{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "e0-cVHMOUuff"
      },
      "outputs": [],
      "source": [
        "if (!require(tidyverse)) install.packages(\"tidyverse\")\n",
        "library(tidyverse)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data <- read.csv(\"/content/titanic.csv\", stringsAsFactors = FALSE)"
      ],
      "metadata": {
        "id": "0mGIiP2bUxwk"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dim(data)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "FLBgs89qUxzL",
        "outputId": "521db262-45fd-4097-a1e0-f01928b47468"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>891</li><li>7</li></ol>\n"
            ],
            "text/markdown": "1. 891\n2. 7\n\n\n",
            "text/latex": "\\begin{enumerate*}\n\\item 891\n\\item 7\n\\end{enumerate*}\n",
            "text/plain": [
              "[1] 891   7"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "head(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "8kzcwQZfZWgc",
        "outputId": "7945c59c-70c3-40f3-f942-f412e95427cc"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 7</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Survived</th><th scope=col>Pclass</th><th scope=col>Sex</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Fare</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>0</td><td>3</td><td>male  </td><td>22</td><td>1</td><td>0</td><td> 7.2500</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>1</td><td>1</td><td>female</td><td>38</td><td>1</td><td>0</td><td>71.2833</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>1</td><td>3</td><td>female</td><td>26</td><td>0</td><td>0</td><td> 7.9250</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>1</td><td>1</td><td>female</td><td>35</td><td>1</td><td>0</td><td>53.1000</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>0</td><td>3</td><td>male  </td><td>35</td><td>0</td><td>0</td><td> 8.0500</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>0</td><td>3</td><td>male  </td><td>NA</td><td>0</td><td>0</td><td> 8.4583</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA data.frame: 6 × 7\n\n| <!--/--> | Survived &lt;int&gt; | Pclass &lt;int&gt; | Sex &lt;chr&gt; | Age &lt;dbl&gt; | SibSp &lt;int&gt; | Parch &lt;int&gt; | Fare &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|\n| 1 | 0 | 3 | male   | 22 | 1 | 0 |  7.2500 |\n| 2 | 1 | 1 | female | 38 | 1 | 0 | 71.2833 |\n| 3 | 1 | 3 | female | 26 | 0 | 0 |  7.9250 |\n| 4 | 1 | 1 | female | 35 | 1 | 0 | 53.1000 |\n| 5 | 0 | 3 | male   | 35 | 0 | 0 |  8.0500 |\n| 6 | 0 | 3 | male   | NA | 0 | 0 |  8.4583 |\n\n",
            "text/latex": "A data.frame: 6 × 7\n\\begin{tabular}{r|lllllll}\n  & Survived & Pclass & Sex & Age & SibSp & Parch & Fare\\\\\n  & <int> & <int> & <chr> & <dbl> & <int> & <int> & <dbl>\\\\\n\\hline\n\t1 & 0 & 3 & male   & 22 & 1 & 0 &  7.2500\\\\\n\t2 & 1 & 1 & female & 38 & 1 & 0 & 71.2833\\\\\n\t3 & 1 & 3 & female & 26 & 0 & 0 &  7.9250\\\\\n\t4 & 1 & 1 & female & 35 & 1 & 0 & 53.1000\\\\\n\t5 & 0 & 3 & male   & 35 & 0 & 0 &  8.0500\\\\\n\t6 & 0 & 3 & male   & NA & 0 & 0 &  8.4583\\\\\n\\end{tabular}\n",
            "text/plain": [
              "  Survived Pclass Sex    Age SibSp Parch Fare   \n",
              "1 0        3      male   22  1     0      7.2500\n",
              "2 1        1      female 38  1     0     71.2833\n",
              "3 1        3      female 26  0     0      7.9250\n",
              "4 1        1      female 35  1     0     53.1000\n",
              "5 0        3      male   35  0     0      8.0500\n",
              "6 0        3      male   NA  0     0      8.4583"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "str(data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjnDCVQfUx1U",
        "outputId": "4d633359-5d2a-429e-d116-41c363e1d348"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'data.frame':\t891 obs. of  7 variables:\n",
            " $ Survived: int  0 1 1 1 0 0 0 0 1 1 ...\n",
            " $ Pclass  : int  3 1 3 1 3 3 1 3 3 2 ...\n",
            " $ Sex     : chr  \"male\" \"female\" \"female\" \"female\" ...\n",
            " $ Age     : num  22 38 26 35 35 NA 54 2 27 14 ...\n",
            " $ SibSp   : int  1 1 0 1 0 0 0 3 0 1 ...\n",
            " $ Parch   : int  0 0 0 0 0 0 0 1 2 0 ...\n",
            " $ Fare    : num  7.25 71.28 7.92 53.1 8.05 ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summary(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "P7hHwJ54WLql",
        "outputId": "7b50d256-b29e-431d-a641-732a8052a28c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    Survived          Pclass          Sex                 Age       \n",
              " Min.   :0.0000   Min.   :1.000   Length:891         Min.   : 0.42  \n",
              " 1st Qu.:0.0000   1st Qu.:2.000   Class :character   1st Qu.:20.12  \n",
              " Median :0.0000   Median :3.000   Mode  :character   Median :28.00  \n",
              " Mean   :0.3838   Mean   :2.309                      Mean   :29.70  \n",
              " 3rd Qu.:1.0000   3rd Qu.:3.000                      3rd Qu.:38.00  \n",
              " Max.   :1.0000   Max.   :3.000                      Max.   :80.00  \n",
              "                                                     NA's   :177    \n",
              "     SibSp           Parch             Fare       \n",
              " Min.   :0.000   Min.   :0.0000   Min.   :  0.00  \n",
              " 1st Qu.:0.000   1st Qu.:0.0000   1st Qu.:  7.91  \n",
              " Median :0.000   Median :0.0000   Median : 14.45  \n",
              " Mean   :0.523   Mean   :0.3816   Mean   : 32.20  \n",
              " 3rd Qu.:1.000   3rd Qu.:0.0000   3rd Qu.: 31.00  \n",
              " Max.   :8.000   Max.   :6.0000   Max.   :512.33  \n",
              "                                                  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data$Age  <- as.numeric(data$Age)\n",
        "data$Fare <- as.numeric(data$Fare)\n",
        "data$Sex  <- as.factor(data$Sex)"
      ],
      "metadata": {
        "id": "zwfRHaAwWLum"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data$Age[is.na(data$Age)] <- median(data$Age, na.rm = TRUE)\n",
        "\n",
        "data$Fare[is.na(data$Fare)] <- median(data$Fare, na.rm = TRUE)\n",
        "\n",
        "\n",
        "mode_sex <- names(sort(table(data$Sex), decreasing = TRUE))[1]\n",
        "data$Sex[is.na(data$Sex)] <- mode_sex"
      ],
      "metadata": {
        "id": "Tn43s1JeWR-E"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data <- distinct(data)"
      ],
      "metadata": {
        "id": "MUigd-knWSAj"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data$Age  <- as.numeric(scale(data$Age))\n",
        "data$Fare <- as.numeric(scale(data$Fare))"
      ],
      "metadata": {
        "id": "1KChucTLUx4Q"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data$Fare <- (data$Fare - min(data$Fare)) /\n",
        "             (max(data$Fare) - min(data$Fare))\n",
        "\n",
        "data$Age <- (data$Age - min(data$Age)) /\n",
        "            (max(data$Age) - min(data$Age))\n"
      ],
      "metadata": {
        "id": "OULJnohPdLqH"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_data <- cbind(data, model.matrix(~ Sex - 1, data))\n",
        "encoded_data$Sex <- NULL"
      ],
      "metadata": {
        "id": "1ijWXPrIWmrU"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "boxplot(data$Fare, main = \"Before Removing Outliers\")\n",
        "\n",
        "Q1 <- quantile(data$Fare, 0.25)\n",
        "Q3 <- quantile(data$Fare, 0.75)\n",
        "IQR_value <- Q3 - Q1\n",
        "\n",
        "lower_bound <- Q1 - 1.5 * IQR_value\n",
        "upper_bound <- Q3 + 1.5 * IQR_value\n",
        "\n",
        "data <- data[data$Fare >= lower_bound & data$Fare <= upper_bound, ]\n",
        "\n",
        "boxplot(data$Fare, main = \"After Removing Outliers\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "KEoG_FzfWmtn",
        "outputId": "7cc50b2e-84bb-4a5d-e783-e862d94ea74d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “Before Removing Outliers”"
            ],
            "image/png": 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SFF4WbpvNLzVnZueIgHRcZCmhW2FUKK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\nkFfD2jQAAAAASUVORK5CYII="
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Plot with title “After Removing Outliers”"
            ],
            "image/png": 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Dw1JD1sWHrI067nkUR2Dza80OWq5s3d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"
          },
          "metadata": {
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "summary(data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "WERCtkhdUx6b",
        "outputId": "9c3fc0d0-c528-4375-edd4-842861d4ac97"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    Survived         Pclass          Sex           Age             SibSp      \n",
              " Min.   :0.000   Min.   :1.000   female:229   Min.   :0.0000   Min.   :0.000  \n",
              " 1st Qu.:0.000   1st Qu.:2.000   male  :446   1st Qu.:0.2586   1st Qu.:0.000  \n",
              " Median :0.000   Median :3.000                Median :0.3466   Median :0.000  \n",
              " Mean   :0.363   Mean   :2.422                Mean   :0.3580   Mean   :0.517  \n",
              " 3rd Qu.:1.000   3rd Qu.:3.000                3rd Qu.:0.4408   3rd Qu.:1.000  \n",
              " Max.   :1.000   Max.   :3.000                Max.   :1.0000   Max.   :8.000  \n",
              "     Parch             Fare        \n",
              " Min.   :0.0000   Min.   :0.00000  \n",
              " 1st Qu.:0.0000   1st Qu.:0.01547  \n",
              " Median :0.0000   Median :0.02635  \n",
              " Mean   :0.3956   Mean   :0.03894  \n",
              " 3rd Qu.:0.0000   3rd Qu.:0.05182  \n",
              " Max.   :6.0000   Max.   :0.14346  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "cor(data[, sapply(data, is.numeric)])\n",
        "\n",
        "set.seed(123)\n",
        "train_index <- sample(seq_len(nrow(data)), size = 0.7*nrow(data))\n",
        "train_data <- data[train_index, ]\n",
        "test_data  <- data[-train_index, ]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "F9AcU2iaUx87",
        "outputId": "03bcb5ba-50e8-4bb3-e40c-3cec4b07b1cd"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A matrix: 6 × 6 of type dbl</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>Survived</th><th scope=col>Pclass</th><th scope=col>Age</th><th scope=col>SibSp</th><th scope=col>Parch</th><th scope=col>Fare</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>Survived</th><td> 1.00000000</td><td>-0.2407369</td><td>-0.13011112</td><td>-0.05314774</td><td> 0.07774987</td><td> 0.17059489</td></tr>\n",
              "\t<tr><th scope=row>Pclass</th><td>-0.24073693</td><td> 1.0000000</td><td>-0.35325883</td><td> 0.13617358</td><td> 0.11247487</td><td>-0.59298176</td></tr>\n",
              "\t<tr><th scope=row>Age</th><td>-0.13011112</td><td>-0.3532588</td><td> 1.00000000</td><td>-0.31574713</td><td>-0.20205772</td><td> 0.07634925</td></tr>\n",
              "\t<tr><th scope=row>SibSp</th><td>-0.05314774</td><td> 0.1361736</td><td>-0.31574713</td><td> 1.00000000</td><td> 0.39096631</td><td> 0.38109812</td></tr>\n",
              "\t<tr><th scope=row>Parch</th><td> 0.07774987</td><td> 0.1124749</td><td>-0.20205772</td><td> 0.39096631</td><td> 1.00000000</td><td> 0.29426378</td></tr>\n",
              "\t<tr><th scope=row>Fare</th><td> 0.17059489</td><td>-0.5929818</td><td> 0.07634925</td><td> 0.38109812</td><td> 0.29426378</td><td> 1.00000000</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ],
            "text/markdown": "\nA matrix: 6 × 6 of type dbl\n\n| <!--/--> | Survived | Pclass | Age | SibSp | Parch | Fare |\n|---|---|---|---|---|---|---|\n| Survived |  1.00000000 | -0.2407369 | -0.13011112 | -0.05314774 |  0.07774987 |  0.17059489 |\n| Pclass | -0.24073693 |  1.0000000 | -0.35325883 |  0.13617358 |  0.11247487 | -0.59298176 |\n| Age | -0.13011112 | -0.3532588 |  1.00000000 | -0.31574713 | -0.20205772 |  0.07634925 |\n| SibSp | -0.05314774 |  0.1361736 | -0.31574713 |  1.00000000 |  0.39096631 |  0.38109812 |\n| Parch |  0.07774987 |  0.1124749 | -0.20205772 |  0.39096631 |  1.00000000 |  0.29426378 |\n| Fare |  0.17059489 | -0.5929818 |  0.07634925 |  0.38109812 |  0.29426378 |  1.00000000 |\n\n",
            "text/latex": "A matrix: 6 × 6 of type dbl\n\\begin{tabular}{r|llllll}\n  & Survived & Pclass & Age & SibSp & Parch & Fare\\\\\n\\hline\n\tSurvived &  1.00000000 & -0.2407369 & -0.13011112 & -0.05314774 &  0.07774987 &  0.17059489\\\\\n\tPclass & -0.24073693 &  1.0000000 & -0.35325883 &  0.13617358 &  0.11247487 & -0.59298176\\\\\n\tAge & -0.13011112 & -0.3532588 &  1.00000000 & -0.31574713 & -0.20205772 &  0.07634925\\\\\n\tSibSp & -0.05314774 &  0.1361736 & -0.31574713 &  1.00000000 &  0.39096631 &  0.38109812\\\\\n\tParch &  0.07774987 &  0.1124749 & -0.20205772 &  0.39096631 &  1.00000000 &  0.29426378\\\\\n\tFare &  0.17059489 & -0.5929818 &  0.07634925 &  0.38109812 &  0.29426378 &  1.00000000\\\\\n\\end{tabular}\n",
            "text/plain": [
              "         Survived    Pclass     Age         SibSp       Parch       Fare       \n",
              "Survived  1.00000000 -0.2407369 -0.13011112 -0.05314774  0.07774987  0.17059489\n",
              "Pclass   -0.24073693  1.0000000 -0.35325883  0.13617358  0.11247487 -0.59298176\n",
              "Age      -0.13011112 -0.3532588  1.00000000 -0.31574713 -0.20205772  0.07634925\n",
              "SibSp    -0.05314774  0.1361736 -0.31574713  1.00000000  0.39096631  0.38109812\n",
              "Parch     0.07774987  0.1124749 -0.20205772  0.39096631  1.00000000  0.29426378\n",
              "Fare      0.17059489 -0.5929818  0.07634925  0.38109812  0.29426378  1.00000000"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eIDj_omwUx_T"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "i-CeUqWqUyCE"
      },
      "execution_count": 37,
      "outputs": []
    }
  ]
}