Linearregression!.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import linear_model"
      ],
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        "df = pd.read_csv('homeprices.csv')\n",
        "df.head()"
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              "   area   price\n",
              "0  2600  550000\n",
              "1  3000  565000\n",
              "2  3200  610000\n",
              "3  3600  680000\n",
              "4  4000  725000"
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      "cell_type": "code",
      "source": [
        "plt.xlabel('area(sqr ft)')\n",
        "plt.ylabel('price(USD$)')\n",
        "plt.scatter(df.area,df.price, color = 'red', marker = '+')"
      ],
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              "<matplotlib.collections.PathCollection at 0x7b214258df30>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg = linear_model.LinearRegression()\n",
        "reg.fit(df[['area']],df.price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "zpUfdSFvP6-j",
        "outputId": "3f38a5a2-75af-4302-cbdf-588e8b0be397"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[3300]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISWoPyL8QXmN",
        "outputId": "984767c2-5ff6-4e5c-8691-e319467f169e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([628715.75342466])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJYWLRYsR_cM",
        "outputId": "acb28440-83a8-4fe0-fea6-30ba9fd45087"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([135.78767123])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "me4NxUWxSHQ3",
        "outputId": "b7124175-4d36-4fa4-df6e-f22aa01f33f4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "180616.43835616432"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "135.78767123 * 3300 + 180616.43835616432"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lbkSaAJBSTG6",
        "outputId": "bd6adf36-8764-4e01-9f53-4655fca4aefd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "628715.7534151643"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = pd.read_csv('areas.csv')\n",
        "d.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "aTuyeNlbT6k9",
        "outputId": "f2a56b5e-477f-4fe4-b617-066f4c2138d6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   area\n",
              "0  1000\n",
              "1  1500\n",
              "2  2300\n",
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              "4  4120"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "p = reg.predict(d)"
      ],
      "metadata": {
        "id": "1DEqdWDcT_rq"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d['prices'] = p\n",
        "d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "bvjydV0JUMRB",
        "outputId": "3ce3321f-5cbf-40d6-b090-cdc3dfe5e17f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    area        prices\n",
              "0   1000  3.164041e+05\n",
              "1   1500  3.842979e+05\n",
              "2   2300  4.929281e+05\n",
              "3   3540  6.613048e+05\n",
              "4   4120  7.400616e+05\n",
              "5   4560  7.998082e+05\n",
              "6   5490  9.260908e+05\n",
              "7   3460  6.504418e+05\n",
              "8   4750  8.256079e+05\n",
              "9   2300  4.929281e+05\n",
              "10  9000  1.402705e+06\n",
              "11  8600  1.348390e+06\n",
              "12  7100  1.144709e+06"
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              "      <th>3</th>\n",
              "      <td>3540</td>\n",
              "      <td>6.613048e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4120</td>\n",
              "      <td>7.400616e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4560</td>\n",
              "      <td>7.998082e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5490</td>\n",
              "      <td>9.260908e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3460</td>\n",
              "      <td>6.504418e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4750</td>\n",
              "      <td>8.256079e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2300</td>\n",
              "      <td>4.929281e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9000</td>\n",
              "      <td>1.402705e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8600</td>\n",
              "      <td>1.348390e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>7100</td>\n",
              "      <td>1.144709e+06</td>\n",
              "    </tr>\n",
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              "    </button>\n",
              "    <script>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d.to_csv('prediction.csv',index = 'True')"
      ],
      "metadata": {
        "id": "dsyv81hnU2bo"
      },
      "execution_count": 21,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "Добро пожаловать в Colaboratory!",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}