MLClassificationAlgorithms.ipynb

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        "## Supervised Learning Algorithms \n",
        "\n",
        "We will use the [Palmer's Penguins dataset](https://github.com/allisonhorst/palmerpenguins) and create different models for classifying the dataset by species. \n",
        "\n",
        "We will use the [scikit-learn library](https://scikit-learn.org/stable/index.html) functions for [Supervised learning](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning) cases. \n"
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      "source": [
        "# Basic libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm \n",
        "\n",
        "# Visualization libraries\n",
        "import matplotlib.pyplot as plt \n",
        "import plotly.express as px  \n",
        "import seaborn as sns \n",
        "\n",
        "# Specific scikit-learn libraries\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import svm\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import Normalizer \n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import label_binarize \n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, roc_curve,roc_auc_score \n",
        "#import sklearn.metrics \n",
        "\n",
        "\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore')\n"
      ]
    },
    {
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      "source": [
        "# Read the Penguins dataset fom Github\n",
        "filename = \"https://raw.githubusercontent.com/clizarraga-UAD7/Datasets/main/penguins/penguins_size.csv\"\n",
        "df_dist = pd.read_csv(filename)\n",
        "df_dist.head()"
      ],
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              "  species     island  culmen_length_mm  culmen_depth_mm  flipper_length_mm  \\\n",
              "0  Adelie  Torgersen              39.1             18.7              181.0   \n",
              "1  Adelie  Torgersen              39.5             17.4              186.0   \n",
              "2  Adelie  Torgersen              40.3             18.0              195.0   \n",
              "3  Adelie  Torgersen               NaN              NaN                NaN   \n",
              "4  Adelie  Torgersen              36.7             19.3              193.0   \n",
              "\n",
              "   body_mass_g     sex  \n",
              "0       3750.0    MALE  \n",
              "1       3800.0  FEMALE  \n",
              "2       3250.0  FEMALE  \n",
              "3          NaN     NaN  \n",
              "4       3450.0  FEMALE  "
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              "      <th></th>\n",
              "      <th>species</th>\n",
              "      <th>island</th>\n",
              "      <th>culmen_length_mm</th>\n",
              "      <th>culmen_depth_mm</th>\n",
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              "      <th>0</th>\n",
              "      <td>Adelie</td>\n",
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              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>MALE</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie</td>\n",
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              "      <th>2</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>FEMALE</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
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      "cell_type": "markdown",
      "source": [
        "### Data cleaning and manipulation\n",
        "Check for missimg values and appropriatedata type of the variables"
      ],
      "metadata": {
        "id": "eKqlrhgvdlcx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inquiry about the state of the original dataset\n",
        "df_dist.info()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "l-3mcrVyYOAa",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 344 entries, 0 to 343\n",
            "Data columns (total 7 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   species            344 non-null    object \n",
            " 1   island             344 non-null    object \n",
            " 2   culmen_length_mm   342 non-null    float64\n",
            " 3   culmen_depth_mm    342 non-null    float64\n",
            " 4   flipper_length_mm  342 non-null    float64\n",
            " 5   body_mass_g        342 non-null    float64\n",
            " 6   sex                334 non-null    object \n",
            "dtypes: float64(4), object(3)\n",
            "memory usage: 18.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if any missing values\n",
        "df_dist.isnull().any()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWtj6z-DZauW",
        "outputId": "04914c6c-e381-47ad-e90b-b151c3a0016c"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "species              False\n",
              "island               False\n",
              "culmen_length_mm      True\n",
              "culmen_depth_mm       True\n",
              "flipper_length_mm     True\n",
              "body_mass_g           True\n",
              "sex                   True\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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      "cell_type": "code",
      "source": [
        "# Eliminate the rows with mising values, and define a new dataframe df\n",
        "df = df_dist.dropna(axis = 0, how ='any')"
      ],
      "metadata": {
        "id": "fg7pR-y6aks8"
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      "execution_count": null,
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      "cell_type": "code",
      "source": [
        "# Should have a clean dataframe with no missing values\n",
        "df.info()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "jU9jLV8BawFU",
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 334 entries, 0 to 343\n",
            "Data columns (total 7 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   species            334 non-null    object \n",
            " 1   island             334 non-null    object \n",
            " 2   culmen_length_mm   334 non-null    float64\n",
            " 3   culmen_depth_mm    334 non-null    float64\n",
            " 4   flipper_length_mm  334 non-null    float64\n",
            " 5   body_mass_g        334 non-null    float64\n",
            " 6   sex                334 non-null    object \n",
            "dtypes: float64(4), object(3)\n",
            "memory usage: 20.9+ KB\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "### Label encoding of categorical variables using the built-in function LabelEncoder()\n",
        "\n",
        "The categorial variables are:\n",
        "- species\n",
        "- island\n",
        "- sex"
      ],
      "metadata": {
        "id": "e783bvXmd4cY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# See the different values of the above variables\n",
        "print('Species: \\n', df.species.value_counts())\n",
        "print('Island: \\n', df.island.value_counts())\n",
        "print('Gender: \\n', df.sex.value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fy4M4he2ay_H",
        "outputId": "332f4692-a8fa-4532-d482-373c7f75f7cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Species: \n",
            "  Adelie       146\n",
            "Gentoo       120\n",
            "Chinstrap     68\n",
            "Name: species, dtype: int64\n",
            "Island: \n",
            " Biscoe       164\n",
            "Dream        123\n",
            "Torgersen     47\n",
            "Name: island, dtype: int64\n",
            "Gender: \n",
            " MALE      168\n",
            "FEMALE    165\n",
            ".           1\n",
            "Name: sex, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is a row hwre the gender of the penguin has value of \".\", then we need to eliminte that row, using a filter condition. "
      ],
      "metadata": {
        "id": "wOieGyDegFuD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Keep or filter out all rows where the sex of the Penguins are not equal to '.'\n",
        "df = df[df.sex != '.'] \n",
        "print('Gender: \\n', df.sex.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1tzTglgfqmy",
        "outputId": "40cc7a15-f8b4-4b14-a424-4080b4d0933c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gender: \n",
            " MALE      168\n",
            "FEMALE    165\n",
            "Name: sex, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
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        "id": "HfHJhpb5hqO-",
        "outputId": "f8a4d78c-8646-4d38-bc25-01ddbd2d81e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 333 entries, 0 to 343\n",
            "Data columns (total 7 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   species            333 non-null    object \n",
            " 1   island             333 non-null    object \n",
            " 2   culmen_length_mm   333 non-null    float64\n",
            " 3   culmen_depth_mm    333 non-null    float64\n",
            " 4   flipper_length_mm  333 non-null    float64\n",
            " 5   body_mass_g        333 non-null    float64\n",
            " 6   sex                333 non-null    object \n",
            "dtypes: float64(4), object(3)\n",
            "memory usage: 20.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ready to encode categorical variables (encode names with values)\n",
        "le = LabelEncoder()\n",
        "\n",
        "df['species_encoded']= le.fit_transform(df['species'])\n",
        "df['island_encoded']= le.fit_transform(df['island'])\n",
        "df['sex_encoded']= le.fit_transform(df['sex'])\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IkVGsnVshtAU",
        "outputId": "3a95c02c-c6f5-4eac-f17c-dc63868edf4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  species     island  culmen_length_mm  culmen_depth_mm  flipper_length_mm  \\\n",
            "0  Adelie  Torgersen              39.1             18.7              181.0   \n",
            "1  Adelie  Torgersen              39.5             17.4              186.0   \n",
            "2  Adelie  Torgersen              40.3             18.0              195.0   \n",
            "4  Adelie  Torgersen              36.7             19.3              193.0   \n",
            "5  Adelie  Torgersen              39.3             20.6              190.0   \n",
            "\n",
            "   body_mass_g     sex  species_encoded  island_encoded  sex_encoded  \n",
            "0       3750.0    MALE                0               2            1  \n",
            "1       3800.0  FEMALE                0               2            0  \n",
            "2       3250.0  FEMALE                0               2            0  \n",
            "4       3450.0  FEMALE                0               2            0  \n",
            "5       3650.0    MALE                0               2            1  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check for outliers in dataset\n",
        "\n",
        "Tukey also gives a criteria for defining outlier data. If Q1, and Q3 are the first and third quartile positions, the interquartile range IQR = Q3 - Q1 , then an outlier value will fall below Q1 - 1.5 IQR or above Q3 + 1.5 IQR.\n",
        "\n",
        "We can define an additional Tukey outer fences, lower outer fence Q1 - 3.0 IQR and the upper outer fence above Q3 + 3.0 IQR.\n",
        "\n"
      ],
      "metadata": {
        "id": "xw728FTclBg7"
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    {
      "cell_type": "code",
      "source": [
        "#Tukey's method\n",
        "def tukeys_method(df, variable):\n",
        "    #Takes two parameters: dataframe & variable of interest as string\n",
        "    q1 = df[variable].quantile(0.25)\n",
        "    q3 = df[variable].quantile(0.75)\n",
        "    iqr = q3-q1\n",
        "    inner_fence = 1.5*iqr\n",
        "    outer_fence = 3*iqr\n",
        "    \n",
        "    #inner fence lower and upper end\n",
        "    inner_fence_le = q1-inner_fence\n",
        "    inner_fence_ue = q3+inner_fence\n",
        "    ifl, ifu = inner_fence_le, inner_fence_ue\n",
        "    \n",
        "    #outer fence lower and upper end\n",
        "    outer_fence_le = q1-outer_fence\n",
        "    outer_fence_ue = q3+outer_fence\n",
        "    ofl, ofu = outer_fence_le, outer_fence_ue\n",
        "    \n",
        "    \n",
        "    outliers_prob = []\n",
        "    outliers_poss = []\n",
        "    for index, x in enumerate(df[variable]):\n",
        "        if x <= outer_fence_le or x >= outer_fence_ue:\n",
        "            outliers_prob.append(index)\n",
        "    for index, x in enumerate(df[variable]):\n",
        "        if x <= inner_fence_le or x >= inner_fence_ue:\n",
        "            outliers_poss.append(index)\n",
        "    return outliers_prob, outliers_poss, ifl, ifu, ofl, ofu\n",
        " \n",
        "     "
      ],
      "metadata": {
        "id": "N0zX7lY9ltIj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for putliers in each numeric variable: culmen length\n",
        "\n",
        "probable_outliers_tm, possible_outliers_tm, ifl, ifu, ofl, ofu = tukeys_method(df, 'culmen_length_mm')\n",
        "print(len(probable_outliers_tm))\n",
        "print(len(possible_outliers_tm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3dS2IIGl6-u",
        "outputId": "f7b14584-9fb5-4dd1-fd92-124f488ad2d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for putliers in each numeric variable: culmen depth\n",
        "\n",
        "probable_outliers_tm, possible_outliers_tm, ifl, ifu, ofl, ofu = tukeys_method(df, 'culmen_depth_mm')\n",
        "print(len(probable_outliers_tm))\n",
        "print(len(possible_outliers_tm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JrLCPgWymRBv",
        "outputId": "5c054054-4de8-46ce-a32e-a7fa2dd89961"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for putliers in each numeric variable: flipper length\n",
        "\n",
        "probable_outliers_tm, possible_outliers_tm, ifl, ifu, ofl, ofu = tukeys_method(df, 'flipper_length_mm')\n",
        "print(len(probable_outliers_tm))\n",
        "print(len(possible_outliers_tm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ulXx7hXmdkU",
        "outputId": "346185a2-d431-4541-b4d8-b161c92c3a81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for putliers in each numeric variable: body mass\n",
        "\n",
        "probable_outliers_tm, possible_outliers_tm, ifl, ifu, ofl, ofu = tukeys_method(df, 'body_mass_g')\n",
        "print(len(probable_outliers_tm))\n",
        "print(len(possible_outliers_tm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hP8_1XI9m5kw",
        "outputId": "2f9141c1-62ea-461b-c23c-97c7363240d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no outliers"
      ],
      "metadata": {
        "id": "GYGHhHj7nE9Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot data frequency distribution for first two variables\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2,figsize=(12, 8))\n",
        "\n",
        "sns.histplot(df['culmen_length_mm'], bins=25, kde=True, ax=ax1);\n",
        "sns.histplot(df['culmen_depth_mm'], bins=25, kde=True, ax=ax2);\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "DxAjSheukE1y",
        "outputId": "2ffc0fe8-ee21-4acf-eba5-78a03b736020"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 8))\n",
        "\n",
        "sns.barplot(x='species', y='culmen_length_mm', data=df, ax=ax1)\n",
        "sns.barplot(x='species', y='culmen_depth_mm', data=df, ax=ax2)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "1-T80Fv6qLbM",
        "outputId": "e0c8bb10-d1ac-4572-e31c-e844a40f022e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot data frequency distribution for last two variables\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2,figsize=(12, 8))\n",
        "\n",
        "sns.histplot(df['flipper_length_mm'], bins=25, kde=True, ax=ax1);\n",
        "sns.histplot(df['body_mass_g'], bins=25, kde=True, ax=ax2);\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "hgZ9-N0mmnZ9",
        "outputId": "b89c43f2-af81-4258-bd9f-c5674706dd61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 8))\n",
        "\n",
        "sns.barplot(x='species', y='flipper_length_mm', data=df, ax=ax1)\n",
        "sns.barplot(x='species', y='body_mass_g', data=df, ax=ax2)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "k9SyDOUYpARj",
        "outputId": "c0789f5b-ac17-4640-d636-495cb6c2c3f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting the data into Training & Testing datasets"
      ],
      "metadata": {
        "id": "7fiVeBgqtZlT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features=['culmen_length_mm', 'culmen_depth_mm','flipper_length_mm', 'body_mass_g','island_encoded', 'sex_encoded']\n",
        "\n",
        "x=df[features]# since these are the features we take them as x\n",
        "y=df['species_encoded']# since species is the output or label we'll take it as y\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2,random_state=42)\n",
        "\n",
        "print(\"\\nShape of x_train:\\n{}\".format(x_train.shape))\n",
        "print(\"\\nShape of x_test:\\n{}\".format(x_test.shape))\n",
        "print(\"\\nShape of y_train:\\n{}\".format(y_train.shape))\n",
        "print(\"\\nShape of y_test:\\n{}\".format(y_test.shape))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OceLb_qMsfE2",
        "outputId": "f01fc110-9d55-43ef-82fe-f4c985b39990"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Shape of x_train:\n",
            "(266, 6)\n",
            "\n",
            "Shape of x_test:\n",
            "(67, 6)\n",
            "\n",
            "Shape of y_train:\n",
            "(266,)\n",
            "\n",
            "Shape of y_test:\n",
            "(67,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scaling"
      ],
      "metadata": {
        "id": "RF__XqfvuDVn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "sc = StandardScaler()\n",
        "\n",
        "scaled_x_train = sc.fit_transform(x_train)\n",
        "scaled_x_test = sc.transform(x_test)\n",
        "\n",
        "print(x_train)\n",
        "print(\"____________________________________________________________________________\")\n",
        "print(\"\",scaled_x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7KeIfCbt4K-",
        "outputId": "c05bdaaa-e3d9-4ff3-be07-5987c2d2f73a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     culmen_length_mm  culmen_depth_mm  flipper_length_mm  body_mass_g  \\\n",
            "230              40.9             13.7              214.0       4650.0   \n",
            "84               37.3             17.8              191.0       3350.0   \n",
            "303              50.0             15.9              224.0       5350.0   \n",
            "22               35.9             19.2              189.0       3800.0   \n",
            "29               40.5             18.9              180.0       3950.0   \n",
            "..                ...              ...                ...          ...   \n",
            "194              50.9             19.1              196.0       3550.0   \n",
            "77               37.2             19.4              184.0       3900.0   \n",
            "112              39.7             17.7              193.0       3200.0   \n",
            "277              45.5             15.0              220.0       5000.0   \n",
            "108              38.1             17.0              181.0       3175.0   \n",
            "\n",
            "     island_encoded  sex_encoded  \n",
            "230               0            0  \n",
            "84                1            0  \n",
            "303               0            1  \n",
            "22                0            0  \n",
            "29                0            1  \n",
            "..              ...          ...  \n",
            "194               1            1  \n",
            "77                2            1  \n",
            "112               0            0  \n",
            "277               0            1  \n",
            "108               0            0  \n",
            "\n",
            "[266 rows x 6 columns]\n",
            "____________________________________________________________________________\n",
            " [[-0.59372706 -1.75093934  0.9359428   0.53973681 -0.91721936 -1.03832798]\n",
            " [-1.26104283  0.32310698 -0.719956   -1.07236416  0.52645135 -1.03832798]\n",
            " [ 1.09309891 -0.63803644  1.65589881  1.40779118 -0.91721936  0.96308682]\n",
            " ...\n",
            " [-0.81616565  0.27252048 -0.5759648  -1.25837581 -0.91721936 -1.03832798]\n",
            " [ 0.2589542  -1.0933149   1.36791641  0.97376399 -0.91721936  0.96308682]\n",
            " [-1.11275044 -0.08158499 -1.43991201 -1.28937775 -0.91721936 -1.03832798]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train the model"
      ],
      "metadata": {
        "id": "QKFc8lSyu2Ws"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#KNN model\n",
        "x_train=scaled_x_train\n",
        "x_test=scaled_x_test\n",
        "\n",
        "model1=KNeighborsClassifier(n_neighbors=3)\n",
        "model1.fit(x_train, y_train)\n",
        "y_prediction1= model1.predict(x_test)\n",
        "\n",
        "report=pd.DataFrame()\n",
        "report['Actual values']=y_test\n",
        "report['Predicted values KNN']= y_prediction1\n",
        "print(report)\n",
        "\n",
        "#Decision tree\n",
        "model2= DecisionTreeClassifier()\n",
        "model2 = model2.fit(x_train,y_train)\n",
        "#Predict the response for test dataset\n",
        "y_prediction2 = model2.predict(x_test)\n",
        "report['Predicted values Decision tree']= y_prediction2\n",
        "print(report)\n",
        "\n",
        "#SVM\n",
        "model3= svm.SVC(kernel='linear',probability=True) # Linear Kernel\n",
        "model3.fit(x_train, y_train)\n",
        "#Predict the response for test dataset\n",
        "y_prediction3 = model3.predict(x_test)\n",
        "report['Predicted values SVM']= y_prediction3\n",
        "print(report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4DSrLEZsuUql",
        "outputId": "e9a00634-bc43-4801-d25e-0e12b940c3af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Actual values  Predicted values KNN\n",
            "30               0                     1\n",
            "317              2                     2\n",
            "79               0                     0\n",
            "201              1                     1\n",
            "63               0                     0\n",
            "..             ...                   ...\n",
            "288              2                     2\n",
            "4                0                     0\n",
            "83               0                     0\n",
            "319              2                     2\n",
            "66               0                     0\n",
            "\n",
            "[67 rows x 2 columns]\n",
            "     Actual values  Predicted values KNN  Predicted values Decision tree\n",
            "30               0                     1                               0\n",
            "317              2                     2                               2\n",
            "79               0                     0                               0\n",
            "201              1                     1                               1\n",
            "63               0                     0                               0\n",
            "..             ...                   ...                             ...\n",
            "288              2                     2                               2\n",
            "4                0                     0                               0\n",
            "83               0                     0                               0\n",
            "319              2                     2                               2\n",
            "66               0                     0                               0\n",
            "\n",
            "[67 rows x 3 columns]\n",
            "     Actual values  Predicted values KNN  Predicted values Decision tree  \\\n",
            "30               0                     1                               0   \n",
            "317              2                     2                               2   \n",
            "79               0                     0                               0   \n",
            "201              1                     1                               1   \n",
            "63               0                     0                               0   \n",
            "..             ...                   ...                             ...   \n",
            "288              2                     2                               2   \n",
            "4                0                     0                               0   \n",
            "83               0                     0                               0   \n",
            "319              2                     2                               2   \n",
            "66               0                     0                               0   \n",
            "\n",
            "     Predicted values SVM  \n",
            "30                      0  \n",
            "317                     2  \n",
            "79                      0  \n",
            "201                     1  \n",
            "63                      0  \n",
            "..                    ...  \n",
            "288                     2  \n",
            "4                       0  \n",
            "83                      0  \n",
            "319                     2  \n",
            "66                      0  \n",
            "\n",
            "[67 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model evaluation"
      ],
      "metadata": {
        "id": "tbX7gXVVvvwn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ConfusionMatrix1=confusion_matrix(y_test,y_prediction1)\n",
        "print(ConfusionMatrix1)\n",
        "\n",
        "ConfusionMatrix2=confusion_matrix(y_test,y_prediction2)\n",
        "print(ConfusionMatrix2)\n",
        "\n",
        "ConfusionMatrix3=confusion_matrix(y_test,y_prediction3)\n",
        "print(ConfusionMatrix3)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-0-Lh2hvfdS",
        "outputId": "7e3313fd-d5a1-44fe-e1fc-acf0300c39e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[30  1  0]\n",
            " [ 0 13  0]\n",
            " [ 0  0 23]]\n",
            "[[30  1  0]\n",
            " [ 0 13  0]\n",
            " [ 0  0 23]]\n",
            "[[31  0  0]\n",
            " [ 0 13  0]\n",
            " [ 0  0 23]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Heatmap of First Confusion Matrix\n",
        "ax=sns.heatmap(ConfusionMatrix1,annot=True,cmap=\"YlGnBu\")\n",
        "ax.set_title('Confusion matrix')\n",
        "ax.set_xlabel('Predicted values')\n",
        "ax.set_ylabel('Actual values')\n",
        "#Ticket labels:\n",
        "ax.xaxis.set_ticklabels(['No','Yes'])\n",
        "ax.xaxis.set_ticklabels(['No','Yes'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "l6h7_kT2v60O",
        "outputId": "02e4a7d3-0cc3-45d5-83f6-83190ac15494"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Second Confusion Matrix\n",
        "\n",
        "ax=sns.heatmap(ConfusionMatrix2,annot=True,cmap=\"YlGnBu\")\n",
        "ax.set_title('Confusion matrix')\n",
        "ax.set_xlabel('Predicted values')\n",
        "ax.set_ylabel('Actual values')\n",
        "#Ticket labels:\n",
        "ax.xaxis.set_ticklabels(['No','Yes'])\n",
        "ax.xaxis.set_ticklabels(['No','Yes'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "2CWyTqvSwG5M",
        "outputId": "a3a2c8de-bfb8-4815-878b-ddfd96db54a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Third Confusion Matrix (SVC)\n",
        "\n",
        "ax=sns.heatmap(ConfusionMatrix3,annot=True,cmap=\"YlGnBu\")\n",
        "ax.set_title('Confusion matrix')\n",
        "ax.set_xlabel('Predicted values')\n",
        "ax.set_ylabel('Actual values')\n",
        "#Ticket labels:\n",
        "ax.xaxis.set_ticklabels(['No','Yes'])\n",
        "ax.xaxis.set_ticklabels(['No','Yes'])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Lcr1nAdCwT49",
        "outputId": "c5676a1e-da92-4c3a-c618-acbe8dac9dfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ROC Scores and Curve "
      ],
      "metadata": {
        "id": "7U93PA3bwojU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#roc auc score\n",
        "roc_auc_score(y_test, y_pred_prob1, multi_class='ovo', average='weighted')\n",
        "roc_auc_score(y_test, y_pred_prob2, multi_class='ovo', average='weighted')\n",
        "roc_auc_score(y_test, y_pred_prob3, multi_class='ovo', average='weighted')\n",
        "fpr1, tpr1, thresh1 = roc_curve(y_test, y_pred_prob1[:,1], pos_label=1)\n",
        "fpr2, tpr2, thresh2 = roc_curve(y_test, y_pred_prob2[:,1], pos_label=1)\n",
        "fpr3, tpr3, thresh3 = roc_curve(y_test, y_pred_prob3[:,1], pos_label=1)\n",
        "\n",
        "# roc curve for tpr = fpr\n",
        "random_probs = [0 for i in range(len(y_test))]\n",
        "p_fpr, p_tpr, _ = roc_curve(y_test, random_probs, pos_label=1)\n",
        "plt.plot(fpr1, tpr1, linestyle='--',color='black', label='KNN')\n",
        "plt.plot(fpr2, tpr2, linestyle='--',color='green', label='Descision Tree')\n",
        "plt.plot(fpr3, tpr3, linestyle='--',color='red', label='SVM')\n",
        "plt.plot(p_fpr, p_tpr, linestyle='--', color='blue')\n",
        "\n",
        "# title\n",
        "plt.title('ROC curve')\n",
        "# x label\n",
        "plt.xlabel('False Positive Rate')\n",
        "# y label\n",
        "plt.ylabel('True Positive rate')\n",
        "plt.legend(loc='best')\n",
        "#plt.savefig('ROC',dpi=300)\n",
        "plt.show();\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "vxnXbXGzwfXZ",
        "outputId": "aa3311d9-a52e-460d-dc23-fb303f5b1058"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-45-ec0113f15a8c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#roc auc score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mroc_auc_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_prob1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_class\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ovo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'weighted'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mroc_auc_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_prob2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_class\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ovo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'weighted'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mroc_auc_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_prob3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_class\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ovo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'weighted'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mfpr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtpr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthresh1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_prob1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'y_pred_prob1' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H7STPQs1w4_s"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}