diff --git a/HandwrittenDigitRecognition_SVM.ipynb b/HandwrittenDigitRecognition_SVM.ipynb
new file mode 100644
index 0000000..aebd2e6
--- /dev/null
+++ b/HandwrittenDigitRecognition_SVM.ipynb
@@ -0,0 +1,1397 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Orrm23/DeepSeek-Coder/blob/main/HandwrittenDigitRecognition_SVM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l-FmNW1v-wEx"
+      },
+      "source": [
+        "#Handwritten Digit Recognition | SVM**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aPZ-cSea-8Pz"
+      },
+      "source": [
+        "### *Importing Basic Libraries*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KgOTIpQe-Sij"
+      },
+      "source": [
+        "import numpy as np\n",
+        "from sklearn.datasets import load_digits"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tAEv1LgB_OxV"
+      },
+      "source": [
+        "### *Load Dataset*\n",
+        "![image.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mEkoGFM3_Rl6"
+      },
+      "source": [
+        "dataset = load_digits()"
+      ],
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fIw9RCUrACG9"
+      },
+      "source": [
+        "### *Summarize Dataset*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OKcoy2EJAFj2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6ca48cd3-8140-4383-f915-8586948f2b80"
+      },
+      "source": [
+        "print(dataset.data)\n",
+        "print(dataset.target)\n",
+        "\n",
+        "print(dataset.data.shape)\n",
+        "print(dataset.images.shape)\n",
+        "\n",
+        "dataimageLength = len(dataset.images)\n",
+        "print(dataimageLength)"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[ 0.  0.  5. ...  0.  0.  0.]\n",
+            " [ 0.  0.  0. ... 10.  0.  0.]\n",
+            " [ 0.  0.  0. ... 16.  9.  0.]\n",
+            " ...\n",
+            " [ 0.  0.  1. ...  6.  0.  0.]\n",
+            " [ 0.  0.  2. ... 12.  0.  0.]\n",
+            " [ 0.  0. 10. ... 12.  1.  0.]]\n",
+            "[0 1 2 ... 8 9 8]\n",
+            "(1797, 64)\n",
+            "(1797, 8, 8)\n",
+            "1797\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "epmSBzRtCgdh"
+      },
+      "source": [
+        "### *Visualize the Dataset*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sGYJfmAvCj3a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 593
+        },
+        "outputId": "992865b4-a1ad-4ebd-91c9-1918361c48b6"
+      },
+      "source": [
+        "n=7 #No. of Sample out of Samples total 1797\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.gray()\n",
+        "plt.matshow(dataset.images[n])\n",
+        "plt.show()\n",
+        "\n",
+        "dataset.images[n]"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 480x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.,  0.,  7.,  8., 13., 16., 15.,  1.],\n",
+              "       [ 0.,  0.,  7.,  7.,  4., 11., 12.,  0.],\n",
+              "       [ 0.,  0.,  0.,  0.,  8., 13.,  1.,  0.],\n",
+              "       [ 0.,  4.,  8.,  8., 15., 15.,  6.,  0.],\n",
+              "       [ 0.,  2., 11., 15., 15.,  4.,  0.,  0.],\n",
+              "       [ 0.,  0.,  0., 16.,  5.,  0.,  0.,  0.],\n",
+              "       [ 0.,  0.,  9., 15.,  1.,  0.,  0.,  0.],\n",
+              "       [ 0.,  0., 13.,  5.,  0.,  0.,  0.,  0.]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "woyBgG8hGXI-"
+      },
+      "source": [
+        "### *Segregate Dataset into X(Input/IndependentVariable) & Y(Output/DependentVariable)*\n",
+        "\n",
+        "### *Input - Pixel | Output - Class*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OYBk3bVmGbxW",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7c8e67fc-c7d2-46b7-faea-74357fe4eba2"
+      },
+      "source": [
+        "X = dataset.images.reshape((dataimageLength,-1))\n",
+        "X"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],\n",
+              "       [ 0.,  0.,  0., ..., 10.,  0.,  0.],\n",
+              "       [ 0.,  0.,  0., ..., 16.,  9.,  0.],\n",
+              "       ...,\n",
+              "       [ 0.,  0.,  1., ...,  6.,  0.,  0.],\n",
+              "       [ 0.,  0.,  2., ..., 12.,  0.,  0.],\n",
+              "       [ 0.,  0., 10., ..., 12.,  1.,  0.]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Of_82fBDHC4R",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3f1ff0b9-0774-4f1d-c2b1-7815cd863753"
+      },
+      "source": [
+        "Y = dataset.target\n",
+        "Y"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 1, 2, ..., 8, 9, 8])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QbO0qzzeHM7d"
+      },
+      "source": [
+        "### *Splitting Dataset into Train & Test*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1760lRsBHNw2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6f87d4c7-8adf-4809-a053-dd8e776af375"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)\n",
+        "print(X_train.shape)\n",
+        "print(X_test.shape)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(1347, 64)\n",
+            "(450, 64)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e8DmcF1oHzTS"
+      },
+      "source": [
+        "### *Training*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uI79KiPYH3Ud",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "outputId": "9ab5c2bf-7039-4578-95ba-fc5e2f2edda3"
+      },
+      "source": [
+        "from sklearn import svm\n",
+        "model = svm.SVC(kernel='linear')\n",
+        "model.fit(X_train,y_train)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVC(kernel='linear')"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "umVph3jWO1_l"
+      },
+      "source": [
+        "### *Predicting, what the digit is from Test Data*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Cf1TTKbAO8i9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 535
+        },
+        "outputId": "cc0a3dca-d97c-4390-f1c0-6d1f52fa84c0"
+      },
+      "source": [
+        "n=13\n",
+        "result = model.predict(dataset.images[n].reshape((1,-1)))\n",
+        "plt.imshow(dataset.images[n], cmap=plt.cm.gray_r, interpolation='nearest')\n",
+        "print(result)\n",
+        "print(\"\\n\")\n",
+        "plt.axis('off')\n",
+        "plt.title('%i' %result)\n",
+        "plt.show()"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[3]\n",
+            "\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-11-e7c448382e0f>:7: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+            "  plt.title('%i' %result)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7LeUrxqkJP6w"
+      },
+      "source": [
+        "### *Prediction for Test Data*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2mcLDrhzJTwh",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c1e6ad1e-6f04-4441-deba-6d4e9c4cca64"
+      },
+      "source": [
+        "y_pred = model.predict(X_test)\n",
+        "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[2 2]\n",
+            " [8 8]\n",
+            " [2 2]\n",
+            " [6 6]\n",
+            " [6 6]\n",
+            " [7 7]\n",
+            " [1 1]\n",
+            " [9 9]\n",
+            " [8 8]\n",
+            " [5 5]\n",
+            " [2 2]\n",
+            " [8 8]\n",
+            " [6 6]\n",
+            " [6 6]\n",
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+            " [1 1]\n",
+            " [0 0]\n",
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+            " [4 4]\n",
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+            " [2 2]\n",
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+            " [4 4]\n",
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+            " [6 6]\n",
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+            " [4 4]\n",
+            " [3 3]\n",
+            " [1 1]\n",
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+            " [0 0]\n",
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+            " [6 6]\n",
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+            " [1 1]\n",
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+            " [6 6]\n",
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+            " [3 3]\n",
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+            " [9 9]\n",
+            " [8 8]\n",
+            " [3 3]\n",
+            " [6 6]\n",
+            " [2 2]\n",
+            " [1 1]\n",
+            " [6 6]\n",
+            " [2 2]\n",
+            " [1 1]\n",
+            " [7 7]\n",
+            " [5 5]\n",
+            " [5 5]\n",
+            " [1 1]\n",
+            " [9 9]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gfNRnb-MJi9P"
+      },
+      "source": [
+        "### *Evaluate Model - Accuracy Score*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "irsPBj9KJnl-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9fcd5e90-df75-4aee-d182-d3c938032b54"
+      },
+      "source": [
+        "from sklearn.metrics import accuracy_score\n",
+        "print(\"Accuracy of the Model: {0}%\".format(accuracy_score(y_test, y_pred)*100))"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy of the Model: 97.11111111111111%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C95vmesVLUrO"
+      },
+      "source": [
+        "### *Play with the Different Method*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BVv7Pux6LdpH",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cb5ae2d4-1583-4f09-8641-98028ef13223"
+      },
+      "source": [
+        "from sklearn import svm\n",
+        "model1 = svm.SVC(kernel='linear')\n",
+        "model2 = svm.SVC(kernel='rbf')\n",
+        "model3 = svm.SVC(gamma=0.001)\n",
+        "model4 = svm.SVC(gamma=0.001,C=0.1)\n",
+        "\n",
+        "model1.fit(X_train,y_train)\n",
+        "model2.fit(X_train,y_train)\n",
+        "model3.fit(X_train,y_train)\n",
+        "model4.fit(X_train,y_train)\n",
+        "\n",
+        "y_predModel1 = model1.predict(X_test)\n",
+        "y_predModel2 = model2.predict(X_test)\n",
+        "y_predModel3 = model3.predict(X_test)\n",
+        "y_predModel4 = model4.predict(X_test)\n",
+        "\n",
+        "print(\"Accuracy of the Model 1: {0}%\".format(accuracy_score(y_test, y_predModel1)*100))\n",
+        "print(\"Accuracy of the Model 2: {0}%\".format(accuracy_score(y_test, y_predModel2)*100))\n",
+        "print(\"Accuracy of the Model 3: {0}%\".format(accuracy_score(y_test, y_predModel3)*100))\n",
+        "print(\"Accuracy of the Model 4: {0}%\".format(accuracy_score(y_test, y_predModel4)*100))"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy of the Model 1: 97.11111111111111%\n",
+            "Accuracy of the Model 2: 99.11111111111111%\n",
+            "Accuracy of the Model 3: 99.55555555555556%\n",
+            "Accuracy of the Model 4: 96.66666666666667%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z22DqlQn_DaS"
+      },
+      "source": []
+    }
+  ]
+}
\ No newline at end of file