Assignment2.ipynb

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        "\n",
        "\n",
        "# CAR PRICE ASSIGNMENT\n",
        "\n"
      ],
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        "id": "Vhh5XVTTCv-9"
      }
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      "source": [
        "***LINEAR REGRESSION***"
      ],
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        "id": "1hbCNfRuyUgg"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"CarPrice_Assignment.csv\")"
      ],
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        "id": "DYLmmKLfyocQ"
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      "execution_count": null,
      "outputs": []
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        "df"
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            "text/plain": [
              "     car_ID  symboling                   CarName fueltype aspiration  \\\n",
              "0         1          3        alfa-romero giulia      gas        std   \n",
              "1         2          3       alfa-romero stelvio      gas        std   \n",
              "2         3          1  alfa-romero Quadrifoglio      gas        std   \n",
              "3         4          2               audi 100 ls      gas        std   \n",
              "4         5          2                audi 100ls      gas        std   \n",
              "..      ...        ...                       ...      ...        ...   \n",
              "200     201         -1           volvo 145e (sw)      gas        std   \n",
              "201     202         -1               volvo 144ea      gas      turbo   \n",
              "202     203         -1               volvo 244dl      gas        std   \n",
              "203     204         -1                 volvo 246   diesel      turbo   \n",
              "204     205         -1               volvo 264gl      gas      turbo   \n",
              "\n",
              "    doornumber      carbody drivewheel enginelocation  wheelbase  ...  \\\n",
              "0          two  convertible        rwd          front       88.6  ...   \n",
              "1          two  convertible        rwd          front       88.6  ...   \n",
              "2          two    hatchback        rwd          front       94.5  ...   \n",
              "3         four        sedan        fwd          front       99.8  ...   \n",
              "4         four        sedan        4wd          front       99.4  ...   \n",
              "..         ...          ...        ...            ...        ...  ...   \n",
              "200       four        sedan        rwd          front      109.1  ...   \n",
              "201       four        sedan        rwd          front      109.1  ...   \n",
              "202       four        sedan        rwd          front      109.1  ...   \n",
              "203       four        sedan        rwd          front      109.1  ...   \n",
              "204       four        sedan        rwd          front      109.1  ...   \n",
              "\n",
              "     enginesize  fuelsystem  boreratio  stroke compressionratio horsepower  \\\n",
              "0           130        mpfi       3.47    2.68              9.0        111   \n",
              "1           130        mpfi       3.47    2.68              9.0        111   \n",
              "2           152        mpfi       2.68    3.47              9.0        154   \n",
              "3           109        mpfi       3.19    3.40             10.0        102   \n",
              "4           136        mpfi       3.19    3.40              8.0        115   \n",
              "..          ...         ...        ...     ...              ...        ...   \n",
              "200         141        mpfi       3.78    3.15              9.5        114   \n",
              "201         141        mpfi       3.78    3.15              8.7        160   \n",
              "202         173        mpfi       3.58    2.87              8.8        134   \n",
              "203         145         idi       3.01    3.40             23.0        106   \n",
              "204         141        mpfi       3.78    3.15              9.5        114   \n",
              "\n",
              "     peakrpm citympg  highwaympg    price  \n",
              "0       5000      21          27  13495.0  \n",
              "1       5000      21          27  16500.0  \n",
              "2       5000      19          26  16500.0  \n",
              "3       5500      24          30  13950.0  \n",
              "4       5500      18          22  17450.0  \n",
              "..       ...     ...         ...      ...  \n",
              "200     5400      23          28  16845.0  \n",
              "201     5300      19          25  19045.0  \n",
              "202     5500      18          23  21485.0  \n",
              "203     4800      26          27  22470.0  \n",
              "204     5400      19          25  22625.0  \n",
              "\n",
              "[205 rows x 26 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>car_ID</th>\n",
              "      <th>symboling</th>\n",
              "      <th>CarName</th>\n",
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              "      <th>aspiration</th>\n",
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              "      <th>compressionratio</th>\n",
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              "      <th>peakrpm</th>\n",
              "      <th>citympg</th>\n",
              "      <th>highwaympg</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romero giulia</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>...</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
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              "      <td>alfa-romero stelvio</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>convertible</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>88.6</td>\n",
              "      <td>...</td>\n",
              "      <td>130</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.47</td>\n",
              "      <td>2.68</td>\n",
              "      <td>9.0</td>\n",
              "      <td>111</td>\n",
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              "      <td>27</td>\n",
              "      <td>16500.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>alfa-romero Quadrifoglio</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>two</td>\n",
              "      <td>hatchback</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>94.5</td>\n",
              "      <td>...</td>\n",
              "      <td>152</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>2.68</td>\n",
              "      <td>3.47</td>\n",
              "      <td>9.0</td>\n",
              "      <td>154</td>\n",
              "      <td>5000</td>\n",
              "      <td>19</td>\n",
              "      <td>26</td>\n",
              "      <td>16500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100 ls</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>fwd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.8</td>\n",
              "      <td>...</td>\n",
              "      <td>109</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>10.0</td>\n",
              "      <td>102</td>\n",
              "      <td>5500</td>\n",
              "      <td>24</td>\n",
              "      <td>30</td>\n",
              "      <td>13950.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>audi 100ls</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>4wd</td>\n",
              "      <td>front</td>\n",
              "      <td>99.4</td>\n",
              "      <td>...</td>\n",
              "      <td>136</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.19</td>\n",
              "      <td>3.40</td>\n",
              "      <td>8.0</td>\n",
              "      <td>115</td>\n",
              "      <td>5500</td>\n",
              "      <td>18</td>\n",
              "      <td>22</td>\n",
              "      <td>17450.0</td>\n",
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              "      <td>201</td>\n",
              "      <td>-1</td>\n",
              "      <td>volvo 145e (sw)</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
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              "      <td>16845.0</td>\n",
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              "      <th>201</th>\n",
              "      <td>202</td>\n",
              "      <td>-1</td>\n",
              "      <td>volvo 144ea</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
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              "      <td>141</td>\n",
              "      <td>mpfi</td>\n",
              "      <td>3.78</td>\n",
              "      <td>3.15</td>\n",
              "      <td>8.7</td>\n",
              "      <td>160</td>\n",
              "      <td>5300</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>19045.0</td>\n",
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              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>203</td>\n",
              "      <td>-1</td>\n",
              "      <td>volvo 244dl</td>\n",
              "      <td>gas</td>\n",
              "      <td>std</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
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              "      <td>173</td>\n",
              "      <td>mpfi</td>\n",
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              "      <td>2.87</td>\n",
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              "      <th>203</th>\n",
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              "      <td>volvo 246</td>\n",
              "      <td>diesel</td>\n",
              "      <td>turbo</td>\n",
              "      <td>four</td>\n",
              "      <td>sedan</td>\n",
              "      <td>rwd</td>\n",
              "      <td>front</td>\n",
              "      <td>109.1</td>\n",
              "      <td>...</td>\n",
              "      <td>145</td>\n",
              "      <td>idi</td>\n",
              "      <td>3.01</td>\n",
              "      <td>3.40</td>\n",
              "      <td>23.0</td>\n",
              "      <td>106</td>\n",
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              "      <td>22470.0</td>\n",
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              "      <th>204</th>\n",
              "      <td>205</td>\n",
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              "      <td>volvo 264gl</td>\n",
              "      <td>gas</td>\n",
              "      <td>turbo</td>\n",
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              "      <td>sedan</td>\n",
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              "      <td>109.1</td>\n",
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              "      <td>114</td>\n",
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              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>22625.0</td>\n",
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              "        <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-ce3ba410-14dd-4ca7-85a2-d48683aa8a12 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 220
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8lPkxqi0dHh",
        "outputId": "f7d24673-276e-4a45-f150-dca1121b5258"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "car_ID              0\n",
              "symboling           0\n",
              "CarName             0\n",
              "fueltype            0\n",
              "aspiration          0\n",
              "doornumber          0\n",
              "carbody             0\n",
              "drivewheel          0\n",
              "enginelocation      0\n",
              "wheelbase           0\n",
              "carlength           0\n",
              "carwidth            0\n",
              "carheight           0\n",
              "curbweight          0\n",
              "enginetype          0\n",
              "cylindernumber      0\n",
              "enginesize          0\n",
              "fuelsystem          0\n",
              "boreratio           0\n",
              "stroke              0\n",
              "compressionratio    0\n",
              "horsepower          0\n",
              "peakrpm             0\n",
              "citympg             0\n",
              "highwaympg          0\n",
              "price               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=df[\"car_ID\"]\n",
        "Y=df[\"price\"]"
      ],
      "metadata": {
        "id": "Z91c-isD0n5W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X,Y)\n",
        "plt.xlabel(\"carID\")\n",
        "plt.ylabel(\"price\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "VHlhwd3q_-6-",
        "outputId": "874e8bcd-3ce3-4028-8bd8-a99421b38ed1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'price')"
            ]
          },
          "metadata": {},
          "execution_count": 223
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test ,y_train,y_test= train_test_split(X,Y , test_size=0.3, random_state=26)\n",
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OGefKgUE2nGM",
        "outputId": "71a43330-e87d-4168-c036-7c681843e780"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43      44\n",
              "171    172\n",
              "28      29\n",
              "18      19\n",
              "118    119\n",
              "      ... \n",
              "193    194\n",
              "176    177\n",
              "6        7\n",
              "62      63\n",
              "53      54\n",
              "Name: car_ID, Length: 143, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.array(X_train).reshape(-1,1)\n",
        "X_train.flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGPm7kDB4hSF",
        "outputId": "82b38166-067e-4a11-cbdf-ad8e9cef2734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 44, 172,  29,  19, 119, 175, 196,  14,  85,  73, 189, 195,  30,\n",
              "        59, 149,   9,  70, 199,  18, 140, 164, 104, 170,  98,  37, 183,\n",
              "        74,  95,  92, 137, 128,  20,  38, 182,  75,  42,  39,  90, 187,\n",
              "       114, 204,  76,  50, 193,  10, 133,  28,  16,   5, 127, 145,  40,\n",
              "        93, 136,   4, 121,  69, 203,  33,   1,  36,  56,  46, 186, 135,\n",
              "        62, 198, 185,  34, 124,   8, 107,  87, 167,  24,  12,  65,  58,\n",
              "        60,  23, 102,  25, 153,  55, 162, 122,  64, 169,  41,  47, 163,\n",
              "       141,  53,  80, 200, 120,  97,  79, 129,  71, 113, 201,  45,  82,\n",
              "       157, 100,  52, 158, 148,  84,  77, 115, 178,  17, 180,  13, 118,\n",
              "        48,  83,  57, 176, 152, 171, 144, 159, 150, 111, 181, 205, 116,\n",
              "       184, 146, 174,  99, 197, 161,  78, 103, 194, 177,   7,  63,  54])"
            ]
          },
          "metadata": {},
          "execution_count": 225
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X_test = np.array(X_test).reshape(-1,1)\n",
        "X_test\n",
        "X_test.flatten()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eOxJ53ZvEpWW",
        "outputId": "e577922f-d389-4447-b4ef-229667820689"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 72,  26,  61, 109, 125, 112,  35,  88,  91, 191, 156, 165, 168,\n",
              "       108,  81, 173, 190, 132,  43, 126,  22,  31, 142, 202, 139,  94,\n",
              "       130, 154,  68, 138,  67,  51, 110,   2,  89,  27, 123,  21, 188,\n",
              "       192, 117, 155,   6, 179, 106, 147, 134, 151,  86, 131,  15, 105,\n",
              "        49, 101,  32, 160, 166,  66,   3,  11, 143,  96])"
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "BOfIEHw85_1w",
        "outputId": "a6211d25-93b1-4bed-9778-aae160f6163b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c=lr.intercept_\n",
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EI1Qmxta7Bb9",
        "outputId": "86b1bf96-298c-4dee-e4c9-63902357f593"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15165.697744488109"
            ]
          },
          "metadata": {},
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m=lr.coef_\n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDB70tQf7z0t",
        "outputId": "177e9997-0b15-4133-8400-0e7a6659b5c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-15.3558251])"
            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_train = m*X_train + c\n",
        "Y_pred_train.flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0u-p7eaC74Oc",
        "outputId": "a3ac3727-fd57-4970-d85a-c6e80ac82133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([14490.04144027, 12524.49582798, 14720.37881671, 14873.93706767,\n",
              "       13338.35455807, 12478.4283527 , 12155.95602568, 14950.71619314,\n",
              "       13860.45261133, 14044.72251248, 12263.44680135, 12171.31185078,\n",
              "       14705.02299161, 14259.70406383, 12877.67980519, 15027.49531862,\n",
              "       14090.78998777, 12109.88855039, 14889.29289276, 13015.88223106,\n",
              "       12647.34242875, 13568.69193451, 12555.20747818, 13660.82688509,\n",
              "       14597.53221594, 12355.58175193, 14029.36668739, 13706.89436037,\n",
              "       13752.96183566, 13061.94970634, 13200.15213221, 14858.58124257,\n",
              "       14582.17639084, 12370.93757703, 14014.01086229, 14520.75309046,\n",
              "       14566.82056575, 13783.67348585, 12294.15845155, 13415.13368355,\n",
              "       12033.10942491, 13998.6550372 , 14397.90648969, 12202.02350097,\n",
              "       15012.13949353, 13123.37300673, 14735.7346418 , 14920.00454295,\n",
              "       15088.91861901, 13215.5079573 , 12939.10310558, 14551.46474065,\n",
              "       13737.60601056, 13077.30553144, 15104.2744441 , 13307.64290788,\n",
              "       14106.14581287, 12048.46525001, 14658.95551632, 15150.34191939,\n",
              "       14612.88804103, 14305.77153911, 14459.32979007, 12309.51427664,\n",
              "       13092.66135653, 14213.63658854, 12125.24437549, 12324.87010174,\n",
              "       14643.59969123, 13261.57543259, 15042.85114372, 13522.62445922,\n",
              "       13829.74096114, 12601.27495346, 14797.15794219, 14981.42784334,\n",
              "       14167.56911325, 14275.05988892, 14244.34823873, 14812.51376728,\n",
              "       13599.4035847 , 14781.80211709, 12816.25650481, 14321.12736421,\n",
              "       12678.05407894, 13292.28708278, 14182.92493835, 12570.56330327,\n",
              "       14536.10891555, 14443.97396498, 12662.69825385, 13000.52640596,\n",
              "       14351.8390144 , 13937.23173681, 12094.5327253 , 13322.99873297,\n",
              "       13676.18271018, 13952.58756191, 13184.79630711, 14075.43416268,\n",
              "       13430.48950865, 12079.1769002 , 14474.68561517, 13906.52008662,\n",
              "       12754.83320442, 13630.11523489, 14367.1948395 , 12739.47737933,\n",
              "       12893.03563029, 13875.80843643, 13983.2992121 , 13399.77785845,\n",
              "       12432.36087741, 14904.64871786, 12401.64922722, 14966.07201824,\n",
              "       13353.71038317, 14428.61813988, 13891.16426152, 14290.41571402,\n",
              "       12463.0725276 , 12831.6123299 , 12539.85165308, 12954.45893067,\n",
              "       12724.12155423, 12862.3239801 , 13461.20115884, 12386.29340212,\n",
              "       12017.75359982, 13384.42203336, 12340.22592683, 12923.74728048,\n",
              "       12493.78417779, 13645.47105999, 12140.60020059, 12693.40990404,\n",
              "       13967.943387  , 13584.04775961, 12186.66767587, 12447.71670251,\n",
              "       15058.20696882, 14198.28076344, 14336.48318931])"
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_train1=lr.predict(X_train)\n",
        "Y_pred_train1.flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPoGcU4p8FZh",
        "outputId": "fdba0690-0e0b-46b4-d48b-c9c495df6ed5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([14490.04144027, 12524.49582798, 14720.37881671, 14873.93706767,\n",
              "       13338.35455807, 12478.4283527 , 12155.95602568, 14950.71619314,\n",
              "       13860.45261133, 14044.72251248, 12263.44680135, 12171.31185078,\n",
              "       14705.02299161, 14259.70406383, 12877.67980519, 15027.49531862,\n",
              "       14090.78998777, 12109.88855039, 14889.29289276, 13015.88223106,\n",
              "       12647.34242875, 13568.69193451, 12555.20747818, 13660.82688509,\n",
              "       14597.53221594, 12355.58175193, 14029.36668739, 13706.89436037,\n",
              "       13752.96183566, 13061.94970634, 13200.15213221, 14858.58124257,\n",
              "       14582.17639084, 12370.93757703, 14014.01086229, 14520.75309046,\n",
              "       14566.82056575, 13783.67348585, 12294.15845155, 13415.13368355,\n",
              "       12033.10942491, 13998.6550372 , 14397.90648969, 12202.02350097,\n",
              "       15012.13949353, 13123.37300673, 14735.7346418 , 14920.00454295,\n",
              "       15088.91861901, 13215.5079573 , 12939.10310558, 14551.46474065,\n",
              "       13737.60601056, 13077.30553144, 15104.2744441 , 13307.64290788,\n",
              "       14106.14581287, 12048.46525001, 14658.95551632, 15150.34191939,\n",
              "       14612.88804103, 14305.77153911, 14459.32979007, 12309.51427664,\n",
              "       13092.66135653, 14213.63658854, 12125.24437549, 12324.87010174,\n",
              "       14643.59969123, 13261.57543259, 15042.85114372, 13522.62445922,\n",
              "       13829.74096114, 12601.27495346, 14797.15794219, 14981.42784334,\n",
              "       14167.56911325, 14275.05988892, 14244.34823873, 14812.51376728,\n",
              "       13599.4035847 , 14781.80211709, 12816.25650481, 14321.12736421,\n",
              "       12678.05407894, 13292.28708278, 14182.92493835, 12570.56330327,\n",
              "       14536.10891555, 14443.97396498, 12662.69825385, 13000.52640596,\n",
              "       14351.8390144 , 13937.23173681, 12094.5327253 , 13322.99873297,\n",
              "       13676.18271018, 13952.58756191, 13184.79630711, 14075.43416268,\n",
              "       13430.48950865, 12079.1769002 , 14474.68561517, 13906.52008662,\n",
              "       12754.83320442, 13630.11523489, 14367.1948395 , 12739.47737933,\n",
              "       12893.03563029, 13875.80843643, 13983.2992121 , 13399.77785845,\n",
              "       12432.36087741, 14904.64871786, 12401.64922722, 14966.07201824,\n",
              "       13353.71038317, 14428.61813988, 13891.16426152, 14290.41571402,\n",
              "       12463.0725276 , 12831.6123299 , 12539.85165308, 12954.45893067,\n",
              "       12724.12155423, 12862.3239801 , 13461.20115884, 12386.29340212,\n",
              "       12017.75359982, 13384.42203336, 12340.22592683, 12923.74728048,\n",
              "       12493.78417779, 13645.47105999, 12140.60020059, 12693.40990404,\n",
              "       13967.943387  , 13584.04775961, 12186.66767587, 12447.71670251,\n",
              "       15058.20696882, 14198.28076344, 14336.48318931])"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X_train, y_train)\n",
        "plt.plot(X_train, Y_pred_train1, color='k')\n",
        "plt.xlabel(\"carID\")\n",
        "plt.ylabel(\"price\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "m2pZDM438V6I",
        "outputId": "1bc8ce06-5ce3-4c9c-8179-39ec3ef26e7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'price')"
            ]
          },
          "metadata": {},
          "execution_count": 232
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_test1= lr.predict(X_test)\n",
        "Y_pred_test1.flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szpDK0nzBhGN",
        "outputId": "4deb9ffe-fd33-4b16-adf2-423deb48f68d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([14060.07833758, 14766.44629199, 14228.99241364, 13491.91280903,\n",
              "       13246.21960749, 13445.84533374, 14628.24386613, 13814.38513604,\n",
              "       13768.31766076, 12232.73515116, 12770.18902952, 12631.98660366,\n",
              "       12585.91912837, 13507.26863413, 13921.87591172, 12509.14000289,\n",
              "       12248.09097626, 13138.72883182, 14505.39726536, 13230.8637824 ,\n",
              "       14827.86959238, 14689.66716651, 12985.17058086, 12063.82107511,\n",
              "       13031.23805615, 13722.25018547, 13169.44048201, 12800.90067971,\n",
              "       14121.50163796, 13046.59388125, 14136.85746306, 14382.55066459,\n",
              "       13476.55698393, 15134.9860943 , 13799.02931095, 14751.0904669 ,\n",
              "       13276.93125769, 14843.22541747, 12278.80262645, 12217.37932607,\n",
              "       13369.06620826, 12785.54485462, 15073.56279391, 12417.00505231,\n",
              "       13537.98028432, 12908.39145538, 13108.01718163, 12846.968155  ,\n",
              "       13845.09678624, 13154.08465692, 14935.36036805, 13553.33610941,\n",
              "       14413.26231479, 13614.7594098 , 14674.31134142, 12708.76572914,\n",
              "       12616.63077856, 14152.21328816, 15119.6302692 , 14996.78366843,\n",
              "       12969.81475577, 13691.53853528])"
            ]
          },
          "metadata": {},
          "execution_count": 236
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X_test, y_test)\n",
        "plt.plot(X_test, Y_pred_test1,color=\"r\")\n",
        "plt.xlabel(\"carID\")\n",
        "plt.ylabel(\"price\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "7cRbqscvCUFo",
        "outputId": "ee5bad24-9c5c-4b3b-f792-ac0361aae88f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'price')"
            ]
          },
          "metadata": {},
          "execution_count": 235
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***MULTIPLE REGRESSION***"
      ],
      "metadata": {
        "id": "yCjlzYCkO_Yq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['CarName']=df['CarName'].astype('category')\n",
        "df['CarName']=df['CarName'].cat.codes\n",
        "df['fueltype']=df['fueltype'].astype('category')\n",
        "df['fueltype']=df['fueltype'].cat.codes\n",
        "df['aspiration']=df['aspiration'].astype('category')\n",
        "df['aspiration']=df['aspiration'].cat.codes\n",
        "df['doornumber']=df['doornumber'].astype('category')\n",
        "df['doornumber']=df['doornumber'].cat.codes\n",
        "df['carbody']=df['carbody'].astype('category')\n",
        "df['carbody']=df['carbody'].cat.codes\n",
        "df['drivewheel']=df['drivewheel'].astype('category')\n",
        "df['drivewheel']=df['drivewheel'].cat.codes\n",
        "df['enginelocation']=df['enginelocation'].astype('category')\n",
        "df['enginelocation']=df['enginelocation'].cat.codes\n",
        "df['fuelsystem']=df['fuelsystem'].astype('category')\n",
        "df['fuelsystem']=df['fuelsystem'].cat.codes\n",
        "df['cylindernumber']=df['cylindernumber'].astype('category')\n",
        "df['cylindernumber']=df['cylindernumber'].cat.codes\n",
        "df['enginetype']=df['enginetype'].astype('category')\n",
        "df['enginetype']=df['enginetype'].cat.codes\n"
      ],
      "metadata": {
        "id": "l-DFJJtaQqBW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X2=df.drop(['price'],axis=1).values\n",
        "Y2=df['price'].values"
      ],
      "metadata": {
        "id": "7yU4IiYtPHGP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dhetd8CyPHCw",
        "outputId": "1a413517-91c0-4753-c7fd-512ce3cefb2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.00e+00  3.00e+00  2.00e+00 ...  5.00e+03  2.10e+01  2.70e+01]\n",
            " [ 2.00e+00  3.00e+00  3.00e+00 ...  5.00e+03  2.10e+01  2.70e+01]\n",
            " [ 3.00e+00  1.00e+00  1.00e+00 ...  5.00e+03  1.90e+01  2.60e+01]\n",
            " ...\n",
            " [ 2.03e+02 -1.00e+00  1.40e+02 ...  5.50e+03  1.80e+01  2.30e+01]\n",
            " [ 2.04e+02 -1.00e+00  1.42e+02 ...  4.80e+03  2.60e+01  2.70e+01]\n",
            " [ 2.05e+02 -1.00e+00  1.43e+02 ...  5.40e+03  1.90e+01  2.50e+01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yuz4EgP9PG_m",
        "outputId": "9b40961e-e7ed-4a8f-aaf7-9d4cb390d9a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[13495.    16500.    16500.    13950.    17450.    15250.    17710.\n",
            " 18920.    23875.    17859.167 16430.    16925.    20970.    21105.\n",
            " 24565.    30760.    41315.    36880.     5151.     6295.     6575.\n",
            "  5572.     6377.     7957.     6229.     6692.     7609.     8558.\n",
            "  8921.    12964.     6479.     6855.     5399.     6529.     7129.\n",
            "  7295.     7295.     7895.     9095.     8845.    10295.    12945.\n",
            " 10345.     6785.     8916.5    8916.5   11048.    32250.    35550.\n",
            " 36000.     5195.     6095.     6795.     6695.     7395.    10945.\n",
            " 11845.    13645.    15645.     8845.     8495.    10595.    10245.\n",
            " 10795.    11245.    18280.    18344.    25552.    28248.    28176.\n",
            " 31600.    34184.    35056.    40960.    45400.    16503.     5389.\n",
            "  6189.     6669.     7689.     9959.     8499.    12629.    14869.\n",
            " 14489.     6989.     8189.     9279.     9279.     5499.     7099.\n",
            "  6649.     6849.     7349.     7299.     7799.     7499.     7999.\n",
            "  8249.     8949.     9549.    13499.    14399.    13499.    17199.\n",
            " 19699.    18399.    11900.    13200.    12440.    13860.    15580.\n",
            " 16900.    16695.    17075.    16630.    17950.    18150.     5572.\n",
            "  7957.     6229.     6692.     7609.     8921.    12764.    22018.\n",
            " 32528.    34028.    37028.    31400.5    9295.     9895.    11850.\n",
            " 12170.    15040.    15510.    18150.    18620.     5118.     7053.\n",
            "  7603.     7126.     7775.     9960.     9233.    11259.     7463.\n",
            " 10198.     8013.    11694.     5348.     6338.     6488.     6918.\n",
            "  7898.     8778.     6938.     7198.     7898.     7788.     7738.\n",
            "  8358.     9258.     8058.     8238.     9298.     9538.     8449.\n",
            "  9639.     9989.    11199.    11549.    17669.     8948.    10698.\n",
            "  9988.    10898.    11248.    16558.    15998.    15690.    15750.\n",
            "  7775.     7975.     7995.     8195.     8495.     9495.     9995.\n",
            " 11595.     9980.    13295.    13845.    12290.    12940.    13415.\n",
            " 15985.    16515.    18420.    18950.    16845.    19045.    21485.\n",
            " 22470.    22625.   ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X2_train,X2_test ,Y2_train,Y2_test= train_test_split(X2,Y2, test_size=0.3, random_state=0)\n"
      ],
      "metadata": {
        "id": "AfP9eog4PG9g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X2_train, Y2_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "-2WLpLMBPG7V",
        "outputId": "b5dec281-0041-4fe2-c0b9-d8019b8d8306"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y2_pred=lr.predict(X2_test)\n",
        "print(Y2_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqvbLwCJPG5K",
        "outputId": "1ffadd6c-b89c-47b8-c78d-132c3df21c39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 4892.38237559 17948.77680365 13436.86696928  4260.56959143\n",
            " 10183.50054958 12095.55135398  6975.40443827  5041.60982065\n",
            " 19294.1104416   7908.9267982  17235.68249768 22449.94679794\n",
            " 12125.68125529 12296.0885611   7087.60108809 10850.92600657\n",
            "  8917.57616638 19653.28850345  9893.3651494   7308.11292865\n",
            " 10658.04025805 19918.71194518  8920.25885007 13127.8217426\n",
            " 21151.97167137  7195.38847472  5484.34814439 20533.95553172\n",
            "  8106.65375748 15251.95424203  7864.56211431 10718.00484462\n",
            " 15632.03930507  9610.93424968  6627.55237506 24530.59837772\n",
            "  8555.85823509 14614.22320329  7409.64667866 37914.79615534\n",
            "  5818.34161714 15285.8764862  33727.94609429 17424.25396079\n",
            "  9529.95567282  8403.61730424  8669.83324185 14215.16771346\n",
            "  9113.92654453  9746.4237669  21755.01557536  4380.53725232\n",
            "  6321.97806001  9972.75138402 17015.2138317  21708.42406292\n",
            " 10031.1207807  14623.99911737  9848.21098976  8441.78564403\n",
            "  3991.7583163  12892.7795125 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y2_pred_train=lr.predict(X2_train)"
      ],
      "metadata": {
        "id": "MYie3q6UUq7C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(Y2_test, Y2_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikO2w-J9PGtN",
        "outputId": "c4ea669f-d67c-445a-c817-a651d393c0f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7961110382079515"
            ]
          },
          "metadata": {},
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(Y2_train, Y2_pred_train,color='m')\n",
        "plt.xlabel(\"Actual\")\n",
        "plt.ylabel(\"Predicted\")\n",
        "plt.title(\"Actual vs Predicted (Training)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "f5SH6mWWUQ6E",
        "outputId": "ddbfffde-b183-42e0-c72b-00b99b6e6eee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(Y2_test, Y2_pred,color='c')\n",
        "plt.xlabel(\"Actual \")\n",
        "plt.ylabel(\"Predicted\")\n",
        "plt.title(\"Actual vs Predicted (Testing)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "vlR4D06YPGRh",
        "outputId": "0b829e47-ff8e-4088-afa1-329c75cad29c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_Y2_df=pd.DataFrame({\"Actual Value\":Y2_test,'Predicted Value':Y2_pred,'Difference':Y2_test-Y2_pred})\n",
        "pred_Y2_df[0:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "ppKKGmG_VNTe",
        "outputId": "ab493652-ad93-425c-a846-6de7ee835701"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Actual Value  Predicted Value   Difference\n",
              "0         6795.0      4892.382376  1902.617624\n",
              "1        15750.0     17948.776804 -2198.776804\n",
              "2        15250.0     13436.866969  1813.133031\n",
              "3         5151.0      4260.569591   890.430409\n",
              "4         9995.0     10183.500550  -188.500550\n",
              "5        11199.0     12095.551354  -896.551354\n",
              "6         5389.0      6975.404438 -1586.404438\n",
              "7         7898.0      5041.609821  2856.390179\n",
              "8        17199.0     19294.110442 -2095.110442\n",
              "9         6529.0      7908.926798 -1379.926798\n",
              "10       20970.0     17235.682498  3734.317502\n",
              "11       31400.5     22449.946798  8950.553202\n",
              "12       10945.0     12125.681255 -1180.681255\n",
              "13       18344.0     12296.088561  6047.911439\n",
              "14        8916.5      7087.601088  1828.898912\n",
              "15        9989.0     10850.926007  -861.926007\n",
              "16        9295.0      8917.576166   377.423834\n",
              "17       18920.0     19653.288503  -733.288503\n",
              "18        7895.0      9893.365149 -1998.365149\n",
              "19        6488.0      7308.112929  -820.112929"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe628c51-db7f-4f51-b062-92fce5bc841d\" 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual Value</th>\n",
              "      <th>Predicted Value</th>\n",
              "      <th>Difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6795.0</td>\n",
              "      <td>4892.382376</td>\n",
              "      <td>1902.617624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15750.0</td>\n",
              "      <td>17948.776804</td>\n",
              "      <td>-2198.776804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15250.0</td>\n",
              "      <td>13436.866969</td>\n",
              "      <td>1813.133031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5151.0</td>\n",
              "      <td>4260.569591</td>\n",
              "      <td>890.430409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9995.0</td>\n",
              "      <td>10183.500550</td>\n",
              "      <td>-188.500550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>11199.0</td>\n",
              "      <td>12095.551354</td>\n",
              "      <td>-896.551354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5389.0</td>\n",
              "      <td>6975.404438</td>\n",
              "      <td>-1586.404438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7898.0</td>\n",
              "      <td>5041.609821</td>\n",
              "      <td>2856.390179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>17199.0</td>\n",
              "      <td>19294.110442</td>\n",
              "      <td>-2095.110442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>6529.0</td>\n",
              "      <td>7908.926798</td>\n",
              "      <td>-1379.926798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>20970.0</td>\n",
              "      <td>17235.682498</td>\n",
              "      <td>3734.317502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>31400.5</td>\n",
              "      <td>22449.946798</td>\n",
              "      <td>8950.553202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>10945.0</td>\n",
              "      <td>12125.681255</td>\n",
              "      <td>-1180.681255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>18344.0</td>\n",
              "      <td>12296.088561</td>\n",
              "      <td>6047.911439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>8916.5</td>\n",
              "      <td>7087.601088</td>\n",
              "      <td>1828.898912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>9989.0</td>\n",
              "      <td>10850.926007</td>\n",
              "      <td>-861.926007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>9295.0</td>\n",
              "      <td>8917.576166</td>\n",
              "      <td>377.423834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18920.0</td>\n",
              "      <td>19653.288503</td>\n",
              "      <td>-733.288503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>7895.0</td>\n",
              "      <td>9893.365149</td>\n",
              "      <td>-1998.365149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>6488.0</td>\n",
              "      <td>7308.112929</td>\n",
              "      <td>-820.112929</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\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",
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              "\n",
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              "    60% {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 310
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# FAST FOOD ASSIGNMENT"
      ],
      "metadata": {
        "id": "MGXKKBs2Eyl_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***LINEAR REGRESSION***"
      ],
      "metadata": {
        "id": "48nA9R5PLcFu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1=pd.read_csv(\"fastfood.csv\")"
      ],
      "metadata": {
        "id": "flrj4h5wFaES"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ieFy4Mq6F3gb",
        "outputId": "d8f9ec41-8c40-4230-abed-895ebae7d0a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  restaurant                                       item  calories  cal_fat  \\\n",
              "0  Mcdonalds           Artisan Grilled Chicken Sandwich       380       60   \n",
              "1  Mcdonalds             Single Bacon Smokehouse Burger       840      410   \n",
              "2  Mcdonalds             Double Bacon Smokehouse Burger      1130      600   \n",
              "3  Mcdonalds  Grilled Bacon Smokehouse Chicken Sandwich       750      280   \n",
              "4  Mcdonalds   Crispy Bacon Smokehouse Chicken Sandwich       920      410   \n",
              "\n",
              "   total_fat  sat_fat  trans_fat  cholesterol  sodium  total_carb  fiber  \\\n",
              "0          7      2.0        0.0           95    1110          44    3.0   \n",
              "1         45     17.0        1.5          130    1580          62    2.0   \n",
              "2         67     27.0        3.0          220    1920          63    3.0   \n",
              "3         31     10.0        0.5          155    1940          62    2.0   \n",
              "4         45     12.0        0.5          120    1980          81    4.0   \n",
              "\n",
              "   sugar  protein  vit_a  vit_c  calcium  Healthy  \n",
              "0     11     37.0    4.0   20.0     20.0        1  \n",
              "1     18     46.0    6.0   20.0     20.0        0  \n",
              "2     18     70.0   10.0   20.0     50.0        0  \n",
              "3     18     55.0    6.0   25.0     20.0        1  \n",
              "4     18     46.0    6.0   20.0     20.0        0  "
            ],
            "text/html": [
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              "      <th>2</th>\n",
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              "      <td>920</td>\n",
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              "        const element = document.querySelector('#df-b0f4f278-3ce8-4865-a269-b77126422c44');\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-ab231acb-4ae3-4d8d-b9b7-487c08b9af62\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ab231acb-4ae3-4d8d-b9b7-487c08b9af62')\"\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-ab231acb-4ae3-4d8d-b9b7-487c08b9af62 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VixjQLROJNN9",
        "outputId": "36b09f94-8a9a-4378-caad-0637e836c619"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "restaurant       0\n",
              "item             0\n",
              "calories         0\n",
              "cal_fat          0\n",
              "total_fat        0\n",
              "sat_fat          0\n",
              "trans_fat        0\n",
              "cholesterol      0\n",
              "sodium           0\n",
              "total_carb       0\n",
              "fiber           12\n",
              "sugar            0\n",
              "protein          1\n",
              "vit_a          214\n",
              "vit_c          210\n",
              "calcium        210\n",
              "Healthy          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1= df1[\"total_carb\"]\n",
        "y1= df1['total_fat']"
      ],
      "metadata": {
        "id": "4krWtFpTMMaO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x1_train,x1_test ,y1_train,y1_test= train_test_split(x1,y1, test_size=0.3, random_state=32)\n",
        "x1_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cvt-zNtK-2q",
        "outputId": "6b1bc924-e363-43ce-968d-cad77da72ead"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "400    59\n",
              "182    21\n",
              "373    13\n",
              "389    10\n",
              "240    18\n",
              "       ..\n",
              "71     40\n",
              "252    19\n",
              "403    56\n",
              "88     32\n",
              "310    51\n",
              "Name: total_carb, Length: 360, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anNRnHHZM9wE",
        "outputId": "60fd2cd3-6c55-4a94-ac20-5bb1d4088df1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "456    31\n",
              "144    53\n",
              "320    94\n",
              "111    16\n",
              "369    10\n",
              "       ..\n",
              "162    28\n",
              "298    11\n",
              "447    19\n",
              "45     63\n",
              "174    44\n",
              "Name: total_carb, Length: 155, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1_train = np.array(x1_train).reshape(-1,1)\n",
        "x1_train.flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bp491DXNEMT",
        "outputId": "b1f9d327-505b-4b1f-f85b-00889a8ba7a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 59,  21,  13,  10,  18,  51,  96,  44,  37,  55,  43,  64,  34,\n",
              "        48,  20,  92,  63,  66,  94,  53,  67,  53,  16,  35,  48,  92,\n",
              "        47,  62,  48,  92,  22,  46,  12,  33,  62, 105,  59,  69,  45,\n",
              "        50,  94,  49,  92,  45,  40,  49,  22,  21,   3,  35,  62,  44,\n",
              "        74,  85,   9,  18,  30,  67,  26,  34,  31,  94,  25,  59,  27,\n",
              "        14,  43,  56,   7,  43,  69,  95,  41,  46,  56,  13,  26,   1,\n",
              "        15, 118,  87,  40,  42,  96,  59,  68,  50,  62,  12,  50,  52,\n",
              "        46,  47,  35,  44,  23, 121,  30,  44,  34,  18,  31,  63,  46,\n",
              "        44,  13,  32,  48,   2,  23,  81,  14,  45,  98,  29,  41,  55,\n",
              "        46,  64,  30,  76,  30,  26,  27,  32,  31,  51,  57,  54,  44,\n",
              "        32,  44,  36,  49,  29,  58,  70,   5,  38,  18,  10,  79,  50,\n",
              "        19,  90,  46,  47, 102,  45,  15,  21,  51,  34,  69,  68,  43,\n",
              "        47,  17,  30,  52,  59,  30,  26,  27,  39,  17,  22,  75,  52,\n",
              "        66, 102,   8,  22,  38,  48,  26,  50,  39,  44,  15,  48,  45,\n",
              "        59,  68,  37,  34,  64,  12,  51,  96,   7,  46,  48,  34,  83,\n",
              "        28,  56,  39,   9,  11,  70,  41,  53,  40,  21,  92,  25,  16,\n",
              "        57,  34,  45,  92,  54,  28,  43,  92,  68,  26,  32,  39,  45,\n",
              "        11,  29,  13,  66,  33,  44,  18,  47,  35,  47,  65,  53,  80,\n",
              "        18,  52,   8,  28,  66,  64,  27,  32,  55,  40,  40, 126,  26,\n",
              "        46,  44,  42,  71, 118,  51,  36,  44,  44,  33,  40,  53,  13,\n",
              "        32,  44,  15,  90,  49,  39,  47,  52,  96,  49,  47,  94,  46,\n",
              "        62,  54,  62,  53,   9,  11,  25,  66,  51,  48,  28,  68,  45,\n",
              "        49,  28,   4,  90,  46,  91,  14,  44,  64,  44,  80,  50,  10,\n",
              "        91,  17, 112,  37,  45,  28,  13,  64,   2,  54,  47,  69,  46,\n",
              "        41,  49,  19,  59,  43,  11,  38,  42,  63,  35,  29,   9,  37,\n",
              "        29, 109,  62,  22,  34,  48,  79,  39,  28,  21,  53,  47,  36,\n",
              "        39,  58,  70,  26,  52,  21,  52,   5,  41,  13,  45,  81,  42,\n",
              "        57,  64,  35,  33,  40,  19,  56,  32,  51])"
            ]
          },
          "metadata": {},
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1_test = np.array(x1_test).reshape(-1,1)\n",
        "x1_test\n",
        "x1_test.flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFdV6ky6NKaN",
        "outputId": "50e22f12-4509-49cd-a076-737cb4260fe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 31,  53,  94,  16,  10,  52,  36,  34,  13,  70,  46,  53,  38,\n",
              "        29,  45,  22,  44, 105,  29,  36,  42,  40,  63,  51,  23,  98,\n",
              "        22,  46,   6,  59,  48,  40,  32,  41,  15,  56,  94, 118,  24,\n",
              "        88,  53,  74,  23,  92,  21,  46,  28,  75,  65,  82,  88,  62,\n",
              "        24,  40,   0,  11,  50,  12,  50,  48,  47,  97,  30,  41, 100,\n",
              "        50,  53,  40,  45,  37,  19, 103,  39,  26,  42,  28, 107,  73,\n",
              "        88,  41,  44,  29,  11,  38,   7,  34,  55,  40,  59,  40,  44,\n",
              "        11,  98,  29,  67,  80,  11,  94,  11,  41,  48,  53,  12,  46,\n",
              "        19,  48,  46,  62,  30,  42,  22,  65,  30,  65,  51, 156,  35,\n",
              "        31,  41,  86,  26,  30,  57,  21,  45,  47,  55,  10,  10,  75,\n",
              "        10,  61,  21,  46,  53,  70,  24,  30,  94,  37,  47,  32,  56,\n",
              "        28,  47,  16,  15,  36,  43,  52,  28,  11,  19,  63,  44])"
            ]
          },
          "metadata": {},
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(x1_train, y1_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "rJ9ueNEZNTbC",
        "outputId": "ff759a0f-f318-4c34-e34e-d2d4959235ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c=lr.intercept_\n",
        "c"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2_JPOX3NbO-",
        "outputId": "4d17ce0e-86f8-41c9-ff78-8d1bfc2a66f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.409131794090044"
            ]
          },
          "metadata": {},
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m=lr.coef_\n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vLjRSdpjLGHS",
        "outputId": "2d276468-f778-40d2-b1ff-8949863b398d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.29995203])"
            ]
          },
          "metadata": {},
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y1_pred_train=m*x1_train+c\n",
        "y1_pred_train.flatten()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBBte75eNfgz",
        "outputId": "f56f4a21-833c-4600-cf4e-e4787890a169"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([30.1063015 , 18.7081244 , 16.30850817, 15.40865208, 17.80826831,\n",
              "       27.70668527, 41.20452657, 25.60702106, 23.50735686, 28.90649338,\n",
              "       25.30706904, 31.60606164, 22.60750078, 26.80682918, 18.40817237,\n",
              "       40.00471845, 31.30610961, 32.2059657 , 40.60462251, 28.30658932,\n",
              "       32.50591773, 28.30658932, 17.20836426, 22.90745281, 26.80682918,\n",
              "       40.00471845, 26.50687715, 31.00615758, 26.80682918, 40.00471845,\n",
              "       19.00807643, 26.20692512, 16.00855614, 22.30754875, 31.00615758,\n",
              "       43.90409483, 30.1063015 , 33.10582179, 25.90697309, 27.40673324,\n",
              "       40.60462251, 27.10678121, 40.00471845, 25.90697309, 24.40721295,\n",
              "       27.10678121, 19.00807643, 18.7081244 , 13.30898788, 22.90745281,\n",
              "       31.00615758, 25.60702106, 34.60558193, 37.90505425, 15.10870005,\n",
              "       17.80826831, 21.40769266, 32.50591773, 20.20788455, 22.60750078,\n",
              "       21.70764469, 40.60462251, 19.90793252, 30.1063015 , 20.50783657,\n",
              "       16.6084602 , 25.30706904, 29.20644541, 14.508796  , 25.30706904,\n",
              "       33.10582179, 40.90457454, 24.70716498, 26.20692512, 29.20644541,\n",
              "       16.30850817, 20.20788455, 12.70908382, 16.90841223, 47.8034712 ,\n",
              "       38.50495831, 24.40721295, 25.00711701, 41.20452657, 30.1063015 ,\n",
              "       32.80586976, 27.40673324, 31.00615758, 16.00855614, 27.40673324,\n",
              "       28.0066373 , 26.20692512, 26.50687715, 22.90745281, 25.60702106,\n",
              "       19.30802846, 48.70332729, 21.40769266, 25.60702106, 22.60750078,\n",
              "       17.80826831, 21.70764469, 31.30610961, 26.20692512, 25.60702106,\n",
              "       16.30850817, 22.00759672, 26.80682918, 13.00903585, 19.30802846,\n",
              "       36.70524613, 16.6084602 , 25.90697309, 41.80443062, 21.10774063,\n",
              "       24.70716498, 28.90649338, 26.20692512, 31.60606164, 21.40769266,\n",
              "       35.20548599, 21.40769266, 20.20788455, 20.50783657, 22.00759672,\n",
              "       21.70764469, 27.70668527, 29.50639744, 28.60654135, 25.60702106,\n",
              "       22.00759672, 25.60702106, 23.20740483, 27.10678121, 21.10774063,\n",
              "       29.80634947, 33.40577382, 13.90889194, 23.80730889, 17.80826831,\n",
              "       15.40865208, 36.10534208, 27.40673324, 18.10822034, 39.40481439,\n",
              "       26.20692512, 26.50687715, 43.00423874, 25.90697309, 16.90841223,\n",
              "       18.7081244 , 27.70668527, 22.60750078, 33.10582179, 32.80586976,\n",
              "       25.30706904, 26.50687715, 17.50831629, 21.40769266, 28.0066373 ,\n",
              "       30.1063015 , 21.40769266, 20.20788455, 20.50783657, 24.10726092,\n",
              "       17.50831629, 19.00807643, 34.90553396, 28.0066373 , 32.2059657 ,\n",
              "       43.00423874, 14.80874803, 19.00807643, 23.80730889, 26.80682918,\n",
              "       20.20788455, 27.40673324, 24.10726092, 25.60702106, 16.90841223,\n",
              "       26.80682918, 25.90697309, 30.1063015 , 32.80586976, 23.50735686,\n",
              "       22.60750078, 31.60606164, 16.00855614, 27.70668527, 41.20452657,\n",
              "       14.508796  , 26.20692512, 26.80682918, 22.60750078, 37.30515019,\n",
              "       20.8077886 , 29.20644541, 24.10726092, 15.10870005, 15.70860411,\n",
              "       33.40577382, 24.70716498, 28.30658932, 24.40721295, 18.7081244 ,\n",
              "       40.00471845, 19.90793252, 17.20836426, 29.50639744, 22.60750078,\n",
              "       25.90697309, 40.00471845, 28.60654135, 20.8077886 , 25.30706904,\n",
              "       40.00471845, 32.80586976, 20.20788455, 22.00759672, 24.10726092,\n",
              "       25.90697309, 15.70860411, 21.10774063, 16.30850817, 32.2059657 ,\n",
              "       22.30754875, 25.60702106, 17.80826831, 26.50687715, 22.90745281,\n",
              "       26.50687715, 31.90601367, 28.30658932, 36.4052941 , 17.80826831,\n",
              "       28.0066373 , 14.80874803, 20.8077886 , 32.2059657 , 31.60606164,\n",
              "       20.50783657, 22.00759672, 28.90649338, 24.40721295, 24.40721295,\n",
              "       50.20308743, 20.20788455, 26.20692512, 25.60702106, 25.00711701,\n",
              "       33.70572584, 47.8034712 , 27.70668527, 23.20740483, 25.60702106,\n",
              "       25.60702106, 22.30754875, 24.40721295, 28.30658932, 16.30850817,\n",
              "       22.00759672, 25.60702106, 16.90841223, 39.40481439, 27.10678121,\n",
              "       24.10726092, 26.50687715, 28.0066373 , 41.20452657, 27.10678121,\n",
              "       26.50687715, 40.60462251, 26.20692512, 31.00615758, 28.60654135,\n",
              "       31.00615758, 28.30658932, 15.10870005, 15.70860411, 19.90793252,\n",
              "       32.2059657 , 27.70668527, 26.80682918, 20.8077886 , 32.80586976,\n",
              "       25.90697309, 27.10678121, 20.8077886 , 13.60893991, 39.40481439,\n",
              "       26.20692512, 39.70476642, 16.6084602 , 25.60702106, 31.60606164,\n",
              "       25.60702106, 36.4052941 , 27.40673324, 15.40865208, 39.70476642,\n",
              "       17.50831629, 46.00375903, 23.50735686, 25.90697309, 20.8077886 ,\n",
              "       16.30850817, 31.60606164, 13.00903585, 28.60654135, 26.50687715,\n",
              "       33.10582179, 26.20692512, 24.70716498, 27.10678121, 18.10822034,\n",
              "       30.1063015 , 25.30706904, 15.70860411, 23.80730889, 25.00711701,\n",
              "       31.30610961, 22.90745281, 21.10774063, 15.10870005, 23.50735686,\n",
              "       21.10774063, 45.10390294, 31.00615758, 19.00807643, 22.60750078,\n",
              "       26.80682918, 36.10534208, 24.10726092, 20.8077886 , 18.7081244 ,\n",
              "       28.30658932, 26.50687715, 23.20740483, 24.10726092, 29.80634947,\n",
              "       33.40577382, 20.20788455, 28.0066373 , 18.7081244 , 28.0066373 ,\n",
              "       13.90889194, 24.70716498, 16.30850817, 25.90697309, 36.70524613,\n",
              "       25.00711701, 29.50639744, 31.60606164, 22.90745281, 22.30754875,\n",
              "       24.40721295, 18.10822034, 29.20644541, 22.00759672, 27.70668527])"
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y1_pred_train1= lr.predict(x1_train)\n",
        "y1_pred_train1.flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xXoAPpZNklh",
        "outputId": "bde2cdcb-7c86-441d-fada-1939ae95b215"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([30.1063015 , 18.7081244 , 16.30850817, 15.40865208, 17.80826831,\n",
              "       27.70668527, 41.20452657, 25.60702106, 23.50735686, 28.90649338,\n",
              "       25.30706904, 31.60606164, 22.60750078, 26.80682918, 18.40817237,\n",
              "       40.00471845, 31.30610961, 32.2059657 , 40.60462251, 28.30658932,\n",
              "       32.50591773, 28.30658932, 17.20836426, 22.90745281, 26.80682918,\n",
              "       40.00471845, 26.50687715, 31.00615758, 26.80682918, 40.00471845,\n",
              "       19.00807643, 26.20692512, 16.00855614, 22.30754875, 31.00615758,\n",
              "       43.90409483, 30.1063015 , 33.10582179, 25.90697309, 27.40673324,\n",
              "       40.60462251, 27.10678121, 40.00471845, 25.90697309, 24.40721295,\n",
              "       27.10678121, 19.00807643, 18.7081244 , 13.30898788, 22.90745281,\n",
              "       31.00615758, 25.60702106, 34.60558193, 37.90505425, 15.10870005,\n",
              "       17.80826831, 21.40769266, 32.50591773, 20.20788455, 22.60750078,\n",
              "       21.70764469, 40.60462251, 19.90793252, 30.1063015 , 20.50783657,\n",
              "       16.6084602 , 25.30706904, 29.20644541, 14.508796  , 25.30706904,\n",
              "       33.10582179, 40.90457454, 24.70716498, 26.20692512, 29.20644541,\n",
              "       16.30850817, 20.20788455, 12.70908382, 16.90841223, 47.8034712 ,\n",
              "       38.50495831, 24.40721295, 25.00711701, 41.20452657, 30.1063015 ,\n",
              "       32.80586976, 27.40673324, 31.00615758, 16.00855614, 27.40673324,\n",
              "       28.0066373 , 26.20692512, 26.50687715, 22.90745281, 25.60702106,\n",
              "       19.30802846, 48.70332729, 21.40769266, 25.60702106, 22.60750078,\n",
              "       17.80826831, 21.70764469, 31.30610961, 26.20692512, 25.60702106,\n",
              "       16.30850817, 22.00759672, 26.80682918, 13.00903585, 19.30802846,\n",
              "       36.70524613, 16.6084602 , 25.90697309, 41.80443062, 21.10774063,\n",
              "       24.70716498, 28.90649338, 26.20692512, 31.60606164, 21.40769266,\n",
              "       35.20548599, 21.40769266, 20.20788455, 20.50783657, 22.00759672,\n",
              "       21.70764469, 27.70668527, 29.50639744, 28.60654135, 25.60702106,\n",
              "       22.00759672, 25.60702106, 23.20740483, 27.10678121, 21.10774063,\n",
              "       29.80634947, 33.40577382, 13.90889194, 23.80730889, 17.80826831,\n",
              "       15.40865208, 36.10534208, 27.40673324, 18.10822034, 39.40481439,\n",
              "       26.20692512, 26.50687715, 43.00423874, 25.90697309, 16.90841223,\n",
              "       18.7081244 , 27.70668527, 22.60750078, 33.10582179, 32.80586976,\n",
              "       25.30706904, 26.50687715, 17.50831629, 21.40769266, 28.0066373 ,\n",
              "       30.1063015 , 21.40769266, 20.20788455, 20.50783657, 24.10726092,\n",
              "       17.50831629, 19.00807643, 34.90553396, 28.0066373 , 32.2059657 ,\n",
              "       43.00423874, 14.80874803, 19.00807643, 23.80730889, 26.80682918,\n",
              "       20.20788455, 27.40673324, 24.10726092, 25.60702106, 16.90841223,\n",
              "       26.80682918, 25.90697309, 30.1063015 , 32.80586976, 23.50735686,\n",
              "       22.60750078, 31.60606164, 16.00855614, 27.70668527, 41.20452657,\n",
              "       14.508796  , 26.20692512, 26.80682918, 22.60750078, 37.30515019,\n",
              "       20.8077886 , 29.20644541, 24.10726092, 15.10870005, 15.70860411,\n",
              "       33.40577382, 24.70716498, 28.30658932, 24.40721295, 18.7081244 ,\n",
              "       40.00471845, 19.90793252, 17.20836426, 29.50639744, 22.60750078,\n",
              "       25.90697309, 40.00471845, 28.60654135, 20.8077886 , 25.30706904,\n",
              "       40.00471845, 32.80586976, 20.20788455, 22.00759672, 24.10726092,\n",
              "       25.90697309, 15.70860411, 21.10774063, 16.30850817, 32.2059657 ,\n",
              "       22.30754875, 25.60702106, 17.80826831, 26.50687715, 22.90745281,\n",
              "       26.50687715, 31.90601367, 28.30658932, 36.4052941 , 17.80826831,\n",
              "       28.0066373 , 14.80874803, 20.8077886 , 32.2059657 , 31.60606164,\n",
              "       20.50783657, 22.00759672, 28.90649338, 24.40721295, 24.40721295,\n",
              "       50.20308743, 20.20788455, 26.20692512, 25.60702106, 25.00711701,\n",
              "       33.70572584, 47.8034712 , 27.70668527, 23.20740483, 25.60702106,\n",
              "       25.60702106, 22.30754875, 24.40721295, 28.30658932, 16.30850817,\n",
              "       22.00759672, 25.60702106, 16.90841223, 39.40481439, 27.10678121,\n",
              "       24.10726092, 26.50687715, 28.0066373 , 41.20452657, 27.10678121,\n",
              "       26.50687715, 40.60462251, 26.20692512, 31.00615758, 28.60654135,\n",
              "       31.00615758, 28.30658932, 15.10870005, 15.70860411, 19.90793252,\n",
              "       32.2059657 , 27.70668527, 26.80682918, 20.8077886 , 32.80586976,\n",
              "       25.90697309, 27.10678121, 20.8077886 , 13.60893991, 39.40481439,\n",
              "       26.20692512, 39.70476642, 16.6084602 , 25.60702106, 31.60606164,\n",
              "       25.60702106, 36.4052941 , 27.40673324, 15.40865208, 39.70476642,\n",
              "       17.50831629, 46.00375903, 23.50735686, 25.90697309, 20.8077886 ,\n",
              "       16.30850817, 31.60606164, 13.00903585, 28.60654135, 26.50687715,\n",
              "       33.10582179, 26.20692512, 24.70716498, 27.10678121, 18.10822034,\n",
              "       30.1063015 , 25.30706904, 15.70860411, 23.80730889, 25.00711701,\n",
              "       31.30610961, 22.90745281, 21.10774063, 15.10870005, 23.50735686,\n",
              "       21.10774063, 45.10390294, 31.00615758, 19.00807643, 22.60750078,\n",
              "       26.80682918, 36.10534208, 24.10726092, 20.8077886 , 18.7081244 ,\n",
              "       28.30658932, 26.50687715, 23.20740483, 24.10726092, 29.80634947,\n",
              "       33.40577382, 20.20788455, 28.0066373 , 18.7081244 , 28.0066373 ,\n",
              "       13.90889194, 24.70716498, 16.30850817, 25.90697309, 36.70524613,\n",
              "       25.00711701, 29.50639744, 31.60606164, 22.90745281, 22.30754875,\n",
              "       24.40721295, 18.10822034, 29.20644541, 22.00759672, 27.70668527])"
            ]
          },
          "metadata": {},
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(x1_train, y1_train)\n",
        "plt.plot(x1_train, y1_pred_train1, color='k')\n",
        "plt.xlabel(\"total_carb\")\n",
        "plt.ylabel(\"total_fat\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "xpewO1ybNsac",
        "outputId": "464c4106-73e5-4cca-c37d-bb01a682123a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'total_fat')"
            ]
          },
          "metadata": {},
          "execution_count": 270
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y1_pred_test1= lr.predict(x1_test)\n",
        "y1_pred_test1.flatten()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Od7nait2OAaB",
        "outputId": "90ccc47f-ab09-4415-84a1-f0e6383f684c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([21.70764469, 28.30658932, 40.60462251, 17.20836426, 15.40865208,\n",
              "       28.0066373 , 23.20740483, 22.60750078, 16.30850817, 33.40577382,\n",
              "       26.20692512, 28.30658932, 23.80730889, 21.10774063, 25.90697309,\n",
              "       19.00807643, 25.60702106, 43.90409483, 21.10774063, 23.20740483,\n",
              "       25.00711701, 24.40721295, 31.30610961, 27.70668527, 19.30802846,\n",
              "       41.80443062, 19.00807643, 26.20692512, 14.20884397, 30.1063015 ,\n",
              "       26.80682918, 24.40721295, 22.00759672, 24.70716498, 16.90841223,\n",
              "       29.20644541, 40.60462251, 47.8034712 , 19.60798049, 38.80491034,\n",
              "       28.30658932, 34.60558193, 19.30802846, 40.00471845, 18.7081244 ,\n",
              "       26.20692512, 20.8077886 , 34.90553396, 31.90601367, 37.00519816,\n",
              "       38.80491034, 31.00615758, 19.60798049, 24.40721295, 12.40913179,\n",
              "       15.70860411, 27.40673324, 16.00855614, 27.40673324, 26.80682918,\n",
              "       26.50687715, 41.5044786 , 21.40769266, 24.70716498, 42.40433468,\n",
              "       27.40673324, 28.30658932, 24.40721295, 25.90697309, 23.50735686,\n",
              "       18.10822034, 43.30419077, 24.10726092, 20.20788455, 25.00711701,\n",
              "       20.8077886 , 44.50399888, 34.3056299 , 38.80491034, 24.70716498,\n",
              "       25.60702106, 21.10774063, 15.70860411, 23.80730889, 14.508796  ,\n",
              "       22.60750078, 28.90649338, 24.40721295, 30.1063015 , 24.40721295,\n",
              "       25.60702106, 15.70860411, 41.80443062, 21.10774063, 32.50591773,\n",
              "       36.4052941 , 15.70860411, 40.60462251, 15.70860411, 24.70716498,\n",
              "       26.80682918, 28.30658932, 16.00855614, 26.20692512, 18.10822034,\n",
              "       26.80682918, 26.20692512, 31.00615758, 21.40769266, 25.00711701,\n",
              "       19.00807643, 31.90601367, 21.40769266, 31.90601367, 27.70668527,\n",
              "       59.2016483 , 22.90745281, 21.70764469, 24.70716498, 38.20500628,\n",
              "       20.20788455, 21.40769266, 29.50639744, 18.7081244 , 25.90697309,\n",
              "       26.50687715, 28.90649338, 15.40865208, 15.40865208, 34.90553396,\n",
              "       15.40865208, 30.70620556, 18.7081244 , 26.20692512, 28.30658932,\n",
              "       33.40577382, 19.60798049, 21.40769266, 40.60462251, 23.50735686,\n",
              "       26.50687715, 22.00759672, 29.20644541, 20.8077886 , 26.50687715,\n",
              "       17.20836426, 16.90841223, 23.20740483, 25.30706904, 28.0066373 ,\n",
              "       20.8077886 , 15.70860411, 18.10822034, 31.30610961, 25.60702106])"
            ]
          },
          "metadata": {},
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(x1_test, y1_test)\n",
        "plt.plot(x1_test, y1_pred_test1, color='red')\n",
        "plt.xlabel(\"total_carb\")\n",
        "plt.ylabel(\"total_fat\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "ow0HC1koOJwD",
        "outputId": "fd1bf5f9-dbeb-4f00-d0ed-b0b412bc53f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'total_fat')"
            ]
          },
          "metadata": {},
          "execution_count": 272
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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iIsVg4ENERESKwcCHiIiIFIOBDxERESkGAx8iIiJSDAY+REREpBgMfIiIiEgxGPgQERGRYjDwISIiIsVg4ENERESKwcCHiIiIFIOBDxERESkGAx8iIiJSDAY+REREpBgMfIiIiEgxGPgQERGRYjDwISIiIsVg4ENERESKwcCHiIiIFIOBDxERESkGAx8iIiJSDAY+REREpBgMfIiIiEgxGPgQERGRYjDwISIiIsVwKvDp2rUrSkpK6txeVlaGrl27utonIiIiIrcIcuZBmzdvRlVVVZ3bKyoqsG3bNpc7RUTyotML2JNfjKIrFYiLDEOn5BgEBqi83S0iIoc5FPj88ssvpn/n5eVBq9WavtfpdMjOzkajRo0k65xOp8PUqVOxfPlyaLVaJCYm4vnnn8ebb74JlcrwS1cQBGRlZWHx4sUoKSlBeno6FixYgObNm0vWDyIlyz5cgGlr81BQWmG6LUEdhqzeKeiRmuDFnhEROc6hwKdNmzZQqVRQqVQWl7TCw8Mxd+5cyTo3c+ZMLFiwAJ9++inuuusu7Nu3D0OGDIFarcbo0aMBALNmzcKcOXPw6aefIjk5GZmZmejevTvy8vIQFhYmWV+IlCj7cAFeWp4Lodbt2tIKvLQ8Fwuebcfgh4h8ikoQhNq/06w6c+YMBEHArbfeij179uCWW24x3RcSEoK4uDgEBgZK1rlHH30U8fHxWLJkiem2/v37Izw8HMuXL4cgCEhMTMSECRPwyiuvAABKS0sRHx+PZcuWYcCAAaJep6ysDGq1GqWlpYiKipKs/0S+TKcX0GXmRrOZnppUADTqMGyf1JXLXkTkFc78/XZoxqdp06YAAL1e73jvnHDPPfdg0aJFOH78OO644w78/PPP2L59Oz788EMAQH5+PrRaLTIyMkyPUavV6Ny5M3JycqwGPpWVlaisrDR9X1ZW5t6BEPmgPfnFVoMeABAAFJRWYE9+MdJui/Vcx4iIXOBUcrNRXl4ezp49WyfRuU+fPi51yui1115DWVkZWrRogcDAQOh0OrzzzjsYOHAgAJhyjOLj480eFx8fb5Z/VNuMGTMwbdo0SfpI5K+KrlgPepxpR0QkB04FPr/99hv69u2LQ4cOQaVSwbhaZkw41ul0knTu888/x4oVK7By5UrcddddOHjwIMaOHYvExEQMHjzY6eedPHkyxo8fb/q+rKwMTZo0kaLLRH4jLlJcjpzYdkREcuBUHZ8xY8YgOTkZRUVFqFevHo4cOYKtW7eiQ4cO2Lx5s2Sde/XVV/Haa69hwIABaNWqFQYNGoRx48ZhxowZAACNRgMAKCwsNHtcYWGh6T5LQkNDERUVZfZFROY6JccgQR0Ga9k7Khh2d3VKjvFkt4iIXOJU4JOTk4O33noLDRs2REBAAAICAtClSxfMmDHDtNtKCteuXUNAgHkXAwMDTTlGycnJ0Gg02LBhg+n+srIy7N69G2lpaZL1g0iJAgNUyOqdAgB1gh/j91m9U5jYTEQ+xanAR6fTITIyEgDQsGFDXLx4EYAh+fnYsWOSda53795455138N133+H06dNYs2YNPvzwQ/Tt2xeAYWlt7NixePvtt/Htt9/i0KFDeO6555CYmIjHH39csn4QKVWP1AQseLYdNGrz5SyNOoxb2YnIJzmV45Oamoqff/4ZycnJ6Ny5M2bNmoWQkBAsWrQIt956q2Sdmzt3LjIzM/Hyyy+jqKgIiYmJeOGFFzBlyhRTm4kTJ6K8vBzDhw9HSUkJunTpguzsbNbwIZJIj9QEPJSiYeVmIvILDtXxMfrxxx9RXl6Ofv364eTJk3j00Udx/PhxxMbG4rPPPvO587pYx4eIiMj3OPP3W3Tg88svvyA1NbVOzo1RcXExGjRoYNrZ5UsY+BAREfkeZ/5+i87xadu2LS5dugQAuPXWW3H58mWz+2NiYnwy6CEiIiLlEB34REdHIz8/HwBw+vRpj1VvJiIiIpKK6OTm/v374/7770dCQgJUKhU6dOhg9Vyu3377TbIOEpF76fQCE5eJSDFEBz6LFi0yJTOPHj0aw4YNM21pJyLflH24ANPW5pmdyZWgDkNW7xRuVSciv+TUrq4hQ4Zgzpw5dgOf8+fPIzEx0WpCtFwwuZmUKPtwAV5anovavwCMcz2s00NEcufW5Oaali5dKmq2JyUlBadPn3bmJYjIjXR6AdPW5tUJegCYbpu2Ng86vcPXRUREsubWqRgnJpOIyAP25BebLW/VJgAoKK3Anvxiz3WKiMgD5L0GRURuUXTFetDjTDsiIl/BwIdIgeIixR3pIrYdEZGvYOBDpECdkmOQoA6rc+q6kQqG3V2dkmM82S0iIrdza+DDSs5E8hQYoEJW7xQAqBP8GL/P6p3Cej5E5HeY3EzkY3R6ATmnLuObgxeQc+qy0zuveqQmYMGz7aBRmy9nadRh3MpORH5LdAFDZ+Tl5SExMdGdL0GkKFIXHOyRmoCHUjSs3ExEiiG6gGG/fv1EP+lXX33ldIe8gQUMyRew4CARkTln/n6LnvFRq9VOd4yIXGOv4KAKhoKDD6VoOFtDRGSD6MBn6dKl7uwHEdngSMHBtNtiPdcxIiIfw+3sRD6ABQeJiKThdHLzf//7X3z++ec4e/YsqqqqzO7Lzc11uWNEdBMLDhIRScOpGZ85c+ZgyJAhiI+Px4EDB9CpUyfExsbit99+wyOPPCJ1H4kUjwUHiYik4VTg849//AOLFi3C3LlzERISgokTJ2L9+vUYPXo0SktLpe4jkeKx4CARkTScCnzOnj2Le+65BwAQHh6OK1euAAAGDRqEVatWSdc7IjJhwUEiItc5leOj0WhQXFyMpk2bIikpCbt27ULr1q2Rn5/Pas1EbsSCg0TkS3R6QXa/r5wKfLp27Ypvv/0Wbdu2xZAhQzBu3Dj897//xb59+xwqdEhEjgsMUHHLOhHJntSV5qUiunJzTXq9Hnq9HkFBhrhp9erV2LlzJ5o3b44XXngBISEhknfUnVi5mbxJjldERESu8FSleWf+fjsV+Jw9exZNmjSpc/q6IAg4d+4ckpKSHH1Kr2LgQ94i1ysiIiJn6fQCuszcaLXoqgqG3MTtk7q6fJHnzN9vp5Kbk5OT8fvvv9e5vbi4GMnJyc48JZHiGK+Iav9y0JZW4KXlucg+XOClnhEROc+RSvPe4FTgIwhCndkeALh69SrCwlhAjcgee2dvAYazt3R639gsoNMLyDl1Gd8cvICcU5d9pt9EJD25V5p3KLl5/PjxAACVSoXMzEzUq1fPdJ9Op8Pu3bvRpk0bSTtI5I/86ewtLtcRUU1yrzTvUOBz4MABAIYZn0OHDpklMYeEhKB169Z45ZVXpO0hkQ+zlrgs9ysisawlMBqX61hfiEh5jJXmtaUVFme1jTk+3qo071Dgs2nTJgDAkCFD8PHHHzMRmMgGWzMhcr8iEsPecp0KhuW6h1I03KVGpCDGSvMvLc+FCjD7HSGHSvNO5fgsXbrUFPScP38e58+fl7RTRL7OXuLyH+VVPn/2ltwTGInIe+Rcad6pAoZ6vR5vv/02PvjgA1y9ehUAEBkZiQkTJuCNN95AQIBT8RSRXxAzEzL9uzxk9mqJESsPyPKKSAx/Wa4jIveQa6V5pwKfN954A0uWLMF7772H9PR0AMD27dsxdepUVFRU4J133pG0k0S+ROxMSIOIUCx4tl2d5TCNjyQG+8NyHRG5lxwrzTsV+Hz66af45z//iT59+phu+8tf/oJGjRrh5ZdfZuBDiubITMhjbRrJ8opIDLknMBIRWeJU4FNcXIwWLVrUub1FixYoLuZ6PimbozMhcrwiEkPuCYxERJY4lYzTunVrzJs3r87t8+bNQ+vWrV3uFJEvM86E+HLislhyTmAkIrLEqRmfWbNmoVevXvjpp5+QlpYGAMjJycG5c+fw/fffS9rBCxcuYNKkSfjhhx9w7do13H777Vi6dCk6dOgAwFBTKCsrC4sXL0ZJSQnS09OxYMECNG/eXNJ+EImltJkQuSYwEhFZ4vRZXcePH0ffvn1RUlKCkpIS9OvXD8eOHUPTpk0l69wff/yB9PR0BAcH44cffkBeXh4++OADNGjQwNRm1qxZmDNnDhYuXIjdu3cjIiIC3bt3R0UFd5KQ9yhtJsS4XPdYm0ZIuy2WQQ8RyZZTp7MHBgaioKAAcXFxZrdfvnwZcXFx0Ol0knTutddew44dO7Bt2zaL9wuCgMTEREyYMMFUMbq0tBTx8fFYtmwZBgwYIOp1eDo7uUvVDT3+k3MaZ4qvoWlMPQxKa4aQIJZ7ICKSgsdOZ7cWK0l9SOm3336LDh064IknnkBcXBzatm2LxYsXm+7Pz8+HVqtFRkaG6Ta1Wo3OnTsjJydHsn4QOSP7cAHuf38Tpn93FP/OOYPp3x3F/e9v4qnrRERe5PQhpVOmTHH7IaW//fYbFixYgPHjx+P111/H3r17MXr0aISEhGDw4MHQarUAgPj4eLPHxcfHm+6zpLKyEpWVlabvy8rKJOszEcAzrMg11s54IyLXyfqQUr1ejw4dOuDdd98FALRt2xaHDx/GwoULMXjwYKefd8aMGZg2bZpU3SQywzOsyBU87Z78yqZNQFYW8MQTwMiRgMr7v/NkfUhpQkICUlJSzG5r2bIlvvzySwCARqMBABQWFiIh4eYvhMLCQpszT5MnTzbNXgGGGZ8mTZpI2HNSMkfOsPLF+j3kPpwpJJ+3ZYsh0Nmyxfz2bduALl2Atm29068aXD6k1J3S09Nx7Ngxs9uOHz9u2jmWnJwMjUaDDRs2mO4vKyvD7t27TdvsLQkNDUVUVJTZF5FU3HGGlU4vIOfUZXxz8AJyTl2GTu/wngSSOXszhYBhppCfPcnK1q1A166GmRyVCnjggbpBDwD06AG0auXx7lniVB0fTxk3bhzuuecevPvuu3jyySexZ88eLFq0CIsWLQJgyDUaO3Ys3n77bTRv3hzJycnIzMxEYmIiHn/8ce92nhRL6jOsuPShDJwpJJ+wfTswdSpQY8LBqldfBV55Bai1A9zbZB34dOzYEWvWrMHkyZPx1ltvITk5GR999BEGDhxoajNx4kSUl5dj+PDhKCkpQZcuXZCdnS3p7jIiR0h5hhWXPpSDp92TLO3caQh01q+333bCBEOwU2vDkdw4VcfH37COD0nNGLAAlis3iwlYdHoBXWZutDoLYAygtk/qyiRpP5Bz6jKeXrzLbrtVw+7mjA+5z65dhkDnxx/ttx03Dpg4Efgz39YbPFbHh4hsk6JysyNLH+T7OiXHILpesM02DeoF+8UZbyQju3cDjzxyM0cnLc160DNmDHDxIiAIhq8PP/Rq0OMsWS91EfkyV8+w4tIH1ab46Xly3Z49hhmdH36w33b0aGDSJCAx0e3d8iQGPkRuZDzDyhlSJ0mTvO3JL0bJtWqbbUquVTO5mRyzb58h0PnuO/ttR44EXnsNaNTI7d3yJi51EcmUMUna2vyQCobdXVz68A+c4SNJ7N8P9Olzc+mqY0frQc+IEcC5czeXrubO9fugB2DgQyRbgQEqZPU2FPCsHfwYv8/qncLEZj/BGT5yyoEDwOOP3wx0OnQA1q613Pall4AzZ24GOvPmAY0be7S7csDAh0jGpEiSJt/AGT4S5eefgX79bgY67doB33xjue0LLwCnT98MdP7xDyApyaPdlSPm+BDJnKtJ0uQbjDN8Ly3PhQqWyyBwhk+BfvkFeOst4M+jmmwaNgx4/XWgWTO3d8uXsY4PWMeHiOSDlboV7vBhYNo04L//td926FBDoHPrre7vl0w58/ebMz5ERDLCGT6FOXLEMKPz+ef22/7tb8Abbyg60JECAx8iIplxpQwCyVxeniHQ+ewz+22ff94Q6Nx+u9u7pSQMfIiIiNzl118Ngc6qVfbbDh5sCHSaN3d/vxSMgQ8REZFUjh0Dpk8HVqyw33bQIODNN4E77nB/v8iEgQ8REZGzjh8H3n4b+M9/7LcdONAQ6LRo4f5+kVUMfMhv6PQCE0KJyL1OnjQEOp9+ar/t008DmZlAy5bu7xeJxsCH/AK3ABORW5w6ZQh0li2z3/appwyBzl13ub1b5DxWbiafl324AC8tzzULegBAW1qBl5bnIvtwgZd6RkQ+Jz/fUB/HWBn59tutBz1PPAEcOnSzMvLq1Qx6fAADH/JpOr2AaWvzYKkKp/G2aWvzoNMrvk4nEVmSnw/83//dDHRuvRX4178st/3rXw2VlI2BzuefA6mpnu0vuYyBD/m0PfnFdWZ6ahIAFJRWYE9+sec6RUTydeYMMHy4eaCzZInltv36AQcP3gx0vvgCaNXKo90l6THHh3yatsx60FNT0RVx7aRiL9Gaidj+i5+tzJw9C7z7LvDJJ/bb9u0LTJkCtGnj9m6R9zDwIZ+VfbgA09cdEdU2LjLMfiOJ2Eu0ZiK2/+JnKwPnzgEzZgALFthv+9hjhkCnXTv394tkg4eUgoeU+iJjQrO9H14VAI06DNsndfXIVbe1fhlfefh9yVi0Nd/q/QuebefQH0jOLsiHvc/e0c+WRDp/HnjvPWD+fPtte/cGsrKA9u3d3y/yCB5SSopgK6G5JuMfnKzeKR4JBsQkWi/eVjfoMd6vgiER+6EUjaj+cnZBPux99o5+tmTDxYuGQGfuXPtte/UyBDodO7q/X+QzmNxMPsdeQrNRTESIR6+yxfTL1uYyRxKxuYVfXphk70YFBcCYMTeTkRs1sh70PPIIsGvXzWTkdesY9FAdDHzI54hNVH6zV0uPznxIlUBt73m4hV9+xH72nk6y90laLTB27M1AJzERmDPHctvu3YGcnJuBzvffA507e7S75HsY+JDPEZuorFGHu7kn5qRKoLb3PJxdkB+xn70nk+x9hlYLjB9/M9BJSAA+/thy24cfBnbuvBnoZGcDd9/t2f6Sz2Pg4yY6vYCcU5fxzcELyDl1mVffEuqUHIMEdRisZUqoYMh16ZQc48lu2e0XAASo4HK/ObsgP3L9mZSlwkLglVfMA53Zsy23zcgAtm+/Gej8+COQlubZ/pLfYeDjBtmHC9Bl5kY8vXgXxqw+iKcX70KXmRuZdyGRwAAVsnqnAKgbRHg6odmRfqkADLs32er9gLh+c3ZBfuT6MykLv/8OTJx4M9DRaIAPPrDctls3YOvWm4HO+vVAerpn+0t+j4GPxJh06hk9UhOw4Nl20KjN/7hr1GFe3TZsr1+Te6a43G/OLsiTXH8mPe7SJeC1124GOnFxwPvvW2774IPA5s03A52ffgLuvdej3SXlYR0fSFfHR6cX0GXmRqv5F56uKaMEcq1j4+7KzcYAG4BZkjNrxnifXH8m3ebyZcMMzowZ9tvefz8wdSrwwAPu7hUphDN/vxn4QLrAJ+fUZTy9eJfddquG3Y2022Kdfh0igHV8yEuKiw2Bzrvv2m97772GQOfBBw2zP0QSYwFDL2PSqbI5cqUvxaxAj9QEPJSiUdbsAnneH38AH34IvP22/bbp6YZAp1s3BjokWwx8JMSkU+VyZPZFypmawAAVZw9JWn/8YdhlNX26/bZpacC0aYbdVwx0yEcwuVlCTDpVJkcS2pn87hqllInw6DhLSgyzNMZk5JgY60HP3XcbtpTr9YZk5J07gYceYtBDPoUzPhIybml9aXkuVLCcdKrYLa1+ypEzmvDnv3mek3OUktPk9nGWlhoKBE6daghebOnUyTCj0707gxvyG5zxkRi3tCqLI1WUWXHZeUqZKXPLOMvKDPk5QUGG4CU62nBwp6Wgp2NH4Lvvbs7o7N4N9OjBoIf8Cmd83IBJp8rhjoR2Jr+bq7qhx+trDvn9TJlkJ7xfuWI4xHPqVKC62vaLtm9vaNerF4MbUgwGPm7CpFNlcEdCO5Pfb8o+XIDX1xxGcbn1P+A1Z8p8+f+cIzOCZuO8cgWYN88wi2Mv0Gnb1hDo9O7NQIcUy6eWut577z2oVCqMHTvWdFtFRQVGjBiB2NhY1K9fH/3790dhYaH3OkmK4khCO5PfHWNc9ikurxLV3tdnysT2/3LhZWDmTCAszBC8REUBr79uOehp3RpYs+bm0lVuLtCnD4MeUjSfCXz27t2LTz75BH/5y1/Mbh83bhzWrl2LL774Alu2bMHFixfRr18/L/WSlMaRM5p4npN4tpZ9rPH1mTJr/Q+vqsALu/+Lox/0x+mZj+LR9DsNR0JUVtZt3KoV8OWXgE5nCHQOHgQef5yBDlENPrHUdfXqVQwcOBCLFy/G2zWKaJWWlmLJkiVYuXIlunbtCgBYunQpWrZsiV27duHuu+/2VpdJQYwJ7bV34mgs7MRxpK0Ynj4ewVOvZ2/ZpybjUTCuzpR5+6gJ44xg6e8lGHjge4zdsRIR1Xbeg7vuMuy66tsXCPCZ61gir/KJwGfEiBHo1asXMjIyzAKf/fv3o7q6GhkZGabbWrRogaSkJOTk5DDwIY9xJKFdquR3T2/v9uTrObps5epMmVe3yl+7BixciMCpU5Fz5YrNplduuwOR770D9OvHQIfISbIPfFavXo3c3Fzs3bu3zn1arRYhISGIjo42uz0+Ph5ardbqc1ZWVqKyxjRxWVmZZP0l5XIkod3V5Hdj/kvtpSDjtmepSyd4+vXELlvFRATj3b6tXHptT48N168Dn3xiSDIuLbXZ9GRMY8zuMhAHOnXDlMdSWQ6DSAKyvmQ4d+4cxowZgxUrViAsTLr1+xkzZkCtVpu+mjRpItlzE7mbvW3PgGHbs1TVfj39eoD9pHEAiI0Iwa7JGS4FAx4Z2/XrwJw5horIKhVQrx4wbpzloOfOO4FVq6CrqkbOyUs4smEXnv3wVWxzcZxEdJOsA5/9+/ejqKgI7dq1Q1BQEIKCgrBlyxbMmTMHQUFBiI+PR1VVFUpKSsweV1hYCI1GY/V5J0+ejNLSUtPXuXPn3DwSIul4uhCiNwov2ksEVwF4p28qQoJc+xXmlrFVVBi2l8fG3gx0xowxnIFVW/PmwIoVwI0bhmTkX38FBgxAYHAQ0m6LxWNtGiHttlgmvBNJSNZLXd26dcOhQ4fMbhsyZAhatGiBSZMmoUmTJggODsaGDRvQv39/AMCxY8dw9uxZpKWlWX3e0NBQhIaGurXv5DpvJ5uKYa2P7uy7O4omSvE8O07+Lul4pU4Et0SS97KyEliyxFBH59Il2090222GJa4BAwyVlInI42T9Py8yMhKpqalmt0VERCA2NtZ0+9ChQzF+/HjExMQgKioKo0aNQlpaGhObfZwvnMtkrY99Wifg258L3NZ3dxRNlOJ55m06Zfq3VON1dxV0p97LykrgX/8yBDq//277gcnJhkDnmWcY6BDJhKyXusSYPXs2Hn30UfTv3x/33XcfNBoNvvrqK293i1zgC+cyWetjQWkFPtma79a+e7oQoph8m9qkHK8xEdwdyz5i3ssmEYHo/OPngEZjWLoKCwNeftly0NO0KbBsmaGYoCAAv/0GPPccgx4iGVEJgr3jef1fWVkZ1Go1SktLERUV5e3uKJpOL6DLzI1W8y6MNVu2T+rq0WWvmktXDeuHYsLnB6Ets1BAzgYp+24MvACYJeYan9Vdu7pqv54tYsfrjSXNmq95+lI5Zv90AioYxhasq8ZfD23A2B0rEX/VTm5PUpKhjs7AgUBwsFv7rHS+sPRNnufM329ehpCsOH1ekRtZWtJyhpR990T+i5jXs0XMeL2xpFn7NYN0NzDk+Ga8uHk54svs5Og0bmwIdAYNYqDjQb6w9E2+g4EPyYqnE3ftsVbjxRVi+27vCrdHagK6tojHf3JO40zxNTSNqYdBac1c3ulkrS/q8BBM7NECxVcrERMRglO/l2PeppN2H2ttvNbe2wJ31c/58zVHfboHjx/ZiLHbV6HRFTs5Oo0aGXJ0nnsOCAmRtC8kjsfrLJHfY+BDsuLpxF1bnDkvSoyGEfZ3FIq5wrXU5p/b8yW/CrbWlwEdk0Q93tJnZe+9FWCon/NQisb15YwbN4DlyyFkZaHH2bM4YaPp75GxiJ31DgL+NoSBjgzYq7OkgoQ/J6QYDHxIVozJptrSCou/7Jw5l8ksPyciFFABl65W2s0T2HXqssvLWxbVernaMzt/lFdixMoDNq9wAXjkKtjW1fZHPx1HdL1glF6rtvpZxUeFQi8I+ObgBbP3W8xZXE4vC964Aaxcadh1dfq0WX9q+z0iGh+lP4MvWj2EqiDD0tWqh+5Gmg8EPUrIeZHj0jf5PgY+JCvGwnUvLc81JZsaOXOCub38HGt5AtmHC/Dal4csPsZVl67eTIq21L8AleUEYuMV7tRvjwBQ2a027OpVsJir7dqfUe02FTf0GPjP3abbjO/39SqdqD5oS6/bb3TjBrBqlSHQyc+32fRSPTU+Sn8Gn//lYVOgU5unllFdoZScF7ktfZN/8Pnt7OR/jIm0GrX5EolGHebQTIa1Lec1Wdp2bXxcyfVq5wZgh3Hpx1r/bJ2OIADQllVCWyZutsQVYq62/7hm+z0qqXW/8f3ecfKyqD4Ul1fVvVGnA5YvB26/3bC9PDjYkINjKeiJjQXmzgWuX0fOyUvoMGoFlrfrZTXoATyzjOoKXyj3IBU5LX2T/+CMD8mSq4XrxObn1M4TwJ//dkeNh5rLdFU39Hh9zSG3vI6RveDIHndcRRvf75+OFopqH1M/1BDofPaZIcn4hK0MHQANGhh2XQ0bZqi3U0On5FDJl1E9TWk5L+5Y+iZi4EOy5coJ5mJySIxqn8fkjryemst06/O0eH3NYRSXu2dGyaj4qmN1hmpz11W0ANicTQvQ69Dr1+0Yu2MVbpt53vaTqdWGQGf4cCA83GZTqZdRvUFpOS/+8JmR/DDwIb/kzGyFlDMcASrzJSuNOgyZvVJwTHsVs3867vTzqgBEhQeh9PoNu21jIlxL0LV3te2q6PBglFyvhkrQo+evOzB2x0o0v2znwOCoqJuBTr16Dr+mp+sfSU3sz6irs31y4uufGckPAx/yS87MVkgxw2G87pz3dFs0iAg126n11rojDld7tvTcf0u/VVTwpFHbngGxx97VtrPBkErQo8exnZj+85doeNrO0lVkpGGJ68UXnQp0LHH3+V/uJPZndPq6IwgPDvCboMCXPzOSHwY+5Jccma2onSfgyixHTEQI3umbavYHJ/twgcXt6fZYmjXK6p2Ch1I0WL33rM0lD6nO6rJ1tZ3ZqyWmf3fU7nulEvR4+PgujNu+Ai0unbH5euXBYfhXxnNoMXUiHup0m8v9t8aVZVRvEvtzXVxe7XfF/Xz1MyP54Vld4Fld/krM+VKWzrZy5lwqo9lPtkbfdo1N39s7e8waFYD5z7RDg4gQi1e4tipKq2qMR6paL9aex+J7JQjofiIHY7evRMvfT9t+4vBw6LOysPeRAdDqg3glL4LYn09vnWtH5EnO/P1m4AMGPv7MlTo+zpzPtWrY3WZXpTmnLuPpxbsceo6YiGC827eV3St1e7VcPFXrJfvQRWyc9U88/79lSCmyXUenMigYp0e8gjunTzYsY5FTsg8X/Jkgb2G7fy21fyaJ/AkPKSXZkEtV2dq5AfYqNxv7XXlDj7//tbWprfFE9sKySoe21TqaMB0bEYKcyd1EnbdlK+/BrecbCQKwdq0h9+bAAfQA0MNa0+BgnBs1EYf6DUKMpiE6JcfgTs4+uKxHagKuV+kw7vOf7bZlcT8icwx8SHJyqyorNjfAVr/Tb2+IqX3ucnhbrSMJ0yoA7/RNdeiQUUtjk7zWiyAA331nCHT277fToUDDrqtRo4CoKKgAJP35RdISm7zO4n5E5li5mSTlq1VlxfTbmYrSxmRUe+FFgoNVqW1xpNaL5QYC8P33QKdOhsrIAQFA796Wg56AAOCtt4CSEsPjbtwA3njDsO2c3Mrez5YK0iW5E/kTzvgogKeWnXy1qqwj/XZ0W62tLeFG4zKaY2TX5pK9J46eb6TT6XH80/8i/qP3EHPogP0HTpsGjB4NREe70EtyFYv7ETmHgY+f8+Syk69WlXW0345uq7W2Jdxdn4PdpQ1BwL2nD6DrkKnAwX0IBNDSVvusLGDMGMNxECQrLO5H5DgGPn7MrQmuFvhqVVmx/d5x8ndRCdKW9EhNQNcW8fhPzmmcKb6GpjH1MCitmUP5PGLVqfUiCEg/8zPGbV+BDheO2n38nHsG4F8dHsN7w+7nH04f4MmfLSJ/wMDHT3lj2clXq8qK7fe8Tacs3i5m5sbSzNs/t+e75ao8UAV8fMtlqOZPQ8cLeXbbz017Cks6PoaS8Jt5OXJdlqS6PPmzReQPeEngp1xOcHWC2EReY1VZuSQ6i+23NfYStz2S8L1pE3DffaZk5E7DnrIa9Jz6v1HYu/c4mk1ah2aT1uGD+waZBT2A9Z8PnV5AzqnL+ObgBeScugydXvFlwLzKVzcTEHkTAx8/5WiCqxSMyZYARAUR09bmyeIPp6P9rk3488vSeOzNvMHK4+zavBl44AFDoKNSAV27Atu2WWx6Yfho/LDxF+ScvASdTo/bFs/BxSBx517V/PnIPlyALjM34unFuzBm9UE8vXgXuszcyD+uXuK2ny0iP8fAx0+JXb6RusaHMdmygZ2Twd0x4+QKa1vVHWFpPJLNvG3daghujIHOgw8CW7ZYbjtxIlBUZNheLgho9MnHeOTBVqbEbMDxnw/OLMiPN2Z1ifwBc3z8lL3DDK1VGpaCO6rKemJLfu2t6icKr2LeppMOPUftxG2nZ962bzcUDNywwf6DX30VeOUVIC5OZC8d+/nw1TIF/s4bs7pyqchO5AoGPn7K2zU+pKwq68kt+TW3quecuuxw4FN8tdLse7EzK7ee+AWYOARYv95+4wkTDMFOfLxDfavJkZ+PnFOXfbJMgb/z9Kyu3CqyEzmLS11+zJlKw1KRqqqsN5dYnEl6jg4PrvMc0fWC67Rre+FX/PuzTJye+ShOz3wUrZ7saT3oGTcOKCgwLV3h7393KuipnZj8UIpG1M+HN2YWyD4x/8diI0KgLb3uciI6lzrJn3DGx885WmlYKlLMOHl7iaXmGMR654dfEREWVCeobHPxGMZuX4kH8u2cdQUYigVOnAgkJjraZatsXa1vn9TV5s+Ht/LFyDZ7VcEFAJfLq0xLzs7Oznj7/yGR1FSCICg+5d+ZY+1JHFemx3NOXcbTi3fZfY1Vw+526xJL9uECTP32CLRllXbbGn/tf5lQiHZjh4p6/qXte6PVnHfR4Z5UF3ppnbVClsa+2pv90+kFdJm50W4+0PZJXfmHzwss/R+zROznXZtc/h8SWeLM32/O+JBbiakqay1hUi5LLMZZs3kbT2L2T8cttsk4sRv//Gq6qOf7tF0vLOj8BLRRDU23zQ6LRgcn+mYv2VSKq3Vv54tZw0Rbg5qzutqyCkxfdwTF5dV12jk7OyOX/4dEUmHgQ25lr6qsrRkhOS2xBAaoMCajOe7U1Mfraw6hzc878K8v3xL12H+37YV/3G0e6NRWXF7lcJ/EzKZJdX6a3M6EYqKtOWNSfs6pyxaDHiNnEtHl9P+QSAoMfPyUHK6G7Z0VNvy+ZCzamm/1/vnPtHPblvya74+os7d++AHo2RM9APSw89wXIm/BkCeyMGLU49ALwJTPDtrtT0z9UIf6L/YcNimv1r2VL1abp8+g8yXumJ3xZmkMIndg4OOH5HA1LKaq7OJtdYMe4/0qANO/y0Nmr5Z4eeUBi68hwLklFns5EQnqMMyNKUKHF58R9XwF9WMx5Imp+DUu2ex2R66ANVHi2zqyfCX11bqjJ9NLjYm2trljdkauS51EzmLg42fkcjVsb4kFAGztrjVOyZ8ouippv6y9P+mnD2LFZ2+Keg5Bo8Hgv07FtojGoq6AE9RhNt8LMdv6axK7fDV7/XGk3RoLTVQYCsscv1qXw6xhbVIt3cmds++9u2Zn5LbUSeQKBj5+RE5Xw1IlOi7dcdrqfY6Op+b7k3bmZ6xa/Ya4TtxyC5CdDbRrZ3rdZw4XYJvIK+CaW+KluFoW+97O23QS8zadRHS9YNPnL/b15TBraIkSEm1dee/dOTsjl6VOIlexgKEfkdPZPVIlOpZcF5eoademTQgMDEDO6xk4PfNRm0HPH2GR6P3cbDSbtA45Jy8Zzr36M+gxcqQ4pNSFJB19b0uvGd5Dda1CitZeX87F6vw90VaK996dhUuNS52PtWlkdvYbkS/hjI8fkepqWIolDntT7gAQoPqzGLGF+1Qw/KEuuWY98DHacfJS3b4aD/XU6ew+vjQ0As89+RZ+Tryzzn223itHroClvFoW897WZJztCQsKwIr/62wziVtOs4aW+HOirZTvPWdniKxj4ONHpLgalmqJQ8yU+7B7Dbu6rN0/5J5kq3Vzapq36SQ6nD+CFavfQKDuht32V0LC8dyT03GgUQu7be29p44k+0qVGGyvYq8lAgBtWSUCVCo81qaR1XZyz6Exjv1FK9W0nU14lwOp33tvJ6ITyRUDHz/i6tWw1InR1hIiG0QEo2+bRnjgzni0bhyN6d8dtZgw+VCKBqv3nrU4nlsvn8eonavRN2+z3X6UB4dh0FPTkduopei+e3LmwJkZNmvvrT32ZvuUkEMjV3zviTxD9oHPjBkz8NVXX+HXX39FeHg47rnnHsycORN33nlzWaKiogITJkzA6tWrUVlZie7du+Mf//gH4l04vdoXuZLY6K4ljppT7uvztPj64EUUl1dhyY7TWLLjNBLUYcjslYIGESEW//Abx3Nr8QWM2Lka/Y9ssvuaFUEhGDNkJn5u1graMsf/SHhyi64rM2w139sdJ3/HvE2n7L6evRksuefQGH9OrfH2Upwr5P7eE/kL2Sc3b9myBSNGjMCuXbuwfv16VFdX4+GHH0Z5ebmpzbhx47B27Vp88cUX2LJlCy5evIh+/fp5sdfe42xiozsTowMDVCi9XoWlO07XqVCsLa3AiJW5KL1eZZ4wefIkMHgwerRKRP7MR7Fx8QtWg57L4VF48pn30GzSOjSbtA4tJnyFH2OaOxX0AJ45vR6QJpHVuJwx7qE77Z7ULWbbvJgTvx3dfi8lOSXwS03u7z2Rv5D9jE92drbZ98uWLUNcXBz279+P++67D6WlpViyZAlWrlyJrl27AgCWLl2Kli1bYteuXbj77ru90W2vEnM+Vm3unGYXM5v0z2U/4eFLGxHw6TK7z7e2xb2Yc88AnLilqcN9qe25tKbonqKxX7m5BimSvx2dYbP3mmJOkhczgyX3YnX+vBwk9/eeyF/IPvCprbS0FAAQE2O46tm/fz+qq6uRkZFhatOiRQskJSUhJyfHYuBTWVmJysqbJ22XlZW5udeeZe98LEvcOc1u6Sq9SYkWo3auxpOHfrL/BE88gYNDRuPxLaUOv7Y9j6QmOJQAKlXytyMzF6XXq0S9Zo/UBAy/LxmLt+WbFYcMUBkSycX2T87F6vx9OUjO7z2Rv/CpwEev12Ps2LFIT09HamoqAECr1SIkJATR0dFmbePj46HVai0+z4wZMzBt2jR3d9crnE1Qduc24aIrFWhcosXInM8x4Jf/2X/AX/8KTJkCtGpluunU/vMAfhb1eioA8VGhAFQOVyy2NrOi0wtWT2d3Jvlb7IzE+jwtlu44LerzzD5cYPHsM0EAFm3NR9ukBg4FP3LcDi2n7ezuqmwt1/eeyF/4VOAzYsQIHD58GNu3b3fpeSZPnozx48ebvi8rK0OTJk1c7Z7XuZKgLPk0++nTwLvvAosX4zEAj9lo+sMd92BO+gBMeeMZi7MvM77Pwydb88W97p+m9rkLABwaj7XZnD6tE/DNwYvQllXCEmeSv8XOSHx98KKozxN//lvK5HQ5boeWy3KQuytby/G9J/IXsk9uNho5ciTWrVuHTZs2oXHjxqbbNRoNqqqqUFJSYta+sLAQGo3G4nOFhoYiKirK7MsfuJr46VLF17NngRdfBFQqw1dyMrB4scWm2Xekoefzc0zJyC/3fR0lzVMsXqU7GvQ0qBds6qsj47GWaFxQWoFPtuZbDXqMHE2qFZPIGhMRXCcZ3Npr+nPSb23urEwshpwrWxORfbKf8REEAaNGjcKaNWuwefNmJCebn4Ddvn17BAcHY8OGDejfvz8A4NixYzh79izS0tK80WWvkSLxU/Q0+7lzwIwZwIIFdl+vOOMR5P3fGAw6YCguKPYq/XqVDoscnOmZ0vuuOnkv9sZja6bMUWI/AzEzF33bNMISG2eVGe04eQllFfYrXDvSP7nz1nKQ3CtbE5F9sg98RowYgZUrV+Kbb75BZGSkKW9HrVYjPDwcarUaQ4cOxfjx4xETE4OoqCiMGjUKaWlpitvRJVXip8Vp9vPngffeA+bPt/v8RQ88hIl39cXm+kmm2xJ+C8Lw+5rg258LRCVtZh8uwKv//cXhYEQTVXds9pYNxJwkL5YjSbX2ElnV4SGiAp95m066pX9y543lILlXtiYi+2Qf+Cz4c0bhgQceMLt96dKleP755wEAs2fPRkBAAPr3729WwFBpJE38vHDBEOjMm2e3qdCzF1RTs4COHW0mVy/amo/5z7RFg4hQm1fp1p7Dnuh6wU4nX7uq9nsrNvHV1sxF1Q09YiJCbC53Ods/co4/b6cnUgrZBz6CYP/PX1hYGObPn4/5ImYj/JlLiZ8FBYZAZ84cu6+z845OmNX5KRz881DPBHUYssIb4yERywDTvzuK7ZO6Wl0GcGXZydmFBalmQYzvraOJr5ZmLozPIVXQU7N/5Dx/305PpAQ+k9xM4ohO/CwoAMaOvZmMnJhoPejp3h3YuRPZhy4iedI6PNN3iinoAW4mdc7beNLlBFtXlp3+uFaNZTvy8c3BC8g5dRk6vbjwyV6isT0JNd5bW0nSLy7Pxfe/XLT7fNaew1meSvpVAlZXJvJ9sp/xIcdZXD4Jr0Lg398HZs+2/wQPPwxMnQrUSA7X6QVMm7nR5mzO0p3iEpFtLQO4ukQw/bujpn+L3V7szGnnNen1egDiZqtGrjqAeVCh518s90nMc0SEBqK8Ume3X8+lNcUjqQmsASMhuWynJyLnccbHTwX+XoS0BTPwWNvGSLu9IQIbJVoPejIygO3bDZXuBAH48UezoAcQl9RZck3cziJbywBSLhE4sr3Y2kyZGIVXqv6c8Tphd5ZGLwAvr7TeJzEzXmKCHuBmVWr+EZaWt7fTE5FrOOPjL4qKgL//HXj/ffttu3UDsrKAe++1eLelxFyxMzG2ZiPEJNjaS9B2hNjtxcbxVt7Q4+9/bW06t+t44RXMF3HiudFSETuwjKz1Sez7HB0ejNLr1V6vXqxUrK5M5LsY+PiqS5cMgc7MmfbbPvigIdC5/367Ta0l5g7oKK6yta2gB7C/DGBcSnjRxmGbjrC3vdhWInJMvRCHXqfkurgZL9jok9gZryHpzfDRTye43OJFrK5M5Ju41OUrLl8GJk++mYx8yy3Wg5777wc2bbq5dLVxo+igx1pF2tk/nUC9kECnu29cBngoRYOcU5dtJiAfOPuHzeeKcKIftWdSdHoBH/90HC/aqMB77o9rDr9OdHiw030CxCfPjuzanMstRERO4IyPXBUXAx98YDjvyp577zUkIz/4oCEocoK9irQAcL1aXG5JbbERIdjy6oPY+GshuszcaHObd9UNPRZvs50kfa1Kh//8rROKr1Xh0pVKs4Rma2rOpGQfLsDUb/OgLbO8rGRcIlv7s+NHDwxJT7Z4kKm9Phk5kjzL5RYiIsdxxkcuiouBN9+8OaMTG2s96ElPB9avB/R6w4zO1q1A165OBz2AuKRaESWVLLpcXoUFm0+JOt/oPzmnYW8XugDgeOEVPNamEZ5PT3Zoe7FxVsta0FPzNS6XVyEmQtwMzs2ZmNvxj2fawlbsYW/LsyPJs8bllsfaNGIiMxGRCJzx8ZY//jDsspo+3X7btDRg2jTD7isXghtbtKXX3fK8Rkt35Is63+hMsbjlJWM7R2ZInCmO2LdNI/xrx2mbj6n9Oj3/koh5UOHllXXzlMTm4NiazRFbFZq8g58Pkbwx8PGUkhLgo48MAYw9d99taPfQQ24LdGrKPlwgarnIFbYSf2smIDeNqSfq+Wq2s3fmlXGGxJniiBkpGnRMjqnz3DVZOm+s518SsDDAfp9ssVXRWWxVaPIsfj5E8qcSxJwJ4efKysqgVqtRWlqKqKgo6Z748mXgttuA0lLb7Tp1MgQ63bu7FOjUvNJsWD8UEIBL5ZU2rzodORcrQPVnvrQDfVIBUIcHi9rx9PGANngkNQEtMn+wudwVoAJ+nf4IQoLMV2rtXWl/c/ACxqw+KLrfGnWY6XiNqht6/CfnNM4UX0OTBvXQQhOJ4mtVdq/opbz6t/ZZGZ+NSc3exc+HyPOc+fvNGR93eu01y0FPx46GZORHHpFsRsfSlWZNlq46HVn6UQEYdm8yPtkqrjqz8TGAYev17J9O2G0fFxmGkKAAu68z7N7kOkEPYH97saPFEcWcvWVvO7NUW57tJZ+LqVdE7sPPh8h3MLnZnV54AUhOBtq3B9auvZmMvGcP0LOnpEGPvbOdjGdFffzTCdMWcrFLPzERwVjwbDu0TWpgs110PfNE4AYRwfhbejN0aBYDTVSozbOwYiKCoS2rQM6py5jYoyVeuC+5ToJwgAp44b5kTO6ZYrfPlog9k0sTFWr37C1HqkLbo9MLdrf4i6mcbe8cNHIffj5EvoMzPu7UoQPw229ufQlHE3Zn/3Qcq/acwdQ+d+F6lbjt6W/0TMFDKRp0mbnRahsVgPDgQMwf2g4bfi3E1wcvori8Ckt2nMaSHacRXS/YdOVrqa/F5dUY99lBADdnU36d3sK0vNQ0ph4GpTWzONMjlpgzucZl3IGRXW+3mwwt1VW82JwQsRWdXT3rjJzDz4fId3DGx8c5k7CrLavES8tzsePkZVHtS65Vib6i3XfmDyzdcRrF5VVm95f+eY6Xup797eHG2ZSNvxZi6L234q3HUjH03ltdCnqMrG0VT1CHYeGz7TAmo7kpiHH3Vbwjs0lil+mkPOuMxOPnQ+Q7OOPjQ1w5Q6s2AcAPR8Qt08TUDxX9Ova2rRtnhYquVGD6d0frBEg127orJ0Js4T93XsU7Optk7wwzns/lXfx8iHwHAx8f4eoZWpaIPeVbEyX+KlXMtvWAABU06nCLQU/tttbO2HKVmKRjd17FOzKbZCxMKLZeEXkePx8i38GlLh9g7wyt6HrBdhN2nWWsMCzmDCmx51QVXanwiZwIsedmOXMVL3ZcO07+bkp6fihFw/O5ZMyRittGYhLbiUhanPGROTFLIsZ/S8nSVaq9K1pHtq2L5c2cCHdexYsd17xNp0z/NiY9b5/UlZWBZcqR89NY7JDIOzjjI3NilkRKrtkvDugoS1ep9q5oR3ZtLnqGxJ2zKVJy5ipeDLFb62syJj2vz9PyfC4ZE3N+mifKJBCRZZzxkTlPLvWMfPA2NI+PtHmVau8MqQEdm1ic9XFmBkkuORHuOAVdzNb62pxJ+ua5UfLDYodE3sXAR+Y8udSTfvstohKJxZ4hVZOlM6rEnrElB1JVYK7J2vhtcSTpm0sp8uRoYjsRSYuBj8y1b9oAMREhNndAiREREohyKwULXd1qa++8r5pFAWtzx2yKGHKZCak9/hOFVzFv00m7j7M3E2jtMzEupTAZ2n3s/Wz5QmI/kT9j4CNjxit2a0GP2CUSABh+32346KfjAKRdVrJXOVoFYPXesxjZ9Xarz+GO2RRb5DYTUnP8Oacuiwp8bM0EcinFe8T8bLHYIZF3MblZpsScv6VRh+Efz7QTlSQ8suvtbknS9bUziuSeVCpF0revfSb+QuzPlq8k9hP5K874yJCY87diIoKx5dUHERIUgIAA4KXluVbbGmdz3LGs5EvT9t6eCam5BNIwIhRQAZeuVpp9DlJsofelz8RfOPqz5SuJ/UT+iIGPDIk5f6u4vBr7z/yBtNti0SM1AcPvS8bibfmoWf8sQAUMuzfZbDZH6mUlX5q292ZSqb3k75rLIa4mffvSZ+IvHP3Z8qXEfiJ/w8BHhhy9Ys8+XIBFW+uekaUXgE+25qNeSLDV5GJX+dIZRd6aCbGX/A3UTTp2ZXbOlz4Tf+HMz5a3EvuJlI45PjLkyBW7mGWx2T8dR/p7G9ySv2KctgdQJ2dBbtP23pgJEfP5ADeXO6atzTMdWyCmEJ4lvvSZ+Atnf7ac/YyJyHkMfGTIkeRHMctiAKAtq3Rb8q4j1Y2lOJvI2efwRlKp2M8HkDbp2F0Vp8kyJiwT+Q4udcmQI8mPjizLCHBf8q6YaXsptpG78hzeSCrVll53+DFSLbVxKcVzmLBM5Ds44yNTYq/YHV2Wcec2ZlvT9lJsI5fiOTw9E+JM4Ukpl9q4lOI5nGUj8g2c8ZExMVfs7Zs2QIAKcGTFyJlZCFdIsY1cyq3onpwJiakfKrotk459H2fZiOSPgY/M2dt+vv/MHw4FPYBzsxCukGIbudRb0T1VLVoT5djsDZdDfJ+nK5ETkWO41OXjnMkHcWQWQgpSbCP31aJ8xqRXezRRoVwOISLyAAY+Ps6ZfJC4SM8GPlJsI28oMlgT285TjEmvKtTdWm40LuMO7HitG4MeIiIPYODj4+xto7XI8R3kLpFkq6/YPnt4bGJYS3pNUIdh4bPtMCajOZe3iIg8xG8Cn/nz56NZs2YICwtD586dsWfPHm93ySNqFqsT61J5pZt6Y5kUBfXE9tnTYxOrR2oCtk/qilXD7sbHA9pg1bC7sX1SV87yEBF5mF8EPp999hnGjx+PrKws5ObmonXr1ujevTuKioq83TWPMM4oxESEiGrvjTOaXN3q6w/nT3FrORGR96kEQZDh4oBjOnfujI4dO2LevHkAAL1ejyZNmmDUqFF47bXX7D6+rKwMarUapaWliIqKcnd33abqhh53z/gJxeXVFu83bpfePqmr1/7o1jyh3JGtvjq9gC4zN9o9f8qbYyMiIs9y5u+3z8/4VFVVYf/+/cjIyDDdFhAQgIyMDOTk5HixZ54XEhSAd/u2sphIK5fqsTx/ioiIvMnnA59Lly5Bp9MhPj7e7Pb4+HhotVqLj6msrERZWZnZl7/w5+qx/jw2IiLyDEUWMJwxYwamTZvm7W64jT9Xj/XnsRERkfv5fODTsGFDBAYGorCw0Oz2wsJCaDQai4+ZPHkyxo8fb/q+rKwMTZo0cWs/Pc2fq8f689iIiMi9fH6pKyQkBO3bt8eGDRtMt+n1emzYsAFpaWkWHxMaGoqoqCizLyIiIvJ/Pj/jAwDjx4/H4MGD0aFDB3Tq1AkfffQRysvLMWTIEG93jYiIiGTELwKfp556Cr///jumTJkCrVaLNm3aIDs7u07CMxERESmbX9TxcZW/1PEhIiJSEkXW8SEiIiISi4EPERERKQYDHyIiIlIMBj5ERESkGAx8iIiISDH8Yju7q4wb2/zpzC4iIiJ/Z/y77cgGdQY+AK5cuQIAfndsBRERkRJcuXIFarVaVFvW8YHhiIuLFy8iMjISKpV0h10azwA7d+6cX9cH4jj9C8fpP5QwRoDj9DeOjFMQBFy5cgWJiYkICBCXvcMZHwABAQFo3Lix255fKeeBcZz+heP0H0oYI8Bx+hux4xQ702PE5GYiIiJSDAY+REREpBgMfNwoNDQUWVlZCA0N9XZX3Irj9C8cp/9QwhgBjtPfuHucTG4mIiIixeCMDxERESkGAx8iIiJSDAY+REREpBgMfNxo/vz5aNasGcLCwtC5c2fs2bPH211y2owZM9CxY0dERkYiLi4Ojz/+OI4dO2bWpqKiAiNGjEBsbCzq16+P/v37o7Cw0Es9lsZ7770HlUqFsWPHmm7zl3FeuHABzz77LGJjYxEeHo5WrVph3759pvsFQcCUKVOQkJCA8PBwZGRk4MSJE17sseN0Oh0yMzORnJyM8PBw3HbbbZg+fbpZeXtfHOfWrVvRu3dvJCYmQqVS4euvvza7X8yYiouLMXDgQERFRSE6OhpDhw7F1atXPTgK+2yNs7q6GpMmTUKrVq0QERGBxMREPPfcc7h48aLZc8h9nPY+y5pefPFFqFQqfPTRR2a3y32MgLhxHj16FH369IFarUZERAQ6duyIs2fPmu6X6ncvAx83+eyzzzB+/HhkZWUhNzcXrVu3Rvfu3VFUVOTtrjlly5YtGDFiBHbt2oX169ejuroaDz/8MMrLy01txo0bh7Vr1+KLL77Ali1bcPHiRfTr18+LvXbN3r178cknn+Avf/mL2e3+MM4//vgD6enpCA4Oxg8//IC8vDx88MEHaNCgganNrFmzMGfOHCxcuBC7d+9GREQEunfvjoqKCi/23DEzZ87EggULMG/ePBw9ehQzZ87ErFmzMHfuXFMbXxxneXk5Wrdujfnz51u8X8yYBg4ciCNHjmD9+vVYt24dtm7diuHDh3tqCKLYGue1a9eQm5uLzMxM5Obm4quvvsKxY8fQp08fs3ZyH6e9z9JozZo12LVrFxITE+vcJ/cxAvbHeerUKXTp0gUtWrTA5s2b8csvvyAzMxNhYWGmNpL97hXILTp16iSMGDHC9L1OpxMSExOFGTNmeLFX0ikqKhIACFu2bBEEQRBKSkqE4OBg4YsvvjC1OXr0qABAyMnJ8VY3nXblyhWhefPmwvr164X7779fGDNmjCAI/jPOSZMmCV26dLF6v16vFzQajfD++++bbispKRFCQ0OFVatWeaKLkujVq5fwt7/9zey2fv36CQMHDhQEwT/GCUBYs2aN6XsxY8rLyxMACHv37jW1+eGHHwSVSiVcuHDBY313RO1xWrJnzx4BgHDmzBlBEHxvnNbGeP78eaFRo0bC4cOHhaZNmwqzZ8823edrYxQEy+N86qmnhGeffdbqY6T83csZHzeoqqrC/v37kZGRYbotICAAGRkZyMnJ8WLPpFNaWgoAiImJAQDs378f1dXVZmNu0aIFkpKSfHLMI0aMQK9evczGA/jPOL/99lt06NABTzzxBOLi4tC2bVssXrzYdH9+fj60Wq3ZONVqNTp37uxT47znnnuwYcMGHD9+HADw888/Y/v27XjkkUcA+M84axIzppycHERHR6NDhw6mNhkZGQgICMDu3bs93meplJaWQqVSITo6GoB/jFOv12PQoEF49dVXcdddd9W531/G+N133+GOO+5A9+7dERcXh86dO5sth0n5u5eBjxtcunQJOp0O8fHxZrfHx8dDq9V6qVfS0ev1GDt2LNLT05GamgoA0Gq1CAkJMf3CMfLFMa9evRq5ubmYMWNGnfv8ZZy//fYbFixYgObNm+PHH3/ESy+9hNGjR+PTTz8FANNYfP1n+LXXXsOAAQPQokULBAcHo23bthg7diwGDhwIwH/GWZOYMWm1WsTFxZndHxQUhJiYGJ8dd0VFBSZNmoSnn37adL6TP4xz5syZCAoKwujRoy3e7w9jLCoqwtWrV/Hee++hR48e+N///oe+ffuiX79+2LJlCwBpf/fykFJy2IgRI3D48GFs377d212R3Llz5zBmzBisX7/ebG3Z3+j1enTo0AHvvvsuAKBt27Y4fPgwFi5ciMGDB3u5d9L5/PPPsWLFCqxcuRJ33XUXDh48iLFjxyIxMdGvxql01dXVePLJJyEIAhYsWODt7khm//79+Pjjj5GbmwuVSuXt7riNXq8HADz22GMYN24cAKBNmzbYuXMnFi5ciPvvv1/S1+OMjxs0bNgQgYGBdbLNCwsLodFovNQraYwcORLr1q3Dpk2bzE6012g0qKqqQklJiVl7Xxvz/v37UVRUhHbt2iEoKAhBQUHYsmUL5syZg6CgIMTHx/vFOBMSEpCSkmJ2W8uWLU07KIxj8fWf4VdffdU069OqVSsMGjQI48aNM83m+cs4axIzJo1GU2ejxY0bN1BcXOxz4zYGPWfOnMH69evNTvP29XFu27YNRUVFSEpKMv0+OnPmDCZMmIBmzZoB8P0xAoa/mUFBQXZ/J0n1u5eBjxuEhISgffv22LBhg+k2vV6PDRs2IC0tzYs9c54gCBg5ciTWrFmDjRs3Ijk52ez+9u3bIzg42GzMx44dw9mzZ31qzN26dcOhQ4dw8OBB01eHDh0wcOBA07/9YZzp6el1yhEcP34cTZs2BQAkJydDo9GYjbOsrAy7d+/2qXFeu3YNAQHmv+YCAwNNV5j+Ms6axIwpLS0NJSUl2L9/v6nNxo0bodfr0blzZ4/32VnGoOfEiRP46aefEBsba3a/r49z0KBB+OWXX8x+HyUmJuLVV1/Fjz/+CMD3xwgY/mZ27NjR5u8kSf/GOJQKTaKtXr1aCA0NFZYtWybk5eUJw4cPF6KjowWtVuvtrjnlpZdeEtRqtbB582ahoKDA9HXt2jVTmxdffFFISkoSNm7cKOzbt09IS0sT0tLSvNhradTc1SUI/jHOPXv2CEFBQcI777wjnDhxQlixYoVQr149Yfny5aY27733nhAdHS188803wi+//CI89thjQnJysnD9+nUv9twxgwcPFho1aiSsW7dOyM/PF7766iuhYcOGwsSJE01tfHGcV65cEQ4cOCAcOHBAACB8+OGHwoEDB0y7mcSMqUePHkLbtm2F3bt3C9u3bxeaN28uPP30094akkW2xllVVSX06dNHaNy4sXDw4EGz30uVlZWm55D7OO19lrXV3tUlCPIfoyDYH+dXX30lBAcHC4sWLRJOnDghzJ07VwgMDBS2bdtmeg6pfvcy8HGjuXPnCklJSUJISIjQqVMnYdeuXd7uktMAWPxaunSpqc3169eFl19+WWjQoIFQr149oW/fvkJBQYH3Oi2R2oGPv4xz7dq1QmpqqhAaGiq0aNFCWLRokdn9er1eyMzMFOLj44XQ0FChW7duwrFjx7zUW+eUlZUJY8aMEZKSkoSwsDDh1ltvFd544w2zP4y+OM5NmzZZ/P84ePBgQRDEjeny5cvC008/LdSvX1+IiooShgwZIly5csULo7HO1jjz8/Ot/l7atGmT6TnkPk57n2VtlgIfuY9REMSNc8mSJcLtt98uhIWFCa1btxa+/vprs+eQ6ncvT2cnIiIixWCODxERESkGAx8iIiJSDAY+REREpBgMfIiIiEgxGPgQERGRYjDwISIiIsVg4ENERESKwcCHiIiIFIOBDxH5heeffx6PP/64V/vwwAMPYOzYsV7tAxHZxsCHiNzGmUCAwQMRuRMDHyIiF1VVVXm7C0QkEgMfInKL559/Hlu2bMHHH38MlUoFlUqF06dPY8uWLejUqRNCQ0ORkJCA1157DTdu3LD5GJ1Oh6FDhyI5ORnh4eG488478fHHHzvdN71ej1mzZuH2229HaGgokpKS8M4775junzRpEu644w7Uq1cPt956KzIzM1FdXW26f+rUqWjTpg3++c9/Ijk5GWFhYab7bty4gZEjR0KtVqNhw4bIzMwEj0Qkko8gb3eAiPzTxx9/jOPHjyM1NRVvvfUWAECn06Fnz554/vnn8e9//xu//vorhg0bhrCwMEydOtXiY2655Rbo9Xo0btwYX3zxBWJjY7Fz504MHz4cCQkJePLJJx3u2+TJk7F48WLMnj0bXbp0QUFBAX799VfT/ZGRkVi2bBkSExNx6NAhDBs2DJGRkZg4caKpzcmTJ/Hll1/iq6++QmBgoOn2Tz/9FEOHDsWePXuwb98+DB8+HElJSRg2bJizbyURScmlc+aJiGy4//77hTFjxpi+f/3114U777xT0Ov1ptvmz58v1K9fX9DpdBYfY82IESOE/v37m74fPHiw8Nhjj9l9XFlZmRAaGiosXrxY9Djef/99oX379qbvs7KyhODgYKGoqMis3f333y+0bNnSbHyTJk0SWrZsKfq1iMi9uNRFRB5z9OhRpKWlQaVSmW5LT0/H1atXcf78eZuPnT9/Ptq3b49bbrkF9evXx6JFi3D27Fmn+lBZWYlu3bpZbfPZZ58hPT0dGo0G9evXx5tvvlnntZo2bYpbbrmlzmPvvvtus/GlpaXhxIkT0Ol0DveViKTHwIeIZG/16tV45ZVXMHToUPzvf//DwYMHMWTIEKeSisPDw23en5OTg4EDB6Jnz55Yt24dDhw4gDfeeKPOa0VERDj82kTkfczxISK3CQkJMZvpaNmyJb788ksIgmCaFdmxYwciIyPRuHFji48xtrnnnnvw8ssvm247deqUU31q3rw5wsPDsWHDBvzf//1fnft37tyJpk2b4o033jDddubMGdHPv3v3brPvd+3ahebNm5vlARGR93DGh4jcplmzZti9ezdOnz6NS5cu4eWXX8a5c+cwatQo/Prrr/jmm2+QlZWF8ePHIyAgwOJj9Ho9mjdvjn379uHHH3/E8ePHkZmZib179zrVp7CwMEyaNAkTJ07Ev//9b5w6dQq7du3CkiVLABgCo7Nnz2L16tU4deoU5syZgzVr1oh+/rNnz2L8+PE4duwYVq1ahblz52LMmDFO9ZWIpMfAh4jc5pVXXkFgYCBSUlJwyy23oLq6Gt9//z327NmD1q1b48UXX8TQoUPx5ptvWn3M2bNn8cILL6Bfv3546qmn0LlzZ1y+fNls9sdRmZmZmDBhAqZMmYKWLVviqaeeQlFREQCgT58+GDduHEaOHIk2bdpg586dyMzMFP3czz33HK5fv45OnTphxIgRGDNmDIYPH+50X4lIWipBYIEJIiIiUgbO+BAREZFiMPAhIr9y9uxZ1K9f3+qXM1vgich/cKmLiPzKjRs3cPr0aav3N2vWDEFB3NBKpFQMfIiIiEgxuNRFREREisHAh4iIiBSDgQ8REREpBgMfIiIiUgwGPkRERKQYDHyIiIhIMRj4EBERkWIw8CEiIiLF+H9mNG8PbxeF3QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***MULTIPLE REGRESSION***"
      ],
      "metadata": {
        "id": "ttdDspLTPIoX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1['restaurant']=df1['restaurant'].astype('category')\n",
        "df1['restaurant']=df1['restaurant'].cat.codes\n",
        "df1['item']=df1['item'].astype('category')\n",
        "df1['item']=df1['item'].cat.codes"
      ],
      "metadata": {
        "id": "7YH-qliJOW3d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1['fiber']=df1['fiber'].fillna(value=4)\n",
        "df1['vit_a']=df1['vit_a'].fillna(value=10)\n",
        "df1['vit_c']=df1['vit_c'].fillna(value=20)\n",
        "df1['calcium']=df1['calcium'].fillna(value=30)\n",
        "df1.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "z5pNOvVtaO0T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X3=df1.drop(['calories'],axis=1).values\n",
        "Y3=df1['calories'].values"
      ],
      "metadata": {
        "id": "BpG7Xt9IYCu3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lv1WPX9OX3zZ",
        "outputId": "0fb2d926-c01a-4b2d-8c8b-8dc6226dd15f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[  4.  91.  60. ...  20.   1.  12.]\n",
            " [  4. 420. 410. ...  20.   0.  12.]\n",
            " [  4. 242. 600. ...  50.   0.  20.]\n",
            " ...\n",
            " [  7. 259. 380. ...   0.   0.  17.]\n",
            " [  7. 260. 320. ...   0.   0.  17.]\n",
            " [  7. 261. 320. ...   0.   0.  17.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wo4FmPc5YfFP",
        "outputId": "c4f21965-9df9-4385-add4-07d19526a3df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 380  840 1130  750  920  540  300  510  430  770  380  620  530  700\n",
            "  250  290  640  580  740  350  380  480  580  520  680  570  530  530\n",
            "  670  560  730  690  630  800  370  480  760 1210 1510 2430  180  270\n",
            "  440  890 1770  640  960 1600  140  270  490  190  320  490  220  350\n",
            "  520  430  310  270  120  230  350  470  500  130  190  260  390  970\n",
            "  490  440   70  110  140  210  430  450  500  450  540  350  860  720\n",
            "  710  640  340  410  380  450  600  870  640  650  740  710  720  800\n",
            " 1280 1120 1130 1220 1120  450  450  450  610  680  430  570  450  280\n",
            "  470  740 1000 1350  380  560  350  610  970 1080 1190  330  440  550\n",
            "  730  100  370  430  410  340  320  500  210  830  320  330  400  450\n",
            "  630  650  690  690  540  650  690  550  540  360  510  640  480  710\n",
            "  740  750  610  300  680  710  840  240  360  600  680  550  710  520\n",
            "  800  620  740  590  600  430  650 1030  730  470  980  290  290  220\n",
            "  210  240  300  240  200   70  430  420  230  720 1550 1000  330  290\n",
            " 1040  730 1100  300  520  450  360  900  580 1220  260  550  990  940\n",
            "  310 1250  730  970 1100  770  900  990  660  760  340  380  590  720\n",
            "  550  690  530  670  560  700  320  450  220  230  830  440  530  410\n",
            "  480  730  290  190  290  950  470  570  580  430  670  470  330  310\n",
            "  300  630  340  410  840  210  530  410  700  760 1000  800  630  540\n",
            "  570  400  630 1030 1260  420  390  380  330  290  350  310  250  550\n",
            " 1050  760  260  470  400  640  540  350  780  580  910  350  500  640\n",
            "  520  280  600  350  380  150  360  280   20  550  320  640  430  860\n",
            "  580 1160  500 1000  180  290  580  330  660  570 1140  570 1140  460\n",
            "  920  370  740  470  940  410  820  550 1100  480  960  320  640  200\n",
            "  320  640  350  700  480  960  380  760  310  620  370  740  420  840\n",
            "  380  760  470  940  390  780  180  280  560  280  560  490  980  150\n",
            "  230  460  390  780  300  150  400  330  110  360  280  150  510  180\n",
            "  220  230  230  310  140  140  310  210  140  200  200  310  110  110\n",
            "   50  760  730  810  740  680  790  820  540  460  510  370  550  440\n",
            "  410  420  390  390  760  780  740  420  380  610  610  630  550  620\n",
            "  630  650  400  710  650  670  540  550  570  410  880  830  820  170\n",
            "  320  160  200  170  200  320  350  340  350  380  320  170  200  250\n",
            "  320  170  200  230  200  250  340  340  370  600  420  440  600  350\n",
            "  340  340  150  140  150  170  490  490  490  490  570  300  270  270\n",
            "  710  760  430  320  260  410  210  420  430  560  580  540  480  190\n",
            "  520  620  380  290  650  540  270  580  470  540  270  440  440  460\n",
            "  180  400  200  390  520  700  780  580  780  720  720]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X3_train,X3_test ,Y3_train,Y3_test= train_test_split(X3,Y3, test_size=0.3, random_state=0)"
      ],
      "metadata": {
        "id": "elhZwqgnYiP6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X3_train, Y3_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "e97kRtH4Ysor",
        "outputId": "b6b1852e-46ae-46bd-bdce-8e211afad16d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 433
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y3_pred_train=lr.predict(X3_train)"
      ],
      "metadata": {
        "id": "hdp1EkjCePKT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y3_pred=lr.predict(X3_test)\n",
        "print(Y3_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZKqmQRNKYz1L",
        "outputId": "3f536ef2-1943-4797-9f35-671ffc26c0cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 381.76599461 1104.78597637 1207.02094587  960.77820315  327.39271823\n",
            "  596.71926889  489.71121077  270.2903603   343.10609201  229.95864706\n",
            "  329.41785307  697.81840906  474.10236155  325.56638457  194.56894984\n",
            "  465.2245776   697.27672014  241.695828    318.27292104  650.35700063\n",
            "  442.06459061  418.86846121  391.64223087  416.85648967  465.09912162\n",
            " 1002.43866308  975.53424661  359.3359087   722.80536333 1143.69945445\n",
            "  623.32710083  326.08498009  301.93075566  962.45269074  218.39792402\n",
            "   18.45259874 1036.89479026  783.75588415  365.38200226  675.72587868\n",
            "  337.56705385  770.79917346  414.74099545  702.77060015  763.81174029\n",
            "  395.27685797  228.02192575  347.45938766  330.14806525  827.97331566\n",
            "  803.76758827  243.97739049  423.14451639  547.27526723  951.40135771\n",
            "  755.02860042  512.49059579  703.6602224   424.14161023  527.34542461\n",
            "  532.41829576  539.89153053  431.88690256  554.68509876 1088.37699397\n",
            "  273.21121611  416.89045228  367.36661799  584.97506276  543.6466549\n",
            "  361.95112491  621.05429058  446.04991711  690.98830336  458.78889964\n",
            "  137.26228662  386.59965547  769.47912958  387.03183574  526.80001141\n",
            " 1137.33497718  293.05781584  768.00755961  484.47325966  730.61367937\n",
            "  768.29152982  425.49685175  328.27798647  629.09513999  482.22838648\n",
            "  169.69270727  643.67611647  631.30944164  220.95623894  687.97411704\n",
            "  753.74751685  384.42309278  797.34331816  515.47143269  137.99897357\n",
            "  506.10644931  431.11457409  132.1236443   392.83266318  491.30374999\n",
            "  154.28762146  444.62259501  730.33003993  302.10655691  542.18246489\n",
            "  747.01870139  293.72421791  576.74201831  696.21393022  491.3931715\n",
            "  506.71655912  307.99231549  425.91952457  585.8693105   189.62291264\n",
            "  214.28192425  606.35330419  529.22822704  350.34455775  320.50167117\n",
            "  270.42375401  260.78334689  447.33209949  440.33745063  387.03183574\n",
            "  262.8436662   497.27496132  436.46308673  539.35695094  199.66992416\n",
            "  419.40117126  699.75759407  357.68982591  939.06137252  550.87743387\n",
            "  295.91721399  471.27668857  639.21947456  710.45033807  768.01963021\n",
            "  346.65480362  150.37635702  522.65992845  300.18342937  904.06513891\n",
            "  412.17656364  467.90554154  332.94614778  531.31947635  656.15310384]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(Y2_test, Y2_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NVMVbc52cciF",
        "outputId": "8a9121ad-6a97-4247-a7f5-d782728c2715"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7961110382079515"
            ]
          },
          "metadata": {},
          "execution_count": 436
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(Y3_train, Y3_pred_train,color='mediumspringgreen')\n",
        "plt.xlabel(\"Actual\")\n",
        "plt.ylabel(\"Predicted\")\n",
        "plt.title(\"Actual vs Predicted (Training)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "sDFmFbAccjFU",
        "outputId": "f6444c51-9647-4ea7-9c74-0ae50ef580f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(Y3_test, Y3_pred,color='violet')\n",
        "plt.xlabel(\"Actual \")\n",
        "plt.ylabel(\"Predicted\")\n",
        "plt.title(\"Actual vs Predicted (Testing)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "3yppLhGqd0At",
        "outputId": "18e5fd62-1fa3-4ee7-def2-5d6e6b20ef08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_Y3_df1=pd.DataFrame({\"Actual Value\":Y3_test,'Predicted Value':Y3_pred,'Difference':Y3_test-Y3_pred})\n",
        "pred_Y3_df1[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Zx1NFrgPd8_T",
        "outputId": "7a8700aa-6cbb-4bb1-8a46-2d5f783d420e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Actual Value  Predicted Value  Difference\n",
              "0           400       381.765995   18.234005\n",
              "1          1130      1104.785976   25.214024\n",
              "2          1210      1207.020946    2.979054\n",
              "3           960       960.778203   -0.778203\n",
              "4           340       327.392718   12.607282\n",
              "5           600       596.719269    3.280731\n",
              "6           480       489.711211   -9.711211\n",
              "7           290       270.290360   19.709640\n",
              "8           330       343.106092  -13.106092\n",
              "9           230       229.958647    0.041353"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-668a6fac-aa07-4423-822c-3418b8cf30a3\" 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",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual Value</th>\n",
              "      <th>Predicted Value</th>\n",
              "      <th>Difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>400</td>\n",
              "      <td>381.765995</td>\n",
              "      <td>18.234005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1130</td>\n",
              "      <td>1104.785976</td>\n",
              "      <td>25.214024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1210</td>\n",
              "      <td>1207.020946</td>\n",
              "      <td>2.979054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>960</td>\n",
              "      <td>960.778203</td>\n",
              "      <td>-0.778203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>340</td>\n",
              "      <td>327.392718</td>\n",
              "      <td>12.607282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>600</td>\n",
              "      <td>596.719269</td>\n",
              "      <td>3.280731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>480</td>\n",
              "      <td>489.711211</td>\n",
              "      <td>-9.711211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>290</td>\n",
              "      <td>270.290360</td>\n",
              "      <td>19.709640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>330</td>\n",
              "      <td>343.106092</td>\n",
              "      <td>-13.106092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>230</td>\n",
              "      <td>229.958647</td>\n",
              "      <td>0.041353</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-668a6fac-aa07-4423-822c-3418b8cf30a3')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-668a6fac-aa07-4423-822c-3418b8cf30a3 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-668a6fac-aa07-4423-822c-3418b8cf30a3');\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-a18d6ad4-acbc-4407-8f8a-4b30d38a68b1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a18d6ad4-acbc-4407-8f8a-4b30d38a68b1')\"\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-a18d6ad4-acbc-4407-8f8a-4b30d38a68b1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 439
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***LOGISTIC REGRESSION***"
      ],
      "metadata": {
        "id": "MmfMTd1ZfMHp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x4=df1.drop(columns='Healthy')\n",
        "y4=df1['Healthy']"
      ],
      "metadata": {
        "id": "mP8sKMkYeVJv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x4_train, x4_test, y4_train, y4_test=train_test_split(x4, y4, test_size=0.3, random_state=21)"
      ],
      "metadata": {
        "id": "02LM6A8hnrwQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()"
      ],
      "metadata": {
        "id": "q54Zcumznzng"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x4_train_=scaler.fit_transform(x4_train)\n",
        "x4_test_=scaler.transform(x4_test)"
      ],
      "metadata": {
        "id": "U0XpnAAbn91t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lr=LogisticRegression(random_state=0).fit(x4_train_, y4_train)\n",
        "print(\"Predicted values of health TRAINING: \\n\", lr.predict(x4_train_))\n",
        "\n",
        "print(\"Predicted values of health TESTING: \\n\", lr.predict(x4_test_))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GqiSeAeDoKfF",
        "outputId": "cab885d1-7dd8-414f-9809-b9d95e88eec1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted values of health TRAINING: \n",
            " [0 0 1 0 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 1 0 0\n",
            " 1 1 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 0 0 0\n",
            " 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 0 1 1 0 1 0\n",
            " 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 1 0 1 0 1 0 0 0 0 1 0 1 0 1 1 0 1 1 1 0 1\n",
            " 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 1 0 0 1 0 1 1 1 0\n",
            " 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 0 0 1\n",
            " 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 1 1 1 1\n",
            " 1 1 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 1 0 0 1 0 1 0 1 1\n",
            " 1 0 0 1 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 1 0\n",
            " 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 1 1 0 1 1 0 0 0 0 1 1 0]\n",
            "Predicted values of health TESTING: \n",
            " [0 0 0 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1\n",
            " 1 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1\n",
            " 1 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 0 1 1 0 1 0 0 1 1 0 1 0 1 1 1 0 1 1 1 0 0\n",
            " 0 1 0 0 0 1 1 1 0 0 1 0 1 1 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 0 0 1 0 1 0 0 1\n",
            " 0 1 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.score(x4_train_, y4_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6hMS77oBxrk5",
        "outputId": "d22120f6-8b0e-4223-a1b9-26776583a097"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.95"
            ]
          },
          "metadata": {},
          "execution_count": 460
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.score(x4_test_, y4_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcPH-QJTxkv-",
        "outputId": "1e3b5cad-586a-4dac-8632-43ba6b06be96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9419354838709677"
            ]
          },
          "metadata": {},
          "execution_count": 461
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr1=LogisticRegression(random_state=0, C=1, fit_intercept=True).fit(x4_train_, y4_train)\n",
        "print(\"Predicted values of health TRAINING: \\n\", lr1.predict(x4_train_))\n",
        "\n",
        "print(\"Predicted values of health TESTING: \\n\", lr1.predict(x4_test_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FgU0HFSpZU_",
        "outputId": "9bbeffcf-4183-4e08-d2f5-4806610b0628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted values of health TRAINING: \n",
            " [0 0 1 0 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 1 0 0\n",
            " 1 1 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 1 0 0 0\n",
            " 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 1 1 1 0 0 1 0 1 1 0 1 0\n",
            " 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 1 0 1 0 1 0 0 0 0 1 0 1 0 1 1 0 1 1 1 0 1\n",
            " 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 1 0 0 1 0 1 1 1 0\n",
            " 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 1 0 0 1 0 1 0 1 1 0 1 0 0 0 1\n",
            " 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 0 1 1 1 1\n",
            " 1 1 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 0 1 0 0 1 0 1 1 0 0 1 0 1 0 1 1\n",
            " 1 0 0 1 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 1 0\n",
            " 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 1 1 0 1 1 0 0 0 0 1 1 0]\n",
            "Predicted values of health TESTING: \n",
            " [0 0 0 0 0 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1\n",
            " 1 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1\n",
            " 1 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 0 1 1 0 1 0 0 1 1 0 1 0 1 1 1 0 1 1 1 0 0\n",
            " 0 1 0 0 0 1 1 1 0 0 1 0 1 1 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 0 0 1 0 1 0 0 1\n",
            " 0 1 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr1.score(x4_train_, y4_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BNfRwi6yRxE",
        "outputId": "ca6527a6-3331-4f85-a3c4-9fbb5fd7b37e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.95"
            ]
          },
          "metadata": {},
          "execution_count": 462
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr1.score(x4_test_, y4_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1ekEO1NySas",
        "outputId": "aba3a566-9abe-491a-dbec-b098a6b4d35f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9419354838709677"
            ]
          },
          "metadata": {},
          "execution_count": 464
        }
      ]
    }
  ]
}