Digit_Recognizer.ipynb

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  "metadata": {
    "colab": {
      "name": "Digit Recognizer.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/surajdurgesht/Digit-Recognizer/blob/master/Digit_Recognizer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uBtp9NoJfgD_",
        "colab_type": "code",
        "outputId": "f1c40ad9-0cfb-45f5-8a94-12cfd94f1789",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "\n",
        "!ls \"/content/drive/My Drive/Colab Notebooks\""
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n",
            "'Digit Recognizer'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nnztsaMfosY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a2967e90-c2d4-44e6-8d75-8650f965256e"
      },
      "source": [
        "pwd"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/My Drive'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2KlcSoHtgLBx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip -q \"/content/drive/My Drive/Colab Notebooks/Digit Recognizer/digit-recognizer.zip\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kho05mXRlYZl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import keras\n",
        "from keras.models import Model\n",
        "from keras.layers import *\n",
        "from keras import optimizers\n",
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxT3HZ2Ulon2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Digit Recognizer/train.csv')\n",
        "df_test = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Digit Recognizer/test.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vj3F5_ZVmdK-",
        "colab_type": "code",
        "outputId": "3a21b9e4-9f0c-4507-fc5d-7ed1275512da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(df_train.shape)\n",
        "#42000 rows and 785 columns\n",
        "print(df_test.shape)\n",
        "#28000 rows in test"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(42000, 785)\n",
            "(28000, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_iQITYrmoQO",
        "colab_type": "code",
        "outputId": "92b5c1db-4641-4212-9c85-f7f0150ce000",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "df_train.head()\n",
        "#784 features and 1 label "
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  pixel0  pixel1  pixel2  ...  pixel780  pixel781  pixel782  pixel783\n",
              "0      1       0       0       0  ...         0         0         0         0\n",
              "1      0       0       0       0  ...         0         0         0         0\n",
              "2      1       0       0       0  ...         0         0         0         0\n",
              "3      4       0       0       0  ...         0         0         0         0\n",
              "4      0       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Auh9jSFamtwK",
        "colab_type": "code",
        "outputId": "03cada01-702e-4161-c420-31d6d87d4b96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "#iloc The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position\n",
        "#iloc basically used for dataframe slicing\n",
        "df_features = df_train.iloc[:, 1:785]\n",
        "print(df_features.shape)\n",
        "\n",
        "#extracting al lables tha contains our actual data\n",
        "df_label = df_train.iloc[:, 0]\n",
        "print(df_label.shape)\n",
        "\n",
        "X_test = df_test.iloc[:, 0:784]\n",
        "\n",
        "print(X_test.shape)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(42000, 784)\n",
            "(42000,)\n",
            "(28000, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FpLkfBNm--u",
        "colab_type": "code",
        "outputId": "480a95de-1cb6-4ad1-d7c6-56ccd3250f29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "#splitting data into train and test\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_val,y_train,y_val=train_test_split(df_features,df_label,\n",
        "                                          test_size=0.2,\n",
        "                                          random_state=1212)\n",
        "\n",
        "X_train = X_train.as_matrix().reshape(33600, 784) #(33600, 784)\n",
        "X_val = X_val.as_matrix().reshape(8400, 784) #(8400, 784)\n",
        "\n",
        "X_test = X_test.as_matrix().reshape(28000, 784)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  import sys\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
            "  if __name__ == '__main__':\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1iytUO3gnHsS",
        "colab_type": "code",
        "outputId": "7d961123-605b-48b7-eb1f-3690068336cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train=tf.keras.utils.normalize(X_train,axis=1)\n",
        "X_test=tf.keras.utils.normalize(X_test,axis=1)\n",
        "print((min(X_train[1]), max(X_train[1])))\n",
        "#normalizing the values same as above code"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0.0, 0.09835373608323213)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQKMqjw0nVvc",
        "colab_type": "code",
        "outputId": "99a79991-c25a-4f6b-928c-410738fcfba1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#convert labels to one hot encoders\n",
        "num_digits = 10\n",
        "#Converts a class vector (integers) to binary class matrix.\n",
        "y_train = keras.utils.to_categorical(y_train, num_digits)\n",
        "y_val = keras.utils.to_categorical(y_val, num_digits)\n",
        "\n",
        "print(y_train[2])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QOat3me8nZEO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Model fitting \n",
        "#building a neural network of 5 layers\n",
        "model=tf.keras.models.Sequential()\n",
        "#creating input layer\n",
        "#model.add(tf.keras.layers.Flatten())\n",
        "\n",
        "#hidden layers creation\n",
        "model.add(tf.keras.layers.Dense(300,activation=tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dense(200,activation=tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dense(200,activation=tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dense(200,activation=tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dense(100,activation=tf.nn.relu))\n",
        "\n",
        "#output layer\n",
        "model.add(tf.keras.layers.Dense(10,activation=tf.nn.softmax))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jr3MVPJIndBL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training_epochs = 30\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHJ9KbTpoAgT",
        "colab_type": "code",
        "outputId": "db03f053-0a4f-40e3-a122-e7600b1d2f8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "training_model = model.fit(X_train, y_train,\n",
        "                     epochs = training_epochs,\n",
        "                     verbose = 2,\n",
        "                     validation_data=(X_val, y_val))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 33600 samples, validate on 8400 samples\n",
            "Epoch 1/30\n",
            "33600/33600 - 8s - loss: 0.3337 - acc: 0.8923 - val_loss: 235.0272 - val_acc: 0.9524\n",
            "Epoch 2/30\n",
            "33600/33600 - 8s - loss: 0.1324 - acc: 0.9600 - val_loss: 191.5331 - val_acc: 0.9607\n",
            "Epoch 3/30\n",
            "33600/33600 - 9s - loss: 0.0928 - acc: 0.9716 - val_loss: 214.2098 - val_acc: 0.9607\n",
            "Epoch 4/30\n",
            "33600/33600 - 9s - loss: 0.0726 - acc: 0.9785 - val_loss: 269.9282 - val_acc: 0.9568\n",
            "Epoch 5/30\n",
            "33600/33600 - 9s - loss: 0.0611 - acc: 0.9811 - val_loss: 185.6606 - val_acc: 0.9656\n",
            "Epoch 6/30\n",
            "33600/33600 - 8s - loss: 0.0490 - acc: 0.9847 - val_loss: 244.9302 - val_acc: 0.9600\n",
            "Epoch 7/30\n",
            "33600/33600 - 8s - loss: 0.0387 - acc: 0.9884 - val_loss: 215.6793 - val_acc: 0.9643\n",
            "Epoch 8/30\n",
            "33600/33600 - 9s - loss: 0.0359 - acc: 0.9892 - val_loss: 222.5214 - val_acc: 0.9702\n",
            "Epoch 9/30\n",
            "33600/33600 - 8s - loss: 0.0336 - acc: 0.9893 - val_loss: 300.3965 - val_acc: 0.9606\n",
            "Epoch 10/30\n",
            "33600/33600 - 8s - loss: 0.0276 - acc: 0.9917 - val_loss: 245.0293 - val_acc: 0.9642\n",
            "Epoch 11/30\n",
            "33600/33600 - 9s - loss: 0.0241 - acc: 0.9930 - val_loss: 470.5289 - val_acc: 0.9483\n",
            "Epoch 12/30\n",
            "33600/33600 - 9s - loss: 0.0245 - acc: 0.9931 - val_loss: 272.4960 - val_acc: 0.9655\n",
            "Epoch 13/30\n",
            "33600/33600 - 8s - loss: 0.0223 - acc: 0.9933 - val_loss: 292.0166 - val_acc: 0.9681\n",
            "Epoch 14/30\n",
            "33600/33600 - 8s - loss: 0.0180 - acc: 0.9946 - val_loss: 365.3703 - val_acc: 0.9630\n",
            "Epoch 15/30\n",
            "33600/33600 - 8s - loss: 0.0187 - acc: 0.9946 - val_loss: 302.1232 - val_acc: 0.9698\n",
            "Epoch 16/30\n",
            "33600/33600 - 8s - loss: 0.0170 - acc: 0.9949 - val_loss: 238.6424 - val_acc: 0.9665\n",
            "Epoch 17/30\n",
            "33600/33600 - 8s - loss: 0.0155 - acc: 0.9954 - val_loss: 308.6780 - val_acc: 0.9670\n",
            "Epoch 18/30\n",
            "33600/33600 - 9s - loss: 0.0140 - acc: 0.9955 - val_loss: 367.2165 - val_acc: 0.9656\n",
            "Epoch 19/30\n",
            "33600/33600 - 8s - loss: 0.0185 - acc: 0.9951 - val_loss: 379.4439 - val_acc: 0.9599\n",
            "Epoch 20/30\n",
            "33600/33600 - 8s - loss: 0.0156 - acc: 0.9958 - val_loss: 466.6485 - val_acc: 0.9606\n",
            "Epoch 21/30\n",
            "33600/33600 - 8s - loss: 0.0132 - acc: 0.9967 - val_loss: 334.5121 - val_acc: 0.9649\n",
            "Epoch 22/30\n",
            "33600/33600 - 8s - loss: 0.0155 - acc: 0.9959 - val_loss: 339.3885 - val_acc: 0.9630\n",
            "Epoch 23/30\n",
            "33600/33600 - 8s - loss: 0.0141 - acc: 0.9961 - val_loss: 301.5667 - val_acc: 0.9725\n",
            "Epoch 24/30\n",
            "33600/33600 - 8s - loss: 0.0107 - acc: 0.9971 - val_loss: 390.4891 - val_acc: 0.9695\n",
            "Epoch 25/30\n",
            "33600/33600 - 8s - loss: 0.0107 - acc: 0.9973 - val_loss: 371.7385 - val_acc: 0.9713\n",
            "Epoch 26/30\n",
            "33600/33600 - 8s - loss: 0.0115 - acc: 0.9965 - val_loss: 410.2335 - val_acc: 0.9683\n",
            "Epoch 27/30\n",
            "33600/33600 - 8s - loss: 0.0101 - acc: 0.9972 - val_loss: 368.5121 - val_acc: 0.9743\n",
            "Epoch 28/30\n",
            "33600/33600 - 9s - loss: 0.0110 - acc: 0.9976 - val_loss: 515.3728 - val_acc: 0.9631\n",
            "Epoch 29/30\n",
            "33600/33600 - 9s - loss: 0.0110 - acc: 0.9971 - val_loss: 524.0083 - val_acc: 0.9681\n",
            "Epoch 30/30\n",
            "33600/33600 - 8s - loss: 0.0120 - acc: 0.9968 - val_loss: 434.8362 - val_acc: 0.9733\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BKP4MLZQoFB0",
        "colab_type": "code",
        "outputId": "cb3d736a-b240-44d7-954f-0e8898db9298",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "source": [
        "predictions=model.predict(X_test)\n",
        "print(predictions)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[9.7391606e-20 2.6777075e-18 1.0000000e+00 ... 3.5780210e-17\n",
            "  4.1509908e-16 1.7786541e-27]\n",
            " [1.0000000e+00 1.0086368e-20 1.4841392e-18 ... 3.3071565e-19\n",
            "  5.3196793e-16 2.9596746e-15]\n",
            " [3.8565801e-08 5.0586652e-07 2.5970396e-08 ... 5.9132032e-07\n",
            "  8.8741370e-05 9.9985504e-01]\n",
            " ...\n",
            " [2.1444447e-26 1.7417746e-16 4.3916853e-16 ... 2.3983638e-22\n",
            "  2.3667530e-15 4.9644154e-14]\n",
            " [5.2002221e-08 5.2098322e-08 9.6215680e-10 ... 9.0260244e-08\n",
            "  4.2580559e-05 9.9995506e-01]\n",
            " [2.0523830e-14 2.5344237e-13 9.9999928e-01 ... 3.6161997e-13\n",
            "  3.6388004e-11 1.6790367e-19]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbZeYOHfpGg4",
        "colab_type": "code",
        "outputId": "8fe2fbcd-8f7c-4ff1-f34c-471a6ed1792f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(np.argmax(predictions[11]))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXGz98D5pMig",
        "colab_type": "code",
        "outputId": "5df8d3dd-331d-4538-ea67-80f11e93dbbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        }
      },
      "source": [
        "#pd.dataframe converts output to a dataframe\n",
        "pred = pd.DataFrame(model.predict(X_test))\n",
        "pred = pd.DataFrame(pred.idxmax(axis = 1))\n",
        "pred.index.name = 'ImageId'\n",
        "pred = pred.rename(columns = {0: 'Label'}).reset_index()\n",
        "pred['ImageId'] = pred['ImageId'] + 1\n",
        "\n",
        "pred.head(20)"
      ],
      "execution_count": 42,
      "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>ImageId</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    ImageId  Label\n",
              "0         1      2\n",
              "1         2      0\n",
              "2         3      9\n",
              "3         4      0\n",
              "4         5      3\n",
              "5         6      7\n",
              "6         7      0\n",
              "7         8      3\n",
              "8         9      0\n",
              "9        10      3\n",
              "10       11      5\n",
              "11       12      7\n",
              "12       13      4\n",
              "13       14      0\n",
              "14       15      4\n",
              "15       16      3\n",
              "16       17      3\n",
              "17       18      1\n",
              "18       19      9\n",
              "19       20      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZKoBz_6pRCp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred.to_csv('/content/drive/My Drive/Colab Notebooks/Digit Recognizer/final_submission.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}