Housing_Price_prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/Abubacker10/colabtools/blob/main/Housing_Price_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "id": "9cb1c845",
      "metadata": {
        "id": "9cb1c845"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import statsmodels.api as sm\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be503dfe",
      "metadata": {
        "id": "be503dfe"
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      "source": [
        "data=pd.read_csv(\"C:\\\\Users\\\\navab\\\\Downloads\\\\Housing_dataset_kaggle.zip\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7ba13fe2",
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        "id": "7ba13fe2",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Avg. Area Income</th>\n",
              "      <th>Avg. Area House Age</th>\n",
              "      <th>Avg. Area Number of Rooms</th>\n",
              "      <th>Avg. Area Number of Bedrooms</th>\n",
              "      <th>Area Population</th>\n",
              "      <th>Price</th>\n",
              "      <th>Address</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>79545.458574</td>\n",
              "      <td>5.682861</td>\n",
              "      <td>7.009188</td>\n",
              "      <td>4.09</td>\n",
              "      <td>23086.800503</td>\n",
              "      <td>1.059034e+06</td>\n",
              "      <td>208 Michael Ferry Apt. 674\\nLaurabury, NE 3701...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>79248.642455</td>\n",
              "      <td>6.002900</td>\n",
              "      <td>6.730821</td>\n",
              "      <td>3.09</td>\n",
              "      <td>40173.072174</td>\n",
              "      <td>1.505891e+06</td>\n",
              "      <td>188 Johnson Views Suite 079\\nLake Kathleen, CA...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>5.865890</td>\n",
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              "      <td>5.13</td>\n",
              "      <td>36882.159400</td>\n",
              "      <td>1.058988e+06</td>\n",
              "      <td>9127 Elizabeth Stravenue\\nDanieltown, WI 06482...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>63345.240046</td>\n",
              "      <td>7.188236</td>\n",
              "      <td>5.586729</td>\n",
              "      <td>3.26</td>\n",
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              "      <td>1.260617e+06</td>\n",
              "      <td>USS Barnett\\nFPO AP 44820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>59982.197226</td>\n",
              "      <td>5.040555</td>\n",
              "      <td>7.839388</td>\n",
              "      <td>4.23</td>\n",
              "      <td>26354.109472</td>\n",
              "      <td>6.309435e+05</td>\n",
              "      <td>USNS Raymond\\nFPO AE 09386</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
              "0      79545.458574             5.682861                   7.009188   \n",
              "1      79248.642455             6.002900                   6.730821   \n",
              "2      61287.067179             5.865890                   8.512727   \n",
              "3      63345.240046             7.188236                   5.586729   \n",
              "4      59982.197226             5.040555                   7.839388   \n",
              "\n",
              "   Avg. Area Number of Bedrooms  Area Population         Price  \\\n",
              "0                          4.09     23086.800503  1.059034e+06   \n",
              "1                          3.09     40173.072174  1.505891e+06   \n",
              "2                          5.13     36882.159400  1.058988e+06   \n",
              "3                          3.26     34310.242831  1.260617e+06   \n",
              "4                          4.23     26354.109472  6.309435e+05   \n",
              "\n",
              "                                             Address  \n",
              "0  208 Michael Ferry Apt. 674\\nLaurabury, NE 3701...  \n",
              "1  188 Johnson Views Suite 079\\nLake Kathleen, CA...  \n",
              "2  9127 Elizabeth Stravenue\\nDanieltown, WI 06482...  \n",
              "3                          USS Barnett\\nFPO AP 44820  \n",
              "4                         USNS Raymond\\nFPO AE 09386  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
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      "id": "984ec258",
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        "id": "984ec258",
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              "      <th>Avg. Area Income</th>\n",
              "      <th>Avg. Area House Age</th>\n",
              "      <th>Avg. Area Number of Rooms</th>\n",
              "      <th>Avg. Area Number of Bedrooms</th>\n",
              "      <th>Area Population</th>\n",
              "      <th>Price</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5.000000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>68583.108984</td>\n",
              "      <td>5.977222</td>\n",
              "      <td>6.987792</td>\n",
              "      <td>3.981330</td>\n",
              "      <td>36163.516039</td>\n",
              "      <td>1.232073e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10657.991214</td>\n",
              "      <td>0.991456</td>\n",
              "      <td>1.005833</td>\n",
              "      <td>1.234137</td>\n",
              "      <td>9925.650114</td>\n",
              "      <td>3.531176e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17796.631190</td>\n",
              "      <td>2.644304</td>\n",
              "      <td>3.236194</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>172.610686</td>\n",
              "      <td>1.593866e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>61480.562388</td>\n",
              "      <td>5.322283</td>\n",
              "      <td>6.299250</td>\n",
              "      <td>3.140000</td>\n",
              "      <td>29403.928702</td>\n",
              "      <td>9.975771e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>68804.286404</td>\n",
              "      <td>5.970429</td>\n",
              "      <td>7.002902</td>\n",
              "      <td>4.050000</td>\n",
              "      <td>36199.406689</td>\n",
              "      <td>1.232669e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>75783.338666</td>\n",
              "      <td>6.650808</td>\n",
              "      <td>7.665871</td>\n",
              "      <td>4.490000</td>\n",
              "      <td>42861.290769</td>\n",
              "      <td>1.471210e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>107701.748378</td>\n",
              "      <td>9.519088</td>\n",
              "      <td>10.759588</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>69621.713378</td>\n",
              "      <td>2.469066e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Avg. Area Income  Avg. Area House Age  Avg. Area Number of Rooms  \\\n",
              "count       5000.000000          5000.000000                5000.000000   \n",
              "mean       68583.108984             5.977222                   6.987792   \n",
              "std        10657.991214             0.991456                   1.005833   \n",
              "min        17796.631190             2.644304                   3.236194   \n",
              "25%        61480.562388             5.322283                   6.299250   \n",
              "50%        68804.286404             5.970429                   7.002902   \n",
              "75%        75783.338666             6.650808                   7.665871   \n",
              "max       107701.748378             9.519088                  10.759588   \n",
              "\n",
              "       Avg. Area Number of Bedrooms  Area Population         Price  \n",
              "count                   5000.000000      5000.000000  5.000000e+03  \n",
              "mean                       3.981330     36163.516039  1.232073e+06  \n",
              "std                        1.234137      9925.650114  3.531176e+05  \n",
              "min                        2.000000       172.610686  1.593866e+04  \n",
              "25%                        3.140000     29403.928702  9.975771e+05  \n",
              "50%                        4.050000     36199.406689  1.232669e+06  \n",
              "75%                        4.490000     42861.290769  1.471210e+06  \n",
              "max                        6.500000     69621.713378  2.469066e+06  "
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          "metadata": {},
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        "data.describe()"
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      "cell_type": "code",
      "execution_count": null,
      "id": "6ca3bad7",
      "metadata": {
        "id": "6ca3bad7"
      },
      "outputs": [],
      "source": [
        "x=data[['Avg. Area Income','Avg. Area House Age','Avg. Area Number of Rooms','Avg. Area Number of Bedrooms','Area Population']]\n",
        "y=data['Price']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b3cf237f",
      "metadata": {
        "id": "b3cf237f",
        "outputId": "4ab07e52-6b43-426a-c136-e289d19985cb"
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        {
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            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "reg=LinearRegression()\n",
        "reg.fit(x,y)"
      ]
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    {
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      "execution_count": null,
      "id": "2a539d11",
      "metadata": {
        "id": "2a539d11",
        "outputId": "e952597b-8ddb-4ec7-b7a9-131002c042ca"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9180238195089554"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "reg.score(x,y)"
      ]
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    {
      "cell_type": "code",
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      "id": "f7c67b11",
      "metadata": {
        "id": "f7c67b11",
        "outputId": "36fbaa9d-978b-49db-92df-7d2987718380"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "-2637299.033328597"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
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        "reg.intercept_"
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    },
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      "cell_type": "code",
      "execution_count": null,
      "id": "1b3b5f27",
      "metadata": {
        "id": "1b3b5f27",
        "outputId": "827d47dd-51f1-48cc-bfd6-1e263acfef18"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([2.15780494e+01, 1.65637027e+05, 1.20659949e+05, 1.65113905e+03,\n",
              "       1.52007439e+01])"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
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        "reg.coef_"
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      "cell_type": "code",
      "execution_count": null,
      "id": "b7662f38",
      "metadata": {
        "id": "b7662f38"
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      "outputs": [],
      "source": [
        "n=pd.DataFrame({'Avg. Area Income':[344448],'Avg. Area House Age':[3],'Avg. Area Number of Rooms':[4],'Avg. Area Number of Bedrooms':[3],'Area Population':[4432]})"
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        {
          "data": {
            "text/plain": [
              "array([5847090.93337655])"
            ]
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          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "reg.predict(n)"
      ]
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      "cell_type": "code",
      "execution_count": null,
      "id": "a55a1e59",
      "metadata": {
        "id": "a55a1e59"
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      "outputs": [],
      "source": [
        "x1=data['Avg. Area Income']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "779af030",
      "metadata": {
        "id": "779af030"
      },
      "outputs": [],
      "source": [
        "yhat=reg.intercept_ + x1*reg.coef_[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "24f85b2f",
      "metadata": {
        "id": "24f85b2f",
        "outputId": "db3fd855-58fc-4a81-e845-80bb145fa69f"
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        {
          "data": {
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              "<matplotlib.collections.PathCollection at 0x1f068eb9480>"
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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(x1,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a579a68e",
      "metadata": {
        "id": "a579a68e"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    },
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}