CODE_PYTHON_1.ipynb

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    {
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      "metadata": {
        "id": "OFs2zKZA0PJe"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data='/content/communities.data'"
      ],
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        "id": "YF5vpkwM0k_h"
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      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df=pd.read_csv(data)"
      ],
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        "id": "foMmI7L50oYB"
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      "outputs": []
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        "df"
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              "       8   ?    ?.1         Lakewoodcity   1  0.19  0.33  0.02   0.9  0.12  \\\n",
              "0     53   ?      ?          Tukwilacity   1  0.00  0.16  0.12  0.74  0.45   \n",
              "1     24   ?      ?         Aberdeentown   1  0.00  0.42  0.49  0.56  0.17   \n",
              "2     34   5  81440  Willingborotownship   1  0.04  0.77  1.00  0.08  0.12   \n",
              "3     42  95   6096    Bethlehemtownship   1  0.01  0.55  0.02  0.95  0.09   \n",
              "4      6   ?      ?    SouthPasadenacity   1  0.02  0.28  0.06  0.54  1.00   \n",
              "...   ..  ..    ...                  ...  ..   ...   ...   ...   ...   ...   \n",
              "1988  12   ?      ?    TempleTerracecity  10  0.01  0.40  0.10  0.87  0.12   \n",
              "1989   6   ?      ?          Seasidecity  10  0.05  0.96  0.46  0.28  0.83   \n",
              "1990   9   9  80070        Waterburytown  10  0.16  0.37  0.25  0.69  0.04   \n",
              "1991  25  17  72600          Walthamcity  10  0.08  0.51  0.06  0.87  0.22   \n",
              "1992   6   ?      ?          Ontariocity  10  0.20  0.78  0.14  0.46  0.24   \n",
              "\n",
              "      ...  0.12.2  0.26.1  0.2.1  0.06.3  0.04.2  0.9.1  0.5.2  0.32.2  \\\n",
              "0     ...    0.02    0.12   0.45       ?       ?      ?      ?    0.00   \n",
              "1     ...    0.01    0.21   0.02       ?       ?      ?      ?    0.00   \n",
              "2     ...    0.02    0.39   0.28       ?       ?      ?      ?    0.00   \n",
              "3     ...    0.04    0.09   0.02       ?       ?      ?      ?    0.00   \n",
              "4     ...    0.01    0.58   0.10       ?       ?      ?      ?    0.00   \n",
              "...   ...     ...     ...    ...     ...     ...    ...    ...     ...   \n",
              "1988  ...    0.01    0.28   0.05       ?       ?      ?      ?    0.00   \n",
              "1989  ...    0.02    0.37   0.20       ?       ?      ?      ?    0.00   \n",
              "1990  ...    0.08    0.32   0.18    0.08    0.06   0.78      0    0.91   \n",
              "1991  ...    0.03    0.38   0.33    0.02    0.02   0.79      0    0.22   \n",
              "1992  ...    0.11    0.30   0.05    0.08    0.04   0.73    0.5    1.00   \n",
              "\n",
              "      0.14.3  0.2.2  \n",
              "0          ?   0.67  \n",
              "1          ?   0.43  \n",
              "2          ?   0.12  \n",
              "3          ?   0.03  \n",
              "4          ?   0.14  \n",
              "...      ...    ...  \n",
              "1988       ?   0.09  \n",
              "1989       ?   0.45  \n",
              "1990    0.28   0.23  \n",
              "1991    0.18   0.19  \n",
              "1992    0.13   0.48  \n",
              "\n",
              "[1993 rows x 128 columns]"
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              "      <th>?.1</th>\n",
              "      <th>Lakewoodcity</th>\n",
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              "      <th>0.12</th>\n",
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              "      <th>0.12.2</th>\n",
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              "      <th>0</th>\n",
              "      <td>53</td>\n",
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              "      <td>Tukwilacity</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.16</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.74</td>\n",
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              "      <td>0.67</td>\n",
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              "      <th>1</th>\n",
              "      <td>24</td>\n",
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              "      <td>?</td>\n",
              "      <td>Aberdeentown</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.17</td>\n",
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              "      <td>0.01</td>\n",
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              "      <td>0.43</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34</td>\n",
              "      <td>5</td>\n",
              "      <td>81440</td>\n",
              "      <td>Willingborotownship</td>\n",
              "      <td>1</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.77</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.12</td>\n",
              "      <td>...</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.28</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>0.00</td>\n",
              "      <td>?</td>\n",
              "      <td>0.12</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>42</td>\n",
              "      <td>95</td>\n",
              "      <td>6096</td>\n",
              "      <td>Bethlehemtownship</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>SouthPasadenacity</td>\n",
              "      <td>1</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.54</td>\n",
              "      <td>1.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.10</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>0.00</td>\n",
              "      <td>?</td>\n",
              "      <td>0.14</td>\n",
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              "    <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",
              "      <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",
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              "      <th>1988</th>\n",
              "      <td>12</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>TempleTerracecity</td>\n",
              "      <td>10</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.12</td>\n",
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              "      <td>0.01</td>\n",
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              "      <td>0.05</td>\n",
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              "      <td>?</td>\n",
              "      <td>0.00</td>\n",
              "      <td>?</td>\n",
              "      <td>0.09</td>\n",
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              "    <tr>\n",
              "      <th>1989</th>\n",
              "      <td>6</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>Seasidecity</td>\n",
              "      <td>10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.83</td>\n",
              "      <td>...</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.20</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>0.00</td>\n",
              "      <td>?</td>\n",
              "      <td>0.45</td>\n",
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              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>80070</td>\n",
              "      <td>Waterburytown</td>\n",
              "      <td>10</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.04</td>\n",
              "      <td>...</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.32</td>\n",
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              "      <td>0.08</td>\n",
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              "      <td>0.78</td>\n",
              "      <td>0</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.23</td>\n",
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              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>25</td>\n",
              "      <td>17</td>\n",
              "      <td>72600</td>\n",
              "      <td>Walthamcity</td>\n",
              "      <td>10</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.22</td>\n",
              "      <td>...</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.19</td>\n",
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              "      <th>1992</th>\n",
              "      <td>6</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>Ontariocity</td>\n",
              "      <td>10</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.24</td>\n",
              "      <td>...</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.48</td>\n",
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              "<p>1993 rows × 128 columns</p>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ff66d43f-229e-4feb-ac10-f765c11a33e9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_codes = [\n",
        "    \"state\",\n",
        "    \"county\",\n",
        "    \"community\",\n",
        "    \"communityname\",\n",
        "    \"fold\",\n",
        "    \"population\",\n",
        "    \"householdsize\",\n",
        "    \"racepctblack\",\n",
        "    \"racePctWhite\",\n",
        "    \"racePctAsian\",\n",
        "    \"racePctHisp\",\n",
        "    \"agePct12t21\",\n",
        "    \"agePct12t29\",\n",
        "    \"agePct16t24\",\n",
        "    \"agePct65up\",\n",
        "    \"numbUrban\",\n",
        "    \"pctUrban\",\n",
        "    \"medIncome\",\n",
        "    \"pctWWage\",\n",
        "    \"pctWFarmSelf\",\n",
        "    \"pctWInvInc\",\n",
        "    \"pctWSocSec\",\n",
        "    \"pctWPubAsst\",\n",
        "    \"pctWRetire\",\n",
        "    \"medFamInc\",\n",
        "    \"perCapInc\",\n",
        "    \"whitePerCap\",\n",
        "    \"blackPerCap\",\n",
        "    \"indianPerCap\",\n",
        "    \"AsianPerCap\",\n",
        "    \"OtherPerCap\",\n",
        "    \"HispPerCap\",\n",
        "    \"NumUnderPov\",\n",
        "    \"PctPopUnderPov\",\n",
        "    \"PctLess9thGrade\",\n",
        "    \"PctNotHSGrad\",\n",
        "    \"PctBSorMore\",\n",
        "    \"PctUnemployed\",\n",
        "    \"PctEmploy\",\n",
        "    \"PctEmplManu\",\n",
        "    \"PctEmplProfServ\",\n",
        "    \"PctOccupManu\",\n",
        "    \"PctOccupMgmtProf\",\n",
        "    \"MalePctDivorce\",\n",
        "    \"MalePctNevMarr\",\n",
        "    \"FemalePctDiv\",\n",
        "    \"TotalPctDiv\",\n",
        "    \"PersPerFam\",\n",
        "    \"PctFam2Par\",\n",
        "    \"PctKids2Par\",\n",
        "    \"PctYoungKids2Par\",\n",
        "    \"PctTeen2Par\",\n",
        "    \"PctWorkMomYoungKids\",\n",
        "    \"PctWorkMom\",\n",
        "    \"NumIlleg\",\n",
        "    \"PctIlleg\",\n",
        "    \"NumImmig\",\n",
        "    \"PctImmigRecent\",\n",
        "    \"PctImmigRec5\",\n",
        "    \"PctImmigRec8\",\n",
        "    \"PctImmigRec10\",\n",
        "    \"PctRecentImmig\",\n",
        "    \"PctRecImmig5\",\n",
        "    \"PctRecImmig8\",\n",
        "    \"PctRecImmig10\",\n",
        "    \"PctSpeakEnglOnly\",\n",
        "    \"PctNotSpeakEnglWell\",\n",
        "    \"PctLargHouseFam\",\n",
        "    \"PctLargHouseOccup\",\n",
        "    \"PersPerOccupHous\",\n",
        "    \"PersPerOwnOccHous\",\n",
        "    \"PersPerRentOccHous\",\n",
        "    \"PctPersOwnOccup\",\n",
        "    \"PctPersDenseHous\",\n",
        "    \"PctHousLess3BR\",\n",
        "    \"MedNumBR\",\n",
        "    \"HousVacant\",\n",
        "    \"PctHousOccup\",\n",
        "    \"PctHousOwnOcc\",\n",
        "    \"PctVacantBoarded\",\n",
        "    \"PctVacMore6Mos\",\n",
        "    \"MedYrHousBuilt\",\n",
        "    \"PctHousNoPhone\",\n",
        "    \"PctWOFullPlumb\",\n",
        "    \"OwnOccLowQuart\",\n",
        "    \"OwnOccMedVal\",\n",
        "    \"OwnOccHiQuart\",\n",
        "    \"RentLowQ\",\n",
        "    \"RentMedian\",\n",
        "    \"RentHighQ\",\n",
        "    \"MedRent\",\n",
        "    \"MedRentPctHousInc\",\n",
        "    \"MedOwnCostPctInc\",\n",
        "    \"MedOwnCostPctIncNoMtg\",\n",
        "    \"NumInShelters\",\n",
        "    \"NumStreet\",\n",
        "    \"PctForeignBorn\",\n",
        "    \"PctBornSameState\",\n",
        "    \"PctSameHouse85\",\n",
        "    \"PctSameCity85\",\n",
        "    \"PctSameState85\",\n",
        "    \"LemasSwornFT\",\n",
        "    \"LemasSwFTPerPop\",\n",
        "    \"LemasSwFTFieldOps\",\n",
        "    \"LemasSwFTFieldPerPop\",\n",
        "    \"LemasTotalReq\",\n",
        "    \"LemasTotReqPerPop\",\n",
        "    \"PolicReqPerOffic\",\n",
        "    \"PolicPerPop\",\n",
        "    \"RacialMatchCommPol\",\n",
        "    \"PctPolicWhite\",\n",
        "    \"PctPolicBlack\",\n",
        "    \"PctPolicHisp\",\n",
        "    \"PctPolicAsian\",\n",
        "    \"PctPolicMinor\",\n",
        "    \"OfficAssgnDrugUnits\",\n",
        "    \"NumKindsDrugsSeiz\",\n",
        "    \"PolicAveOTWorked\",\n",
        "    \"LandArea\",\n",
        "    \"PopDens\",\n",
        "    \"PctUsePubTrans\",\n",
        "    \"PolicCars\",\n",
        "    \"PolicOperBudg\",\n",
        "    \"LemasPctPolicOnPatr\",\n",
        "    \"LemasGangUnitDeploy\",\n",
        "    \"LemasPctOfficDrugUn\",\n",
        "    \"PolicBudgPerPop\",\n",
        "    \"ViolentCrimesPerPop\"\n",
        "]\n",
        "df = pd.read_csv(data, names=column_codes, na_values=[\"?\"])\n"
      ],
      "metadata": {
        "id": "V8pRvli61WRX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "RNX_R9vd2B3H",
        "outputId": "8c3122c2-66b6-4f64-a650-d8324c087655"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      state  county  community        communityname  fold  population  \\\n",
              "0         8     NaN        NaN         Lakewoodcity     1        0.19   \n",
              "1        53     NaN        NaN          Tukwilacity     1        0.00   \n",
              "2        24     NaN        NaN         Aberdeentown     1        0.00   \n",
              "3        34     5.0    81440.0  Willingborotownship     1        0.04   \n",
              "4        42    95.0     6096.0    Bethlehemtownship     1        0.01   \n",
              "...     ...     ...        ...                  ...   ...         ...   \n",
              "1989     12     NaN        NaN    TempleTerracecity    10        0.01   \n",
              "1990      6     NaN        NaN          Seasidecity    10        0.05   \n",
              "1991      9     9.0    80070.0        Waterburytown    10        0.16   \n",
              "1992     25    17.0    72600.0          Walthamcity    10        0.08   \n",
              "1993      6     NaN        NaN          Ontariocity    10        0.20   \n",
              "\n",
              "      householdsize  racepctblack  racePctWhite  racePctAsian  ...  LandArea  \\\n",
              "0              0.33          0.02          0.90          0.12  ...      0.12   \n",
              "1              0.16          0.12          0.74          0.45  ...      0.02   \n",
              "2              0.42          0.49          0.56          0.17  ...      0.01   \n",
              "3              0.77          1.00          0.08          0.12  ...      0.02   \n",
              "4              0.55          0.02          0.95          0.09  ...      0.04   \n",
              "...             ...           ...           ...           ...  ...       ...   \n",
              "1989           0.40          0.10          0.87          0.12  ...      0.01   \n",
              "1990           0.96          0.46          0.28          0.83  ...      0.02   \n",
              "1991           0.37          0.25          0.69          0.04  ...      0.08   \n",
              "1992           0.51          0.06          0.87          0.22  ...      0.03   \n",
              "1993           0.78          0.14          0.46          0.24  ...      0.11   \n",
              "\n",
              "      PopDens  PctUsePubTrans  PolicCars  PolicOperBudg  LemasPctPolicOnPatr  \\\n",
              "0        0.26            0.20       0.06           0.04                 0.90   \n",
              "1        0.12            0.45        NaN            NaN                  NaN   \n",
              "2        0.21            0.02        NaN            NaN                  NaN   \n",
              "3        0.39            0.28        NaN            NaN                  NaN   \n",
              "4        0.09            0.02        NaN            NaN                  NaN   \n",
              "...       ...             ...        ...            ...                  ...   \n",
              "1989     0.28            0.05        NaN            NaN                  NaN   \n",
              "1990     0.37            0.20        NaN            NaN                  NaN   \n",
              "1991     0.32            0.18       0.08           0.06                 0.78   \n",
              "1992     0.38            0.33       0.02           0.02                 0.79   \n",
              "1993     0.30            0.05       0.08           0.04                 0.73   \n",
              "\n",
              "      LemasGangUnitDeploy  LemasPctOfficDrugUn  PolicBudgPerPop  \\\n",
              "0                     0.5                 0.32             0.14   \n",
              "1                     NaN                 0.00              NaN   \n",
              "2                     NaN                 0.00              NaN   \n",
              "3                     NaN                 0.00              NaN   \n",
              "4                     NaN                 0.00              NaN   \n",
              "...                   ...                  ...              ...   \n",
              "1989                  NaN                 0.00              NaN   \n",
              "1990                  NaN                 0.00              NaN   \n",
              "1991                  0.0                 0.91             0.28   \n",
              "1992                  0.0                 0.22             0.18   \n",
              "1993                  0.5                 1.00             0.13   \n",
              "\n",
              "      ViolentCrimesPerPop  \n",
              "0                    0.20  \n",
              "1                    0.67  \n",
              "2                    0.43  \n",
              "3                    0.12  \n",
              "4                    0.03  \n",
              "...                   ...  \n",
              "1989                 0.09  \n",
              "1990                 0.45  \n",
              "1991                 0.23  \n",
              "1992                 0.19  \n",
              "1993                 0.48  \n",
              "\n",
              "[1994 rows x 128 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>county</th>\n",
              "      <th>community</th>\n",
              "      <th>communityname</th>\n",
              "      <th>fold</th>\n",
              "      <th>population</th>\n",
              "      <th>householdsize</th>\n",
              "      <th>racepctblack</th>\n",
              "      <th>racePctWhite</th>\n",
              "      <th>racePctAsian</th>\n",
              "      <th>...</th>\n",
              "      <th>LandArea</th>\n",
              "      <th>PopDens</th>\n",
              "      <th>PctUsePubTrans</th>\n",
              "      <th>PolicCars</th>\n",
              "      <th>PolicOperBudg</th>\n",
              "      <th>LemasPctPolicOnPatr</th>\n",
              "      <th>LemasGangUnitDeploy</th>\n",
              "      <th>LemasPctOfficDrugUn</th>\n",
              "      <th>PolicBudgPerPop</th>\n",
              "      <th>ViolentCrimesPerPop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lakewoodcity</td>\n",
              "      <td>1</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.12</td>\n",
              "      <td>...</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tukwilacity</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.45</td>\n",
              "      <td>...</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Aberdeentown</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.17</td>\n",
              "      <td>...</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>34</td>\n",
              "      <td>5.0</td>\n",
              "      <td>81440.0</td>\n",
              "      <td>Willingborotownship</td>\n",
              "      <td>1</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.77</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.12</td>\n",
              "      <td>...</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>42</td>\n",
              "      <td>95.0</td>\n",
              "      <td>6096.0</td>\n",
              "      <td>Bethlehemtownship</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.09</td>\n",
              "      <td>...</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.02</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.03</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",
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              "      <td>...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1989</th>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TempleTerracecity</td>\n",
              "      <td>10</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.12</td>\n",
              "      <td>...</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Seasidecity</td>\n",
              "      <td>10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.83</td>\n",
              "      <td>...</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>9</td>\n",
              "      <td>9.0</td>\n",
              "      <td>80070.0</td>\n",
              "      <td>Waterburytown</td>\n",
              "      <td>10</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.04</td>\n",
              "      <td>...</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992</th>\n",
              "      <td>25</td>\n",
              "      <td>17.0</td>\n",
              "      <td>72600.0</td>\n",
              "      <td>Walthamcity</td>\n",
              "      <td>10</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.22</td>\n",
              "      <td>...</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993</th>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Ontariocity</td>\n",
              "      <td>10</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.24</td>\n",
              "      <td>...</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.48</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1994 rows × 128 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac1a4d3e-917f-43eb-b82b-89a3078585d8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ac1a4d3e-917f-43eb-b82b-89a3078585d8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ac1a4d3e-917f-43eb-b82b-89a3078585d8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c3bb6f38-b278-4dda-8d69-41eed17b3dfd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c3bb6f38-b278-4dda-8d69-41eed17b3dfd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c3bb6f38-b278-4dda-8d69-41eed17b3dfd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hokLQIxI4kgN",
        "outputId": "37b3f9e2-91d5-4a8c-8ef7-ee21172d396c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['state', 'county', 'community', 'communityname', 'fold', 'population',\n",
              "       'householdsize', 'racepctblack', 'racePctWhite', 'racePctAsian',\n",
              "       ...\n",
              "       'LandArea', 'PopDens', 'PctUsePubTrans', 'PolicCars', 'PolicOperBudg',\n",
              "       'LemasPctPolicOnPatr', 'LemasGangUnitDeploy', 'LemasPctOfficDrugUn',\n",
              "       'PolicBudgPerPop', 'ViolentCrimesPerPop'],\n",
              "      dtype='object', length=128)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "metadata": {
        "id": "-m3YDAKa2Izq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.boxplot(x=df['ViolentCrimesPerPop'])\n",
        "plt.xlabel('Violent Crimes Per Population')\n",
        "plt.title('Boxplot of Violent Crimes Per Population')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "3C-Sa8g23Urw",
        "outputId": "569c8c6f-5233-475b-a638-ee275bc77249"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Grouping data\n",
        "state_grouped = df.groupby('state')['ViolentCrimesPerPop'].mean()\n",
        "\n",
        "# Bar chart\n",
        "plt.figure(figsize=(12, 8))\n",
        "state_grouped.plot(kind='bar', color='lightcoral')\n",
        "plt.title('Mean Violent Crimes Per Population by State')\n",
        "plt.xlabel('State')\n",
        "plt.ylabel('Mean Violent Crimes Per Population')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "id": "9bFiqn0G3Qha",
        "outputId": "dc84030a-bd99-418f-d13c-a7df10314b0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, columns=['state', 'LemasGangUnitDeploy'], drop_first=True)"
      ],
      "metadata": {
        "id": "N8IpTdzdFP-X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "-PB7W6hq_ayz",
        "outputId": "ea50f404-f127-4b32-96ed-b9451e646673"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      county  community        communityname  fold  population  householdsize  \\\n",
              "0        NaN        NaN         Lakewoodcity     1        0.19           0.33   \n",
              "1        NaN        NaN          Tukwilacity     1        0.00           0.16   \n",
              "2        NaN        NaN         Aberdeentown     1        0.00           0.42   \n",
              "3        5.0    81440.0  Willingborotownship     1        0.04           0.77   \n",
              "4       95.0     6096.0    Bethlehemtownship     1        0.01           0.55   \n",
              "...      ...        ...                  ...   ...         ...            ...   \n",
              "1989     NaN        NaN    TempleTerracecity    10        0.01           0.40   \n",
              "1990     NaN        NaN          Seasidecity    10        0.05           0.96   \n",
              "1991     9.0    80070.0        Waterburytown    10        0.16           0.37   \n",
              "1992    17.0    72600.0          Walthamcity    10        0.08           0.51   \n",
              "1993     NaN        NaN          Ontariocity    10        0.20           0.78   \n",
              "\n",
              "      racepctblack  racePctWhite  racePctAsian  racePctHisp  ...  state_48  \\\n",
              "0             0.02          0.90          0.12         0.17  ...     False   \n",
              "1             0.12          0.74          0.45         0.07  ...     False   \n",
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              "...            ...           ...           ...          ...  ...       ...   \n",
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              "1993          0.14          0.46          0.24         0.77  ...     False   \n",
              "\n",
              "      state_49  state_50  state_51  state_53  state_54  state_55  state_56  \\\n",
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              "4        False     False     False     False     False     False     False   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "1989     False     False     False     False     False     False     False   \n",
              "1990     False     False     False     False     False     False     False   \n",
              "1991     False     False     False     False     False     False     False   \n",
              "1992     False     False     False     False     False     False     False   \n",
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              "      LemasGangUnitDeploy_0.5  LemasGangUnitDeploy_1.0  \n",
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              "...                       ...                      ...  \n",
              "1989                    False                    False  \n",
              "1990                    False                    False  \n",
              "1991                    False                    False  \n",
              "1992                    False                    False  \n",
              "1993                     True                    False  \n",
              "\n",
              "[1994 rows x 173 columns]"
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-110ebdcf-224e-4767-a33d-a5b58705d156 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check\n",
        "duplicated_rows = df[df.duplicated()]\n",
        "print(\"Number of duplicated rows:\", duplicated_rows.shape[0])\n",
        "\n",
        "# Remove\n",
        "df = df.drop_duplicates()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvw9VFXNFDpX",
        "outputId": "cbfc7773-6d75-4a1b-ed75-d41ad61b6b9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicated rows: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['crime_rate_per_capita'] = df['ViolentCrimesPerPop'] / df['population']\n",
        "df['percent_households_with_public_assistance_income'] = (df['pctWPubAsst'] + df['pctWSocSec']) / 2\n",
        "\n"
      ],
      "metadata": {
        "id": "9WSQAT0iGNGE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "KnzNuQB3N6WD",
        "outputId": "0746bf2f-0c78-42d7-e0f5-f43441b0650a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   county  community        communityname  fold  population  householdsize  \\\n",
              "0     NaN        NaN         Lakewoodcity     1        0.19           0.33   \n",
              "1     NaN        NaN          Tukwilacity     1        0.00           0.16   \n",
              "2     NaN        NaN         Aberdeentown     1        0.00           0.42   \n",
              "3     5.0    81440.0  Willingborotownship     1        0.04           0.77   \n",
              "4    95.0     6096.0    Bethlehemtownship     1        0.01           0.55   \n",
              "\n",
              "   racepctblack  racePctWhite  racePctAsian  racePctHisp  ...  state_50  \\\n",
              "0          0.02          0.90          0.12         0.17  ...     False   \n",
              "1          0.12          0.74          0.45         0.07  ...     False   \n",
              "2          0.49          0.56          0.17         0.04  ...     False   \n",
              "3          1.00          0.08          0.12         0.10  ...     False   \n",
              "4          0.02          0.95          0.09         0.05  ...     False   \n",
              "\n",
              "   state_51  state_53  state_54  state_55  state_56  LemasGangUnitDeploy_0.5  \\\n",
              "0     False     False     False     False     False                     True   \n",
              "1     False      True     False     False     False                    False   \n",
              "2     False     False     False     False     False                    False   \n",
              "3     False     False     False     False     False                    False   \n",
              "4     False     False     False     False     False                    False   \n",
              "\n",
              "   LemasGangUnitDeploy_1.0  crime_rate_per_capita  \\\n",
              "0                    False               1.052632   \n",
              "1                    False                    inf   \n",
              "2                    False                    inf   \n",
              "3                    False               3.000000   \n",
              "4                    False               3.000000   \n",
              "\n",
              "   percent_households_with_public_assistance_income  \n",
              "0                                             0.220  \n",
              "1                                             0.270  \n",
              "2                                             0.390  \n",
              "3                                             0.280  \n",
              "4                                             0.275  \n",
              "\n",
              "[5 rows x 175 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-949ac1a7-9172-4270-9e52-6f6e9e40ee62\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>county</th>\n",
              "      <th>community</th>\n",
              "      <th>communityname</th>\n",
              "      <th>fold</th>\n",
              "      <th>population</th>\n",
              "      <th>householdsize</th>\n",
              "      <th>racepctblack</th>\n",
              "      <th>racePctWhite</th>\n",
              "      <th>racePctAsian</th>\n",
              "      <th>racePctHisp</th>\n",
              "      <th>...</th>\n",
              "      <th>state_50</th>\n",
              "      <th>state_51</th>\n",
              "      <th>state_53</th>\n",
              "      <th>state_54</th>\n",
              "      <th>state_55</th>\n",
              "      <th>state_56</th>\n",
              "      <th>LemasGangUnitDeploy_0.5</th>\n",
              "      <th>LemasGangUnitDeploy_1.0</th>\n",
              "      <th>crime_rate_per_capita</th>\n",
              "      <th>percent_households_with_public_assistance_income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Lakewoodcity</td>\n",
              "      <td>1</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.17</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>1.052632</td>\n",
              "      <td>0.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tukwilacity</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.07</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>inf</td>\n",
              "      <td>0.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Aberdeentown</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.04</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>inf</td>\n",
              "      <td>0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>81440.0</td>\n",
              "      <td>Willingborotownship</td>\n",
              "      <td>1</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.77</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.10</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>95.0</td>\n",
              "      <td>6096.0</td>\n",
              "      <td>Bethlehemtownship</td>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.05</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.275</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 175 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-949ac1a7-9172-4270-9e52-6f6e9e40ee62')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-949ac1a7-9172-4270-9e52-6f6e9e40ee62 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-949ac1a7-9172-4270-9e52-6f6e9e40ee62');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2bae28cc-ffea-48b5-8ed6-943b30200806\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2bae28cc-ffea-48b5-8ed6-943b30200806')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2bae28cc-ffea-48b5-8ed6-943b30200806 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "df[['population', 'householdsize', 'medIncome']] = scaler.fit_transform(df[['population', 'householdsize', 'medIncome']])\n"
      ],
      "metadata": {
        "id": "oArjSl5xGq1O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "JOxz1YGWObrE",
        "outputId": "f8c426dc-0eb8-4e0a-9f1f-f2d492d15031"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   county  community        communityname  fold  population  householdsize  \\\n",
              "0     NaN        NaN         Lakewoodcity     1        0.19           0.33   \n",
              "1     NaN        NaN          Tukwilacity     1        0.00           0.16   \n",
              "2     NaN        NaN         Aberdeentown     1        0.00           0.42   \n",
              "3     5.0    81440.0  Willingborotownship     1        0.04           0.77   \n",
              "4    95.0     6096.0    Bethlehemtownship     1        0.01           0.55   \n",
              "\n",
              "   racepctblack  racePctWhite  racePctAsian  racePctHisp  ...  state_50  \\\n",
              "0          0.02          0.90          0.12         0.17  ...     False   \n",
              "1          0.12          0.74          0.45         0.07  ...     False   \n",
              "2          0.49          0.56          0.17         0.04  ...     False   \n",
              "3          1.00          0.08          0.12         0.10  ...     False   \n",
              "4          0.02          0.95          0.09         0.05  ...     False   \n",
              "\n",
              "   state_51  state_53  state_54  state_55  state_56  LemasGangUnitDeploy_0.5  \\\n",
              "0     False     False     False     False     False                     True   \n",
              "1     False      True     False     False     False                    False   \n",
              "2     False     False     False     False     False                    False   \n",
              "3     False     False     False     False     False                    False   \n",
              "4     False     False     False     False     False                    False   \n",
              "\n",
              "   LemasGangUnitDeploy_1.0  crime_rate_per_capita  \\\n",
              "0                    False               1.052632   \n",
              "1                    False                    inf   \n",
              "2                    False                    inf   \n",
              "3                    False               3.000000   \n",
              "4                    False               3.000000   \n",
              "\n",
              "   percent_households_with_public_assistance_income  \n",
              "0                                             0.220  \n",
              "1                                             0.270  \n",
              "2                                             0.390  \n",
              "3                                             0.280  \n",
              "4                                             0.275  \n",
              "\n",
              "[5 rows x 175 columns]"
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              "      <td>Tukwilacity</td>\n",
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              "      <td>0.390</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>81440.0</td>\n",
              "      <td>Willingborotownship</td>\n",
              "      <td>1</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.77</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>6096.0</td>\n",
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              "      <td>0.01</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.describe()"
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          "base_uri": "https://localhost:8080/",
          "height": 349
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        "id": "-7aqjt4fOhke",
        "outputId": "c6a62395-75d3-448d-a12a-7d71f913cfd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           county     community         fold   population  householdsize  \\\n",
              "count  820.000000    817.000000  1994.000000  1994.000000    1994.000000   \n",
              "mean    58.826829  46188.336597     5.493982     0.057593       0.463395   \n",
              "std    126.420560  25299.726569     2.873694     0.126906       0.163717   \n",
              "min      1.000000     70.000000     1.000000     0.000000       0.000000   \n",
              "25%      9.000000  25065.000000     3.000000     0.010000       0.350000   \n",
              "50%     23.000000  48090.000000     5.000000     0.020000       0.440000   \n",
              "75%     59.500000  66660.000000     8.000000     0.050000       0.540000   \n",
              "max    840.000000  94597.000000    10.000000     1.000000       1.000000   \n",
              "\n",
              "       racepctblack  racePctWhite  racePctAsian  racePctHisp  agePct12t21  \\\n",
              "count   1994.000000   1994.000000   1994.000000  1994.000000  1994.000000   \n",
              "mean       0.179629      0.753716      0.153681     0.144022     0.424218   \n",
              "std        0.253442      0.244039      0.208877     0.232492     0.155196   \n",
              "min        0.000000      0.000000      0.000000     0.000000     0.000000   \n",
              "25%        0.020000      0.630000      0.040000     0.010000     0.340000   \n",
              "50%        0.060000      0.850000      0.070000     0.040000     0.400000   \n",
              "75%        0.230000      0.940000      0.170000     0.160000     0.470000   \n",
              "max        1.000000      1.000000      1.000000     1.000000     1.000000   \n",
              "\n",
              "       ...      PopDens  PctUsePubTrans   PolicCars  PolicOperBudg  \\\n",
              "count  ...  1994.000000     1994.000000  319.000000     319.000000   \n",
              "mean   ...     0.232854        0.161685    0.163103       0.076708   \n",
              "std    ...     0.203092        0.229055    0.214778       0.140207   \n",
              "min    ...     0.000000        0.000000    0.000000       0.000000   \n",
              "25%    ...     0.100000        0.020000    0.040000       0.020000   \n",
              "50%    ...     0.170000        0.070000    0.080000       0.030000   \n",
              "75%    ...     0.280000        0.190000    0.195000       0.060000   \n",
              "max    ...     1.000000        1.000000    1.000000       1.000000   \n",
              "\n",
              "       LemasPctPolicOnPatr  LemasPctOfficDrugUn  PolicBudgPerPop  \\\n",
              "count           319.000000          1994.000000       319.000000   \n",
              "mean              0.698589             0.094052         0.195078   \n",
              "std               0.213944             0.240328         0.164718   \n",
              "min               0.000000             0.000000         0.000000   \n",
              "25%               0.620000             0.000000         0.110000   \n",
              "50%               0.750000             0.000000         0.150000   \n",
              "75%               0.840000             0.000000         0.220000   \n",
              "max               1.000000             1.000000         1.000000   \n",
              "\n",
              "       ViolentCrimesPerPop  crime_rate_per_capita  \\\n",
              "count          1994.000000            1991.000000   \n",
              "mean              0.237979                    inf   \n",
              "std               0.232985                    NaN   \n",
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              "50%               0.150000               7.000000   \n",
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              "max               1.000000                    inf   \n",
              "\n",
              "       percent_households_with_public_assistance_income  \n",
              "count                                       1994.000000  \n",
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              "std                                            0.160801  \n",
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              "      <th>count</th>\n",
              "      <td>820.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>58.826829</td>\n",
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              "      <td>0.463395</td>\n",
              "      <td>0.179629</td>\n",
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              "      <td>0.144022</td>\n",
              "      <td>0.424218</td>\n",
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              "      <td>0.126906</td>\n",
              "      <td>0.163717</td>\n",
              "      <td>0.253442</td>\n",
              "      <td>0.244039</td>\n",
              "      <td>0.208877</td>\n",
              "      <td>0.232492</td>\n",
              "      <td>0.155196</td>\n",
              "      <td>...</td>\n",
              "      <td>0.203092</td>\n",
              "      <td>0.229055</td>\n",
              "      <td>0.214778</td>\n",
              "      <td>0.140207</td>\n",
              "      <td>0.213944</td>\n",
              "      <td>0.240328</td>\n",
              "      <td>0.164718</td>\n",
              "      <td>0.232985</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.160801</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>25065.000000</td>\n",
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              "      <td>0.350000</td>\n",
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              "      <td>0.040000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.020000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.110000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>2.571429</td>\n",
              "      <td>0.275000</td>\n",
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              "      <td>48090.000000</td>\n",
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              "      <td>0.020000</td>\n",
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              "      <td>0.070000</td>\n",
              "      <td>0.040000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>0.080000</td>\n",
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              "      <td>0.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.150000</td>\n",
              "      <td>0.150000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.380000</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>66660.000000</td>\n",
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              "      <td>0.540000</td>\n",
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              "      <td>0.170000</td>\n",
              "      <td>0.160000</td>\n",
              "      <td>0.470000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.280000</td>\n",
              "      <td>0.190000</td>\n",
              "      <td>0.195000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.840000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.220000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>27.000000</td>\n",
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              "    <tr>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>inf</td>\n",
              "      <td>0.985000</td>\n",
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              "  </tbody>\n",
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              "<p>8 rows × 127 columns</p>\n",
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              "    0% {\n",
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              "    20% {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17dgpbAJHfnp",
        "outputId": "38ebfab2-ed09-4436-eb1c-81fa879bc79d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['county numeric', 'community numeric', 'communityname string',\n",
              "       'fold numeric', 'population numeric', 'householdsize numeric',\n",
              "       'racepctblack numeric', 'racePctWhite numeric', 'racePctAsian numeric',\n",
              "       'racePctHisp numeric',\n",
              "       ...\n",
              "       'state numeric_49', 'state numeric_50', 'state numeric_51',\n",
              "       'state numeric_53', 'state numeric_54', 'state numeric_55',\n",
              "       'state numeric_56', 'LemasGangUnitDeploy numeric_0.5',\n",
              "       'LemasGangUnitDeploy numeric_1.0', 'crime_rate_per_capita'],\n",
              "      dtype='object', length=174)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify categorical variables\n",
        "categorical_columns = df.select_dtypes(include=['object']).columns\n",
        "\n",
        "# Convert categorical variables to dummy variables\n",
        "df_encoded = pd.get_dummies(df, columns=categorical_columns, drop_first=True)\n",
        "\n"
      ],
      "metadata": {
        "id": "tMn4xfshIWzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df_encoded.corr()\n",
        "correlation_matrix['ViolentCrimesPerPop'].sort_values(ascending=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oy6oQe5eIi2I",
        "outputId": "482f72de-66e1-4e00-8970-30e92e7b6297"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ViolentCrimesPerPop    1.000000\n",
              "PctIlleg               0.737957\n",
              "racepctblack           0.631264\n",
              "pctWPubAsst            0.574665\n",
              "FemalePctDiv           0.556032\n",
              "                         ...   \n",
              "PctTeen2Par           -0.661582\n",
              "PctYoungKids2Par      -0.666059\n",
              "racePctWhite          -0.684770\n",
              "PctFam2Par            -0.706667\n",
              "PctKids2Par           -0.738424\n",
              "Name: ViolentCrimesPerPop, Length: 2001, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "less_significant_features = correlation_matrix[correlation_matrix['ViolentCrimesPerPop'].abs() < 0.1].index\n",
        "less_significant_features = [col for col in less_significant_features if col in df.columns]\n",
        "df = df.drop(columns=less_significant_features)\n"
      ],
      "metadata": {
        "id": "E_B8p9fwRQ3E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "NpgzRyYuixyi",
        "outputId": "ec428dfd-5ce2-469f-e659-9410d5f310da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         communityname  population  racepctblack  racePctWhite  racePctHisp  \\\n",
              "0         Lakewoodcity        0.19          0.02          0.90         0.17   \n",
              "1          Tukwilacity        0.00          0.12          0.74         0.07   \n",
              "2         Aberdeentown        0.00          0.49          0.56         0.04   \n",
              "3  Willingborotownship        0.04          1.00          0.08         0.10   \n",
              "4    Bethlehemtownship        0.01          0.02          0.95         0.05   \n",
              "\n",
              "   agePct12t29  numbUrban  medIncome  pctWWage  pctWFarmSelf  ...  state_22  \\\n",
              "0         0.47       0.20       0.37      0.72          0.34  ...     False   \n",
              "1         0.59       0.02       0.31      0.72          0.11  ...     False   \n",
              "2         0.47       0.00       0.30      0.58          0.19  ...     False   \n",
              "3         0.50       0.06       0.58      0.89          0.21  ...     False   \n",
              "4         0.38       0.02       0.50      0.72          0.16  ...     False   \n",
              "\n",
              "   state_34  state_37  state_42  state_45  state_55  LemasGangUnitDeploy_0.5  \\\n",
              "0     False     False     False     False     False                     True   \n",
              "1     False     False     False     False     False                    False   \n",
              "2     False     False     False     False     False                    False   \n",
              "3      True     False     False     False     False                    False   \n",
              "4     False     False      True     False     False                    False   \n",
              "\n",
              "   LemasGangUnitDeploy_1.0  crime_rate_per_capita  \\\n",
              "0                    False               1.052632   \n",
              "1                    False                    inf   \n",
              "2                    False                    inf   \n",
              "3                    False               3.000000   \n",
              "4                    False               3.000000   \n",
              "\n",
              "   percent_households_with_public_assistance_income  \n",
              "0                                             0.220  \n",
              "1                                             0.270  \n",
              "2                                             0.390  \n",
              "3                                             0.280  \n",
              "4                                             0.275  \n",
              "\n",
              "[5 rows x 115 columns]"
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              "      <th></th>\n",
              "      <th>communityname</th>\n",
              "      <th>population</th>\n",
              "      <th>racepctblack</th>\n",
              "      <th>racePctWhite</th>\n",
              "      <th>racePctHisp</th>\n",
              "      <th>agePct12t29</th>\n",
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              "      <th>0</th>\n",
              "      <td>Lakewoodcity</td>\n",
              "      <td>0.19</td>\n",
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              "      <td>False</td>\n",
              "      <td>1.052632</td>\n",
              "      <td>0.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Tukwilacity</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.11</td>\n",
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              "      <td>inf</td>\n",
              "      <td>0.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aberdeentown</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.19</td>\n",
              "      <td>...</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>inf</td>\n",
              "      <td>0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Willingborotownship</td>\n",
              "      <td>0.04</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.21</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bethlehemtownship</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.72</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df['height_cm'] = df['height_cm'].astype(float)"
      ],
      "metadata": {
        "id": "TmvW0Pwd7oLV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "df.info()"
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      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "HW-oXPANJ7MC",
        "outputId": "48ad5a5b-a617-4917-8039-7b3e6dd25ece"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1994 entries, 0 to 1993\n",
            "Columns: 115 entries, communityname to percent_households_with_public_assistance_income\n",
            "dtypes: bool(11), float64(103), object(1)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "DsXp-N99J9uL",
        "outputId": "2cd62fe9-b3a7-4808-d24f-264401d46589"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1994 entries, 0 to 1993\n",
            "Columns: 115 entries, communityname to percent_households_with_public_assistance_income\n",
            "dtypes: bool(11), float64(103), object(1)\n",
            "memory usage: 1.6+ MB\n"
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        "model=LinearRegression()\n",
        "model.fit=(X_train, y_train)"
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      "metadata": {
        "id": "B8x5_-OwKMe4"
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      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "df.head()"
      ],
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          "height": 255
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        "id": "-DGBHlCfLmOF",
        "outputId": "6c32ca19-a29b-49d4-ada2-443fb6976dc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         communityname  population  racepctblack  racePctWhite  racePctHisp  \\\n",
              "0         Lakewoodcity        0.19          0.02          0.90         0.17   \n",
              "1          Tukwilacity        0.00          0.12          0.74         0.07   \n",
              "2         Aberdeentown        0.00          0.49          0.56         0.04   \n",
              "3  Willingborotownship        0.04          1.00          0.08         0.10   \n",
              "4    Bethlehemtownship        0.01          0.02          0.95         0.05   \n",
              "\n",
              "   agePct12t29  numbUrban  medIncome  pctWWage  pctWFarmSelf  ...  state_22  \\\n",
              "0         0.47       0.20       0.37      0.72          0.34  ...     False   \n",
              "1         0.59       0.02       0.31      0.72          0.11  ...     False   \n",
              "2         0.47       0.00       0.30      0.58          0.19  ...     False   \n",
              "3         0.50       0.06       0.58      0.89          0.21  ...     False   \n",
              "4         0.38       0.02       0.50      0.72          0.16  ...     False   \n",
              "\n",
              "   state_34  state_37  state_42  state_45  state_55  LemasGangUnitDeploy_0.5  \\\n",
              "0     False     False     False     False     False                     True   \n",
              "1     False     False     False     False     False                    False   \n",
              "2     False     False     False     False     False                    False   \n",
              "3      True     False     False     False     False                    False   \n",
              "4     False     False      True     False     False                    False   \n",
              "\n",
              "   LemasGangUnitDeploy_1.0  crime_rate_per_capita  \\\n",
              "0                    False               1.052632   \n",
              "1                    False                    inf   \n",
              "2                    False                    inf   \n",
              "3                    False               3.000000   \n",
              "4                    False               3.000000   \n",
              "\n",
              "   percent_households_with_public_assistance_income  \n",
              "0                                             0.220  \n",
              "1                                             0.270  \n",
              "2                                             0.390  \n",
              "3                                             0.280  \n",
              "4                                             0.275  \n",
              "\n",
              "[5 rows x 115 columns]"
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              "      <th></th>\n",
              "      <th>communityname</th>\n",
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              "      <td>Lakewoodcity</td>\n",
              "      <td>0.19</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>1.052632</td>\n",
              "      <td>0.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Tukwilacity</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.11</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>inf</td>\n",
              "      <td>0.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aberdeentown</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.19</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>inf</td>\n",
              "      <td>0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Willingborotownship</td>\n",
              "      <td>0.04</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.21</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bethlehemtownship</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.16</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.275</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 115 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.info()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YousvJ_dLt5e",
        "outputId": "886d2b47-b32d-4026-c6e5-21de56a3854e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1994 entries, 0 to 1993\n",
            "Columns: 115 entries, communityname to percent_households_with_public_assistance_income\n",
            "dtypes: bool(11), float64(103), object(1)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['communityname'], inplace=True)"
      ],
      "metadata": {
        "id": "Xkgbs7MnLwF9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "6Jd2E-OuMssg",
        "outputId": "7aa6d073-2acc-4f44-f3d7-194011f77660"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      population  racepctblack  racePctWhite  racePctHisp  agePct12t29  \\\n",
              "0           0.19          0.02          0.90         0.17         0.47   \n",
              "1           0.00          0.12          0.74         0.07         0.59   \n",
              "2           0.00          0.49          0.56         0.04         0.47   \n",
              "3           0.04          1.00          0.08         0.10         0.50   \n",
              "4           0.01          0.02          0.95         0.05         0.38   \n",
              "...          ...           ...           ...          ...          ...   \n",
              "1989        0.01          0.10          0.87         0.16         0.51   \n",
              "1990        0.05          0.46          0.28         0.32         0.86   \n",
              "1991        0.16          0.25          0.69         0.25         0.50   \n",
              "1992        0.08          0.06          0.87         0.10         0.74   \n",
              "1993        0.20          0.14          0.46         0.77         0.62   \n",
              "\n",
              "      numbUrban  medIncome  pctWWage  pctWFarmSelf  pctWInvInc  ...  state_22  \\\n",
              "0          0.20       0.37      0.72          0.34        0.60  ...     False   \n",
              "1          0.02       0.31      0.72          0.11        0.45  ...     False   \n",
              "2          0.00       0.30      0.58          0.19        0.39  ...     False   \n",
              "3          0.06       0.58      0.89          0.21        0.43  ...     False   \n",
              "4          0.02       0.50      0.72          0.16        0.68  ...     False   \n",
              "...         ...        ...       ...           ...         ...  ...       ...   \n",
              "1989       0.03       0.42      0.74          0.14        0.64  ...     False   \n",
              "1990       0.06       0.28      0.76          0.10        0.30  ...     False   \n",
              "1991       0.17       0.31      0.47          0.17        0.45  ...     False   \n",
              "1992       0.09       0.44      0.64          0.21        0.55  ...     False   \n",
              "1993       0.21       0.40      0.73          0.22        0.25  ...     False   \n",
              "\n",
              "      state_34  state_37  state_42  state_45  state_55  \\\n",
              "0        False     False     False     False     False   \n",
              "1        False     False     False     False     False   \n",
              "2        False     False     False     False     False   \n",
              "3         True     False     False     False     False   \n",
              "4        False     False      True     False     False   \n",
              "...        ...       ...       ...       ...       ...   \n",
              "1989     False     False     False     False     False   \n",
              "1990     False     False     False     False     False   \n",
              "1991     False     False     False     False     False   \n",
              "1992     False     False     False     False     False   \n",
              "1993     False     False     False     False     False   \n",
              "\n",
              "      LemasGangUnitDeploy_0.5  LemasGangUnitDeploy_1.0  crime_rate_per_capita  \\\n",
              "0                        True                    False               1.052632   \n",
              "1                       False                    False                    inf   \n",
              "2                       False                    False                    inf   \n",
              "3                       False                    False               3.000000   \n",
              "4                       False                    False               3.000000   \n",
              "...                       ...                      ...                    ...   \n",
              "1989                    False                    False               9.000000   \n",
              "1990                    False                    False               9.000000   \n",
              "1991                    False                    False               1.437500   \n",
              "1992                    False                    False               2.375000   \n",
              "1993                     True                    False               2.400000   \n",
              "\n",
              "      percent_households_with_public_assistance_income  \n",
              "0                                                0.220  \n",
              "1                                                0.270  \n",
              "2                                                0.390  \n",
              "3                                                0.280  \n",
              "4                                                0.275  \n",
              "...                                                ...  \n",
              "1989                                             0.180  \n",
              "1990                                             0.315  \n",
              "1991                                             0.530  \n",
              "1992                                             0.330  \n",
              "1993                                             0.365  \n",
              "\n",
              "[1994 rows x 114 columns]"
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              "      <th>1991</th>\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
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          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corrs=df.corr()"
      ],
      "metadata": {
        "id": "8bRqPYC4NDFW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corrs\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "ExTwT8KFNGhf",
        "outputId": "ddf29d32-864d-43e0-9636-d2b585f804cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  population  racepctblack  \\\n",
              "population                                          1.000000      0.231178   \n",
              "racepctblack                                        0.231178      1.000000   \n",
              "racePctWhite                                       -0.300845     -0.794389   \n",
              "racePctHisp                                         0.156218     -0.066581   \n",
              "agePct12t29                                         0.130344      0.153475   \n",
              "...                                                      ...           ...   \n",
              "state_55                                           -0.012392     -0.098337   \n",
              "LemasGangUnitDeploy_0.5                             0.553456      0.184315   \n",
              "LemasGangUnitDeploy_1.0                             0.223858      0.121641   \n",
              "crime_rate_per_capita                              -0.234849      0.312029   \n",
              "percent_households_with_public_assistance_income    0.057457      0.367689   \n",
              "\n",
              "                                                  racePctWhite  racePctHisp  \\\n",
              "population                                           -0.300845     0.156218   \n",
              "racepctblack                                         -0.794389    -0.066581   \n",
              "racePctWhite                                          1.000000    -0.444166   \n",
              "racePctHisp                                          -0.444166     1.000000   \n",
              "agePct12t29                                          -0.266852     0.205870   \n",
              "...                                                        ...          ...   \n",
              "state_55                                              0.132920    -0.090194   \n",
              "LemasGangUnitDeploy_0.5                              -0.239101     0.135319   \n",
              "LemasGangUnitDeploy_1.0                              -0.126271     0.057842   \n",
              "crime_rate_per_capita                                -0.294185     0.091118   \n",
              "percent_households_with_public_assistance_income     -0.370926     0.205035   \n",
              "\n",
              "                                                  agePct12t29  numbUrban  \\\n",
              "population                                           0.130344   0.993112   \n",
              "racepctblack                                         0.153475   0.223469   \n",
              "racePctWhite                                        -0.266852  -0.295237   \n",
              "racePctHisp                                          0.205870   0.153941   \n",
              "agePct12t29                                          1.000000   0.112614   \n",
              "...                                                       ...        ...   \n",
              "state_55                                             0.014276  -0.011088   \n",
              "LemasGangUnitDeploy_0.5                              0.090799   0.553402   \n",
              "LemasGangUnitDeploy_1.0                              0.047800   0.230829   \n",
              "crime_rate_per_capita                                0.003144  -0.248173   \n",
              "percent_households_with_public_assistance_income    -0.094694   0.032321   \n",
              "\n",
              "                                                  medIncome  pctWWage  \\\n",
              "population                                        -0.101615  0.004512   \n",
              "racepctblack                                      -0.360678 -0.227563   \n",
              "racePctWhite                                       0.308686  0.117234   \n",
              "racePctHisp                                       -0.140471  0.019629   \n",
              "agePct12t29                                       -0.342740  0.259699   \n",
              "...                                                     ...       ...   \n",
              "state_55                                          -0.012446  0.025857   \n",
              "LemasGangUnitDeploy_0.5                           -0.089446 -0.013706   \n",
              "LemasGangUnitDeploy_1.0                           -0.052015 -0.021065   \n",
              "crime_rate_per_capita                             -0.312519 -0.278384   \n",
              "percent_households_with_public_assistance_income  -0.680982 -0.866315   \n",
              "\n",
              "                                                  pctWFarmSelf  pctWInvInc  \\\n",
              "population                                           -0.130692   -0.150788   \n",
              "racepctblack                                         -0.152885   -0.492914   \n",
              "racePctWhite                                          0.096982    0.596067   \n",
              "racePctHisp                                           0.082855   -0.417161   \n",
              "agePct12t29                                           0.124626   -0.357157   \n",
              "...                                                        ...         ...   \n",
              "state_55                                             -0.029408    0.173287   \n",
              "LemasGangUnitDeploy_0.5                              -0.096005   -0.131388   \n",
              "LemasGangUnitDeploy_1.0                              -0.053705   -0.056926   \n",
              "crime_rate_per_capita                                 0.045680   -0.374889   \n",
              "percent_households_with_public_assistance_income     -0.084074   -0.565246   \n",
              "\n",
              "                                                  ...  state_22  state_34  \\\n",
              "population                                        ...  0.032277 -0.078670   \n",
              "racepctblack                                      ...  0.185467 -0.005287   \n",
              "racePctWhite                                      ... -0.121645  0.021156   \n",
              "racePctHisp                                       ... -0.049542 -0.021524   \n",
              "agePct12t29                                       ...  0.034619 -0.112636   \n",
              "...                                               ...       ...       ...   \n",
              "state_55                                          ... -0.018604 -0.060592   \n",
              "LemasGangUnitDeploy_0.5                           ...  0.082576 -0.059206   \n",
              "LemasGangUnitDeploy_1.0                           ...  0.000680 -0.050103   \n",
              "crime_rate_per_capita                             ...  0.080134 -0.044695   \n",
              "percent_households_with_public_assistance_income  ...  0.099209 -0.119520   \n",
              "\n",
              "                                                  state_37  state_42  \\\n",
              "population                                       -0.002614 -0.039600   \n",
              "racepctblack                                      0.229500 -0.057969   \n",
              "racePctWhite                                     -0.141728  0.116559   \n",
              "racePctHisp                                      -0.082426 -0.110852   \n",
              "agePct12t29                                       0.056608 -0.109476   \n",
              "...                                                    ...       ...   \n",
              "state_55                                         -0.027066 -0.040685   \n",
              "LemasGangUnitDeploy_0.5                          -0.021273 -0.054458   \n",
              "LemasGangUnitDeploy_1.0                           0.015775 -0.027363   \n",
              "crime_rate_per_capita                             0.158182 -0.061323   \n",
              "percent_households_with_public_assistance_income  0.049561  0.015506   \n",
              "\n",
              "                                                  state_45  state_55  \\\n",
              "population                                       -0.026626 -0.012392   \n",
              "racepctblack                                      0.166874 -0.098337   \n",
              "racePctWhite                                     -0.097375  0.132920   \n",
              "racePctHisp                                      -0.064045 -0.090194   \n",
              "agePct12t29                                       0.078151  0.014276   \n",
              "...                                                    ...       ...   \n",
              "state_55                                         -0.021020  1.000000   \n",
              "LemasGangUnitDeploy_0.5                          -0.009053 -0.002096   \n",
              "LemasGangUnitDeploy_1.0                          -0.004891 -0.009261   \n",
              "crime_rate_per_capita                             0.096755 -0.081466   \n",
              "percent_households_with_public_assistance_income  0.002923 -0.020404   \n",
              "\n",
              "                                                  LemasGangUnitDeploy_0.5  \\\n",
              "population                                                       0.553456   \n",
              "racepctblack                                                     0.184315   \n",
              "racePctWhite                                                    -0.239101   \n",
              "racePctHisp                                                      0.135319   \n",
              "agePct12t29                                                      0.090799   \n",
              "...                                                                   ...   \n",
              "state_55                                                        -0.002096   \n",
              "LemasGangUnitDeploy_0.5                                          1.000000   \n",
              "LemasGangUnitDeploy_1.0                                         -0.050659   \n",
              "crime_rate_per_capita                                           -0.125595   \n",
              "percent_households_with_public_assistance_income                 0.053594   \n",
              "\n",
              "                                                  LemasGangUnitDeploy_1.0  \\\n",
              "population                                                       0.223858   \n",
              "racepctblack                                                     0.121641   \n",
              "racePctWhite                                                    -0.126271   \n",
              "racePctHisp                                                      0.057842   \n",
              "agePct12t29                                                      0.047800   \n",
              "...                                                                   ...   \n",
              "state_55                                                        -0.009261   \n",
              "LemasGangUnitDeploy_0.5                                         -0.050659   \n",
              "LemasGangUnitDeploy_1.0                                          1.000000   \n",
              "crime_rate_per_capita                                           -0.103843   \n",
              "percent_households_with_public_assistance_income                 0.030269   \n",
              "\n",
              "                                                  crime_rate_per_capita  \\\n",
              "population                                                    -0.234849   \n",
              "racepctblack                                                   0.312029   \n",
              "racePctWhite                                                  -0.294185   \n",
              "racePctHisp                                                    0.091118   \n",
              "agePct12t29                                                    0.003144   \n",
              "...                                                                 ...   \n",
              "state_55                                                      -0.081466   \n",
              "LemasGangUnitDeploy_0.5                                       -0.125595   \n",
              "LemasGangUnitDeploy_1.0                                       -0.103843   \n",
              "crime_rate_per_capita                                          1.000000   \n",
              "percent_households_with_public_assistance_income               0.361972   \n",
              "\n",
              "                                                  percent_households_with_public_assistance_income  \n",
              "population                                                                                0.057457  \n",
              "racepctblack                                                                              0.367689  \n",
              "racePctWhite                                                                             -0.370926  \n",
              "racePctHisp                                                                               0.205035  \n",
              "agePct12t29                                                                              -0.094694  \n",
              "...                                                                                            ...  \n",
              "state_55                                                                                 -0.020404  \n",
              "LemasGangUnitDeploy_0.5                                                                   0.053594  \n",
              "LemasGangUnitDeploy_1.0                                                                   0.030269  \n",
              "crime_rate_per_capita                                                                     0.361972  \n",
              "percent_households_with_public_assistance_income                                          1.000000  \n",
              "\n",
              "[114 rows x 114 columns]"
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              "      <th>population</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>-0.444166</td>\n",
              "      <td>-0.266852</td>\n",
              "      <td>-0.295237</td>\n",
              "      <td>0.308686</td>\n",
              "      <td>0.117234</td>\n",
              "      <td>0.096982</td>\n",
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              "      <td>-0.121645</td>\n",
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              "      <td>0.205035</td>\n",
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              "      <td>0.553456</td>\n",
              "      <td>0.184315</td>\n",
              "      <td>-0.239101</td>\n",
              "      <td>0.135319</td>\n",
              "      <td>0.090799</td>\n",
              "      <td>0.553402</td>\n",
              "      <td>-0.089446</td>\n",
              "      <td>-0.013706</td>\n",
              "      <td>-0.096005</td>\n",
              "      <td>-0.131388</td>\n",
              "      <td>...</td>\n",
              "      <td>0.082576</td>\n",
              "      <td>-0.059206</td>\n",
              "      <td>-0.021273</td>\n",
              "      <td>-0.054458</td>\n",
              "      <td>-0.009053</td>\n",
              "      <td>-0.002096</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.050659</td>\n",
              "      <td>-0.125595</td>\n",
              "      <td>0.053594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LemasGangUnitDeploy_1.0</th>\n",
              "      <td>0.223858</td>\n",
              "      <td>0.121641</td>\n",
              "      <td>-0.126271</td>\n",
              "      <td>0.057842</td>\n",
              "      <td>0.047800</td>\n",
              "      <td>0.230829</td>\n",
              "      <td>-0.052015</td>\n",
              "      <td>-0.021065</td>\n",
              "      <td>-0.053705</td>\n",
              "      <td>-0.056926</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000680</td>\n",
              "      <td>-0.050103</td>\n",
              "      <td>0.015775</td>\n",
              "      <td>-0.027363</td>\n",
              "      <td>-0.004891</td>\n",
              "      <td>-0.009261</td>\n",
              "      <td>-0.050659</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.103843</td>\n",
              "      <td>0.030269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>crime_rate_per_capita</th>\n",
              "      <td>-0.234849</td>\n",
              "      <td>0.312029</td>\n",
              "      <td>-0.294185</td>\n",
              "      <td>0.091118</td>\n",
              "      <td>0.003144</td>\n",
              "      <td>-0.248173</td>\n",
              "      <td>-0.312519</td>\n",
              "      <td>-0.278384</td>\n",
              "      <td>0.045680</td>\n",
              "      <td>-0.374889</td>\n",
              "      <td>...</td>\n",
              "      <td>0.080134</td>\n",
              "      <td>-0.044695</td>\n",
              "      <td>0.158182</td>\n",
              "      <td>-0.061323</td>\n",
              "      <td>0.096755</td>\n",
              "      <td>-0.081466</td>\n",
              "      <td>-0.125595</td>\n",
              "      <td>-0.103843</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.361972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>percent_households_with_public_assistance_income</th>\n",
              "      <td>0.057457</td>\n",
              "      <td>0.367689</td>\n",
              "      <td>-0.370926</td>\n",
              "      <td>0.205035</td>\n",
              "      <td>-0.094694</td>\n",
              "      <td>0.032321</td>\n",
              "      <td>-0.680982</td>\n",
              "      <td>-0.866315</td>\n",
              "      <td>-0.084074</td>\n",
              "      <td>-0.565246</td>\n",
              "      <td>...</td>\n",
              "      <td>0.099209</td>\n",
              "      <td>-0.119520</td>\n",
              "      <td>0.049561</td>\n",
              "      <td>0.015506</td>\n",
              "      <td>0.002923</td>\n",
              "      <td>-0.020404</td>\n",
              "      <td>0.053594</td>\n",
              "      <td>0.030269</td>\n",
              "      <td>0.361972</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>114 rows × 114 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "corrs"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "significant_features = corrs['ViolentCrimesPerPop'].sort_values(ascending=False).index[:6]\n",
        "\n",
        "X = df[significant_features]\n",
        "y = df['ViolentCrimesPerPop']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "lr_model = LinearRegression()\n",
        "lr_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = lr_model.predict(X_test)\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n"
      ],
      "metadata": {
        "id": "8HQ-vZ8TNURS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "pKt1tMN0QdOT",
        "outputId": "4443f107-ef00-4e49-9c01-9edba14cc060"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      population  racepctblack  racePctWhite  racePctHisp  agePct12t29  \\\n",
              "0           0.19          0.02          0.90         0.17         0.47   \n",
              "1           0.00          0.12          0.74         0.07         0.59   \n",
              "2           0.00          0.49          0.56         0.04         0.47   \n",
              "3           0.04          1.00          0.08         0.10         0.50   \n",
              "4           0.01          0.02          0.95         0.05         0.38   \n",
              "...          ...           ...           ...          ...          ...   \n",
              "1989        0.01          0.10          0.87         0.16         0.51   \n",
              "1990        0.05          0.46          0.28         0.32         0.86   \n",
              "1991        0.16          0.25          0.69         0.25         0.50   \n",
              "1992        0.08          0.06          0.87         0.10         0.74   \n",
              "1993        0.20          0.14          0.46         0.77         0.62   \n",
              "\n",
              "      numbUrban  medIncome  pctWWage  pctWFarmSelf  pctWInvInc  ...  state_22  \\\n",
              "0          0.20       0.37      0.72          0.34        0.60  ...     False   \n",
              "1          0.02       0.31      0.72          0.11        0.45  ...     False   \n",
              "2          0.00       0.30      0.58          0.19        0.39  ...     False   \n",
              "3          0.06       0.58      0.89          0.21        0.43  ...     False   \n",
              "4          0.02       0.50      0.72          0.16        0.68  ...     False   \n",
              "...         ...        ...       ...           ...         ...  ...       ...   \n",
              "1989       0.03       0.42      0.74          0.14        0.64  ...     False   \n",
              "1990       0.06       0.28      0.76          0.10        0.30  ...     False   \n",
              "1991       0.17       0.31      0.47          0.17        0.45  ...     False   \n",
              "1992       0.09       0.44      0.64          0.21        0.55  ...     False   \n",
              "1993       0.21       0.40      0.73          0.22        0.25  ...     False   \n",
              "\n",
              "      state_34  state_37  state_42  state_45  state_55  \\\n",
              "0        False     False     False     False     False   \n",
              "1        False     False     False     False     False   \n",
              "2        False     False     False     False     False   \n",
              "3         True     False     False     False     False   \n",
              "4        False     False      True     False     False   \n",
              "...        ...       ...       ...       ...       ...   \n",
              "1989     False     False     False     False     False   \n",
              "1990     False     False     False     False     False   \n",
              "1991     False     False     False     False     False   \n",
              "1992     False     False     False     False     False   \n",
              "1993     False     False     False     False     False   \n",
              "\n",
              "      LemasGangUnitDeploy_0.5  LemasGangUnitDeploy_1.0  crime_rate_per_capita  \\\n",
              "0                        True                    False               1.052632   \n",
              "1                       False                    False                    inf   \n",
              "2                       False                    False                    inf   \n",
              "3                       False                    False               3.000000   \n",
              "4                       False                    False               3.000000   \n",
              "...                       ...                      ...                    ...   \n",
              "1989                    False                    False               9.000000   \n",
              "1990                    False                    False               9.000000   \n",
              "1991                    False                    False               1.437500   \n",
              "1992                    False                    False               2.375000   \n",
              "1993                     True                    False               2.400000   \n",
              "\n",
              "      percent_households_with_public_assistance_income  \n",
              "0                                                0.220  \n",
              "1                                                0.270  \n",
              "2                                                0.390  \n",
              "3                                                0.280  \n",
              "4                                                0.275  \n",
              "...                                                ...  \n",
              "1989                                             0.180  \n",
              "1990                                             0.315  \n",
              "1991                                             0.530  \n",
              "1992                                             0.330  \n",
              "1993                                             0.365  \n",
              "\n",
              "[1994 rows x 114 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>population</th>\n",
              "      <th>racepctblack</th>\n",
              "      <th>racePctWhite</th>\n",
              "      <th>racePctHisp</th>\n",
              "      <th>agePct12t29</th>\n",
              "      <th>numbUrban</th>\n",
              "      <th>medIncome</th>\n",
              "      <th>pctWWage</th>\n",
              "      <th>pctWFarmSelf</th>\n",
              "      <th>pctWInvInc</th>\n",
              "      <th>...</th>\n",
              "      <th>state_22</th>\n",
              "      <th>state_34</th>\n",
              "      <th>state_37</th>\n",
              "      <th>state_42</th>\n",
              "      <th>state_45</th>\n",
              "      <th>state_55</th>\n",
              "      <th>LemasGangUnitDeploy_0.5</th>\n",
              "      <th>LemasGangUnitDeploy_1.0</th>\n",
              "      <th>crime_rate_per_capita</th>\n",
              "      <th>percent_households_with_public_assistance_income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.19</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.60</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>1.052632</td>\n",
              "      <td>0.220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.45</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>inf</td>\n",
              "      <td>0.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.39</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>inf</td>\n",
              "      <td>0.390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.04</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.43</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.68</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.275</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",
              "      <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>1989</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.64</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>0.05</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.30</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>0.16</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.45</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1.437500</td>\n",
              "      <td>0.530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992</th>\n",
              "      <td>0.08</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.55</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>2.375000</td>\n",
              "      <td>0.330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993</th>\n",
              "      <td>0.20</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.25</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>2.400000</td>\n",
              "      <td>0.365</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1994 rows × 114 columns</p>\n",
              "</div>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-b3617acd-9684-440e-b446-bd963fde5a80 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b3617acd-9684-440e-b446-bd963fde5a80');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5953ef12-b07f-4afb-a4f3-038522e67193 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r_squared = r2_score(y_test, y_pred)\n",
        "\n",
        "print(\"Mean Squared Error:\", mse)\n",
        "print(\"R-squared:\", r_squared)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlXRFApmNu8h",
        "outputId": "5a175ff1-48cc-4556-804f-00b09599e094"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 1.584452775882253e-32\n",
            "R-squared: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Create an instance of LinearRegression\n",
        "lr_model = LinearRegression()\n",
        "\n",
        "# Train the model using training data\n",
        "lr_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on test data\n",
        "y_pred = lr_model.predict(X_test)\n"
      ],
      "metadata": {
        "id": "aAh79rayQIRx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = np.column_stack((y_pred, y_test))\n",
        "print(\"Predictions vs Actuals:\")\n",
        "print(predictions)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrSZ5mIIQ26T",
        "outputId": "0ed76af5-f91f-4f8b-cff8-e079bb188694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predictions vs Actuals:\n",
            "[[ 2.20000000e-01  2.20000000e-01]\n",
            " [ 4.00000000e-02  4.00000000e-02]\n",
            " [ 1.00000000e-01  1.00000000e-01]\n",
            " [ 8.00000000e-02  8.00000000e-02]\n",
            " [ 4.00000000e-02  4.00000000e-02]\n",
            " [ 5.00000000e-02  5.00000000e-02]\n",
            " [ 2.30000000e-01  2.30000000e-01]\n",
            " [ 4.80000000e-01  4.80000000e-01]\n",
            " [ 4.00000000e-02  4.00000000e-02]\n",
            " [ 2.00000000e-01  2.00000000e-01]\n",
            " [ 6.30000000e-01  6.30000000e-01]\n",
            " [ 1.00000000e-01  1.00000000e-01]\n",
            " [ 7.00000000e-02  7.00000000e-02]\n",
            " [ 1.10000000e-01  1.10000000e-01]\n",
            " [ 5.00000000e-02  5.00000000e-02]\n",
            " [ 4.00000000e-02  4.00000000e-02]\n",
            " [ 1.70000000e-01  1.70000000e-01]\n",
            " [ 5.10000000e-01  5.10000000e-01]\n",
            " [ 5.20000000e-01  5.20000000e-01]\n",
            " [ 1.60000000e-01  1.60000000e-01]\n",
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            " [ 3.10000000e-01  3.10000000e-01]\n",
            " [ 6.00000000e-02  6.00000000e-02]\n",
            " [ 2.85016896e-17  0.00000000e+00]\n",
            " [ 3.40000000e-01  3.40000000e-01]\n",
            " [ 1.50000000e-01  1.50000000e-01]\n",
            " [ 1.60000000e-01  1.60000000e-01]\n",
            " [ 3.60000000e-01  3.60000000e-01]\n",
            " [ 1.00000000e+00  1.00000000e+00]\n",
            " [ 5.90000000e-01  5.90000000e-01]\n",
            " [ 4.00000000e-02  4.00000000e-02]\n",
            " [ 3.00000000e-02  3.00000000e-02]\n",
            " [ 4.00000000e-02  4.00000000e-02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2shFA7-2RRO8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}