412_Group_Project.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# **Data Loading and Pre-processing**\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "R9oSM4alx_pL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FWDHxYKZGV1B"
      },
      "outputs": [],
      "source": [
        "# set up code for this project\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math as math\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import os\n",
        "import seaborn as sea\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "\n",
        "from sklearn.datasets import make_moons\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split as train_test_split\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    confusion_matrix,\n",
        "    ConfusionMatrixDisplay,\n",
        "    f1_score,\n",
        "    classification_report,\n",
        ")\n",
        "import plotly.express as px\n",
        "\n",
        "np.random.seed(1)\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Zacc's Discord message at: https://discord.com/channels/@me/1143389784790810684/1178063081125982278 on how to make this work inside your Google Drive"
      ],
      "metadata": {
        "id": "AVKV2jW9C3Gl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "metadata": {
        "id": "2fcb8HSC9HW5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60d192ec-0bdf-422f-fbf8-e905bdbc4c19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = '/content/drive/MyDrive/412_Project/voice.csv'"
      ],
      "metadata": {
        "id": "V-mS6mQl9MTI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading voice.csv data into a dataframe 'data'\n",
        "data = pd.read_csv(file_path)\n",
        "# Displaying first n values from data\n",
        "data.head(n = 5)"
      ],
      "metadata": {
        "id": "09jZ6ZGkBbx8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "47edbc2d-dee1-408b-b4fd-62a5f3754972"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   meanfreq        sd    median       q25       q75       iqr       skew  \\\n",
              "0  0.059781  0.064241  0.032027  0.015071  0.090193  0.075122  12.863462   \n",
              "1  0.066009  0.067310  0.040229  0.019414  0.092666  0.073252  22.423285   \n",
              "2  0.077316  0.083829  0.036718  0.008701  0.131908  0.123207  30.757155   \n",
              "3  0.151228  0.072111  0.158011  0.096582  0.207955  0.111374   1.232831   \n",
              "4  0.135120  0.079146  0.124656  0.078720  0.206045  0.127325   1.101174   \n",
              "\n",
              "          kurt    sp_ent       sfm  ...  centroid   meanfun    minfun  \\\n",
              "0   274.402906  0.893369  0.491918  ...  0.059781  0.084279  0.015702   \n",
              "1   634.613855  0.892193  0.513724  ...  0.066009  0.107937  0.015826   \n",
              "2  1024.927705  0.846389  0.478905  ...  0.077316  0.098706  0.015656   \n",
              "3     4.177296  0.963322  0.727232  ...  0.151228  0.088965  0.017798   \n",
              "4     4.333713  0.971955  0.783568  ...  0.135120  0.106398  0.016931   \n",
              "\n",
              "     maxfun   meandom    mindom    maxdom   dfrange   modindx  label  \n",
              "0  0.275862  0.007812  0.007812  0.007812  0.000000  0.000000   male  \n",
              "1  0.250000  0.009014  0.007812  0.054688  0.046875  0.052632   male  \n",
              "2  0.271186  0.007990  0.007812  0.015625  0.007812  0.046512   male  \n",
              "3  0.250000  0.201497  0.007812  0.562500  0.554688  0.247119   male  \n",
              "4  0.266667  0.712812  0.007812  5.484375  5.476562  0.208274   male  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <th>meanfreq</th>\n",
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              "      <td>0.846389</td>\n",
              "      <td>0.478905</td>\n",
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              "      <td>0.077316</td>\n",
              "      <td>0.098706</td>\n",
              "      <td>0.015656</td>\n",
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              "      <td>0.007990</td>\n",
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              "      <td>0.015625</td>\n",
              "      <td>0.007812</td>\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e24c266c-b4e8-4023-a5b0-ab5f63991022 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **K-Nearest Neighbor**\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "w_vf3mhYrQuh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "columnList = data.iloc[:,:-1].columns.to_list()\n",
        "fig, axs = plt.subplots(nrows = 5, ncols = 4, figsize=(12, 15))\n",
        "axs = axs.flat\n",
        "# (nrows = 5, ncols = 4, figsize = (12, 15))\n",
        "\n",
        "for i, column in enumerate(columnList):\n",
        "  sea.histplot(data, x = column, kde = True, ax = axs[i])\n",
        "  axs[i].set_title(column, fontweight = 'bold')\n",
        "  axs[i].set_xlabel('')\n",
        "\n",
        "\n",
        "fig.tight_layout()\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1iSgRQDlXsEK",
        "outputId": "a72a44ad-d615-42b8-cee0-b861eb390eff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1500 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = data['label'].to_frame()\n",
        "print(y.size)\n",
        "print(type(y))\n",
        "X = data.drop('label', axis = 1)\n",
        "\n",
        "\n",
        "# We divide into training and test set.\n",
        "# We define the random seed for reproducibility.\n",
        "SEED = 42\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                       test_size = 0.3,\n",
        "                                       random_state = SEED,\n",
        "                                       stratify = y)\n",
        "\n",
        "# Sanity check for propr test and train data split\n",
        "print(\"This is the X train\")\n",
        "print(X_train.head(n = 5))\n",
        "print(\"This is the X test\")\n",
        "print(X_test.head(n=5))\n",
        "print(\"This is the Y train\")\n",
        "y_train = y_train\n",
        "y_train['label'] = y_train['label'].replace({'male': 0, 'female': 1})\n",
        "print(y_train.head(n=5))\n",
        "print(\"This is the Y test\")\n",
        "y_test = y_test\n",
        "y_test['label'] = y_test['label'].replace({'male': 0, 'female': 1})\n",
        "print(y_test.head(n=5))\n",
        "\n",
        "\n",
        "X_train = X_train.to_numpy()\n",
        "X_test = X_test.to_numpy()\n",
        "y_train = y_train.to_numpy()\n",
        "y_test = y_test.to_numpy()\n",
        "\n",
        "y_train = np.array(y_train)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "# print(X_test.shape)\n",
        "# print(X_train.shape)\n",
        "# print(X.shape)\n",
        "# print(y_test.shape)\n",
        "# print(y_train.shape)\n",
        "# print(y.shape)\n"
      ],
      "metadata": {
        "id": "c4px8TiwYb0E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbd68983-d828-4b16-b59b-47fe81ca0a1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3168\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "This is the X train\n",
            "      meanfreq        sd    median       q25       q75       iqr      skew  \\\n",
            "296   0.079369  0.088650  0.040976  0.002119  0.143650  0.141531  9.013217   \n",
            "220   0.163194  0.067451  0.188192  0.102962  0.212503  0.109540  2.278882   \n",
            "2191  0.185102  0.047233  0.175616  0.157696  0.219968  0.062272  2.328919   \n",
            "177   0.141047  0.065363  0.109746  0.091653  0.201398  0.109746  2.902501   \n",
            "2602  0.236635  0.033557  0.243339  0.214010  0.263502  0.049493  1.693354   \n",
            "\n",
            "           kurt    sp_ent       sfm      mode  centroid   meanfun    minfun  \\\n",
            "296   92.766695  0.834213  0.450205  0.001413  0.079369  0.095733  0.016194   \n",
            "220    9.287996  0.927515  0.554522  0.211931  0.163194  0.103156  0.032193   \n",
            "2191   9.042176  0.883298  0.362863  0.158144  0.185102  0.153339  0.048731   \n",
            "177   13.211780  0.927861  0.567241  0.101144  0.141047  0.078778  0.017335   \n",
            "2602   4.969441  0.831598  0.130053  0.253421  0.236635  0.176765  0.046921   \n",
            "\n",
            "        maxfun   meandom    mindom     maxdom    dfrange   modindx  \n",
            "296   0.250000  0.232718  0.007812   6.320312   6.312500  0.065555  \n",
            "220   0.250000  0.409856  0.007812   0.585938   0.578125  0.264128  \n",
            "2191  0.277457  1.317522  0.023438   7.078125   7.054688  0.156884  \n",
            "177   0.271186  0.105469  0.007812   0.140625   0.132812  0.145329  \n",
            "2602  0.279070  2.002604  0.023438  12.328125  12.304688  0.155737  \n",
            "This is the X test\n",
            "      meanfreq        sd    median       q25       q75       iqr       skew  \\\n",
            "2587  0.237042  0.032702  0.244214  0.217133  0.263558  0.046425   2.111834   \n",
            "2746  0.191306  0.034214  0.185684  0.173053  0.212211  0.039158   2.779446   \n",
            "1030  0.192668  0.057447  0.208820  0.145629  0.241505  0.095875   1.480635   \n",
            "868   0.160308  0.058925  0.165000  0.103667  0.197667  0.094000   1.424574   \n",
            "150   0.105873  0.089755  0.097246  0.015808  0.189701  0.173892  24.267693   \n",
            "\n",
            "            kurt    sp_ent       sfm      mode  centroid   meanfun    minfun  \\\n",
            "2587    7.669893  0.828343  0.123204  0.264525  0.237042  0.191185  0.049741   \n",
            "2746   12.163241  0.847855  0.231307  0.176000  0.191306  0.168978  0.048485   \n",
            "1030    5.502262  0.923820  0.423014  0.242231  0.192668  0.130248  0.046921   \n",
            "868     4.944750  0.935771  0.534223  0.187000  0.160308  0.087385  0.023952   \n",
            "150   678.335355  0.906791  0.612515  0.000000  0.105873  0.102800  0.016649   \n",
            "\n",
            "        maxfun   meandom    mindom    maxdom   dfrange   modindx  \n",
            "2587  0.279070  1.466751  0.023438  8.789062  8.765625  0.124532  \n",
            "2746  0.274286  0.692124  0.023438  7.242188  7.218750  0.065747  \n",
            "1030  0.277457  1.463041  0.023438  6.281250  6.257812  0.110827  \n",
            "868   0.202532  0.664062  0.085938  3.601562  3.515625  0.299192  \n",
            "150   0.271186  0.020508  0.007812  0.398438  0.390625  0.067097  \n",
            "This is the Y train\n",
            "      label\n",
            "296       0\n",
            "220       0\n",
            "2191      1\n",
            "177       0\n",
            "2602      1\n",
            "This is the Y test\n",
            "      label\n",
            "2587      1\n",
            "2746      1\n",
            "1030      0\n",
            "868       0\n",
            "150       0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def euclidean_dist(X_test, X_train):\n",
        "  dists = np.add(np.sum(X_test ** 2, axis=1, keepdims=True), np.sum(X_train ** 2, axis=1, keepdims=True).T) - 2* X_test @ X_train.T\n",
        "  return dists\n",
        "\n",
        "# Unit test code here (you can uncomment the four lines below to test)\n",
        "# X_train = np.array([[1, 2], [0, 3], [-1, 1]])\n",
        "# X_test = np.array([[-1, 0], [2, 1]])\n",
        "dists = euclidean_dist(X_test, X_train)\n",
        "print(dists.shape)\n"
      ],
      "metadata": {
        "id": "QYQf5sJu1CSq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7866e0a-3209-4da4-f9e9-27911321b84f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(951, 2217)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a heatmap\n",
        "sea.heatmap(dists, cmap='crest')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "E55bYatY7pFM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b597bd3-27eb-4a90-a2d2-821482a446f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lveUni0IT2G4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cd786fd-a982-48ac-efc2-f7393c9cb9a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(951, 3)\n"
          ]
        }
      ],
      "source": [
        "def find_k_neighbors(dists, y_train, k):\n",
        "  \"\"\"\n",
        "  find the labels of the top k nearest neighbors\n",
        "\n",
        "  Inputs:\n",
        "  - dists: distance matrix of shape (num_test, num_train)\n",
        "  - Y_train: A numpy array of shape (num_train) containing ground truth labels for training data\n",
        "  - k: An integer, k nearest neighbors\n",
        "\n",
        "  Output:\n",
        "  - neighbors: A numpy array of shape (num_test, k), where each row containts the\n",
        "               labels of the k nearest neighbors for each test example\n",
        "  \"\"\"\n",
        "  # TODO:\n",
        "  # find the top k nearest neighbors for each test sample.\n",
        "  # retrieve the corresponding labels of those neighbors.\n",
        "  # Here is the pseudo-code:\n",
        "  # for i = 0 ... num_test-1\n",
        "  #     idx = numpy.argsort(i-th row of dists)\n",
        "  #     neighbors[i] = Y_train(idx[0]), ..., Y_train(idx[k-1])\n",
        "  # return neighbors\n",
        "\n",
        "\n",
        "  # Advanced: You can accelerate the code by, e.g., argsort on the `dists` matrix directly\n",
        "\n",
        "  # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
        "  neighbors = np.zeros((dists.shape[0], k))\n",
        "  # print(\"New print out lines\")\n",
        "  # print(dists.shape[0])\n",
        "\n",
        "  for i in range(0, dists.shape[0]):\n",
        "      idx = dists[i].argsort()[:k]\n",
        "      # print(y_train[idx].shape)\n",
        "      k_labels = y_train[idx]\n",
        "      # print(k_labels[0])\n",
        "      # print(k_labels[1])\n",
        "      # print(k_labels[2])\n",
        "      # print(k_labels.shape)\n",
        "      # print(neighbors[0])\n",
        "      # print(neighbors.shape)\n",
        "      neighbors[i] = k_labels.flatten()\n",
        "\n",
        "  # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
        "  return neighbors\n",
        "\n",
        "\n",
        "# Unit test code here (you can uncomment the lines below to test)\n",
        "# Compute by hand to check if the result is correct.\n",
        "\"\"\"\n",
        "[[1. 0. 1.]\n",
        " [0. 1. 1.]]\n",
        "\"\"\"\n",
        "k = 3  # you can vary it as 1 or 3\n",
        "# Y_train = y_train\n",
        "# print(dists.shape[0])\n",
        "# print(type(dists))\n",
        "# print(y_train.size)\n",
        "# print(type(y_train))\n",
        "neighbors = find_k_neighbors(dists, y_train, k)\n",
        "print(neighbors.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LXqx94yVt41e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d5d0243-6e3d-4fc6-e84d-5925c0028f02"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(951,)\n"
          ]
        }
      ],
      "source": [
        "def knn_predict(X_test, X_train, y_train, k):\n",
        "  \"\"\"\n",
        "  predict labels for test data.\n",
        "\n",
        "  Inputs:\n",
        "  - X_test: A numpy array of shape (num_test, dim_feat) containing test data.\n",
        "  - X_train: A numpy array of shape (num_train, dim_feat) containing training data.\n",
        "  - Y_train: A numpy array of shape (num_train) containing ground truth labels for training data\n",
        "  - k: An integer, k nearest neighbors\n",
        "\n",
        "  Output:\n",
        "  - Y_pred: A numpy array of shape (num_test). Predicted labels for the test data.\n",
        "  \"\"\"\n",
        "  # TODO:\n",
        "  # find the labels of k nearest neighbors for each test example,\n",
        "  # and then find the majority label out of the k labels\n",
        "  #\n",
        "  # Here is the pseudo-code:\n",
        "  # dists = euclidean_dist(X_test, X_train)\n",
        "  # neighbors = find_k_neighbors(dists, Y_train, k)\n",
        "  # Y_pred = np.zeros(num_test, dtype=int)  # force dtype=int in case the dataset\n",
        "  #                                         # stores labels as float-point numbers\n",
        "  # for i = 0 ... num_test-1\n",
        "  #     Y_pred[i] = # the most common/frequent label in neighbors[i], you can\n",
        "  #                 # implement it by using np.unique\n",
        "  # return Y_pred\n",
        "  # -----------------------------------\n",
        "  dists = euclidean_dist(X_test, X_train)\n",
        "  neighbors = find_k_neighbors(dists, y_train, k)\n",
        "  Y_pred = np.zeros(len(X_test), dtype=int)\n",
        "\n",
        "  for i in range(len(X_test)):\n",
        "    values, counts = np.unique(neighbors[i], return_counts=True)\n",
        "    Y_pred[i] = values[counts == counts.max()].min()\n",
        "  return Y_pred\n",
        "\n",
        "  # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
        "\n",
        "  # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
        "\n",
        "# Unit test code here (you can uncomment the lines below to test)\n",
        "# Compute by hand to check if the result is correct.\n",
        "\"\"\"\n",
        "[1 1]\n",
        "\"\"\"\n",
        "Y_pred = knn_predict(X_test, X_train, y_train, k)\n",
        "print(Y_pred.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vr3PVo1_C7_r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74860928-7d48-4d44-ca81-2281d1465081"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Function: compute_error_rate\n",
            "0.29863301787592006\n",
            "The error rate is: 29.86%\n"
          ]
        }
      ],
      "source": [
        "def compute_error_rate(Y_pred, y_test):\n",
        "  \"\"\"\n",
        "  Compute error rate given the predicted results and true lable.\n",
        "  Inputs:\n",
        "  - ypred: array of prediction results.\n",
        "  - ytrue: array of true labels.\n",
        "    ypred and ytrue should be of same length.\n",
        "  Output:\n",
        "  - error rate: float number indicating the error in percentage\n",
        "                (i.e., a number between 0 and 100).\n",
        "  \"\"\"\n",
        "  # Here is the pseudo-code:\n",
        "  # err = 0\n",
        "  # for i in range (num_test):\n",
        "  #     err = err + (ypred[i] != ytrue[i])  # generalizes to multiple classes\n",
        "  # error_rate = err / num_test * 100\n",
        "  # return error_rate\n",
        "  #\n",
        "  # Advanced (optional):\n",
        "  #   implement it in one line by using vector operation and the `mean` function\n",
        "\n",
        "  # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
        "  err = 0\n",
        "  print(\"Function: compute_error_rate\")\n",
        "  # print(Y_pred.shape)\n",
        "  # # print(Y_pred)\n",
        "  # print(y_test.shape)\n",
        "  y_test = y_test.flatten()\n",
        "  # print(y)\n",
        "\n",
        "  for i in range(len(Y_pred)):\n",
        "    err = err + (Y_pred[i] != y_test[i])\n",
        "\n",
        "  error_rate = err / len(Y_pred)\n",
        "  # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
        "  return error_rate\n",
        "\n",
        "error_rates = compute_error_rate(Y_pred, y_test)\n",
        "print(error_rates)\n",
        "print(\"The error rate is: {:.2%}\".format(error_rates))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and split data\n",
        "X, y = make_moons(noise=0.3, random_state=0)\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y.astype(str), test_size=0.25, random_state=0)\n",
        "\n",
        "trace_specs = [\n",
        "    [X_train, y_train, '0', 'Train', 'square'],\n",
        "    [X_train, y_train, '1', 'Train', 'circle'],\n",
        "    [X_test, y_test, '0', 'Test', 'square-dot'],\n",
        "    [X_test, y_test, '1', 'Test', 'circle-dot']\n",
        "]\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Scatter(\n",
        "        x=X[y==label, 0],\n",
        "        y=X[y==label, 1],\n",
        "        name=f'{split} Split, Label {label}',\n",
        "        mode='markers',\n",
        "        marker_symbol=marker\n",
        "    ) for X, y, label, split, marker in trace_specs\n",
        "])\n",
        "\n",
        "fig.update_traces(\n",
        "    marker_size=12,\n",
        "    marker_line_width=1.5,\n",
        "    marker_color=\"lightyellow\"\n",
        ")\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "id": "q4Kxg0GGJ6Dm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "09a3010c-73c4-4661-c8ec-f5fb1fe0557c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "cell_type": "code",
      "source": [
        "# Load and split data\n",
        "# X, y = make_moons(noise=0.3, random_state=0)\n",
        "# X_train, X_test, y_train, y_test = train_test_split(\n",
        "#     X, y.astype(str), test_size=0.25, random_state=0)\n",
        "\n",
        "# Fit the model on training data, predict on test data\n",
        "clf = KNeighborsClassifier(5)\n",
        "clf.fit(X_train, y_train)\n",
        "y_score = clf.predict_proba(X_test)[:, 1]\n",
        "\n",
        "fig = px.scatter(\n",
        "    X_test, x=0, y=1,\n",
        "    color=y_score, color_continuous_scale='RdBu',\n",
        "    symbol=y_test, symbol_map={'male': 'diamond-open-dot', 'female': 'triangle-up-open-dot'},\n",
        "    labels={'symbol': 'label', 'color': 'score of <br>first class'}\n",
        ")\n",
        "fig.update_traces(marker_size=12, marker_line_width=1.5)\n",
        "fig.update_layout(legend_orientation='h')\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "rpTYSFxQ2ARn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "b9cb4b9c-65ac-4357-80c9-3acf16ea5ff3"
      },
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "from sklearn.datasets import make_moons\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "mesh_size = .02\n",
        "margin = 0.25\n",
        "\n",
        "# Load and split data\n",
        "X, y = make_moons(noise=0.3, random_state=0)\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y.astype(str), test_size=0.25, random_state=0)\n",
        "\n",
        "# Create a mesh grid on which we will run our model\n",
        "x_min, x_max = X[:, 0].min() - margin, X[:, 0].max() + margin\n",
        "y_min, y_max = X[:, 1].min() - margin, X[:, 1].max() + margin\n",
        "xrange = np.arange(x_min, x_max, mesh_size)\n",
        "yrange = np.arange(y_min, y_max, mesh_size)\n",
        "xx, yy = np.meshgrid(xrange, yrange)\n",
        "\n",
        "# Create classifier, run predictions on grid\n",
        "clf = KNeighborsClassifier(3, weights='uniform')\n",
        "clf.fit(X, y)\n",
        "Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "\n",
        "# Plot the figure\n",
        "fig = go.Figure(data=[\n",
        "    go.Contour(\n",
        "        x=xrange,\n",
        "        y=yrange,\n",
        "        z=Z,\n",
        "        colorscale='RdBu'\n",
        "    )\n",
        "])\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "kluDq9yQ2qAq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "cca11239-462d-4da3-acc6-e051a2d451a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "from sklearn.datasets import make_moons\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "mesh_size = .02\n",
        "margin = 0.25\n",
        "\n",
        "# Load and split data\n",
        "X, y = make_moons(noise=0.3, random_state=0)\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y.astype(str), test_size=0.25, random_state=0)\n",
        "\n",
        "# Create a mesh grid on which we will run our model\n",
        "x_min, x_max = X[:, 0].min() - margin, X[:, 0].max() + margin\n",
        "y_min, y_max = X[:, 1].min() - margin, X[:, 1].max() + margin\n",
        "xrange = np.arange(x_min, x_max, mesh_size)\n",
        "yrange = np.arange(y_min, y_max, mesh_size)\n",
        "xx, yy = np.meshgrid(xrange, yrange)\n",
        "\n",
        "# Create classifier, run predictions on grid\n",
        "clf = KNeighborsClassifier(3, weights='uniform')\n",
        "clf.fit(X, y)\n",
        "Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "trace_specs = [\n",
        "    [X_train, y_train, '0', 'Train', 'square'],\n",
        "    [X_train, y_train, '1', 'Train', 'circle'],\n",
        "    [X_test, y_test, '0', 'Test', 'square-dot'],\n",
        "    [X_test, y_test, '1', 'Test', 'circle-dot']\n",
        "]\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Scatter(\n",
        "        x=X[y==label, 0], y=X[y==label, 1],\n",
        "        name=f'{split} Split, Label {label}',\n",
        "        mode='markers', marker_symbol=marker\n",
        "    )\n",
        "    for X, y, label, split, marker in trace_specs\n",
        "])\n",
        "fig.update_traces(\n",
        "    marker_size=12, marker_line_width=1.5,\n",
        "    marker_color=\"lightyellow\"\n",
        ")\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Contour(\n",
        "        x=xrange,\n",
        "        y=yrange,\n",
        "        z=Z,\n",
        "        showscale=False,\n",
        "        colorscale='RdBu',\n",
        "        opacity=0.4,\n",
        "        name='Score',\n",
        "        hoverinfo='skip'\n",
        "    )\n",
        ")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "z4OjpdlaINW5",
        "outputId": "bec4d2ab-836c-49c6-d427-36158c81c441"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression Training\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "MN0X9zFbTAMe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# assuming we used the same data prepocessing methods\n",
        "# only using centroid, fundamental frequency. dont use duration, std, or frequency.\n",
        "X = data.drop(['meanfreq', 'sd', 'median', 'q25', 'q75', 'skew', 'kurt', 'sfm', 'mode', 'minfun', 'maxfun', 'mindom', 'maxdom', 'dfrange', 'label'], axis = 1)\n",
        "X.info()\n",
        "\n",
        "print(X.shape)\n",
        "print(y.shape)\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                       test_size = 0.2,\n",
        "                                       random_state = SEED,\n",
        "                                       stratify = y)\n",
        "\n",
        "\n",
        "def softmax(x):\n",
        "    exp_x = np.exp(x - np.max(x, axis=1, keepdims=True))\n",
        "    return exp_x / np.sum(exp_x, axis=1, keepdims=True)\n",
        "\n",
        "def MLR(X, W):\n",
        "    logits = np.dot(X, W)\n",
        "    probabilities = softmax(logits)\n",
        "    return probabilities\n",
        "\n",
        "def cross_entropy_loss(X, W, y):\n",
        "    num_samples = X.shape[0]\n",
        "\n",
        "    logits = np.dot(X, W)\n",
        "    probabilities = softmax(logits)\n",
        "\n",
        "    # One-hot encode the labels\n",
        "    y_one_hot = np.eye(probabilities.shape[1])[y]\n",
        "\n",
        "    # Calculate cross-entropy loss\n",
        "    loss = -np.sum(y_one_hot * np.log(probabilities + 1e-8)) / num_samples\n",
        "\n",
        "    # Calculate the gradient of the loss with respect to W\n",
        "    grad = np.dot(X.T, probabilities - y_one_hot) / num_samples\n",
        "\n",
        "    return loss, grad\n",
        "\n",
        "# Assuming your original labels are in a DataFrame named Y_train\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "y_train_encoded = label_encoder.fit_transform(y_train)\n",
        "\n",
        "\n",
        "y_train = y_train_encoded\n",
        "y_test = label_encoder.transform(y_test)\n",
        "\n",
        "num_iters = 2000\n",
        "lr = 0.5\n",
        "\n",
        "# Initialize weights\n",
        "w = np.random.rand(X_train.shape[1], len(np.unique(y_train)))\n",
        "loss_history = []\n",
        "\n",
        "for i in range(num_iters):\n",
        "    # Update weights using gradient descent\n",
        "    logits = MLR(X_train, w)\n",
        "    loss, grad = cross_entropy_loss(X_train, w, y_train)\n",
        "    w -= lr * grad\n",
        "\n",
        "    # Compute and store the loss for plotting\n",
        "    loss_history.append(loss)\n",
        "\n",
        "# Predict the label for all test examples\n",
        "test_logits = MLR(X_test, w)\n",
        "test_predictions = np.argmax(test_logits, axis=1)\n",
        "\n",
        "# Report the test accuracy\n",
        "acc = np.mean(test_predictions == y_test)\n",
        "print(f'Test Accuracy: {acc * 100:.2f}%')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "5qvlERFeCzIT",
        "outputId": "af5c30a9-bb7e-461f-cff0-e3178d7efd56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3168 entries, 0 to 3167\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   iqr       3168 non-null   float64\n",
            " 1   sp_ent    3168 non-null   float64\n",
            " 2   centroid  3168 non-null   float64\n",
            " 3   meanfun   3168 non-null   float64\n",
            " 4   meandom   3168 non-null   float64\n",
            " 5   modindx   3168 non-null   float64\n",
            "dtypes: float64(6)\n",
            "memory usage: 148.6 KB\n",
            "(3168, 6)\n",
            "(100,)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-d6efa7af6688>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m X_train, X_test, y_train, y_test = train_test_split(X, y,\n\u001b[0m\u001b[1;32m     11\u001b[0m                                        \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m                                        \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSEED\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[0m\n\u001b[1;32m   2557\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"At least one array required as input\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2559\u001b[0;31m     \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marrays\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   2560\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2561\u001b[0m     \u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_make_indexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterables\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\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    444\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    395\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    398\u001b[0m             \u001b[0;34m\"Found input variables with inconsistent numbers of samples: %r\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m             \u001b[0;34m%\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlengths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [3168, 100]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Confusion matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "cm = confusion_matrix(y_test, test_predictions)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
        "            xticklabels=['Predicted male', 'Predicted female'],\n",
        "            yticklabels=['Actual male', 'Actual female'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "LPadAQ-_QXcK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oTB9y6Es0F0s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Naive Bayes\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "Ih9YkB3CHYU-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = data['label'].to_frame()\n",
        "X = data.drop(['label'], axis=1)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                       test_size = 0.3,\n",
        "                                       random_state = SEED,\n",
        "                                       stratify = y)"
      ],
      "metadata": {
        "id": "_kMakw2xICCT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the classifier\n",
        "rf_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Get feature importances\n",
        "feature_importances = rf_classifier.feature_importances_\n",
        "\n",
        "# Assuming X is a Pandas DataFrame\n",
        "feature_names = X.columns.tolist()\n",
        "\n",
        "print(feature_names)\n",
        "\n",
        "# Plot feature importances\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(range(len(feature_importances)), feature_importances, align=\"center\")\n",
        "plt.yticks(range(len(feature_importances)), feature_names)\n",
        "plt.xlabel(\"Feature Importance\")\n",
        "plt.title(\"Random Forest Feature Importance\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "V4OZIAtsHg7i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop(['meanfreq', 'sd', 'median', 'q25', 'q75', 'skew', 'kurt', 'sp_ent', 'sfm', 'mode', 'centroid', 'minfun', 'maxfun', 'meandom', 'mindom', 'maxdom', 'dfrange', 'modindx', 'label'], axis = 1)\n",
        "X.info()"
      ],
      "metadata": {
        "id": "_yY-7msCHOde"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
        "                                       test_size = 0.3,\n",
        "                                       random_state = SEED,\n",
        "                                       stratify = y)"
      ],
      "metadata": {
        "id": "W6WOfb2YIoYu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = GaussianNB()\n",
        "\n",
        "model.fit(X_train, y_train);"
      ],
      "metadata": {
        "id": "k5nx1Wp6Hc1i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_pred, y_test)\n",
        "f1 = f1_score(y_pred, y_test, average=\"weighted\")\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"F1 Score:\", f1)"
      ],
      "metadata": {
        "id": "QNGV1456HeG0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [\"Male\", \"Female\"]\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot();"
      ],
      "metadata": {
        "id": "FesJSa1LHfcO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}