    dataset['Fare'] = dataset['Fare'].astype(int)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHvee7RKwGxx"
      },
      "source": [
        "X_train = train.drop(\"Survived\", axis = 1)\n",
        "y_train = train[\"Survived\"]\n",
        "X_test = test"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hpMV2cQ6-Z7P"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import KFold, cross_validate"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9F53A-ug_dZC"
      },
      "source": [
        "kf = KFold(5, shuffle=True, random_state=42)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SzJRzLycCNXx"
      },
      "source": [
        "model = Pipeline([\n",
        "             (\"poly\", PolynomialFeatures(degree=2, include_bias=False)),\n",
        "             (\"scaler\", StandardScaler()), \n",
        "             (\"logreg\", LogisticRegression(solver = \"saga\", random_state = 42))\n",
        "]) "
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QF53yFGlCjsQ",
        "outputId": "2356039d-7109-4c44-8d3a-6dfec201c3ba"
      },
      "source": [
        "cv = cross_validate(model, X_train, y_train, cv = kf)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hwrrsV03Cr9h",
        "outputId": "ac24eb3f-0230-41b8-ea34-2b71f70d4bbc"
      },
      "source": [
        "cv.keys()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['fit_time', 'score_time', 'test_score'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jUSwI9H6DQEK",
        "outputId": "1b5ddbc4-e02a-475c-fb25-acac16fd56f1"
      },
      "source": [
        "cv[\"test_score\"].mean()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.824938798568828"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2ZqEWCdDjxg"
      },
      "source": [
        "logreg = LogisticRegression()\n",
        "logreg.fit(X_train, y_train)\n",
        "y_pred = logreg.predict(X_test)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "sqcMnNsMHbgF",
        "outputId": "00eebf28-b091-4987-c872-1eeec377bc92"
      },
      "source": [
        "pred = pd.DataFrame(y_pred)\n",
        "pred"
      ],
      "execution_count": 49,
      "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     0\n",
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    1\n",
              "..  ..\n",
              "413  0\n",
              "414  1\n",
              "415  0\n",
              "416  0\n",
              "417  0\n",
              "\n",
              "[418 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "789sTZi6MQqr"
      },
      "source": [
        "pred = pred.set_index(np.arange(892, 1310))"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBDOnyMkMJ-2"
      },
      "source": [
        "pred.to_csv(\"/home/sub\", index=['PassengerId', 'Survived'], header=True)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUVoBc7xIvaA",
        "outputId": "8a598671-fbd0-46e2-c2e1-d63b3e22d61e"
      },
      "source": [
        "%pip install keras"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: keras in /usr/local/lib/python3.7/dist-packages (2.4.3)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras) (2.10.0)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.7/dist-packages (from keras) (1.19.5)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.7/dist-packages (from keras) (1.4.1)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from keras) (3.13)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from h5py->keras) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "88wMAi4fJ13E"
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yWXeerV9P0Iz"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "from keras.layers import Dropout\n",
        "from keras.regularizers import l2\n",
        "\n",
        "model = Sequential([\n",
        "                    Dense(256,  kernel_regularizer=l2(0.001), activation = \"relu\", input_dim = 8),\n",
        "                    Dropout(0.1),\n",
        "                    Dense(128, activation = \"relu\"),\n",
        "                    Dense(64, activation = \"relu\"),\n",
        "                    Dense(32, activation = \"relu\"),\n",
        "                    Dense(16, activation = \"relu\"),\n",
        "                    Dense(1, activation = \"softmax\")\n",
        "])\n"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i9Gy0IHEufGs"
      },
      "source": [
        "model.compile(optimizer=\"adam\", loss=\"mean_squared_error\", metrics=[\"accuracy\"])"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rR7NCY1ux8F",
        "outputId": "b7040282-f48e-42c8-8ee6-66f3697e4e96"
      },
      "source": [
        "fit_model = model.fit(X_train, y_train, batch_size=3, epochs = 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.6257 - accuracy: 0.3801\n",
            "Epoch 2/100\n",
            "297/297 [==============================] - 0s 2ms/step - loss: 0.6156 - accuracy: 0.3844\n",
            "Epoch 3/100\n",
            "297/297 [==============================] - 0s 2ms/step - loss: 0.6233 - accuracy: 0.3767\n",
            "Epoch 4/100\n",
            "297/297 [==============================] - 0s 2ms/step - loss: 0.6154 - accuracy: 0.3846\n",
            "Epoch 5/100\n",
            "297/297 [==============================] - 0s 2ms/step - loss: 0.5984 - accuracy: 0.4016\n",
            "Epoch 6/100\n",
            "297/297 [==============================] - 0s 2ms/step - loss: 0.5862 - accuracy: 0.4138\n",
            "Epoch 7/100\n",
            "297/297 [==============================] - 0s 2ms/step - loss: 0.6192 - accuracy: 0.3808\n",
            "Epoch 8/100\n",
            "297/297 [==============================] - 0s 2ms/step - loss: 0.6190 - accuracy: 0.3810\n",
            "Epoch 9/100\n",
            "297/297 [==============================] - 0s 2ms/step - loss: 0.6355 - accuracy: 0.3645\n",
            "Epoch 10/100\n",
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            "222/297 [=====================>........] - ETA: 0s - loss: 0.6175 - accuracy: 0.3825"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfd6EF1uu8vi"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}