HomePricePridction.ipynb

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    "colab": {
      "name": "HomePricePridction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPVxETZfkEGHTKFaZCj9TQz",
      "include_colab_link": true
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      "name": "python"
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sachin-saroha/Data/blob/main/HomePricePridction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 2,
      "metadata": {
        "id": "unS-snT0hbvC"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import linear_model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "url='https://raw.githubusercontent.com/sachin-saroha/Data/main/HomePrice.csv'\n",
        "df=pd.read_csv(url)\n",
        "df"
      ],
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          "height": 206
        },
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      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2600</td>\n",
              "      <td>550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3000</td>\n",
              "      <td>565000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3200</td>\n",
              "      <td>610000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3600</td>\n",
              "      <td>680000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4000</td>\n",
              "      <td>725000</td>\n",
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            "text/plain": [
              "   area   Price\n",
              "0  2600  550000\n",
              "1  3000  565000\n",
              "2  3200  610000\n",
              "3  3600  680000\n",
              "4  4000  725000"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.xlabel('area(sq ft)')\n",
        "plt.ylabel('Price(USD)')\n",
        "plt.scatter(df.area,df.Price,color='green',marker='+')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "UEYOBGyzkWNi",
        "outputId": "dfbdff4b-ab17-4b4f-e1d7-e29f13b82dc0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fc1193d4590>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
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          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg=linear_model.LinearRegression()\n",
        "reg.fit(df[['area']],df.Price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mewEPeRvlcVR",
        "outputId": "0790c4cc-74d2-4b74-faf4-aed29d851654"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[3300]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTK1KkP9vlr5",
        "outputId": "2d7fe1ae-e7b3-4a28-8271-645cf471de1e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([628715.75342466])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9W2Shyo3_T04",
        "outputId": "ac2d89eb-d827-489c-a991-40617da8aa86"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "180616.43835616432"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "plt.xlabel('area')\n",
        "plt.ylabel('Price')\n",
        "plt.scatter(df.area,df.Price,color='red',marker='+')\n",
        "plt.plot(df.area,reg.predict(df[['area']]),color='blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "b9BJrg0l_0Rr",
        "outputId": "f98215b9-3de2-4472-e647-9533fbb0edd1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc11938dd10>]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('model_pickel','wb') as f:\n",
        "  pickle.dump(reg,f)"
      ],
      "metadata": {
        "id": "p0ePnZt8zNZv"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('model_pickel','rb') as f:\n",
        "  model=pickle.load(f)"
      ],
      "metadata": {
        "id": "DNruqer0z0gp"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[5000]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEE8Xszx068A",
        "outputId": "1f8df6cc-eefc-40ad-91f3-56402b6bbe56"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([859554.79452055])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ]
}