bcml.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "bcml.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ikazreal21/machinelearning_sklearn_pytorch/blob/master/bcml.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_NRnXsJzphf",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "outputId": "e898761f-8b12-4b90-c308-a56516cdb047"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "from google.colab import files\n",
        "file = files.upload()\n",
        "X_train = pd.read_csv(\"xtrain.csv\", header=None)\n",
        "Y_train = pd.read_csv(\"ytrain.csv\", header=None)\n",
        "X_test = pd.read_csv(\"xtest.csv\", header=None)\n",
        "Y_test = pd.read_csv(\"ytest.csv\", header=None)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ac683513-1d12-4207-807d-0b28ebddb808\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ac683513-1d12-4207-807d-0b28ebddb808\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving xtest.csv to xtest.csv\n",
            "Saving xtrain.csv to xtrain.csv\n",
            "Saving ytest.csv to ytest.csv\n",
            "Saving ytrain.csv to ytrain.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoWtmalR80De"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "classifier = Sequential()\n",
        "\n",
        "classifier.add(Dense(units = 16, activation = 'relu', input_dim = 30))\n",
        "classifier.add(Dense(units = 8, activation = 'relu'))\n",
        "classifier.add(Dense(units = 6, activation = 'relu'))\n",
        "classifier.add(Dense(units = 1, activation = 'sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wR2O00E39C7E"
      },
      "source": [
        "classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QlYnFgH9N49",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87567e0b-5345-4fad-dbbd-3def08b75fe8"
      },
      "source": [
        "classifier.fit(X_train, Y_train, batch_size = 1, epochs = 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "455/455 [==============================] - 3s 2ms/step - loss: 0.5058\n",
            "Epoch 2/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.1550\n",
            "Epoch 3/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0933\n",
            "Epoch 4/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0931\n",
            "Epoch 5/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0569\n",
            "Epoch 6/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0654\n",
            "Epoch 7/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0765\n",
            "Epoch 8/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0698\n",
            "Epoch 9/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0657\n",
            "Epoch 10/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0811\n",
            "Epoch 11/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0618\n",
            "Epoch 12/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0604\n",
            "Epoch 13/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0751\n",
            "Epoch 14/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0870\n",
            "Epoch 15/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0393\n",
            "Epoch 16/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.1111\n",
            "Epoch 17/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0869\n",
            "Epoch 18/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0558\n",
            "Epoch 19/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0213\n",
            "Epoch 20/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0372\n",
            "Epoch 21/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0878\n",
            "Epoch 22/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0730\n",
            "Epoch 23/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0538\n",
            "Epoch 24/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0639\n",
            "Epoch 25/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.1218\n",
            "Epoch 26/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0744\n",
            "Epoch 27/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0822\n",
            "Epoch 28/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0866\n",
            "Epoch 29/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0605\n",
            "Epoch 30/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0842\n",
            "Epoch 31/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0648\n",
            "Epoch 32/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0588\n",
            "Epoch 33/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0243\n",
            "Epoch 34/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0512\n",
            "Epoch 35/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0739\n",
            "Epoch 36/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0635\n",
            "Epoch 37/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0351\n",
            "Epoch 38/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0553\n",
            "Epoch 39/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0448\n",
            "Epoch 40/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0428\n",
            "Epoch 41/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0712\n",
            "Epoch 42/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0545\n",
            "Epoch 43/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0515\n",
            "Epoch 44/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0253\n",
            "Epoch 45/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0111\n",
            "Epoch 46/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0308\n",
            "Epoch 47/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0271\n",
            "Epoch 48/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0345\n",
            "Epoch 49/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0104\n",
            "Epoch 50/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0358\n",
            "Epoch 51/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0573\n",
            "Epoch 52/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0418\n",
            "Epoch 53/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0202\n",
            "Epoch 54/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0592\n",
            "Epoch 55/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0283\n",
            "Epoch 56/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0450\n",
            "Epoch 57/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0263\n",
            "Epoch 58/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0109\n",
            "Epoch 59/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0204\n",
            "Epoch 60/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0219\n",
            "Epoch 61/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0084\n",
            "Epoch 62/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0472\n",
            "Epoch 63/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0441\n",
            "Epoch 64/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0699\n",
            "Epoch 65/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0641\n",
            "Epoch 66/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0187\n",
            "Epoch 67/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0505\n",
            "Epoch 68/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0798\n",
            "Epoch 69/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0210\n",
            "Epoch 70/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0099\n",
            "Epoch 71/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0496\n",
            "Epoch 72/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0249\n",
            "Epoch 73/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0258\n",
            "Epoch 74/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0347\n",
            "Epoch 75/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0064\n",
            "Epoch 76/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0156\n",
            "Epoch 77/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0120\n",
            "Epoch 78/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0281\n",
            "Epoch 79/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0339\n",
            "Epoch 80/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0213\n",
            "Epoch 81/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0300\n",
            "Epoch 82/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0071\n",
            "Epoch 83/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0101\n",
            "Epoch 84/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0167\n",
            "Epoch 85/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0450\n",
            "Epoch 86/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0187\n",
            "Epoch 87/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0074\n",
            "Epoch 88/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0115\n",
            "Epoch 89/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0175\n",
            "Epoch 90/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0223\n",
            "Epoch 91/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0116\n",
            "Epoch 92/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0208\n",
            "Epoch 93/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0175\n",
            "Epoch 94/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0060\n",
            "Epoch 95/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0049\n",
            "Epoch 96/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0057\n",
            "Epoch 97/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0115\n",
            "Epoch 98/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0090\n",
            "Epoch 99/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0115\n",
            "Epoch 100/100\n",
            "455/455 [==============================] - 1s 2ms/step - loss: 0.0020\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f1058555e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEaG0Tkx9fUU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1de6327-7ea5-4d6e-dabe-3f3ffe634f55"
      },
      "source": [
        "Y_pred = classifier.predict(X_test)\n",
        "Y_pred = [ 1 if y>=0.5 else 0 for y in Y_pred ]\n",
        "\n",
        "total = 0\n",
        "correct = 0\n",
        "wrong = 0\n",
        "for i in Y_pred:\n",
        "  total=total+1\n",
        "  if(Y_test.at[i,0] == Y_pred[i]):\n",
        "    correct=correct+1\n",
        "  else:\n",
        "    wrong=wrong+1\n",
        "\n",
        "print(\"Total \" + str(total))\n",
        "print(\"Correct \" + str(correct))\n",
        "print(\"Wrong \" + str(wrong))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total 114\n",
            "Correct 114\n",
            "Wrong 0\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}