Day_1_(ANN).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Day 1 (ANN).ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1apztanXUwe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import io\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "import math"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFg-KZ6uX06Z",
        "colab_type": "code",
        "outputId": "a34db776-7141-4a5a-c577-05ed5f64d901",
        "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": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 117
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2d607957-4f3a-4d86-84e4-e6c9f0e03947\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-2d607957-4f3a-4d86-84e4-e6c9f0e03947\">\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 train.csv to train.csv\n",
            "Saving test.csv to test.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1IaOVd7X_Wz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1 = pd.read_csv(io.BytesIO(uploaded['train.csv']))\n",
        "df2 = pd.read_csv(io.BytesIO(uploaded['test.csv']))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zM9oXX81YJez",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1['Victory Status'] = df1['Victory Status'].replace(to_replace=2,value=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diO3TdG_YfQ_",
        "colab_type": "code",
        "outputId": "60cdaefd-733d-4ce7-b12d-6ca7a6b80c2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "Y = df1['Victory Status'].copy()\n",
        "Y.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: Victory Status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iAMPdg8FZ6c9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df1[['Number of soldiers','Number of tanks','Number of aircrafts']].copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZP5_-LjFaKCq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = df2[['Number of soldiers','Number of tanks','Number of aircrafts']].copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2doGEv6aRCW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_test = df2['Id'].copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iomGdcHgagYq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0CnDJUlan1p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size=0.30)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5VCdu_alhvV",
        "colab_type": "code",
        "outputId": "b707fcce-de01-44f5-ed4c-a24b6dda6f0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "X_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.78412493,  1.02183166, -0.55388587],\n",
              "       [-1.0721174 , -1.48993331, -0.55388587],\n",
              "       [ 0.65583738, -1.17596269,  1.70514701],\n",
              "       [-0.11214252,  1.96374352, -0.55388587],\n",
              "       [ 0.55983989,  0.39389041, -0.42100158],\n",
              "       [ 1.23182231, -1.48993331, -0.55388587],\n",
              "       [ 0.46384241, -1.48993331, -0.55388587],\n",
              "       [-0.59212996,  1.33580228, -0.55388587],\n",
              "       [-0.78412493,  0.39389041, -0.55388587],\n",
              "       [ 1.13582482,  1.02183166,  1.43937843],\n",
              "       [-0.20814001,  0.39389041,  0.37630414],\n",
              "       [-1.8400973 ,  1.33580228,  0.37630414],\n",
              "       [ 0.17584994,  1.02183166, -0.55388587],\n",
              "       [ 0.27184743, -0.86199207, -0.55388587],\n",
              "       [ 1.71180975,  1.6497729 , -0.28811729],\n",
              "       [-0.68812745,  0.07991979, -0.55388587],\n",
              "       [-0.97611991,  0.39389041, -0.28811729],\n",
              "       [ 1.51981477,  1.02183166, -0.55388587],\n",
              "       [-1.45610735, -0.86199207, -0.55388587],\n",
              "       [ 0.75183487,  0.39389041, -0.55388587],\n",
              "       [ 0.36784492,  0.39389041, -0.55388587],\n",
              "       [-0.78412493, -1.17596269,  1.30649415],\n",
              "       [ 0.65583738, -0.54802145, -0.42100158],\n",
              "       [ 0.07985246,  0.70786103,  2.50245273],\n",
              "       [-0.97611991, -0.86199207, -0.55388587],\n",
              "       [-0.97611991,  0.07991979,  1.30649415],\n",
              "       [-0.11214252, -1.17596269, -0.28811729],\n",
              "       [ 0.36784492, -0.23405083, -0.55388587],\n",
              "       [-0.01614503,  0.70786103,  1.04072557],\n",
              "       [-0.30413749, -1.48993331, -0.42100158],\n",
              "       [ 0.75183487,  0.70786103,  0.50918843],\n",
              "       [-0.40013498, -0.54802145, -0.55388587],\n",
              "       [-0.40013498, -0.23405083, -0.42100158],\n",
              "       [-0.11214252,  0.07991979, -0.02234872],\n",
              "       [-1.55210483, -1.17596269,  0.24341985],\n",
              "       [-1.16811488, -0.86199207,  2.50245273],\n",
              "       [ 1.03982733,  1.6497729 , -0.55388587],\n",
              "       [-0.68812745, -0.23405083,  0.11053557],\n",
              "       [ 1.80780723,  0.39389041, -0.55388587],\n",
              "       [-1.93609479, -0.86199207, -0.55388587],\n",
              "       [ 0.65583738, -0.54802145,  2.7682213 ],\n",
              "       [-0.97611991, -1.48993331,  6.35609704],\n",
              "       [-0.68812745, -1.48993331, -0.15523301],\n",
              "       [-0.30413749, -0.54802145,  0.24341985],\n",
              "       [-0.01614503, -1.17596269, -0.15523301],\n",
              "       [-0.78412493,  1.6497729 , -0.55388587],\n",
              "       [ 1.90380472,  1.6497729 , -0.55388587],\n",
              "       [-1.45610735,  1.02183166, -0.55388587],\n",
              "       [-0.59212996,  0.07991979,  0.24341985],\n",
              "       [ 0.55983989, -0.86199207, -0.55388587],\n",
              "       [-0.11214252,  1.02183166, -0.02234872],\n",
              "       [-0.30413749,  0.39389041, -0.55388587],\n",
              "       [-0.20814001,  0.70786103, -0.55388587],\n",
              "       [ 1.61581226, -1.17596269,  0.50918843],\n",
              "       [ 0.36784492,  0.39389041, -0.42100158],\n",
              "       [ 0.36784492,  1.33580228, -0.55388587],\n",
              "       [ 1.03982733,  1.02183166, -0.55388587],\n",
              "       [ 1.3278198 ,  0.39389041,  0.50918843],\n",
              "       [-1.36010986,  1.02183166, -0.55388587],\n",
              "       [ 0.36784492,  0.39389041, -0.55388587],\n",
              "       [-1.16811488,  0.39389041, -0.55388587],\n",
              "       [-0.97611991,  0.39389041, -0.55388587],\n",
              "       [-0.11214252,  1.96374352, -0.15523301],\n",
              "       [-0.88012242,  0.07991979, -0.42100158],\n",
              "       [ 0.65583738,  0.70786103, -0.55388587],\n",
              "       [ 1.61581226,  0.07991979, -0.55388587],\n",
              "       [-1.16811488,  0.39389041, -0.55388587],\n",
              "       [-0.78412493,  1.33580228, -0.42100158],\n",
              "       [-2.12808976, -1.17596269, -0.28811729],\n",
              "       [-1.0721174 , -0.23405083,  2.10379987],\n",
              "       [-1.26411237,  0.70786103, -0.55388587],\n",
              "       [-1.45610735, -0.23405083, -0.15523301],\n",
              "       [ 0.46384241, -0.54802145, -0.28811729],\n",
              "       [-0.59212996,  1.6497729 , -0.02234872],\n",
              "       [-0.30413749,  0.70786103, -0.02234872],\n",
              "       [-1.8400973 , -0.86199207, -0.42100158],\n",
              "       [ 1.51981477, -0.86199207, -0.55388587],\n",
              "       [-0.11214252, -0.23405083, -0.15523301],\n",
              "       [ 1.03982733, -0.54802145, -0.55388587],\n",
              "       [-0.11214252, -0.86199207,  0.11053557],\n",
              "       [-0.40013498,  1.33580228, -0.42100158],\n",
              "       [ 1.61581226, -1.48993331, -0.55388587],\n",
              "       [ 1.13582482, -1.48993331, -0.55388587],\n",
              "       [ 0.55983989,  1.33580228, -0.15523301],\n",
              "       [ 0.36784492, -0.23405083,  1.30649415],\n",
              "       [-1.36010986,  0.07991979, -0.02234872],\n",
              "       [-0.40013498, -0.54802145, -0.42100158],\n",
              "       [-0.49613247,  1.02183166, -0.55388587],\n",
              "       [ 0.94382984, -0.86199207, -0.42100158],\n",
              "       [ 1.23182231, -1.48993331, -0.42100158],\n",
              "       [-1.64810232,  1.96374352, -0.55388587],\n",
              "       [ 0.07985246, -1.17596269,  0.37630414],\n",
              "       [ 2.19179718,  1.33580228, -0.55388587],\n",
              "       [ 0.94382984,  1.02183166, -0.55388587],\n",
              "       [-1.55210483, -0.86199207, -0.55388587],\n",
              "       [-1.26411237, -1.48993331, -0.28811729],\n",
              "       [ 1.13582482, -0.23405083,  2.36956844],\n",
              "       [ 1.42381728,  1.33580228, -0.55388587],\n",
              "       [-0.49613247, -0.54802145,  0.50918843],\n",
              "       [-0.40013498, -0.86199207, -0.42100158],\n",
              "       [ 0.17584994,  1.6497729 ,  1.43937843],\n",
              "       [ 0.07985246,  1.33580228,  5.55879132],\n",
              "       [-0.01614503, -0.86199207, -0.28811729],\n",
              "       [ 0.07985246, -1.48993331, -0.42100158],\n",
              "       [-0.78412493,  1.33580228, -0.42100158],\n",
              "       [-1.55210483, -0.86199207,  1.43937843],\n",
              "       [-1.16811488, -1.48993331, -0.55388587],\n",
              "       [ 0.75183487, -1.17596269, -0.55388587],\n",
              "       [ 1.99980221,  0.07991979, -0.55388587],\n",
              "       [-0.40013498,  0.39389041,  0.774957  ],\n",
              "       [ 0.07985246, -0.23405083, -0.55388587],\n",
              "       [-0.30413749,  0.07991979,  1.17360986],\n",
              "       [ 1.13582482,  0.39389041, -0.55388587],\n",
              "       [ 0.84783236, -1.48993331, -0.55388587],\n",
              "       [-0.30413749, -1.17596269, -0.28811729],\n",
              "       [ 1.13582482, -0.54802145, -0.28811729],\n",
              "       [-0.97611991,  1.02183166, -0.02234872],\n",
              "       [-0.68812745, -1.48993331, -0.28811729],\n",
              "       [-0.49613247, -0.23405083, -0.28811729],\n",
              "       [ 2.38379216,  0.70786103, -0.42100158],\n",
              "       [-0.97611991,  0.39389041, -0.15523301],\n",
              "       [-0.88012242, -1.48993331,  0.64207271],\n",
              "       [ 0.84783236,  1.02183166, -0.55388587],\n",
              "       [-1.16811488,  0.70786103, -0.55388587],\n",
              "       [-0.01614503, -0.86199207,  0.64207271],\n",
              "       [-0.68812745,  0.70786103,  2.10379987],\n",
              "       [ 1.80780723, -1.48993331, -0.55388587],\n",
              "       [-0.30413749, -0.54802145, -0.55388587],\n",
              "       [-1.45610735,  1.33580228,  0.11053557],\n",
              "       [ 0.27184743, -0.86199207, -0.55388587],\n",
              "       [-1.16811488,  1.33580228, -0.55388587],\n",
              "       [-0.59212996,  1.02183166,  1.04072557],\n",
              "       [ 0.94382984, -0.54802145,  0.64207271],\n",
              "       [ 1.13582482, -1.17596269, -0.28811729],\n",
              "       [ 0.36784492,  1.96374352, -0.55388587],\n",
              "       [-0.40013498,  1.02183166, -0.55388587],\n",
              "       [-1.45610735,  1.02183166,  0.90784129],\n",
              "       [-2.12808976,  0.70786103, -0.02234872],\n",
              "       [ 1.61581226, -1.48993331, -0.02234872],\n",
              "       [-0.30413749,  1.02183166, -0.42100158],\n",
              "       [ 0.55983989,  0.07991979, -0.55388587],\n",
              "       [-0.30413749,  0.07991979, -0.42100158],\n",
              "       [ 1.23182231, -1.48993331, -0.55388587],\n",
              "       [ 0.07985246,  0.70786103,  0.11053557],\n",
              "       [-1.16811488, -1.17596269, -0.55388587],\n",
              "       [ 1.61581226,  1.02183166,  1.30649415],\n",
              "       [ 1.3278198 ,  0.07991979, -0.42100158],\n",
              "       [-0.78412493,  0.70786103,  0.24341985],\n",
              "       [-2.22408725,  0.39389041, -0.42100158],\n",
              "       [ 0.46384241,  1.33580228, -0.55388587],\n",
              "       [ 0.94382984, -0.23405083, -0.55388587],\n",
              "       [ 0.46384241, -1.48993331, -0.15523301],\n",
              "       [-0.11214252, -0.23405083, -0.42100158],\n",
              "       [-0.30413749, -1.17596269, -0.55388587]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdmilRo4gnLs",
        "colab_type": "code",
        "outputId": "40756468-792b-4d50-da6c-493f22558ed0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from keras.wrappers.scikit_learn import KerasClassifier"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_druXtAwaxoZ",
        "colab_type": "code",
        "outputId": "c713b5ae-4777-467f-ebc5-3a1b2bdd3f76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        }
      },
      "source": [
        "#def create_model(optimizer='adam'):\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Input(shape=(3,)),\n",
        "  tf.keras.layers.Dropout(0.8),\n",
        "  tf.keras.layers.Dense(256, activation='relu'),\n",
        "  tf.keras.layers.Dropout(0.5),\n",
        "  tf.keras.layers.Dense(1,activation='sigmoid')\n",
        "])\n",
        "\n",
        "#optimizer = SGD(lr=0.1)\n",
        "model.compile(optimizer='adam',\n",
        "      loss='binary_crossentropy',\n",
        "      metrics=['accuracy'])\n",
        "  #return model\n",
        "  \n",
        "r = model.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs=20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "5/5 [==============================] - 0s 22ms/step - loss: 0.7230 - accuracy: 0.4610 - val_loss: 0.7120 - val_accuracy: 0.4848\n",
            "Epoch 2/20\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.6805 - accuracy: 0.6429 - val_loss: 0.6962 - val_accuracy: 0.6667\n",
            "Epoch 3/20\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.6560 - accuracy: 0.7208 - val_loss: 0.6871 - val_accuracy: 0.7121\n",
            "Epoch 4/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.6385 - accuracy: 0.6818 - val_loss: 0.6800 - val_accuracy: 0.7273\n",
            "Epoch 5/20\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.6575 - accuracy: 0.7013 - val_loss: 0.6740 - val_accuracy: 0.7424\n",
            "Epoch 6/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.6379 - accuracy: 0.7078 - val_loss: 0.6697 - val_accuracy: 0.7424\n",
            "Epoch 7/20\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.6247 - accuracy: 0.7338 - val_loss: 0.6667 - val_accuracy: 0.7424\n",
            "Epoch 8/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.6107 - accuracy: 0.7208 - val_loss: 0.6640 - val_accuracy: 0.7424\n",
            "Epoch 9/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.6380 - accuracy: 0.7078 - val_loss: 0.6611 - val_accuracy: 0.7424\n",
            "Epoch 10/20\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.6483 - accuracy: 0.7078 - val_loss: 0.6593 - val_accuracy: 0.7273\n",
            "Epoch 11/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.5801 - accuracy: 0.7403 - val_loss: 0.6577 - val_accuracy: 0.7121\n",
            "Epoch 12/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.6002 - accuracy: 0.7338 - val_loss: 0.6564 - val_accuracy: 0.7121\n",
            "Epoch 13/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.5838 - accuracy: 0.7403 - val_loss: 0.6553 - val_accuracy: 0.7273\n",
            "Epoch 14/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.5827 - accuracy: 0.7273 - val_loss: 0.6538 - val_accuracy: 0.7273\n",
            "Epoch 15/20\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.6066 - accuracy: 0.7143 - val_loss: 0.6516 - val_accuracy: 0.7273\n",
            "Epoch 16/20\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.6164 - accuracy: 0.7273 - val_loss: 0.6512 - val_accuracy: 0.7273\n",
            "Epoch 17/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.5773 - accuracy: 0.7273 - val_loss: 0.6516 - val_accuracy: 0.7273\n",
            "Epoch 18/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.5706 - accuracy: 0.7208 - val_loss: 0.6529 - val_accuracy: 0.7273\n",
            "Epoch 19/20\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.5877 - accuracy: 0.7338 - val_loss: 0.6506 - val_accuracy: 0.7273\n",
            "Epoch 20/20\n",
            "5/5 [==============================] - 0s 8ms/step - loss: 0.5638 - accuracy: 0.7143 - val_loss: 0.6499 - val_accuracy: 0.7273\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6mXiMiWjFR-",
        "colab_type": "code",
        "outputId": "2ddf2fa8-1472-45d8-a4ae-83fe84112944",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(\"Train score:\", model.evaluate(X_train, Y_train))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 0s 2ms/step - loss: 4.2522 - accuracy: 0.7208\n",
            "Train score: [4.252191543579102, 0.7207792401313782]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9nZ_KxP0kQcf",
        "colab_type": "code",
        "outputId": "cf9ab93b-5fde-4a7a-f0fd-cec84162f5a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(r.history['loss'], label='loss')\n",
        "plt.plot(r.history['val_loss'], label='val_loss')\n",
        "plt.legend()\n",
        "\n",
        "# Plot the accuracy too\n",
        "plt.plot(r.history['accuracy'], label='acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val_acc')\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f06dd4ea908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 229
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBahYrJSklKC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "P = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4NRghRIkpre",
        "colab_type": "code",
        "outputId": "53480734-262e-4af8-b81b-2dffd48f6678",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "P = np.round(P).flatten()\n",
        "print(P)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
            " 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RaLEP3pkqYb0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "P = P.tolist()\n",
        "Q = []\n",
        "for i in P:\n",
        "  Q.append(math.trunc(i))\n",
        "#Q"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kos6aigL1i7a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "id = df2['Id'].to_list()\n",
        "#id"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iS4sUInV1uG5",
        "colab_type": "code",
        "outputId": "8c7c5555-c534-466c-97c2-906f9f55969b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "Y_pred = pd.DataFrame(list(zip(id, Q)), \n",
        "               columns =['Id', 'Category']) \n",
        "Y_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>8</td>\n",
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              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>293</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>296</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>301</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>303</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>305</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>86 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Id  Category\n",
              "0     1         1\n",
              "1     7         1\n",
              "2     8         1\n",
              "3     9         1\n",
              "4    11         1\n",
              "..  ...       ...\n",
              "81  293         1\n",
              "82  296         1\n",
              "83  301         1\n",
              "84  303         1\n",
              "85  305         1\n",
              "\n",
              "[86 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ETTy2vN_84r8",
        "colab_type": "code",
        "outputId": "e61395dd-99fb-40f3-a837-fb7b91f13a6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "source": [
        "Y_pred.set_index(['Id'], inplace=True)\n",
        "Y_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Id</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>303</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>86 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Category\n",
              "Id           \n",
              "1           1\n",
              "7           1\n",
              "8           1\n",
              "9           1\n",
              "11          1\n",
              "..        ...\n",
              "293         1\n",
              "296         1\n",
              "301         1\n",
              "303         1\n",
              "305         1\n",
              "\n",
              "[86 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CS7F9i1N41N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_pred['Category'] = Y_pred['Category'].replace(to_replace=0,value=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmP9omKe2eEO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_pred.to_csv('pred.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}