Knn_on_mnist.ipynb

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    "colab": {
      "name": "Knn on mnist_readytoshare.ipynb",
      "provenance": [],
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      "cell_type": "markdown",
      "source": [
        "**Importing libraries**"
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      }
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lXD1wxdNNPiQ"
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      "source": [
        "import numpy as np\n",
        "from sklearn.datasets import *\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, balanced_accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fuction in order to display images\n",
        "def displayImage(i):\n",
        "    plt.imshow(digit['images'][i], cmap='Greys_r')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "7cTHzd3mN12W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading data**"
      ],
      "metadata": {
        "id": "kuFhKWe9jNB0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "digit = load_digits()\n",
        "dig = pd.DataFrame(digit['data'])\n",
        "dig.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "XKGxaEbiN_s7",
        "outputId": "76d0a582-55e5-4c5b-d227-baad3c2d7e0c"
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      "execution_count": null,
      "outputs": [
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-531e1f62-47f3-4dda-a996-0b8cfba661cb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-531e1f62-47f3-4dda-a996-0b8cfba661cb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    0    1    2     3     4     5    6   ...   57   58    59    60    61   62   63\n",
              "0  0.0  0.0  5.0  13.0   9.0   1.0  0.0  ...  0.0  6.0  13.0  10.0   0.0  0.0  0.0\n",
              "1  0.0  0.0  0.0  12.0  13.0   5.0  0.0  ...  0.0  0.0  11.0  16.0  10.0  0.0  0.0\n",
              "2  0.0  0.0  0.0   4.0  15.0  12.0  0.0  ...  0.0  0.0   3.0  11.0  16.0  9.0  0.0\n",
              "3  0.0  0.0  7.0  15.0  13.0   1.0  0.0  ...  0.0  7.0  13.0  13.0   9.0  0.0  0.0\n",
              "4  0.0  0.0  0.0   1.0  11.0   0.0  0.0  ...  0.0  0.0   2.0  16.0   4.0  0.0  0.0\n",
              "\n",
              "[5 rows x 64 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "displayImage(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "QuLsTfxWOmue",
        "outputId": "b3e1816d-6264-4037-cbb0-a8511a9d202e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPUAAAD4CAYAAAA0L6C7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKsklEQVR4nO3d34tc9RnH8c+nq5JarZLWFsmGJhcakEKNxIBsEbrBEqtoLyokoNBQyEVQlK1IrFf9Awz2IghLNBVMlTQqBLGmShQrtjY/TBuTTWoaLNlFuwnFn2BD9OnFnkCUtXtm5vzax/cLgruzQ77PoG/PzOyZ83VECEAeX2t7AADVImogGaIGkiFqIBmiBpI5r46/1DZvqVdgaGiosbWuvPLKxtaamJhobK3MIsKz3e46fqVF1NVYuHBhY2vt2rWrsbWuvfbaxtbK7Mui5uk3kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZBMqahtr7Z91PYx2xvrHgpA/+aM2vaQpM2SbpR0laS1tq+qezAA/SlzpF4p6VhEHI+I05KelHRrvWMB6FeZqBdJOnHO95PFbZ9je73tvbb3VjUcgN5V9tHLiBiXNC7xKS2gTWWO1FOSFp/z/XBxG4AOKhP1HklX2F5q+wJJayTtrHcsAP2a8+l3RJyxfaekXZKGJD0aEYdqnwxAX0q9po6I5yQ9V/MsACrAGWVAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMrVsu4NqjI2NNbbWnj17GlsL9eJIDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMmV26HjU9rTtN5sYCMBgyhypfytpdc1zAKjInFFHxCuS/tPALAAqUNmntGyvl7S+qr8PQH/YdgdIhne/gWSIGkimzK+0npD0Z0nLbE/a/kX9YwHoV5m9tNY2MQiAavD0G0iGqIFkiBpIhqiBZIgaSIaogWSIGkjGEdWfpp313O+FCxc2ut7BgwcbW2vTpk2NrbVz587G1mraW2+91dhaEeHZbudIDSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMmWuUbbY9ku2D9s+ZPvuJgYD0J8y1/0+I+mXEbHf9sWS9tl+ISIO1zwbgD6U2XbnnYjYX3z9oaQJSYvqHgxAf3raocP2EknLJb0+y8/YdgfogNJR275I0lOS7omID774c7bdAbqh1Lvfts/XTNDbIuLpekcCMIgy735b0iOSJiKiuU/SA+hLmSP1iKQ7JI3aPlD8+UnNcwHoU5ltd16VNOtlUwB0D2eUAckQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZBMT5/S+qobGxtrdL0m9+568MEHG1tr+/btja116tSpxtaSpA0bNjS63mw4UgPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJEDWQDFEDyZS58OAC23+1/bdi251fNzEYgP6UOU30v5JGI+Kj4lLBr9r+Q0T8pebZAPShzIUHQ9JHxbfnF3+4WD/QUWUv5j9k+4CkaUkvRMSs2+7Y3mt7b9VDAiivVNQR8WlEXC1pWNJK29+f5T7jEbEiIlZUPSSA8np69zsi3pP0kqTV9YwDYFBl3v2+zPalxddfl3SDpCN1DwagP2Xe/b5c0mO2hzTzP4HtEfFsvWMB6FeZd7//rpk9qQHMA5xRBiRD1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAy837bnXXr1jW21gMPPNDYWpK0e/fuRtdrym233dbYWvfee29ja3UFR2ogGaIGkiFqIBmiBpIhaiAZogaSIWogGaIGkiFqIBmiBpIpHXVxQf83bHPRQaDDejlS3y1poq5BAFSj7LY7w5JukrSl3nEADKrskfohSfdJ+uzL7sBeWkA3lNmh42ZJ0xGx7//dj720gG4oc6QekXSL7bclPSlp1PbjtU4FoG9zRh0R90fEcEQskbRG0u6IuL32yQD0hd9TA8n0dDmjiHhZ0su1TAKgEhypgWSIGkiGqIFkiBpIhqiBZIgaSIaogWTm/bY777//fmNrffLJJ42tJUmjo6ONrTU1NdXYWk3aunVr2yM0jiM1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJlDpNtLiS6IeSPpV0hssAA93Vy7nfP4qIU7VNAqASPP0GkikbdUj6o+19ttfPdge23QG6oezT7x9GxJTt70h6wfaRiHjl3DtExLikcUmyHRXPCaCkUkfqiJgq/jkt6RlJK+scCkD/ymyQ9w3bF5/9WtKPJb1Z92AA+lPm6fd3JT1j++z9fxcRz9c6FYC+zRl1RByX9IMGZgFQAX6lBSRD1EAyRA0kQ9RAMkQNJEPUQDJEDSTjiOpP0+bc72qMjIw0ttaLL77Y2FqvvfZaY2utWrWqsbWaFhGe7XaO1EAyRA0kQ9RAMkQNJEPUQDJEDSRD1EAyRA0kQ9RAMkQNJFMqatuX2t5h+4jtCdvX1T0YgP6Uve73byQ9HxE/s32BpAtrnAnAAOaM2vYlkq6X9HNJiojTkk7XOxaAfpV5+r1U0klJW22/YXtLcf3vz2HbHaAbykR9nqRrJD0cEcslfSxp4xfvFBHjEbGCbW6BdpWJelLSZES8Xny/QzORA+igOaOOiHclnbC9rLhplaTDtU4FoG9l3/2+S9K24p3v45LW1TcSgEGUijoiDkjitTIwD3BGGZAMUQPJEDWQDFEDyRA1kAxRA8kQNZAMUQPJlD2jDC2Ynp5ubK0FCxY0ttbmzZsbW+uriCM1kAxRA8kQNZAMUQPJEDWQDFEDyRA1kAxRA8kQNZDMnFHbXmb7wDl/PrB9TxPDAejdnKeJRsRRSVdLku0hSVOSnql5LgB96vXp9ypJ/4yIf9UxDIDB9fqBjjWSnpjtB7bXS1o/8EQABlL6SF1c8/sWSb+f7edsuwN0Qy9Pv2+UtD8i/l3XMAAG10vUa/UlT70BdEepqIuta2+Q9HS94wAYVNltdz6W9K2aZwFQAc4oA5IhaiAZogaSIWogGaIGkiFqIBmiBpIhaiAZR0T1f6l9UlKvH8/8tqRTlQ/TDVkfG4+rPd+LiMtm+0EtUffD9t6sn/DK+th4XN3E028gGaIGkulS1ONtD1CjrI+Nx9VBnXlNDaAaXTpSA6gAUQPJdCJq26ttH7V9zPbGtuepgu3Ftl+yfdj2Idt3tz1TlWwP2X7D9rNtz1Il25fa3mH7iO0J29e1PVOvWn9NXWwQ8A/NXC5pUtIeSWsj4nCrgw3I9uWSLo+I/bYvlrRP0k/n++M6y/aYpBWSvhkRN7c9T1VsPybpTxGxpbiC7oUR8V7bc/WiC0fqlZKORcTxiDgt6UlJt7Y808Ai4p2I2F98/aGkCUmL2p2qGraHJd0kaUvbs1TJ9iWSrpf0iCRFxOn5FrTUjagXSTpxzveTSvIf/1m2l0haLun1diepzEOS7pP0WduDVGyppJOSthYvLbYUF92cV7oQdWq2L5L0lKR7IuKDtucZlO2bJU1HxL62Z6nBeZKukfRwRCyX9LGkefceTxeinpK0+Jzvh4vb5j3b52sm6G0RkeXyyiOSbrH9tmZeKo3afrzdkSozKWkyIs4+o9qhmcjnlS5EvUfSFbaXFm9MrJG0s+WZBmbbmnltNhERm9qepyoRcX9EDEfEEs38u9odEbe3PFYlIuJdSSdsLytuWiVp3r2x2esGeZWLiDO275S0S9KQpEcj4lDLY1VhRNIdkg7aPlDc9quIeK7FmTC3uyRtKw4wxyWta3menrX+Ky0A1erC028AFSJqIBmiBpIhaiAZogaSIWogGaIGkvkfxYKI9dwAr9wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting dataset to train and test**"
      ],
      "metadata": {
        "id": "QzlDw9EslOf_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_x, test_x, train_y, test_y = train_test_split(digit['data'], digit['target'], test_size=0.3, random_state=25)\n",
        "len(train_x), len(train_y), len(test_x), len(test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDAMy3nSjoxW",
        "outputId": "89fd6ba5-ce38-4ea2-be7f-f8199bffc999"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1257, 1257, 540, 540)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Modelling using KNN(K Nearest Neighbour)**"
      ],
      "metadata": {
        "id": "hhCQEwSdlcaR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "KNN = KNeighborsClassifier(10)\n",
        "KNN.fit(train_x, train_y)\n",
        "prd=KNN.predict(test_x)\n",
        "prd[:3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFPkQNvuPYzh",
        "outputId": "70999faa-ae07-4aff-9ac0-6fbc7822fa9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 0, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluation of model**"
      ],
      "metadata": {
        "id": "r9oXrNn3nrhL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy_score(test_y, prd))\n",
        "print('----------------------------')\n",
        "print(balanced_accuracy_score(test_y, prd)) # to see accuracy score in balanced or alltogether form\n",
        "('----------------------------')\n",
        "print(classification_report(test_y, prd))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xguaa08wluow",
        "outputId": "df8903c0-a7e2-4e3b-d69e-f60e9ee060d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9851851851851852\n",
            "----------------------------\n",
            "0.9846871504213588\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        56\n",
            "           1       0.93      1.00      0.96        55\n",
            "           2       0.98      0.98      0.98        46\n",
            "           3       0.98      0.98      0.98        55\n",
            "           4       0.98      1.00      0.99        61\n",
            "           5       1.00      1.00      1.00        57\n",
            "           6       1.00      0.98      0.99        53\n",
            "           7       1.00      1.00      1.00        51\n",
            "           8       0.98      0.96      0.97        53\n",
            "           9       1.00      0.94      0.97        53\n",
            "\n",
            "    accuracy                           0.99       540\n",
            "   macro avg       0.99      0.98      0.98       540\n",
            "weighted avg       0.99      0.99      0.99       540\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking model manually with samples**"
      ],
      "metadata": {
        "id": "FurYyem1oOLc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test = np.array(digit['data'][16])\n",
        "test1 = test.reshape(1,-1)\n",
        "KNN.predict(test1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nk6YTQcHPdoR",
        "outputId": "8e9ef07a-7f44-4a83-ad52-ab12d7b303e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "displayImage(16)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ekyFEMOoQIB-",
        "outputId": "88d83248-d089-4d6b-f798-6491b3ffeb60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}