Linear_regression.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "eCJQcj2XvmfG"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "id": "rQ19Tsu6vnVc",
        "outputId": "aa4bff79-14c6-4c04-f05f-bfcd91223a21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8075e8d6-14e3-42b5-83aa-36e1960f92d3\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8075e8d6-14e3-42b5-83aa-36e1960f92d3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving house.csv to house.csv\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'house.csv': b'area,price\\r\\n2600,55000\\r\\n3000,56500\\r\\n3200,61000\\r\\n3600,68000\\r\\n4000,72000\\r\\n5000,71000\\r\\n2700,38000\\r\\n5000,100000\\r\\n'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"house.csv\")\n",
        "df"
      ],
      "metadata": {
        "id": "w2oxHeUkv73E",
        "outputId": "62c16fe5-2428-46db-88e0-aa72ae726764",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   area   price\n",
              "0  2600   55000\n",
              "1  3000   56500\n",
              "2  3200   61000\n",
              "3  3600   68000\n",
              "4  4000   72000\n",
              "5  5000   71000\n",
              "6  2700   38000\n",
              "7  5000  100000"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area</th>\n",
              "      <th>price</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2600</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2700</td>\n",
              "      <td>38000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5000</td>\n",
              "      <td>100000</td>\n",
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              "                                                     [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",
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              "  "
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          },
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    {
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      "source": [
        "df.head()"
      ],
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        "colab": {
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          "height": 206
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   area  price\n",
              "0  2600  55000\n",
              "1  3000  56500\n",
              "2  3200  61000\n",
              "3  3600  68000\n",
              "4  4000  72000"
            ],
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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>55000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3000</td>\n",
              "      <td>56500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3200</td>\n",
              "      <td>61000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3600</td>\n",
              "      <td>68000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "            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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "2rLE8vwUwhhw",
        "outputId": "928bcdf8-ad15-4517-aba7-19f57a0f2c6d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "pXj7vlm0wr-L",
        "outputId": "18e63e11-bf13-4e7d-905f-8785b5650ea2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "area     0\n",
              "price    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['area'],df['price'])\n",
        "plt.xlabel(\"area in square fit\")\n",
        "plt.ylabel(\"price in taka\")\n",
        "plt.title(\"House price\")"
      ],
      "metadata": {
        "id": "KEcR1clGxtqV",
        "outputId": "10301dac-59ce-477b-dd5b-bc529847e433",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'House price')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=df[['area']]\n",
        "y=df['price']"
      ],
      "metadata": {
        "id": "_hIeGABwxLUe"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "yOr9reFPyDwo"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "YPTET7kpOppn"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain"
      ],
      "metadata": {
        "id": "4b9HxcL1PbMC",
        "outputId": "f8b22106-cb5b-4ac9-bfe1-86196ed69e6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   area\n",
              "1  3000\n",
              "7  5000\n",
              "3  3600\n",
              "0  2600\n",
              "5  5000\n",
              "4  4000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6570f5d7-0dcc-40e2-a6ae-ff27c4d5078b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6570f5d7-0dcc-40e2-a6ae-ff27c4d5078b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6570f5d7-0dcc-40e2-a6ae-ff27c4d5078b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6570f5d7-0dcc-40e2-a6ae-ff27c4d5078b');\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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xtest"
      ],
      "metadata": {
        "id": "r3lboeraPgcr",
        "outputId": "8bbf3a07-ec9a-43d7-fbf1-a5913e0c43f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   area\n",
              "6  2700\n",
              "2  3200"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d66ade5e-0c80-4bcb-aaa2-e91b9eecb117\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>area</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d66ade5e-0c80-4bcb-aaa2-e91b9eecb117')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d66ade5e-0c80-4bcb-aaa2-e91b9eecb117 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d66ade5e-0c80-4bcb-aaa2-e91b9eecb117');\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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg=LinearRegression()\n",
        "reg.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "id": "tRariBNkPsEY",
        "outputId": "42a8a16a-f3d5-4bb9-b905-ff94eb3524c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict(xtest)"
      ],
      "metadata": {
        "id": "CNJQPyBkQtc_",
        "outputId": "75906fe3-283f-47c5-c022-c5ca6af1cf87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([54910.2393617 , 61555.85106383])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.scatter(df['area'],df['price'])\n",
        "plt.xlabel(\"area in square fit\")\n",
        "plt.ylabel(\"price in taka\")\n",
        "plt.title(\"House price\")\n",
        "plt.plot(df.area,reg.predict(df[['area']]))"
      ],
      "metadata": {
        "id": "C56vLaDRRpaI",
        "outputId": "37d62e65-b233-4f20-ec5f-46ef3e2bfd62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7efecda72fd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.predict([[4200]])"
      ],
      "metadata": {
        "id": "WpQ51ka9R5EC",
        "outputId": "345a5097-44e0-42a9-abac-6d155daf817c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: 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([74847.07446809])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#y=mx+c\n",
        "reg.coef_"
      ],
      "metadata": {
        "id": "fFVwOXaGSqiG",
        "outputId": "a17a9fd0-1d68-4f92-8155-b99f14b2e5a3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([13.2912234])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.intercept_"
      ],
      "metadata": {
        "id": "tScPlDITS0UL",
        "outputId": "2fb3d7f9-1106-423a-aa11-ba93fd662691",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19023.93617021277"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=13.2912234*4200+19023.93617021277"
      ],
      "metadata": {
        "id": "UNqJoMesTHh5"
      },
      "execution_count": 31,
      "outputs": []
    },
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        "y"
      ],
      "metadata": {
        "id": "Fssxc0DkTVSV",
        "outputId": "bb9ccaa8-b874-4a0a-9b1a-e6fa0b280462",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "74847.07445021276"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    }
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  "metadata": {
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      "toc_visible": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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}