hand_gestures.ipynb

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        "<a href=\"https://colab.research.google.com/github/abhirukth/Data-Sci.-File/blob/main/hand_gestures.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "id": "naked-colorado",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/kaggle/input/emg-4/1.csv\n",
            "/kaggle/input/emg-4/3.csv\n",
            "/kaggle/input/emg-4/2.csv\n",
            "/kaggle/input/emg-4/0.csv\n"
          ],
          "name": "stdout"
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      ]
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        "tags": [],
        "id": "statutory-tourist"
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      "source": [
        "dfs = []\n",
        "\n",
        "for i in ['0', '1', '2', '3']:\n",
        "    dfs.append(pd.read_csv('../input/emg-4/' + i + '.csv'))"
      ],
      "id": "statutory-tourist",
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
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          "status": "completed"
        },
        "tags": [],
        "id": "referenced-episode"
      },
      "source": [
        "for df in dfs:\n",
        "    df.columns = list(range(len(df.columns)))"
      ],
      "id": "referenced-episode",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "dfs[2]"
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      "id": "hourly-market",
      "execution_count": null,
      "outputs": [
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              "[2942 rows x 65 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:06:25.169290Z",
          "iopub.status.busy": "2021-06-23T13:06:25.168455Z",
          "iopub.status.idle": "2021-06-23T13:06:25.183329Z",
          "shell.execute_reply": "2021-06-23T13:06:25.182680Z",
          "shell.execute_reply.started": "2021-06-23T11:52:49.457748Z"
        },
        "papermill": {
          "duration": 0.038161,
          "end_time": "2021-06-23T13:06:25.183495",
          "exception": false,
          "start_time": "2021-06-23T13:06:25.145334",
          "status": "completed"
        },
        "tags": [],
        "id": "computational-perspective"
      },
      "source": [
        "data = pd.concat([df for df in dfs], axis = 0).reset_index(drop = True)"
      ],
      "id": "computational-perspective",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:06:25.227641Z",
          "iopub.status.busy": "2021-06-23T13:06:25.226932Z",
          "iopub.status.idle": "2021-06-23T13:06:25.277083Z",
          "shell.execute_reply": "2021-06-23T13:06:25.277671Z",
          "shell.execute_reply.started": "2021-06-23T11:52:52.463920Z"
        },
        "papermill": {
          "duration": 0.077623,
          "end_time": "2021-06-23T13:06:25.277892",
          "exception": false,
          "start_time": "2021-06-23T13:06:25.200269",
          "status": "completed"
        },
        "tags": [],
        "id": "light-protection",
        "outputId": "fb4b97f7-5a23-4fb6-9e52-8e1e448d00a9"
      },
      "source": [
        "data"
      ],
      "id": "light-protection",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "         0    1    2     3     4     5      6     7     8    9   ...    55  \\\n",
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              "[11674 rows x 65 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:06:25.319114Z",
          "iopub.status.busy": "2021-06-23T13:06:25.318328Z",
          "iopub.status.idle": "2021-06-23T13:06:25.326178Z",
          "shell.execute_reply": "2021-06-23T13:06:25.326676Z",
          "shell.execute_reply.started": "2021-06-23T11:55:20.679268Z"
        },
        "papermill": {
          "duration": 0.031409,
          "end_time": "2021-06-23T13:06:25.326912",
          "exception": false,
          "start_time": "2021-06-23T13:06:25.295503",
          "status": "completed"
        },
        "tags": [],
        "id": "soviet-remains",
        "outputId": "2707c44c-8ae2-42f3-9cef-c77886a1ec1d"
      },
      "source": [
        "print('Total missing values : ', data.isna().sum().sum())"
      ],
      "id": "soviet-remains",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total missing values :  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:06:25.374700Z",
          "iopub.status.busy": "2021-06-23T13:06:25.373929Z",
          "iopub.status.idle": "2021-06-23T13:06:25.381668Z",
          "shell.execute_reply": "2021-06-23T13:06:25.381056Z",
          "shell.execute_reply.started": "2021-06-23T11:57:30.790942Z"
        },
        "papermill": {
          "duration": 0.037076,
          "end_time": "2021-06-23T13:06:25.381855",
          "exception": false,
          "start_time": "2021-06-23T13:06:25.344779",
          "status": "completed"
        },
        "tags": [],
        "id": "bridal-horse"
      },
      "source": [
        "y = data[64].copy()\n",
        "X = data.drop(64, axis = 1).copy()"
      ],
      "id": "bridal-horse",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:06:25.426292Z",
          "iopub.status.busy": "2021-06-23T13:06:25.425494Z",
          "iopub.status.idle": "2021-06-23T13:06:25.452615Z",
          "shell.execute_reply": "2021-06-23T13:06:25.451949Z",
          "shell.execute_reply.started": "2021-06-23T11:58:21.452064Z"
        },
        "papermill": {
          "duration": 0.05281,
          "end_time": "2021-06-23T13:06:25.452819",
          "exception": false,
          "start_time": "2021-06-23T13:06:25.400009",
          "status": "completed"
        },
        "tags": [],
        "id": "renewable-challenge"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)"
      ],
      "id": "renewable-challenge",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:06:25.497202Z",
          "iopub.status.busy": "2021-06-23T13:06:25.495772Z",
          "iopub.status.idle": "2021-06-23T13:06:25.506763Z",
          "shell.execute_reply": "2021-06-23T13:06:25.506078Z",
          "shell.execute_reply.started": "2021-06-23T11:59:38.175366Z"
        },
        "papermill": {
          "duration": 0.036323,
          "end_time": "2021-06-23T13:06:25.506940",
          "exception": false,
          "start_time": "2021-06-23T13:06:25.470617",
          "status": "completed"
        },
        "tags": [],
        "id": "disturbed-advance"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.7, random_state = 100)"
      ],
      "id": "disturbed-advance",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:06:25.549299Z",
          "iopub.status.busy": "2021-06-23T13:06:25.548307Z",
          "iopub.status.idle": "2021-06-23T13:06:25.552872Z",
          "shell.execute_reply": "2021-06-23T13:06:25.552305Z",
          "shell.execute_reply.started": "2021-06-23T12:05:24.086414Z"
        },
        "papermill": {
          "duration": 0.027952,
          "end_time": "2021-06-23T13:06:25.553033",
          "exception": false,
          "start_time": "2021-06-23T13:06:25.525081",
          "status": "completed"
        },
        "tags": [],
        "id": "fancy-mambo",
        "outputId": "85a521dc-3c02-4a69-e17a-021b73d9bfa0"
      },
      "source": [
        "X.shape"
      ],
      "id": "fancy-mambo",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11674, 64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:06:25.601411Z",
          "iopub.status.busy": "2021-06-23T13:06:25.600626Z",
          "iopub.status.idle": "2021-06-23T13:07:36.220119Z",
          "shell.execute_reply": "2021-06-23T13:07:36.219053Z",
          "shell.execute_reply.started": "2021-06-23T13:04:14.490796Z"
        },
        "papermill": {
          "duration": 70.64917,
          "end_time": "2021-06-23T13:07:36.220344",
          "exception": false,
          "start_time": "2021-06-23T13:06:25.571174",
          "status": "completed"
        },
        "tags": [],
        "id": "romance-liberia",
        "outputId": "df701991-dac6-455a-f5e6-48043ec7d172"
      },
      "source": [
        "inputs = tf.keras.Input(shape = (X.shape[1], ))\n",
        "x = tf.keras.layers.Dense(1024, activation = 'relu')(inputs)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "x = tf.keras.layers.Dense(512, activation = 'relu')(x)\n",
        "x = tf.keras.layers.Dropout(0.2)(x)\n",
        "x = tf.keras.layers.Dense(512, activation = 'sigmoid')(x)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "outputs = tf.keras.layers.Dense(4, activation = 'softmax')(x)\n",
        "\n",
        "model = tf.keras.Model(inputs, outputs)\n",
        "\n",
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    metrics = 'accuracy'\n",
        ")\n",
        "\n",
        "batch_size = 32\n",
        "epochs = 30\n",
        "\n",
        "history = model.fit(\n",
        "    X_train,\n",
        "    y_train,\n",
        "    validation_split = 0.2,\n",
        "    batch_size = batch_size,\n",
        "    epochs = epochs,\n",
        "    callbacks = [\n",
        "        tf.keras.callbacks.ReduceLROnPlateau()\n",
        "    ]\n",
        ")"
      ],
      "id": "romance-liberia",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "205/205 [==============================] - 4s 13ms/step - loss: 1.1586 - accuracy: 0.4700 - val_loss: 0.3409 - val_accuracy: 0.8783\n",
            "Epoch 2/30\n",
            "205/205 [==============================] - 2s 10ms/step - loss: 0.3570 - accuracy: 0.8678 - val_loss: 0.2450 - val_accuracy: 0.9076\n",
            "Epoch 3/30\n",
            "205/205 [==============================] - 2s 10ms/step - loss: 0.2111 - accuracy: 0.9226 - val_loss: 0.2089 - val_accuracy: 0.9254\n",
            "Epoch 4/30\n",
            "205/205 [==============================] - 2s 10ms/step - loss: 0.1690 - accuracy: 0.9425 - val_loss: 0.1944 - val_accuracy: 0.9291\n",
            "Epoch 5/30\n",
            "205/205 [==============================] - 2s 10ms/step - loss: 0.1166 - accuracy: 0.9575 - val_loss: 0.1957 - val_accuracy: 0.9401\n",
            "Epoch 6/30\n",
            "205/205 [==============================] - 2s 10ms/step - loss: 0.0992 - accuracy: 0.9659 - val_loss: 0.2086 - val_accuracy: 0.9394\n",
            "Epoch 7/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0910 - accuracy: 0.9672 - val_loss: 0.2164 - val_accuracy: 0.9284\n",
            "Epoch 8/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0926 - accuracy: 0.9699 - val_loss: 0.2011 - val_accuracy: 0.9370\n",
            "Epoch 9/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0726 - accuracy: 0.9733 - val_loss: 0.2112 - val_accuracy: 0.9401\n",
            "Epoch 10/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0631 - accuracy: 0.9803 - val_loss: 0.2361 - val_accuracy: 0.9303\n",
            "Epoch 11/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0560 - accuracy: 0.9811 - val_loss: 0.2503 - val_accuracy: 0.9339\n",
            "Epoch 12/30\n",
            "205/205 [==============================] - 2s 12ms/step - loss: 0.0511 - accuracy: 0.9819 - val_loss: 0.2391 - val_accuracy: 0.9327\n",
            "Epoch 13/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0522 - accuracy: 0.9806 - val_loss: 0.2491 - val_accuracy: 0.9278\n",
            "Epoch 14/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0650 - accuracy: 0.9748 - val_loss: 0.2292 - val_accuracy: 0.9431\n",
            "Epoch 15/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0317 - accuracy: 0.9880 - val_loss: 0.1865 - val_accuracy: 0.9462\n",
            "Epoch 16/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0288 - accuracy: 0.9904 - val_loss: 0.1891 - val_accuracy: 0.9480\n",
            "Epoch 17/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0187 - accuracy: 0.9937 - val_loss: 0.1899 - val_accuracy: 0.9480\n",
            "Epoch 18/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0163 - accuracy: 0.9943 - val_loss: 0.1912 - val_accuracy: 0.9474\n",
            "Epoch 19/30\n",
            "205/205 [==============================] - 2s 12ms/step - loss: 0.0157 - accuracy: 0.9954 - val_loss: 0.1950 - val_accuracy: 0.9468\n",
            "Epoch 20/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0134 - accuracy: 0.9960 - val_loss: 0.1906 - val_accuracy: 0.9486\n",
            "Epoch 21/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0132 - accuracy: 0.9956 - val_loss: 0.1990 - val_accuracy: 0.9474\n",
            "Epoch 22/30\n",
            "205/205 [==============================] - 2s 12ms/step - loss: 0.0132 - accuracy: 0.9965 - val_loss: 0.1997 - val_accuracy: 0.9505\n",
            "Epoch 23/30\n",
            "205/205 [==============================] - 2s 12ms/step - loss: 0.0120 - accuracy: 0.9970 - val_loss: 0.1997 - val_accuracy: 0.9486\n",
            "Epoch 24/30\n",
            "205/205 [==============================] - 2s 12ms/step - loss: 0.0122 - accuracy: 0.9975 - val_loss: 0.2058 - val_accuracy: 0.9480\n",
            "Epoch 25/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0114 - accuracy: 0.9969 - val_loss: 0.2119 - val_accuracy: 0.9492\n",
            "Epoch 26/30\n",
            "205/205 [==============================] - 2s 12ms/step - loss: 0.0146 - accuracy: 0.9954 - val_loss: 0.2120 - val_accuracy: 0.9492\n",
            "Epoch 27/30\n",
            "205/205 [==============================] - 2s 12ms/step - loss: 0.0079 - accuracy: 0.9974 - val_loss: 0.2122 - val_accuracy: 0.9505\n",
            "Epoch 28/30\n",
            "205/205 [==============================] - 2s 11ms/step - loss: 0.0090 - accuracy: 0.9966 - val_loss: 0.2101 - val_accuracy: 0.9498\n",
            "Epoch 29/30\n",
            "205/205 [==============================] - 2s 12ms/step - loss: 0.0085 - accuracy: 0.9966 - val_loss: 0.2094 - val_accuracy: 0.9492\n",
            "Epoch 30/30\n",
            "205/205 [==============================] - 2s 12ms/step - loss: 0.0112 - accuracy: 0.9959 - val_loss: 0.2113 - val_accuracy: 0.9498\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:07:37.002243Z",
          "iopub.status.busy": "2021-06-23T13:07:37.001371Z",
          "iopub.status.idle": "2021-06-23T13:07:37.254513Z",
          "shell.execute_reply": "2021-06-23T13:07:37.253797Z",
          "shell.execute_reply.started": "2021-06-23T13:05:18.376919Z"
        },
        "papermill": {
          "duration": 0.658497,
          "end_time": "2021-06-23T13:07:37.254686",
          "exception": false,
          "start_time": "2021-06-23T13:07:36.596189",
          "status": "completed"
        },
        "tags": [],
        "id": "prepared-fishing",
        "outputId": "ae5840d0-7885-41f6-b3b2-d124ce2b8fa3"
      },
      "source": [
        "plt.figure(figsize = (20, 10))\n",
        "\n",
        "plt.plot(range(epochs), history.history['loss'], label = 'Training loss')\n",
        "plt.plot(range(epochs), history.history['val_loss'], label = 'Validation loss')\n",
        "\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "id": "prepared-fishing",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:07:38.006116Z",
          "iopub.status.busy": "2021-06-23T13:07:38.004957Z",
          "iopub.status.idle": "2021-06-23T13:07:38.423652Z",
          "shell.execute_reply": "2021-06-23T13:07:38.422912Z",
          "shell.execute_reply.started": "2021-06-23T13:05:31.336658Z"
        },
        "papermill": {
          "duration": 0.800925,
          "end_time": "2021-06-23T13:07:38.423843",
          "exception": false,
          "start_time": "2021-06-23T13:07:37.622918",
          "status": "completed"
        },
        "tags": [],
        "id": "studied-persian",
        "outputId": "e5f7fec6-9cd8-443b-f22b-60d7ae74c177"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "id": "studied-persian",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "110/110 [==============================] - 0s 3ms/step - loss: 0.1879 - accuracy: 0.9552\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.18789543211460114, 0.9551813006401062]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:07:39.187835Z",
          "iopub.status.busy": "2021-06-23T13:07:39.186712Z",
          "iopub.status.idle": "2021-06-23T13:07:39.647699Z",
          "shell.execute_reply": "2021-06-23T13:07:39.647078Z",
          "shell.execute_reply.started": "2021-06-23T13:05:33.857247Z"
        },
        "papermill": {
          "duration": 0.842544,
          "end_time": "2021-06-23T13:07:39.647910",
          "exception": false,
          "start_time": "2021-06-23T13:07:38.805366",
          "status": "completed"
        },
        "tags": [],
        "id": "special-reviewer"
      },
      "source": [
        "y_true = np.array(y_test)\n",
        "y_pred = np.array(list(map(lambda x : np.argmax(x), model.predict(X_test))))"
      ],
      "id": "special-reviewer",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:07:40.408764Z",
          "iopub.status.busy": "2021-06-23T13:07:40.407568Z",
          "iopub.status.idle": "2021-06-23T13:07:40.583693Z",
          "shell.execute_reply": "2021-06-23T13:07:40.583039Z",
          "shell.execute_reply.started": "2021-06-23T13:05:34.319967Z"
        },
        "papermill": {
          "duration": 0.558346,
          "end_time": "2021-06-23T13:07:40.583901",
          "exception": false,
          "start_time": "2021-06-23T13:07:40.025555",
          "status": "completed"
        },
        "tags": [],
        "id": "czech-shame",
        "outputId": "e54550b7-23f6-4321-f7e0-af070b02e1ad"
      },
      "source": [
        "cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "plt.figure(figsize = (6, 6))\n",
        "sns.heatmap(cm, annot = True, fmt = 'g', cmap = 'Blues', cbar = False)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "id": "czech-shame",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-06-23T13:07:41.345677Z",
          "iopub.status.busy": "2021-06-23T13:07:41.344942Z",
          "iopub.status.idle": "2021-06-23T13:07:41.362401Z",
          "shell.execute_reply": "2021-06-23T13:07:41.361652Z",
          "shell.execute_reply.started": "2021-06-23T13:05:37.676612Z"
        },
        "papermill": {
          "duration": 0.399284,
          "end_time": "2021-06-23T13:07:41.362579",
          "exception": false,
          "start_time": "2021-06-23T13:07:40.963295",
          "status": "completed"
        },
        "tags": [],
        "id": "innovative-sixth",
        "outputId": "e955b6d6-00b7-4ef3-dbe6-8ee170e7b4f8"
      },
      "source": [
        "print(classification_report(y_true, y_pred))"
      ],
      "id": "innovative-sixth",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98       864\n",
            "           1       0.95      0.98      0.96       824\n",
            "           2       0.96      0.93      0.94       924\n",
            "           3       0.94      0.94      0.94       891\n",
            "\n",
            "    accuracy                           0.96      3503\n",
            "   macro avg       0.96      0.96      0.96      3503\n",
            "weighted avg       0.96      0.96      0.96      3503\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "papermill": {
          "duration": 0.380012,
          "end_time": "2021-06-23T13:07:42.115968",
          "exception": false,
          "start_time": "2021-06-23T13:07:41.735956",
          "status": "completed"
        },
        "tags": [],
        "id": "genetic-playing"
      },
      "source": [
        ""
      ],
      "id": "genetic-playing",
      "execution_count": null,
      "outputs": []
    }
  ]
}