MNIST.ipynb

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   "source": [
    "# Mnist Classifier "
   ]
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  {
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   "source": [
    "%matplotlib inline\n",
    "from sklearn.datasets import fetch_openml\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# Fetching the MNIST dataset\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X = mnist['data'] \n",
    "y = mnist['target']"
   ]
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       "pandas.core.series.Series"
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69995</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69996</th>\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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69997</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69998</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69999</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70000 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  pixel9  \\\n",
       "0           0       0       0       0       0       0       0       0       0   \n",
       "1           0       0       0       0       0       0       0       0       0   \n",
       "2           0       0       0       0       0       0       0       0       0   \n",
       "3           0       0       0       0       0       0       0       0       0   \n",
       "4           0       0       0       0       0       0       0       0       0   \n",
       "...       ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "69995       0       0       0       0       0       0       0       0       0   \n",
       "69996       0       0       0       0       0       0       0       0       0   \n",
       "69997       0       0       0       0       0       0       0       0       0   \n",
       "69998       0       0       0       0       0       0       0       0       0   \n",
       "69999       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "       pixel10  ...  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0            0  ...         0         0         0         0         0   \n",
       "1            0  ...         0         0         0         0         0   \n",
       "2            0  ...         0         0         0         0         0   \n",
       "3            0  ...         0         0         0         0         0   \n",
       "4            0  ...         0         0         0         0         0   \n",
       "...        ...  ...       ...       ...       ...       ...       ...   \n",
       "69995        0  ...         0         0         0         0         0   \n",
       "69996        0  ...         0         0         0         0         0   \n",
       "69997        0  ...         0         0         0         0         0   \n",
       "69998        0  ...         0         0         0         0         0   \n",
       "69999        0  ...         0         0         0         0         0   \n",
       "\n",
       "       pixel780  pixel781  pixel782  pixel783  pixel784  \n",
       "0             0         0         0         0         0  \n",
       "1             0         0         0         0         0  \n",
       "2             0         0         0         0         0  \n",
       "3             0         0         0         0         0  \n",
       "4             0         0         0         0         0  \n",
       "...         ...       ...       ...       ...       ...  \n",
       "69995         0         0         0         0         0  \n",
       "69996         0         0         0         0         0  \n",
       "69997         0         0         0         0         0  \n",
       "69998         0         0         0         0         0  \n",
       "69999         0         0         0         0         0  \n",
       "\n",
       "[70000 rows x 784 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d823089-9c02-4e58-b2c8-d013186151af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting data \n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test , y_train , y_test = train_test_split(X,y,test_size = 0.20, random_state = 42)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e70174c-dc81-4934-9b1b-52133543ba78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56000, 784)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e99cbb9e-e110-4d08-ad39-1ed137a93c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_model = SGDClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24a02732-fc8c-4081-82cb-80e2855f21ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\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: 1ex;\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>SGDClassifier()</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 fitted\">&nbsp;&nbsp;SGDClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.SGDClassifier.html\">?<span>Documentation for SGDClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SGDClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SGDClassifier()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8be5088-8fd6-4ca0-8f5d-400fd96e4559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pixel1      0\n",
       "pixel2      0\n",
       "pixel3      0\n",
       "pixel4      0\n",
       "pixel5      0\n",
       "           ..\n",
       "pixel780    0\n",
       "pixel781    0\n",
       "pixel782    0\n",
       "pixel783    0\n",
       "pixel784    0\n",
       "Name: 46730, Length: 784, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.loc[46730]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "65b751dc-111c-4cc9-8ed6-3f1d9d6f098c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python312\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but SGDClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['8'], dtype='<U1')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_model.predict([x_test.iloc[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "599fdcf4-c698-4d89-9775-953532e75281",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'8'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Validation\n",
    "y_test.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "39ca5814-54b6-4543-adfb-88c0c9d1cd63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.87165202, 0.83908292, 0.87248178])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "\n",
    "\n",
    "cross_val_score(sgd_model, x_test, y_test, cv=3, scoring = 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "effe0f3f-e422-43c8-9705-ccd090bece70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['8', '4', '8', ..., '2', '7', '1'], dtype='<U1')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "predict = cross_val_predict(sgd_model, x_test, y_test, cv=3)\n",
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6f1dd728-16bc-4ec4-98d8-36bd473513f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14000,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6df5db4c-4c25-499b-b984-b2587cceccfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.8713571428571428)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, predict, average='weighted') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "06d26d23-8660-4814-9a02-4bb75f3d8a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.873619587853503)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, predict, average='weighted') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "544a1021-4b8d-4141-84b2-14e253f28f79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.8716113331612526)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, predict, average='weighted') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9af61852-0a26-4255-b606-0f633038a139",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ebb60a39-6968-4b7a-a08e-c9ab84059414",
   "metadata": {},
   "outputs": [],
   "source": [
    "neigbour = KNeighborsClassifier(n_neighbors = 5).fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7516d6e2-17fa-463e-ae2e-5341df338236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['8', '4', '8', ..., '2', '7', '1'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigbour.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5fb02e47-1856-4569-99c0-8b704132eab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9666256 , 0.9689827 , 0.96657023])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(neigbour, x_train, y_train, cv=3, scoring = 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4e843365-64f8-48cc-9589-12721b4f2959",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_n = cross_val_predict(neigbour, x_train, y_train, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5fec6717-3a01-46ea-9643-00f9ced4d88a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9676641326725988)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_scoring = precision_score(y_train, predict_n,  average='weighted')\n",
    "precision_scoring "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "90861b1b-a48f-4f97-a1c4-87f71706e7ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.9673928571428572)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_scoring = recall_score(y_train, predict_n,  average='weighted')\n",
    "recall_scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "35a50c28-199a-41be-81de-8ffd8a09daed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5522,    5,    2,    2,    1,    5,   19,    1,    0,    3],\n",
       "       [   1, 6241,   13,    3,    1,    0,    5,   10,    1,    2],\n",
       "       [  51,   53, 5362,   22,    4,    2,    6,   93,   12,    5],\n",
       "       [   6,   17,   31, 5500,    2,   63,    3,   39,   25,   22],\n",
       "       [   4,   51,    0,    1, 5335,    0,   15,    5,    1,  117],\n",
       "       [  17,    6,    1,   67,    9, 4834,   56,    8,    9,   33],\n",
       "       [  28,   15,    1,    0,    4,   18, 5414,    0,    0,    0],\n",
       "       [   2,   71,   11,    2,   16,    1,    0, 5623,    2,   62],\n",
       "       [  21,   76,   21,   91,   29,   83,   29,   15, 5051,   52],\n",
       "       [  16,   14,    9,   39,   55,   19,    6,   77,   11, 5292]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "conf_matrix =  confusion_matrix(y_train, predict_n)\n",
    "conf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8565923c-e0e0-45de-b2da-11c7c310c0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(conf_matrix, cmap = \"grey\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9048de9-dde7-4a6e-b5d4-6a362d9fd940",
   "metadata": {},
   "source": [
    "## Precision /recall trade-off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7061a47d-6612-4157-8bff-c8f39809e82c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "# y_scoring = neigbour.predict_proba(x_test)\n",
    "# y_scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9beec002-1f72-42e0-a878-36c871f0bf39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b8bead3e-210c-4a11-b803-228396ba298a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['8', '4', '8', ..., '2', '7', '1'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigbour.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "abe87b96-08c6-42f8-ad24-d2c006d0f6ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto',\n",
       " 'leaf_size': 30,\n",
       " 'metric': 'minkowski',\n",
       " 'metric_params': None,\n",
       " 'n_jobs': None,\n",
       " 'n_neighbors': 5,\n",
       " 'p': 2,\n",
       " 'weights': 'uniform'}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigbour.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d17c5c30-52cd-4793-a263-edd2fc92ee95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_grid = {\n",
    "#     'n_neighbors':[1,3,5],\n",
    "#     'leaf_size':[10,30,50],\n",
    "#     'p':[1,2],\n",
    "#     'weights':['uniform','distance'],\n",
    "# }\n",
    "# param_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5daf0f53-41bd-40af-9114-048b1b6c751f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# grid_s =  GridSearchCV(\n",
    "#     neigbour,\n",
    "#     param_grid,\n",
    "#     scoring = 'accuracy',\n",
    "#     cv = 5,\n",
    "#     verbose = 1,\n",
    "# )\n",
    "\n",
    "# grid_s.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5b1c79a6-28af-466a-ba2f-6aa966bad2f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "model_dump = joblib.dump(neigbour,\"model.pkl\")\n",
    "model_dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f162fe2-074f-4fc1-b6ca-7ed053688e64",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}