LinearRegressionImplementation.ipynb

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      "source": [
        "#importing the required libraries for this model\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import linear_model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#importing data frame into the notebook\n",
        "df = pd.read_csv('data.csv')\n",
        "df"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 206
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        "outputId": "92f6581f-301d-44cb-9e8d-9384f2430ea4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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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              "  <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",
              "    </tr>\n",
              "  </tbody>\n",
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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",
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          "metadata": {},
          "execution_count": 12
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    },
    {
      "cell_type": "code",
      "source": [
        "#getting scatter plot of the data we have\n",
        "%matplotlib inline\n",
        "plt.figure(figsize=(5,3),dpi=130)\n",
        "plt.xlabel('Price in US $')\n",
        "plt.ylabel('Area in sq. feet')\n",
        "plt.title('graph for prices in the town')\n",
        "plt.scatter(df.area,df.price,color='red',marker='+')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
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        "outputId": "cd5f1414-99db-4a57-8038-c89a6f1a397e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 650x390 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#getting object for linear regression\n",
        "reg = linear_model.LinearRegression()\n",
        "#fitting the data to linear regression \n",
        "\n",
        "reg.fit(df[['area']],df.price)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "NvmF06IEetZC",
        "outputId": "0e15401d-370d-43a4-b09d-71ca5132ee4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" 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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" 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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#getting the prediction of our desired area. the data must be in 2d shape\n",
        "reg.predict([[5000]])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FC5JCMcnffpF",
        "outputId": "a61a178c-046d-48d9-c893-ec925aaa4db1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([859554.79452055])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#what parameters did this model use\n",
        "\n",
        "#w was \n",
        "print('w=', reg.coef_)\n",
        "#b was\n",
        "print('b = ', reg.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bexoFOvPg_AQ",
        "outputId": "b6c8f809-68f2-42ae-c2b5-e1f15d95f8dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "w= [135.78767123]\n",
            "b =  180616.43835616432\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## now we are plotting the model according to all the areas and their respective predictions\n",
        "\n",
        "%matplotlib inline\n",
        "plt.figure(figsize=(5,3),dpi=130)\n",
        "plt.xlabel('Price in US $')\n",
        "plt.ylabel('Area in sq. feet')\n",
        "plt.title('graph for prices in the town')\n",
        "plt.scatter(df.area,df.price,color='red',marker='+')\n",
        "plt.plot(df.area,reg.predict(df[['area']]),color='blue')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "OjCGV8iboLBI",
        "outputId": "e1ee0d32-25ea-42e3-c54c-762cbbbd8e45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 650x390 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predicting prices for the file predict.csv \n",
        "pr = pd.read_csv('predict.csv')\n",
        "\n",
        "p=reg.predict(pr)\n",
        "pr['prices'] = p\n",
        "pr.to_csv('predict.csv',index=False)"
      ],
      "metadata": {
        "id": "wcYpyOFSh5i2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}