anomalies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Affected Legal Good</th>\n",
       "      <th>Month</th>\n",
       "      <th>Value</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Patrimonio</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>189</td>\n",
       "      <td>Apodaca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Patrimonio</td>\n",
       "      <td>2015-02</td>\n",
       "      <td>238</td>\n",
       "      <td>Apodaca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Patrimonio</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>203</td>\n",
       "      <td>Apodaca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Patrimonio</td>\n",
       "      <td>2015-04</td>\n",
       "      <td>196</td>\n",
       "      <td>Apodaca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Patrimonio</td>\n",
       "      <td>2015-05</td>\n",
       "      <td>212</td>\n",
       "      <td>Apodaca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6043</th>\n",
       "      <td>Otros Bienes Jurídicos Afectados (Del Fuero Co...</td>\n",
       "      <td>2022-08</td>\n",
       "      <td>55</td>\n",
       "      <td>Santiago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6044</th>\n",
       "      <td>Otros Bienes Jurídicos Afectados (Del Fuero Co...</td>\n",
       "      <td>2022-09</td>\n",
       "      <td>34</td>\n",
       "      <td>Santiago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6045</th>\n",
       "      <td>Otros Bienes Jurídicos Afectados (Del Fuero Co...</td>\n",
       "      <td>2022-10</td>\n",
       "      <td>17</td>\n",
       "      <td>Santiago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6046</th>\n",
       "      <td>Otros Bienes Jurídicos Afectados (Del Fuero Co...</td>\n",
       "      <td>2022-11</td>\n",
       "      <td>15</td>\n",
       "      <td>Santiago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6047</th>\n",
       "      <td>Otros Bienes Jurídicos Afectados (Del Fuero Co...</td>\n",
       "      <td>2022-12</td>\n",
       "      <td>15</td>\n",
       "      <td>Santiago</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6048 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Affected Legal Good    Month  Value  \\\n",
       "0                                            Patrimonio  2015-01    189   \n",
       "1                                            Patrimonio  2015-02    238   \n",
       "2                                            Patrimonio  2015-03    203   \n",
       "3                                            Patrimonio  2015-04    196   \n",
       "4                                            Patrimonio  2015-05    212   \n",
       "...                                                 ...      ...    ...   \n",
       "6043  Otros Bienes Jurídicos Afectados (Del Fuero Co...  2022-08     55   \n",
       "6044  Otros Bienes Jurídicos Afectados (Del Fuero Co...  2022-09     34   \n",
       "6045  Otros Bienes Jurídicos Afectados (Del Fuero Co...  2022-10     17   \n",
       "6046  Otros Bienes Jurídicos Afectados (Del Fuero Co...  2022-11     15   \n",
       "6047  Otros Bienes Jurídicos Afectados (Del Fuero Co...  2022-12     15   \n",
       "\n",
       "          City  \n",
       "0      Apodaca  \n",
       "1      Apodaca  \n",
       "2      Apodaca  \n",
       "3      Apodaca  \n",
       "4      Apodaca  \n",
       "...        ...  \n",
       "6043  Santiago  \n",
       "6044  Santiago  \n",
       "6045  Santiago  \n",
       "6046  Santiago  \n",
       "6047  Santiago  \n",
       "\n",
       "[6048 rows x 4 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page1 = pd.read_csv(\"Page1.csv\")\n",
    "page1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Affected Legal Good</th>\n",
       "      <th>City</th>\n",
       "      <th>Period</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Familia</td>\n",
       "      <td>Apodaca</td>\n",
       "      <td>2015Q1</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Familia</td>\n",
       "      <td>Apodaca</td>\n",
       "      <td>2015Q2</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Familia</td>\n",
       "      <td>Apodaca</td>\n",
       "      <td>2015Q3</td>\n",
       "      <td>723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Familia</td>\n",
       "      <td>Apodaca</td>\n",
       "      <td>2015Q4</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Familia</td>\n",
       "      <td>Apodaca</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>Vida e Integridad Corporal</td>\n",
       "      <td>Santiago</td>\n",
       "      <td>2021Q4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>Vida e Integridad Corporal</td>\n",
       "      <td>Santiago</td>\n",
       "      <td>2022Q1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>Vida e Integridad Corporal</td>\n",
       "      <td>Santiago</td>\n",
       "      <td>2022Q2</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>Vida e Integridad Corporal</td>\n",
       "      <td>Santiago</td>\n",
       "      <td>2022Q3</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>Vida e Integridad Corporal</td>\n",
       "      <td>Santiago</td>\n",
       "      <td>2022Q4</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2016 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Affected Legal Good      City  Period  Value\n",
       "0                        Familia   Apodaca  2015Q1    504\n",
       "1                        Familia   Apodaca  2015Q2    671\n",
       "2                        Familia   Apodaca  2015Q3    723\n",
       "3                        Familia   Apodaca  2015Q4    505\n",
       "4                        Familia   Apodaca  2016Q1    508\n",
       "...                          ...       ...     ...    ...\n",
       "2011  Vida e Integridad Corporal  Santiago  2021Q4     26\n",
       "2012  Vida e Integridad Corporal  Santiago  2022Q1     23\n",
       "2013  Vida e Integridad Corporal  Santiago  2022Q2     36\n",
       "2014  Vida e Integridad Corporal  Santiago  2022Q3     36\n",
       "2015  Vida e Integridad Corporal  Santiago  2022Q4     19\n",
       "\n",
       "[2016 rows x 4 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Assuming your dataframe is named 'df'\n",
    "page1['Month'] = pd.to_datetime(page1['Month'], format='%Y-%m')\n",
    "page1['Period'] = page1['Month'].dt.to_period('Q')  # Create a new column representing the 3-month period\n",
    "\n",
    "grouped_df = page1.groupby(['Affected Legal Good', 'City', 'Period']).agg({'Value': 'sum'}).reset_index()\n",
    "grouped_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyod.models.knn import KNN\n",
    "\n",
    "grouped_df=grouped_df.set_index(\"Period\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_apodaca = grouped_df[grouped_df[\"City\"] == \"Apodaca\"]\n",
    "df_apodaca = df_apodaca.drop([\"City\"], axis=1)\n",
    "df_escobedo = grouped_df[grouped_df[\"City\"] == \"Escobedo\"]\n",
    "df_escobedo = df_escobedo.drop([\"City\"], axis=1)\n",
    "df_garcia = grouped_df[grouped_df[\"City\"] == \"García\"]\n",
    "df_garcia = df_garcia.drop([\"City\"], axis=1)\n",
    "df_guadalupe = grouped_df[grouped_df[\"City\"] == \"Guadalupe\"]\n",
    "df_guadalupe = df_guadalupe.drop([\"City\"], axis=1)\n",
    "df_juarez = grouped_df[grouped_df[\"City\"] == \"Juarez\"]\n",
    "df_juarez = df_juarez.drop([\"City\"], axis=1)\n",
    "df_monterrey = grouped_df[grouped_df[\"City\"] == \"Monterrey\"]\n",
    "df_monterrey = df_monterrey.drop([\"City\"], axis=1)\n",
    "df_san_nico = grouped_df[grouped_df[\"City\"] == \"San Nicolás de los Garza\"]\n",
    "df_san_nico = df_san_nico.drop([\"City\"], axis=1)\n",
    "df_santa = grouped_df[grouped_df[\"City\"] == \"Santa Catarina\"]\n",
    "df_santa = df_santa.drop([\"City\"], axis=1)\n",
    "df_santiago = grouped_df[grouped_df[\"City\"] == \"Santiago\"]\n",
    "df_santiago = df_santiago.drop([\"City\"], axis=1)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apodaca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jp_gl\\.julia\\conda\\3\\lib\\site-packages\\pyod\\models\\base.py:431: UserWarning: y should not be presented in unsupervised learning.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "df_encoded = pd.get_dummies(df_apodaca, columns=[\"Affected Legal Good\"])\n",
    "\n",
    "# Split the dataset into features (X) and target variable (y)\n",
    "X = df_encoded.drop([\"Affected Legal Good_Familia\", \"Affected Legal Good_Libertad Personal\", \"Affected Legal Good_Libertad y Seguridad Sexual\", \"Affected Legal Good_Otros Bienes Jurídicos Afectados (Del Fuero Común)\", \"Affected Legal Good_Patrimonio\", \"Affected Legal Good_Sociedad\", \"Affected Legal Good_Vida e Integridad Corporal\"], axis=1)\n",
    "y = df_encoded[[\"Affected Legal Good_Familia\", \"Affected Legal Good_Libertad Personal\", \"Affected Legal Good_Libertad y Seguridad Sexual\", \"Affected Legal Good_Otros Bienes Jurídicos Afectados (Del Fuero Común)\", \"Affected Legal Good_Patrimonio\", \"Affected Legal Good_Sociedad\", \"Affected Legal Good_Vida e Integridad Corporal\"]]\n",
    "\n",
    "# Initialize the KNN model\n",
    "knn = KNN()\n",
    "\n",
    "# Fit the model\n",
    "knn.fit(X, y)\n",
    "\n",
    "# Make predictions\n",
    "predictions = knn.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "      <th>Affected Legal Good_Familia</th>\n",
       "      <th>Affected Legal Good_Libertad Personal</th>\n",
       "      <th>Affected Legal Good_Libertad y Seguridad Sexual</th>\n",
       "      <th>Affected Legal Good_Otros Bienes Jurídicos Afectados (Del Fuero Común)</th>\n",
       "      <th>Affected Legal Good_Patrimonio</th>\n",
       "      <th>Affected Legal Good_Sociedad</th>\n",
       "      <th>Affected Legal Good_Vida e Integridad Corporal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015Q1</th>\n",
       "      <td>504</td>\n",
       "      <td>1</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>2015Q2</th>\n",
       "      <td>671</td>\n",
       "      <td>1</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>2015Q3</th>\n",
       "      <td>723</td>\n",
       "      <td>1</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>2015Q4</th>\n",
       "      <td>505</td>\n",
       "      <td>1</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>2016Q1</th>\n",
       "      <td>508</td>\n",
       "      <td>1</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>...</th>\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>2021Q4</th>\n",
       "      <td>232</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022Q1</th>\n",
       "      <td>187</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022Q2</th>\n",
       "      <td>184</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022Q3</th>\n",
       "      <td>200</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022Q4</th>\n",
       "      <td>169</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>224 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Value  Affected Legal Good_Familia  \\\n",
       "Period                                       \n",
       "2015Q1    504                            1   \n",
       "2015Q2    671                            1   \n",
       "2015Q3    723                            1   \n",
       "2015Q4    505                            1   \n",
       "2016Q1    508                            1   \n",
       "...       ...                          ...   \n",
       "2021Q4    232                            0   \n",
       "2022Q1    187                            0   \n",
       "2022Q2    184                            0   \n",
       "2022Q3    200                            0   \n",
       "2022Q4    169                            0   \n",
       "\n",
       "        Affected Legal Good_Libertad Personal  \\\n",
       "Period                                          \n",
       "2015Q1                                      0   \n",
       "2015Q2                                      0   \n",
       "2015Q3                                      0   \n",
       "2015Q4                                      0   \n",
       "2016Q1                                      0   \n",
       "...                                       ...   \n",
       "2021Q4                                      0   \n",
       "2022Q1                                      0   \n",
       "2022Q2                                      0   \n",
       "2022Q3                                      0   \n",
       "2022Q4                                      0   \n",
       "\n",
       "        Affected Legal Good_Libertad y Seguridad Sexual  \\\n",
       "Period                                                    \n",
       "2015Q1                                                0   \n",
       "2015Q2                                                0   \n",
       "2015Q3                                                0   \n",
       "2015Q4                                                0   \n",
       "2016Q1                                                0   \n",
       "...                                                 ...   \n",
       "2021Q4                                                0   \n",
       "2022Q1                                                0   \n",
       "2022Q2                                                0   \n",
       "2022Q3                                                0   \n",
       "2022Q4                                                0   \n",
       "\n",
       "        Affected Legal Good_Otros Bienes Jurídicos Afectados (Del Fuero Común)  \\\n",
       "Period                                                                           \n",
       "2015Q1                                                  0                        \n",
       "2015Q2                                                  0                        \n",
       "2015Q3                                                  0                        \n",
       "2015Q4                                                  0                        \n",
       "2016Q1                                                  0                        \n",
       "...                                                   ...                        \n",
       "2021Q4                                                  0                        \n",
       "2022Q1                                                  0                        \n",
       "2022Q2                                                  0                        \n",
       "2022Q3                                                  0                        \n",
       "2022Q4                                                  0                        \n",
       "\n",
       "        Affected Legal Good_Patrimonio  Affected Legal Good_Sociedad  \\\n",
       "Period                                                                 \n",
       "2015Q1                               0                             0   \n",
       "2015Q2                               0                             0   \n",
       "2015Q3                               0                             0   \n",
       "2015Q4                               0                             0   \n",
       "2016Q1                               0                             0   \n",
       "...                                ...                           ...   \n",
       "2021Q4                               0                             0   \n",
       "2022Q1                               0                             0   \n",
       "2022Q2                               0                             0   \n",
       "2022Q3                               0                             0   \n",
       "2022Q4                               0                             0   \n",
       "\n",
       "        Affected Legal Good_Vida e Integridad Corporal  \n",
       "Period                                                  \n",
       "2015Q1                                               0  \n",
       "2015Q2                                               0  \n",
       "2015Q3                                               0  \n",
       "2015Q4                                               0  \n",
       "2016Q1                                               0  \n",
       "...                                                ...  \n",
       "2021Q4                                               1  \n",
       "2022Q1                                               1  \n",
       "2022Q2                                               1  \n",
       "2022Q3                                               1  \n",
       "2022Q4                                               1  \n",
       "\n",
       "[224 rows x 8 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Convert index values to strings\n",
    "df_apodaca.index = df_apodaca.index.astype(str)\n",
    "\n",
    "# Plotting using the updated index\n",
    "plt.plot(df_apodaca.index, predictions)\n",
    "plt.xlabel('Period')\n",
    "plt.ylabel('Predicted Value')\n",
    "plt.title('Predicted Values from KNN Model')\n",
    "plt.xticks(rotation=70)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}