Multi_linear_regression.ipynb

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        "<a href=\"https://colab.research.google.com/github/naidu199/Machine_Learn/blob/main/Multi_linear_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "XwDzceNI9-Rn"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"https://raw.githubusercontent.com/nandu19k/multiple-linear-regression/master/50_Startups.csv\")\n",
        "data.head(10)\n",
        "#to check NULL valus in the dataset data.isna().sum()"
      ],
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
              "0  165349.20       136897.80        471784.10    New York  192261.83\n",
              "1  162597.70       151377.59        443898.53  California  191792.06\n",
              "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
              "3  144372.41       118671.85        383199.62    New York  182901.99\n",
              "4  142107.34        91391.77        366168.42     Florida  166187.94\n",
              "5  131876.90        99814.71        362861.36    New York  156991.12\n",
              "6  134615.46       147198.87        127716.82  California  156122.51\n",
              "7  130298.13       145530.06        323876.68     Florida  155752.60\n",
              "8  120542.52       148718.95        311613.29    New York  152211.77\n",
              "9  123334.88       108679.17        304981.62  California  149759.96"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
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              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>192261.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>California</td>\n",
              "      <td>191792.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>Florida</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>New York</td>\n",
              "      <td>182901.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>Florida</td>\n",
              "      <td>166187.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>131876.90</td>\n",
              "      <td>99814.71</td>\n",
              "      <td>362861.36</td>\n",
              "      <td>New York</td>\n",
              "      <td>156991.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>134615.46</td>\n",
              "      <td>147198.87</td>\n",
              "      <td>127716.82</td>\n",
              "      <td>California</td>\n",
              "      <td>156122.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>130298.13</td>\n",
              "      <td>145530.06</td>\n",
              "      <td>323876.68</td>\n",
              "      <td>Florida</td>\n",
              "      <td>155752.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>120542.52</td>\n",
              "      <td>148718.95</td>\n",
              "      <td>311613.29</td>\n",
              "      <td>New York</td>\n",
              "      <td>152211.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>123334.88</td>\n",
              "      <td>108679.17</td>\n",
              "      <td>304981.62</td>\n",
              "      <td>California</td>\n",
              "      <td>149759.96</td>\n",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
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      "cell_type": "code",
      "source": [
        "x=data.iloc[:,:-2].values\n",
        "y=data.iloc[:,-1].values\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "2a878a49-f61f-4105-b1bf-98255d12ef25"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([192261.83, 191792.06, 191050.39, 182901.99, 166187.94, 156991.12,\n",
              "       156122.51, 155752.6 , 152211.77, 149759.96, 146121.95, 144259.4 ,\n",
              "       141585.52, 134307.35, 132602.65, 129917.04, 126992.93, 125370.37,\n",
              "       124266.9 , 122776.86, 118474.03, 111313.02, 110352.25, 108733.99,\n",
              "       108552.04, 107404.34, 105733.54, 105008.31, 103282.38, 101004.64,\n",
              "        99937.59,  97483.56,  97427.84,  96778.92,  96712.8 ,  96479.51,\n",
              "        90708.19,  89949.14,  81229.06,  81005.76,  78239.91,  77798.83,\n",
              "        71498.49,  69758.98,  65200.33,  64926.08,  49490.75,  42559.73,\n",
              "        35673.41,  14681.4 ])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "xtrain,xtest,ytrain,ytest= train_test_split(x,y,test_size=0.2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kY2l3FPZAH2A",
        "outputId": "a8be40b2-e9a6-4c82-f9a2-9aa125f83c50"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 89949.14, 108552.04, 156122.51,  42559.73, 118474.03, 126992.93,\n",
              "       152211.77, 144259.4 , 141585.52,  96712.8 ])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reg=LinearRegression()\n",
        "reg.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "VUetvP2GAzFa",
        "outputId": "294e0ab2-3341-41ea-968a-22591a6b7168"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yp=reg.predict(xtest)\n",
        "yp"
      ],
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        },
        "id": "eVO32QZjCDaW",
        "outputId": "e948581c-95fb-41c2-b39c-9622b55c20b4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 89205.58494492, 113322.87999409, 158380.89564843,  46418.42229589,\n",
              "       115344.27092221, 115826.18080423, 150533.22978411, 135041.09838769,\n",
              "       128268.57919246,  87746.96838292])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ytest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHbY1tc2CqGx",
        "outputId": "9e22a3d9-4539-4dd9-b330-1f4a98e01903"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 89949.14, 108552.04, 156122.51,  42559.73, 118474.03, 126992.93,\n",
              "       152211.77, 144259.4 , 141585.52,  96712.8 ])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(ytest,yp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nMy0qLGCCtnz",
        "outputId": "3398dadb-9d1a-4fbd-85c0-ca8264dccfc2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52331691.07559253"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(ytest,yp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxTQT50vDBoe",
        "outputId": "c99c3acf-1686-4d4a-c4ad-e8fd6fe5946e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9522979351404004"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FI1TO9gFDN1G",
        "outputId": "f049e002-31f2-4cb5-8ee7-b01801cfcfe8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.81571857, -0.03436096,  0.02003369])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xtest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYh8s0iAE9xZ",
        "outputId": "656c0b0b-768b-42a8-991a-a9ea7cdbede5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 44069.95,  51283.14, 197029.42],\n",
              "       [ 77044.01,  99281.34, 140574.81],\n",
              "       [134615.46, 147198.87, 127716.82],\n",
              "       [     0.  , 135426.92,      0.  ],\n",
              "       [ 76253.86, 113867.3 , 298664.47],\n",
              "       [ 78013.11, 121597.55, 264346.06],\n",
              "       [120542.52, 148718.95, 311613.29],\n",
              "       [100671.96,  91790.61, 249744.55],\n",
              "       [ 93863.75, 127320.38, 249839.44],\n",
              "       [ 46426.07, 157693.92, 210797.67]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qkZ1-IwEGocB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}