Mohammed_Usama_Ahmed.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "\n",
        "from sklearn.datasets import load_boston\n",
        "df = load_boston()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.keys()"
      ],
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        "id": "pQPsX-pMl-ku"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.target)"
      ],
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      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "boston=pd.DataFrame(df.data, columns=df.feature_names)\n",
        "boston.head()"
      ],
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              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
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              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  \n",
              "0     15.3  396.90   4.98  \n",
              "1     17.8  396.90   9.14  \n",
              "2     17.8  392.83   4.03  \n",
              "3     18.7  394.63   2.94  \n",
              "4     18.7  396.90   5.33  "
            ],
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    {
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      "source": [
        "boston['MEDV']=df.target\n",
        "boston.head()"
      ],
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      },
      "execution_count": 18,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  MEDV  \n",
              "0     15.3  396.90   4.98  24.0  \n",
              "1     17.8  396.90   9.14  21.6  \n",
              "2     17.8  392.83   4.03  34.7  \n",
              "3     18.7  394.63   2.94  33.4  \n",
              "4     18.7  396.90   5.33  36.2  "
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              "          const element = document.querySelector('#df-18e5ee9c-6aee-4e78-a970-2f8c044fde5a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "boston.isnull().sum()\n",
        "from sklearn.model_selection import train_test_split\n",
        "x=boston.drop('MEDV', axis=1)\n",
        "y=boston['MEDV']\n",
        "x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=0.15,random_state=5)\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o33x6dWmo3ZI",
        "outputId": "b0b25a8a-d96c-4d33-b945-72f5dc510b0d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(430, 13)\n",
            "(76, 13)\n",
            "(430,)\n",
            "(76,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "lin_model=LinearRegression()\n",
        "lin_model.fit(x_train,y_train)\n",
        "\n",
        "y_train_predict=lin_model.predict(x_train)\n",
        "rmse=(np.sqrt(mean_squared_error(y_train,y_train_predict)))\n",
        "print(\"The model performance for tarining set\")\n",
        "print('RMSE is {}'.format(rmse))\n",
        "print(\"\\n\")\n",
        "\n",
        "y_test_predict=lin_model.predict(x_test)\n",
        "rmse=(np.sqrt(mean_squared_error(y_test,y_test_predict)))\n",
        "print(\"The model performance for tarining set\")\n",
        "print('RMSE is {}'.format(rmse))\n",
        "print(\"\\n\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmzwWCW2vdnA",
        "outputId": "6351ebcd-39a5-4183-f07c-f0855255a255"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model performance for tarining set\n",
            "RMSE is 4.710901797319796\n",
            "\n",
            "\n",
            "The model performance for tarining set\n",
            "RMSE is 4.687543527902972\n",
            "\n",
            "\n"
          ]
        }
      ]
    }
  ]
}