knn exercise.ipynb

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   "execution_count": 2,
   "id": "0e4bc63c-e4d6-4dc7-a42d-c99a8f0370f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "28fa1054-9611-4d32-8925-7f7bf59a6347",
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    {
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     "execution_count": 6,
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     "output_type": "execute_result"
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   ],
   "source": [
    "digits.target_names"
   ]
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   "execution_count": 10,
   "id": "d4526c8d-7908-44ff-9f32-aac6b8fbfdf8",
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "df=pd.DataFrame(digits.data,digits.target)\n",
    "df.head()"
   ]
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    "df['target']=digits.target\n",
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "45b6419a-5850-49dd-ad83-e22db931fc29",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(['target'],axis='columns')\n",
    "y=df.target"
   ]
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  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b49763e4-6250-41ef-b8f3-6bee6d70d63b",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1    2     3     4     5    6    7    8    9   ...   54   55   56  \\\n",
       "0  0.0  0.0  5.0  13.0   9.0   1.0  0.0  0.0  0.0  0.0  ...  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  0.0  ...  0.0  0.0  0.0   \n",
       "2  0.0  0.0  0.0   4.0  15.0  12.0  0.0  0.0  0.0  0.0  ...  5.0  0.0  0.0   \n",
       "3  0.0  0.0  7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  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  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "    57   58    59    60    61   62   63  \n",
       "0  0.0  6.0  13.0  10.0   0.0  0.0  0.0  \n",
       "1  0.0  0.0  11.0  16.0  10.0  0.0  0.0  \n",
       "2  0.0  0.0   3.0  11.0  16.0  9.0  0.0  \n",
       "3  0.0  7.0  13.0  13.0   9.0  0.0  0.0  \n",
       "4  0.0  0.0   2.0  16.0   4.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7f7e5e2c-5d85-4842-9058-7774ddd8dec0",
   "metadata": {},
   "outputs": [],
   "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=0.2, random_state=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ce68c7f2-2228-4104-b0e9-f25faa32f5d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\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-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8 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-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 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-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8 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-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 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-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 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-8 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-8 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-8 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-8 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-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8d252750-e79d-4c75-afc0-1350c05223a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9861111111111112"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ea78e260-cecc-4530-ac85-fa7c997811a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[37,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 31,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0, 28,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 38,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0, 41,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0, 32,  0,  0,  0,  1],\n",
       "       [ 0,  0,  0,  0,  0,  0, 45,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 40,  0,  0],\n",
       "       [ 0,  1,  0,  0,  0,  0,  0,  0, 34,  0],\n",
       "       [ 0,  1,  0,  0,  0,  1,  0,  0,  1, 29]], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = knn.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2978f64e-da59-41b3-9d95-9e8e0a82b97c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(58.222222222222214, 0.5, 'Truth')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure(figsize=(7,5))\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6c304701-6d70-485f-9089-fe392b26e5e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        37\n",
      "           1       0.94      1.00      0.97        31\n",
      "           2       1.00      1.00      1.00        28\n",
      "           3       1.00      1.00      1.00        38\n",
      "           4       1.00      1.00      1.00        41\n",
      "           5       0.97      0.97      0.97        33\n",
      "           6       1.00      1.00      1.00        45\n",
      "           7       1.00      1.00      1.00        40\n",
      "           8       0.97      0.97      0.97        35\n",
      "           9       0.97      0.91      0.94        32\n",
      "\n",
      "    accuracy                           0.99       360\n",
      "   macro avg       0.98      0.98      0.98       360\n",
      "weighted avg       0.99      0.99      0.99       360\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b02b493c-6b4e-4cb4-a5d5-baaaa1b984b6",
   "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
}