Project (1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-JghiFE6AYYC"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "x = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]\n",
        "y = [10.105782,\n",
        "8.11629,\n",
        "8.270114,\n",
        "12.018814,\n",
        "11.390534,\n",
        "12.039276,\n",
        "11.863868,\n",
        "9.276467,\n",
        "10.263554,\n",
        "7.726428,\n",
        "10.095552,\n",
        "14.262409,\n",
        "11.667568,\n",
        "12.402228,\n",
        "10.763497,\n",
        "13.103922,\n",
        "12.244075,\n",
        "12.734343,\n",
        "15.626476,\n",
        "15.856069,\n",
        "21.074627,\n",
        "13.174367,\n",
        "13.401486,\n",
        "15.078455,\n",
        "18.600219,\n",
        "18.049502,\n",
        "17.275087,\n",
        "19.063675,\n",
        "16.806106,\n",
        "16.976447,\n",
        "16.153995,\n",
        "\n",
        "]\n",
        "\n",
        "plt.plot(x, y)\n",
        "  \n",
        "plt.xlabel('Dates')\n",
        "plt.ylabel('Prices')\n",
        "plt.title('Okra (Bhindi) -July')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "dHwHB9whTA75",
        "outputId": "eac5e902-fb54-4799-8c7f-d11d172b6c22"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "import matplotlib.pyplot as plt\n",
        "x = [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32, 33, 34, 35, 36, 37, 38, 40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176]\n",
        "y=[62.446701,\n",
        "41.129213,\n",
        "58.511628,\n",
        "60.188679,\n",
        "60.306818,\n",
        "50.225705,\n",
        "64.411765,\n",
        "51.551724,\n",
        "50,\n",
        "39.247887,\n",
        "53.318182,\n",
        "51.438679,\n",
        "53.75,\n",
        "55,\n",
        "55,\n",
        "45.071531,\n",
        "45,\n",
        "45,\n",
        "45,\n",
        "45,\n",
        "45,\n",
        "47.823944,\n",
        "48.987013,\n",
        "47.365169,\n",
        "47,\n",
        "47.737391,\n",
        "47.994424,\n",
        "47.998004,\n",
        "48,\n",
        "48.743134,\n",
        "51.227197,\n",
        "54.55286,\n",
        "54.230189,\n",
        "54,\n",
        "53.288248,\n",
        "53,\n",
        "53,\n",
        "53,\n",
        "53,\n",
        "53,\n",
        "52.986193,\n",
        "44.72069,\n",
        "44.006258,\n",
        "44.630881,\n",
        "44.997373,\n",
        "43.477934,\n",
        "42.956432,\n",
        "42.528281,\n",
        "39.034547,\n",
        "39.614016,\n",
        "39.776327,\n",
        "42.034861,\n",
        "42.703704,\n",
        "42.877287,\n",
        "35.139556,\n",
        "29.903892,\n",
        "29.848756,\n",
        "29.76672,\n",
        "27.878668,\n",
        "25.530574,\n",
        "20.215899,\n",
        "20.903898,\n",
        "19.003264,\n",
        "21.016714,\n",
        "20.662612,\n",
        "18.021892,\n",
        "20.784834,\n",
        "20.040646,\n",
        "19.87259,\n",
        "17.920522,\n",
        "17.799054,\n",
        "15.378235,\n",
        "13.02406,\n",
        "14.48566,\n",
        "14.590006,\n",
        "13.233906,\n",
        "14.837461,\n",
        "10.108794,\n",
        "12.883067,\n",
        "10.050475,\n",
        "12.060894,\n",
        "18.107379,\n",
        "14.382136,\n",
        "13.875465,\n",
        "12.532073,\n",
        "13.779126,\n",
        "11.829963,\n",
        "10.928226,\n",
        "9.985848,\n",
        "9.898564,\n",
        "11.170087,\n",
        "9.395121,\n",
        "9.11784,\n",
        "11.214194,\n",
        "6.935311,\n",
        "9.129877,\n",
        "10.928727,\n",
        "9.418342,\n",
        "10.665995,\n",
        "12.464158,\n",
        "11.246639,\n",
        "9.114113,\n",
        "9.213163,\n",
        "7.914154,\n",
        "9.227793,\n",
        "9.591982,\n",
        "7.626612,\n",
        "10.724239,\n",
        "10.111858,\n",
        "9.111111,\n",
        "11.335903,\n",
        "8.521606,\n",
        "9.170394,\n",
        "8.18427,\n",
        "7.906231,\n",
        "9.180855,\n",
        "8.992096,\n",
        "10.105782,\n",
        "8.11629,\n",
        "8.270114,\n",
        "12.018814,\n",
        "11.390534,\n",
        "12.039276,\n",
        "11.863868,\n",
        "9.276467,\n",
        "10.263554,\n",
        "7.726428,\n",
        "10.095552,\n",
        "14.262409,\n",
        "11.667568,\n",
        "12.402228,\n",
        "10.763497,\n",
        "13.103922,\n",
        "12.244075,\n",
        "12.734343,\n",
        "15.626476,\n",
        "15.856069,\n",
        "21.074627,\n",
        "13.174367,\n",
        "13.401486,\n",
        "15.078455,\n",
        "18.600219,\n",
        "18.049502,\n",
        "17.275087,\n",
        "19.063675,\n",
        "16.806106,\n",
        "16.976447,\n",
        "16.153995,\n",
        "15.216162,\n",
        "17.090476,\n",
        "17.024778,\n",
        "18.322316,\n",
        "17.38613,\n",
        "15.050362,\n",
        "15.348387,\n",
        "16.428257,\n",
        "14.204798,\n",
        "14.59021,\n",
        "14.155966,\n",
        "12.990761,\n",
        "14.696719,\n",
        "13.406107,\n",
        "14.244707,\n",
        "14.732616,\n",
        "21.283626,\n",
        "15.407961,\n",
        "15.697657,\n",
        "12.17494,\n",
        "12.42572,\n",
        "12.469226]\n",
        "\n",
        "\n",
        "plt.plot(x, y)\n",
        "  \n",
        "plt.xlabel('Dates')\n",
        "plt.ylabel('Prices')\n",
        "plt.title('Okra (Bhindi) -July')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Zu1-cgsHVBJZ",
        "outputId": "3a8cdd2f-659e-42bc-f21b-e34f4c6d50f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "import matplotlib.pyplot as plt\n",
        "x = [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32, 33, 34, 35, 36, 37, 38, 40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165]\n",
        "\n",
        "y=[\n",
        "\n",
        "4.645859,\n",
        "10.222268,\n",
        "16.719044,\n",
        "16.853556,\n",
        "16.427644,\n",
        "17.382589,\n",
        "17.225806,\n",
        "16.451745,\n",
        "12.507437,\n",
        "14.955506,\n",
        "15,\n",
        "15,\n",
        "15,\n",
        "15,\n",
        "15,\n",
        "15,\n",
        "15,\n",
        "15,\n",
        "14.650289,\n",
        "14.707221,\n",
        "14.999,\n",
        "14.163653,\n",
        "14.319532,\n",
        "16.237187,\n",
        "16.993517,\n",
        "17,\n",
        "6.678939,\n",
        "17.363636,\n",
        "17.981111,\n",
        "17.302544,\n",
        "17.002389,\n",
        "17,\n",
        "17,\n",
        "17,\n",
        "17,\n",
        "20.302034,\n",
        "19.996296,\n",
        "20.725917,\n",
        "21.692308,\n",
        "20.495507,\n",
        "19.334699,\n",
        "19,\n",
        "19,\n",
        "19,\n",
        "19,\n",
        "19,\n",
        "19,\n",
        "19.749425,\n",
        "20,\n",
        "19.439024,\n",
        "19.422756,\n",
        "16.049275,\n",
        "17.399433,\n",
        "15,\n",
        "18.326134,\n",
        "18.228145,\n",
        "17.581136,\n",
        "18.96988,\n",
        "18.623529,\n",
        "18.64859,\n",
        "16.789958,\n",
        "16.743032,\n",
        "18.697627,\n",
        "18.85838,\n",
        "19.829686,\n",
        "11.618587,\n",
        "10.578931,\n",
        "11.830152,\n",
        "13.675886,\n",
        "17.856439,\n",
        "18.490583,\n",
        "18.98213,\n",
        "12.206385,\n",
        "12.65264,\n",
        "15.698183,\n",
        "15.321605,\n",
        "15.878202,\n",
        "14.074659,\n",
        "19.998673,\n",
        "19.277957,\n",
        "13.344492,\n",
        "14.545425,\n",
        "27.060134,\n",
        "28.043725,\n",
        "29.860612,\n",
        "28.480331,\n",
        "27.25892,\n",
        "27.930175,\n",
        "28,\n",
        "28,\n",
        "26.736742,\n",
        "28.36205,\n",
        "30.443362,\n",
        "30.212312,\n",
        "30.429185,\n",
        "30.366316,\n",
        "31.2,\n",
        "34.175029,\n",
        "34.004008,\n",
        "31.370441,\n",
        "33.61167,\n",
        "33.20712,\n",
        "33,\n",
        "33.705333,\n",
        "34.818812,\n",
        "38.568259,\n",
        "39.117714,\n",
        "38.150345,\n",
        "11.32567,\n",
        "14.181033,\n",
        "10.873616,\n",
        "15.135965,\n",
        "15.650131,\n",
        "9.870357,\n",
        "7.959559,\n",
        "9.909951,\n",
        "10.261531,\n",
        "8.762011,\n",
        "9.158041,\n",
        "8.114922,\n",
        "11.477943,\n",
        "8.971377,\n",
        "9.5181417,\n",
        "9.235241,\n",
        "7.485473,\n",
        "8.563359,\n",
        "9.564818,\n",
        "7.720124,\n",
        "6.456864,\n",
        "4.896295,\n",
        "6.512567,\n",
        "5.828331,\n",
        "6.480133,\n",
        "7.144279,\n",
        "5.906441,\n",
        "5.643143,\n",
        "5.718771,\n",
        "5.485304,\n",
        "6.13758,\n",
        "6.293481,\n",
        "6.00438,\n",
        "6.185113,\n",
        "6.301407,\n",
        "6.408386,\n",
        "7.051646,\n",
        "6.364311,\n",
        "6.364449,\n",
        "6.668091,\n",
        "8.677487,\n",
        "12.606126,\n",
        "7.587314,\n",
        "10.10802,\n",
        "12.274309,\n",
        "10.980794,\n",
        "8.286688,\n",
        "8.21519,\n",
        "10.790909,\n",
        "11.784903,\n",
        "11.569352]\n",
        "\n",
        "\n",
        "plt.plot(x, y)\n",
        "  \n",
        "plt.xlabel('Dates')\n",
        "plt.ylabel('Prices')\n",
        "plt.title('Tomato Desi')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "q9Ck12-rlzb1",
        "outputId": "6146f50d-1f25-4cc7-9e1e-50cbf0900236"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
        ""
      ],
      "metadata": {
        "id": "eraP2ak0iLi0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "# df=pd.read_csv(\"C:\\Users\\Diya\\OneDrive\\Desktop.csv\")\n",
        "df=pd.read_csv(\"a.csv\")\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "sSz6eyFjo_5q",
        "outputId": "30688f1a-ac80-440e-9428-5452fdf31aec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     prod_id      price      prod_name  n_slot_id  pack_sold  order_count\n",
              "0       1071  62.446701    Okra 500 Gm     220305        203          197\n",
              "1       1071  41.129213    Okra 500 Gm     220306        396          356\n",
              "2       1071  58.511628    Okra 500 Gm     220307        228          215\n",
              "3       1071  60.188679    Okra 500 Gm     220308        284          265\n",
              "4       1071  60.306818    Okra 500 Gm     220309        189          176\n",
              "..       ...        ...            ...        ...        ...          ...\n",
              "165     1071  15.407961  Bhindi (Okra)     220818       4245         3988\n",
              "166     1071  15.697657  Bhindi (Okra)     220819       4028         3764\n",
              "167     1071  12.174940  Bhindi (Okra)     220820       4308         4019\n",
              "168     1071  12.425720  Bhindi (Okra)     220821       4401         4101\n",
              "169     1071  12.469226  Bhindi (Okra)     220822       4703         4304\n",
              "\n",
              "[170 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bfa091cb-8221-45a3-9283-6e2d1360ed64\">\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>prod_id</th>\n",
              "      <th>price</th>\n",
              "      <th>prod_name</th>\n",
              "      <th>n_slot_id</th>\n",
              "      <th>pack_sold</th>\n",
              "      <th>order_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1071</td>\n",
              "      <td>62.446701</td>\n",
              "      <td>Okra 500 Gm</td>\n",
              "      <td>220305</td>\n",
              "      <td>203</td>\n",
              "      <td>197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1071</td>\n",
              "      <td>41.129213</td>\n",
              "      <td>Okra 500 Gm</td>\n",
              "      <td>220306</td>\n",
              "      <td>396</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1071</td>\n",
              "      <td>58.511628</td>\n",
              "      <td>Okra 500 Gm</td>\n",
              "      <td>220307</td>\n",
              "      <td>228</td>\n",
              "      <td>215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1071</td>\n",
              "      <td>60.188679</td>\n",
              "      <td>Okra 500 Gm</td>\n",
              "      <td>220308</td>\n",
              "      <td>284</td>\n",
              "      <td>265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1071</td>\n",
              "      <td>60.306818</td>\n",
              "      <td>Okra 500 Gm</td>\n",
              "      <td>220309</td>\n",
              "      <td>189</td>\n",
              "      <td>176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>1071</td>\n",
              "      <td>15.407961</td>\n",
              "      <td>Bhindi (Okra)</td>\n",
              "      <td>220818</td>\n",
              "      <td>4245</td>\n",
              "      <td>3988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>1071</td>\n",
              "      <td>15.697657</td>\n",
              "      <td>Bhindi (Okra)</td>\n",
              "      <td>220819</td>\n",
              "      <td>4028</td>\n",
              "      <td>3764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>1071</td>\n",
              "      <td>12.174940</td>\n",
              "      <td>Bhindi (Okra)</td>\n",
              "      <td>220820</td>\n",
              "      <td>4308</td>\n",
              "      <td>4019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>1071</td>\n",
              "      <td>12.425720</td>\n",
              "      <td>Bhindi (Okra)</td>\n",
              "      <td>220821</td>\n",
              "      <td>4401</td>\n",
              "      <td>4101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>1071</td>\n",
              "      <td>12.469226</td>\n",
              "      <td>Bhindi (Okra)</td>\n",
              "      <td>220822</td>\n",
              "      <td>4703</td>\n",
              "      <td>4304</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>170 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bfa091cb-8221-45a3-9283-6e2d1360ed64')\"\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-bfa091cb-8221-45a3-9283-6e2d1360ed64 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-bfa091cb-8221-45a3-9283-6e2d1360ed64');\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": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df[\"n_slot_id\"],df[\"price\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "DYxNwz8g9DGo",
        "outputId": "efc0b4b7-20ff-4613-d488-eda8e01eadb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "model.fit(df[[\"n_slot_id\"]],df[[\"price\"]])\n",
        "print(model.predict([[220925]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cPlnLEbr9rCQ",
        "outputId": "524bb9b4-7fe3-43e9-e895-698f64a8d27f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-6.06122147]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "X9WPPR5mjCO_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Jq6BEG5a5Rx-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "Bk-sj1TaLQ_K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "QEY0f9pYLRlz"
      }
    }
  ]
}