Machine_Learning.ipynb

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      "cell_type": "markdown",
      "source": [
        "**second Ml project**"
      ],
      "metadata": {
        "id": "NpIQuyS39Cc2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv(\"https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv\")\n",
        "df"
      ],
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      "execution_count": 3,
      "outputs": [
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            "text/plain": [
              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion   logS\n",
              "0     2.59540  167.850                0.0            0.000000 -2.180\n",
              "1     2.37650  133.405                0.0            0.000000 -2.000\n",
              "2     2.59380  167.850                1.0            0.000000 -1.740\n",
              "3     2.02890  133.405                1.0            0.000000 -1.480\n",
              "4     2.91890  187.375                1.0            0.000000 -3.040\n",
              "...       ...      ...                ...                 ...    ...\n",
              "1139  1.98820  287.343                8.0            0.000000  1.144\n",
              "1140  3.42130  286.114                2.0            0.333333 -4.925\n",
              "1141  3.60960  308.333                4.0            0.695652 -3.893\n",
              "1142  2.56214  354.815                3.0            0.521739 -3.790\n",
              "1143  2.02164  179.219                1.0            0.461538 -2.581\n",
              "\n",
              "[1144 rows x 5 columns]"
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              "      <th></th>\n",
              "      <th>MolLogP</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>NumRotatableBonds</th>\n",
              "      <th>AromaticProportion</th>\n",
              "      <th>logS</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.59540</td>\n",
              "      <td>167.850</td>\n",
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              "      <td>-2.180</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.37650</td>\n",
              "      <td>133.405</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.000</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.59380</td>\n",
              "      <td>167.850</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.02890</td>\n",
              "      <td>133.405</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.480</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.91890</td>\n",
              "      <td>187.375</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.040</td>\n",
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              "      <th>1139</th>\n",
              "      <td>1.98820</td>\n",
              "      <td>287.343</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.144</td>\n",
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              "    <tr>\n",
              "      <th>1140</th>\n",
              "      <td>3.42130</td>\n",
              "      <td>286.114</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-4.925</td>\n",
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              "    <tr>\n",
              "      <th>1141</th>\n",
              "      <td>3.60960</td>\n",
              "      <td>308.333</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.695652</td>\n",
              "      <td>-3.893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>2.56214</td>\n",
              "      <td>354.815</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.521739</td>\n",
              "      <td>-3.790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>2.02164</td>\n",
              "      <td>179.219</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.461538</td>\n",
              "      <td>-2.581</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1144,\n  \"fields\": [\n    {\n      \"column\": \"MolLogP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.866002824775323,\n        \"min\": -7.571399999999989,\n        \"max\": 10.388599999999991,\n        \"num_unique_values\": 930,\n        \"samples\": [\n          2.0437,\n          1.0428,\n          0.4903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MolWt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.62049956567412,\n        \"min\": 16.043,\n        \"max\": 780.9490000000001,\n        \"num_unique_values\": 806,\n        \"samples\": [\n          132.12599999999998,\n          194.194,\n          226.276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumRotatableBonds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.627398498656524,\n        \"min\": 0.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.0,\n          3.0,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AromaticProportion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34330517588206755,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          0.5,\n          0.2727272727272727,\n          0.8571428571428571\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"logS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0965019290656026,\n        \"min\": -11.6,\n        \"max\": 1.58,\n        \"num_unique_values\": 743,\n        \"samples\": [\n          -1.7,\n          -6.62,\n          -3.324\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data preparation\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "wzN58_O1JXUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y= df[\"logS\"]\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SCLsDKCJc5H",
        "outputId": "26f4302a-60e3-4de3-b18b-ef1a7afe5783"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      -2.180\n",
              "1      -2.000\n",
              "2      -1.740\n",
              "3      -1.480\n",
              "4      -3.040\n",
              "        ...  \n",
              "1139    1.144\n",
              "1140   -4.925\n",
              "1141   -3.893\n",
              "1142   -3.790\n",
              "1143   -2.581\n",
              "Name: logS, Length: 1144, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x =df.drop('logS', axis=1)\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "oT_1ZwY8Ju_J",
        "outputId": "0bdd45d2-e77c-4118-82b3-3f0ebf0cbfc3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion\n",
              "0     2.59540  167.850                0.0            0.000000\n",
              "1     2.37650  133.405                0.0            0.000000\n",
              "2     2.59380  167.850                1.0            0.000000\n",
              "3     2.02890  133.405                1.0            0.000000\n",
              "4     2.91890  187.375                1.0            0.000000\n",
              "...       ...      ...                ...                 ...\n",
              "1139  1.98820  287.343                8.0            0.000000\n",
              "1140  3.42130  286.114                2.0            0.333333\n",
              "1141  3.60960  308.333                4.0            0.695652\n",
              "1142  2.56214  354.815                3.0            0.521739\n",
              "1143  2.02164  179.219                1.0            0.461538\n",
              "\n",
              "[1144 rows x 4 columns]"
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              "      <th>MolLogP</th>\n",
              "      <th>MolWt</th>\n",
              "      <th>NumRotatableBonds</th>\n",
              "      <th>AromaticProportion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.59540</td>\n",
              "      <td>167.850</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.37650</td>\n",
              "      <td>133.405</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.59380</td>\n",
              "      <td>167.850</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.02890</td>\n",
              "      <td>133.405</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.91890</td>\n",
              "      <td>187.375</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1139</th>\n",
              "      <td>1.98820</td>\n",
              "      <td>287.343</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1140</th>\n",
              "      <td>3.42130</td>\n",
              "      <td>286.114</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1141</th>\n",
              "      <td>3.60960</td>\n",
              "      <td>308.333</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.695652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>2.56214</td>\n",
              "      <td>354.815</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.521739</td>\n",
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              "      <th>1143</th>\n",
              "      <td>2.02164</td>\n",
              "      <td>179.219</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.461538</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1144 rows × 4 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 1144,\n  \"fields\": [\n    {\n      \"column\": \"MolLogP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.866002824775323,\n        \"min\": -7.571399999999989,\n        \"max\": 10.388599999999991,\n        \"num_unique_values\": 930,\n        \"samples\": [\n          2.0437,\n          1.0428,\n          0.4903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MolWt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102.62049956567412,\n        \"min\": 16.043,\n        \"max\": 780.9490000000001,\n        \"num_unique_values\": 806,\n        \"samples\": [\n          132.12599999999998,\n          194.194,\n          226.276\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumRotatableBonds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.627398498656524,\n        \"min\": 0.0,\n        \"max\": 23.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          0.0,\n          3.0,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AromaticProportion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34330517588206755,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          0.5,\n          0.2727272727272727,\n          0.8571428571428571\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###DATA SPLITTING\n"
      ],
      "metadata": {
        "id": "MTTVkO0BK7mk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=0.2, random_state=100)"
      ],
      "metadata": {
        "id": "blZND0DIK-i3"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rEPNjG36S1OE",
        "outputId": "150aacef-b465-41da-bd7c-e08fd1f59a36"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     MolLogP    MolWt  NumRotatableBonds  AromaticProportion\n",
              "107  3.14280  112.216                5.0            0.000000\n",
              "378 -2.07850  142.070                0.0            0.000000\n",
              "529 -0.47730  168.152                0.0            0.000000\n",
              "546 -0.86740  154.125                0.0            0.000000\n",
              "320  1.62150  100.161                2.0            0.000000\n",
              "..       ...      ...                ...                 ...\n",
              "802  3.00254  250.301                1.0            0.842105\n",
              "53   2.13860   82.146                3.0            0.000000\n",
              "350  5.76304  256.348                0.0            0.900000\n",
              "79   3.89960  186.339               10.0            0.000000\n",
              "792  2.52334  310.297                3.0            0.300000\n",
              "\n",
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            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "RC6zdV6uS85i",
        "outputId": "4a2accf0-9c69-4f4f-8d34-e000f4f84488"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MolLogP    MolWt  NumRotatableBonds  AromaticProportion\n",
              "822   2.91000  172.268                7.0            0.000000\n",
              "118   7.27400  360.882                1.0            0.666667\n",
              "347   1.94040  145.161                0.0            0.909091\n",
              "1123  1.98640  119.378                0.0            0.000000\n",
              "924   1.70062  108.140                0.0            0.750000\n",
              "...       ...      ...                ...                 ...\n",
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              "711   0.04430  218.205                5.0            0.000000\n",
              "4     2.91890  187.375                1.0            0.000000\n",
              "948   3.56010  318.328                2.0            0.750000\n",
              "\n",
              "[229 rows x 4 columns]"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1114</th>\n",
              "      <td>1.76210</td>\n",
              "      <td>478.513</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>6.32820</td>\n",
              "      <td>276.338</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>0.04430</td>\n",
              "      <td>218.205</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.91890</td>\n",
              "      <td>187.375</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>948</th>\n",
              "      <td>3.56010</td>\n",
              "      <td>318.328</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>229 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9152f26-c155-4674-8ee4-0487de79967d')\"\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-d9152f26-c155-4674-8ee4-0487de79967d 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-d9152f26-c155-4674-8ee4-0487de79967d');\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-c11cccef-5f9f-4b48-be26-f96e3cf3fc45\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c11cccef-5f9f-4b48-be26-f96e3cf3fc45')\"\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-c11cccef-5f9f-4b48-be26-f96e3cf3fc45 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_da801d07-b757-49b2-9496-9386c30d18a4\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_da801d07-b757-49b2-9496-9386c30d18a4 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_test",
              "summary": "{\n  \"name\": \"x_test\",\n  \"rows\": 229,\n  \"fields\": [\n    {\n      \"column\": \"MolLogP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9781161342582056,\n        \"min\": -5.397199999999993,\n        \"max\": 9.8876,\n        \"num_unique_values\": 213,\n        \"samples\": [\n          3.386800000000001,\n          4.102000000000002,\n          2.935900000000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MolWt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105.78845308303771,\n        \"min\": 16.043,\n        \"max\": 764.9499999999999,\n        \"num_unique_values\": 203,\n        \"samples\": [\n          139.11,\n          232.32700000000003,\n          104.15199999999996\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumRotatableBonds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3074347142542235,\n        \"min\": 0.0,\n        \"max\": 14.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          14.0,\n          6.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AromaticProportion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3432208155181871,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          0.4166666666666667,\n          0.2608695652173913,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model building\n"
      ],
      "metadata": {
        "id": "zpjGM8SxWfoN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Linear regresion\n",
        "training the model"
      ],
      "metadata": {
        "id": "b2K2LD9ZWnhz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "\n",
        "lr = LinearRegression()\n",
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "Qha9Ws8-Wkmc",
        "outputId": "a25d5d34-b0f7-488a-fc3b-1338dc2d9882"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" 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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Applying the model to make a prediction"
      ],
      "metadata": {
        "id": "S4MYd5A7XsSD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "nF2TiWKRXoO5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_lr_train_pred = lr.predict(x_train)\n",
        "y_lr_test_pred = lr.predict(x_test)\n"
      ],
      "metadata": {
        "id": "J4gsct7fXym7"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_lr_train_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4BAMs_XjYKjP",
        "outputId": "2f870792-56f3-4456-c336-ec4e6e4a5d69"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       -1.80406335, -1.80643013, -1.90447849, -5.52193556, -2.07398557,\n",
              "       -3.22132977, -6.70156561, -5.44962012, -1.25610101, -3.35223802,\n",
              "       -3.98606913, -1.89546347, -6.08789325, -3.88711135, -3.78407726])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_lr_test_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9et31CuYlru",
        "outputId": "f14efd45-8f96-4ade-c66b-e968bc1e7616"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       -4.79863925e+00])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluate model perfoemance"
      ],
      "metadata": {
        "id": "AJTDx_ekZLix"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "lr_train_mse= mean_squared_error(y_train, y_lr_train_pred)\n",
        "lr_train_r2= r2_score(y_train, y_lr_train_pred)\n",
        "\n",
        "\n",
        "\n",
        "lr_test_mse= mean_squared_error(y_test, y_lr_test_pred)\n",
        "lr_test_r2= r2_score(y_test, y_lr_test_pred)"
      ],
      "metadata": {
        "id": "u7f4gR2UZSXE"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_train_mse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qcMqoAsUaiDp",
        "outputId": "28e46abc-29b3-473e-837c-1fb4989446ba"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0075362951093687"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_train_r2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6ukzbc4ajti",
        "outputId": "3c1128d0-85b1-4307-91bf-2a1309bbedad"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7645051774663391"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_test_mse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FfuOPwJ3auDl",
        "outputId": "6e6cfa98-c39b-4959-c8ce-dd18aa9b5e84"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0206953660861033"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_test_r2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Exjw_A3Paxzw",
        "outputId": "3644bc51-6ace-48d9-a50a-0fea4dc72053"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7891616188563282"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('LR MSE (Train):',lr_train_mse )\n",
        "print('LR R2 (Train):',lr_train_r2 )\n",
        "print('LR R2 (Test):',lr_test_mse )\n",
        "print('LR R2 (Test):',lr_test_r2 )\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RescWVAOcNys",
        "outputId": "9bf08ad7-c612-4a2b-a550-ea134beefe1c"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR MSE (Train): 1.0075362951093687\n",
            "LR R2 (Train): 0.7645051774663391\n",
            "LR R2 (Test): 1.0206953660861033\n",
            "LR R2 (Test): 0.7891616188563282\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_results = pd.DataFrame(['Linear regression', lr_train_mse,lr_train_r2,lr_test_mse,lr_test_r2]).transpose()"
      ],
      "metadata": {
        "id": "n5jW7tmEdHbP"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_results\n",
        "lr_results.columns=['Method', 'Training MSE','Training R2' , 'Test MSE', 'Test R2']\n"
      ],
      "metadata": {
        "id": "ZznR7pnFbGNr"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "UifGBRLhdQaY",
        "outputId": "311e978b-b52c-46de-9057-8520487990a8"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Method Training MSE Training R2  Test MSE   Test R2\n",
              "0  Linear regression     1.007536    0.764505  1.020695  0.789162"
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('lr_results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "lr_results",
              "repr_error": "'str' object has no attribute 'empty'"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##Random Forest\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "KGUoEl6ldy-7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###training model"
      ],
      "metadata": {
        "id": "xHxYvbDVd-7v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "rf= RandomForestRegressor(max_depth=2, random_state=100)\n",
        "rf.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "SrRR3dz6eRNr",
        "outputId": "3f3ad31c-edb2-4942-e7e0-346a40ff1a08"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_depth=2, random_state=100)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=2, random_state=100)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=2, random_state=100)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###applying the model to make a prediction"
      ],
      "metadata": {
        "id": "kHZZD0OveIVG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_rf_train_pred = rf.predict(x_train)\n",
        "y_rf_test_pred = rf.predict(x_test)"
      ],
      "metadata": {
        "id": "z3RE0n9pfe2U"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Evaluate model performance"
      ],
      "metadata": {
        "id": "GzXiZ9j8f6GL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "rf_train_mse= mean_squared_error(y_train, y_rf_train_pred)\n",
        "rf_train_r2= r2_score(y_train, y_rf_train_pred)\n",
        "\n",
        "\n",
        "\n",
        "rf_test_mse= mean_squared_error(y_test, y_rf_test_pred)\n",
        "rf_test_r2= r2_score(y_test, y_rf_test_pred)"
      ],
      "metadata": {
        "id": "nRZX-IJSf_Bk"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_results = pd.DataFrame(['Random Forest', rf_train_mse,rf_train_r2,rf_test_mse,rf_test_r2]).transpose()\n"
      ],
      "metadata": {
        "id": "nrr24xKFgSKX"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_results.columns=['Method', 'Training MSE','Training R2' , 'Test MSE', 'Test R2']\n"
      ],
      "metadata": {
        "id": "yPeuak91hE3I"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_results\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "0Nm8DItphGpH",
        "outputId": "bbcc2451-0384-43fc-8ad9-e3390149e42f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Method Training MSE Training R2  Test MSE   Test R2\n",
              "0  Random Forest     1.028228    0.759669  1.407688  0.709223"
            ],
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              "\n",
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              "\n",
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              "        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>Method</th>\n",
              "      <th>Training MSE</th>\n",
              "      <th>Training R2</th>\n",
              "      <th>Test MSE</th>\n",
              "      <th>Test R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>1.028228</td>\n",
              "      <td>0.759669</td>\n",
              "      <td>1.407688</td>\n",
              "      <td>0.709223</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-ae6a6131-98e2-4f4a-8d54-91aeac78b9ea')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\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-ae6a6131-98e2-4f4a-8d54-91aeac78b9ea 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-ae6a6131-98e2-4f4a-8d54-91aeac78b9ea');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "  <div id=\"id_b29ab186-8c4c-4927-b6e4-c8597ad38eea\">\n",
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              "      .colab-df-generate {\n",
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              "        height: 32px;\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('rf_results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b29ab186-8c4c-4927-b6e4-c8597ad38eea button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('rf_results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "rf_results",
              "repr_error": "'str' object has no attribute 'empty'"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Comparison"
      ],
      "metadata": {
        "id": "7EnKCMtehed9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_models= pd.concat([lr_results,rf_results], axis=0).reset_index(drop=True)\n",
        "df_models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "LXL0aixBhlTd",
        "outputId": "7a3a1e2e-eaec-45b5-b0bb-715d8cfbc474"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Method Training MSE Training R2  Test MSE   Test R2\n",
              "0  Linear regression     1.007536    0.764505  1.020695  0.789162\n",
              "1      Random Forest     1.028228    0.759669  1.407688  0.709223"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Method</th>\n",
              "      <th>Training MSE</th>\n",
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              "      <th>Test MSE</th>\n",
              "      <th>Test R2</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Linear regression</td>\n",
              "      <td>1.007536</td>\n",
              "      <td>0.764505</td>\n",
              "      <td>1.020695</td>\n",
              "      <td>0.789162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>1.028228</td>\n",
              "      <td>0.759669</td>\n",
              "      <td>1.407688</td>\n",
              "      <td>0.709223</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      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-cc691521-be4f-4b65-8f34-0050c44d6dfe 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-cc691521-be4f-4b65-8f34-0050c44d6dfe');\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-b84ad9fb-23b0-4a3c-aa37-20f4ff8bd80e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b84ad9fb-23b0-4a3c-aa37-20f4ff8bd80e')\"\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-b84ad9fb-23b0-4a3c-aa37-20f4ff8bd80e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_04710693-c476-48d4-9b95-a5d539ec0484\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_models')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_04710693-c476-48d4-9b95-a5d539ec0484 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_models');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_models",
              "repr_error": "'str' object has no attribute 'empty'"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_models.reset_index(drop=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "lCgL9Uj4h3Rv",
        "outputId": "bfb92b65-9475-45c6-c7b5-6daaa4bdf1df"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Method Training MSE Training R2  Test MSE   Test R2\n",
              "0  Linear regression     1.007536    0.764505  1.020695  0.789162\n",
              "1      Random Forest     1.028228    0.759669  1.407688  0.709223"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Method</th>\n",
              "      <th>Training MSE</th>\n",
              "      <th>Training R2</th>\n",
              "      <th>Test MSE</th>\n",
              "      <th>Test R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Linear regression</td>\n",
              "      <td>1.007536</td>\n",
              "      <td>0.764505</td>\n",
              "      <td>1.020695</td>\n",
              "      <td>0.789162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>1.028228</td>\n",
              "      <td>0.759669</td>\n",
              "      <td>1.407688</td>\n",
              "      <td>0.709223</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-77ee33e1-5fa0-4436-aa05-5459c7d824c4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-77ee33e1-5fa0-4436-aa05-5459c7d824c4 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-77ee33e1-5fa0-4436-aa05-5459c7d824c4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-13072f15-1a3a-4a28-9e8b-37208d8002ca\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-13072f15-1a3a-4a28-9e8b-37208d8002ca')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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",
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              "    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-13072f15-1a3a-4a28-9e8b-37208d8002ca button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "'str' object has no attribute 'empty'"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Visualization of prediction results"
      ],
      "metadata": {
        "id": "zt-xd2m6iiAA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "plt.scatter(x=y_train, y=y_lr_train_pred,alpha=0.3)\n",
        "plt.plot(y_train,y_lr_train_pred)\n",
        "plt.ylabel('predict logs')\n",
        "plt.ylabel('Experimental logs')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "gDDLKAQsinJU",
        "outputId": "66d00048-f99a-42ab-de95-334ad2897d3a"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Experimental logs')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "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": [],
      "metadata": {
        "id": "m9AKDmkojSro"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}