Startup_Profit_Prediction/Startup_Profit_Prediction.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Exploration**"
      ],
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      }
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      "metadata": {
        "id": "ewqbUJv1AQaS"
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      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('/content/50_Startups.csv')\n",
        "data.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "OkMSu_Z7Aatz",
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              "  <thead>\n",
              "    <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <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",
              "  </tbody>\n",
              "</table>\n",
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              "  "
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            "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"
            ]
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          "execution_count": 3
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      "source": [
        "data.shape"
      ],
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        "colab": {
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        },
        "id": "_A36EGFvAyfp",
        "outputId": "92794687-844b-4f3a-b2be-9570561b6493"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 5
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "data.dtypes"
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        "colab": {
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        "id": "CW0ecCtGBILi",
        "outputId": "d3d47527-d1cf-4648-9152-f296839e9f04"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "R&D Spend          float64\n",
              "Administration     float64\n",
              "Marketing Spend    float64\n",
              "State               object\n",
              "Profit             float64\n",
              "dtype: object"
            ]
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          "metadata": {},
          "execution_count": 8
        }
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        "id": "60EREBWYCu1s",
        "outputId": "842eabe5-b6c6-44ed-d684-f4f254e8a69c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50 entries, 0 to 49\n",
            "Data columns (total 5 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   R&D Spend        50 non-null     float64\n",
            " 1   Administration   50 non-null     float64\n",
            " 2   Marketing Spend  50 non-null     float64\n",
            " 3   State            50 non-null     object \n",
            " 4   Profit           50 non-null     float64\n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 2.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
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          "height": 300
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      "execution_count": 9,
      "outputs": [
        {
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              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
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              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>73721.615600</td>\n",
              "      <td>121344.639600</td>\n",
              "      <td>211025.097800</td>\n",
              "      <td>112012.639200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>45902.256482</td>\n",
              "      <td>28017.802755</td>\n",
              "      <td>122290.310726</td>\n",
              "      <td>40306.180338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>51283.140000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14681.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>39936.370000</td>\n",
              "      <td>103730.875000</td>\n",
              "      <td>129300.132500</td>\n",
              "      <td>90138.902500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>73051.080000</td>\n",
              "      <td>122699.795000</td>\n",
              "      <td>212716.240000</td>\n",
              "      <td>107978.190000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>101602.800000</td>\n",
              "      <td>144842.180000</td>\n",
              "      <td>299469.085000</td>\n",
              "      <td>139765.977500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>165349.200000</td>\n",
              "      <td>182645.560000</td>\n",
              "      <td>471784.100000</td>\n",
              "      <td>192261.830000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "           R&D Spend  Administration  Marketing Spend         Profit\n",
              "count      50.000000       50.000000        50.000000      50.000000\n",
              "mean    73721.615600   121344.639600    211025.097800  112012.639200\n",
              "std     45902.256482    28017.802755    122290.310726   40306.180338\n",
              "min         0.000000    51283.140000         0.000000   14681.400000\n",
              "25%     39936.370000   103730.875000    129300.132500   90138.902500\n",
              "50%     73051.080000   122699.795000    212716.240000  107978.190000\n",
              "75%    101602.800000   144842.180000    299469.085000  139765.977500\n",
              "max    165349.200000   182645.560000    471784.100000  192261.830000"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Preparation**"
      ],
      "metadata": {
        "id": "-L_2h_QtIkVd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()   #to check for any null/missing values."
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ec-HkrQpCAgY",
        "outputId": "5fe2435f-7aff-4d86-ec0f-64c9b8f69e30"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "R&D Spend          0\n",
              "Administration     0\n",
              "Marketing Spend    0\n",
              "State              0\n",
              "Profit             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum().sum()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "mWqKuJuhCFdl",
        "outputId": "f1521180-11ac-4161-ac6e-99f4594ebf38"
      },
      "execution_count": 11,
      "outputs": [
        {
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            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 11
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    },
    {
      "cell_type": "code",
      "source": [
        "data['State'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8U_WW5jNCfxE",
        "outputId": "5ef09369-8943-4321-dd73-2fa822dbeaec"
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      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['State'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Om248DvSCmMd",
        "outputId": "66081326-6197-4cae-af09-58d72680756a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['New York', 'California', 'Florida'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "# converting State column which is object datatype to int data type.\n",
        "columns=['State']\n",
        "data1=data[columns]\n",
        "dummies=pd.get_dummies(data1,columns=['State'])\n",
        "dummies"
      ],
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        "id": "td3HIRArCowD",
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      "execution_count": 18,
      "outputs": [
        {
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              "    <tr>\n",
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            "text/plain": [
              "    State_California  State_Florida  State_New York\n",
              "0                  0              0               1\n",
              "1                  1              0               0\n",
              "2                  0              1               0\n",
              "3                  0              0               1\n",
              "4                  0              1               0\n",
              "5                  0              0               1\n",
              "6                  1              0               0\n",
              "7                  0              1               0\n",
              "8                  0              0               1\n",
              "9                  1              0               0\n",
              "10                 0              1               0\n",
              "11                 1              0               0\n",
              "12                 0              1               0\n",
              "13                 1              0               0\n",
              "14                 0              1               0\n",
              "15                 0              0               1\n",
              "16                 1              0               0\n",
              "17                 0              0               1\n",
              "18                 0              1               0\n",
              "19                 0              0               1\n",
              "20                 1              0               0\n",
              "21                 0              0               1\n",
              "22                 0              1               0\n",
              "23                 0              1               0\n",
              "24                 0              0               1\n",
              "25                 1              0               0\n",
              "26                 0              1               0\n",
              "27                 0              0               1\n",
              "28                 0              1               0\n",
              "29                 0              0               1\n",
              "30                 0              1               0\n",
              "31                 0              0               1\n",
              "32                 1              0               0\n",
              "33                 0              1               0\n",
              "34                 1              0               0\n",
              "35                 0              0               1\n",
              "36                 0              1               0\n",
              "37                 1              0               0\n",
              "38                 0              0               1\n",
              "39                 1              0               0\n",
              "40                 1              0               0\n",
              "41                 0              1               0\n",
              "42                 1              0               0\n",
              "43                 0              0               1\n",
              "44                 1              0               0\n",
              "45                 0              0               1\n",
              "46                 0              1               0\n",
              "47                 1              0               0\n",
              "48                 0              0               1\n",
              "49                 1              0               0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mergeddata= pd.concat([data,dummies],axis='columns')\n",
        "mergeddata"
      ],
      "metadata": {
        "colab": {
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          "height": 1000
        },
        "id": "-4AyiyUYFy9M",
        "outputId": "e6edd2d4-b0f5-47b2-b9db-9db7ffbfba1d"
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      "execution_count": 19,
      "outputs": [
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
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              "      <td>471784.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>192261.83</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>101913.08</td>\n",
              "      <td>110594.11</td>\n",
              "      <td>229160.95</td>\n",
              "      <td>Florida</td>\n",
              "      <td>146121.95</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>100671.96</td>\n",
              "      <td>91790.61</td>\n",
              "      <td>249744.55</td>\n",
              "      <td>California</td>\n",
              "      <td>144259.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>93863.75</td>\n",
              "      <td>127320.38</td>\n",
              "      <td>249839.44</td>\n",
              "      <td>Florida</td>\n",
              "      <td>141585.52</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>91992.39</td>\n",
              "      <td>135495.07</td>\n",
              "      <td>252664.93</td>\n",
              "      <td>California</td>\n",
              "      <td>134307.35</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>119943.24</td>\n",
              "      <td>156547.42</td>\n",
              "      <td>256512.92</td>\n",
              "      <td>Florida</td>\n",
              "      <td>132602.65</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>114523.61</td>\n",
              "      <td>122616.84</td>\n",
              "      <td>261776.23</td>\n",
              "      <td>New York</td>\n",
              "      <td>129917.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>78013.11</td>\n",
              "      <td>121597.55</td>\n",
              "      <td>264346.06</td>\n",
              "      <td>California</td>\n",
              "      <td>126992.93</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>94657.16</td>\n",
              "      <td>145077.58</td>\n",
              "      <td>282574.31</td>\n",
              "      <td>New York</td>\n",
              "      <td>125370.37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>91749.16</td>\n",
              "      <td>114175.79</td>\n",
              "      <td>294919.57</td>\n",
              "      <td>Florida</td>\n",
              "      <td>124266.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>86419.70</td>\n",
              "      <td>153514.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>New York</td>\n",
              "      <td>122776.86</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>76253.86</td>\n",
              "      <td>113867.30</td>\n",
              "      <td>298664.47</td>\n",
              "      <td>California</td>\n",
              "      <td>118474.03</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>78389.47</td>\n",
              "      <td>153773.43</td>\n",
              "      <td>299737.29</td>\n",
              "      <td>New York</td>\n",
              "      <td>111313.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>73994.56</td>\n",
              "      <td>122782.75</td>\n",
              "      <td>303319.26</td>\n",
              "      <td>Florida</td>\n",
              "      <td>110352.25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>67532.53</td>\n",
              "      <td>105751.03</td>\n",
              "      <td>304768.73</td>\n",
              "      <td>Florida</td>\n",
              "      <td>108733.99</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>77044.01</td>\n",
              "      <td>99281.34</td>\n",
              "      <td>140574.81</td>\n",
              "      <td>New York</td>\n",
              "      <td>108552.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>64664.71</td>\n",
              "      <td>139553.16</td>\n",
              "      <td>137962.62</td>\n",
              "      <td>California</td>\n",
              "      <td>107404.34</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>75328.87</td>\n",
              "      <td>144135.98</td>\n",
              "      <td>134050.07</td>\n",
              "      <td>Florida</td>\n",
              "      <td>105733.54</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>72107.60</td>\n",
              "      <td>127864.55</td>\n",
              "      <td>353183.81</td>\n",
              "      <td>New York</td>\n",
              "      <td>105008.31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>66051.52</td>\n",
              "      <td>182645.56</td>\n",
              "      <td>118148.20</td>\n",
              "      <td>Florida</td>\n",
              "      <td>103282.38</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>65605.48</td>\n",
              "      <td>153032.06</td>\n",
              "      <td>107138.38</td>\n",
              "      <td>New York</td>\n",
              "      <td>101004.64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>61994.48</td>\n",
              "      <td>115641.28</td>\n",
              "      <td>91131.24</td>\n",
              "      <td>Florida</td>\n",
              "      <td>99937.59</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>61136.38</td>\n",
              "      <td>152701.92</td>\n",
              "      <td>88218.23</td>\n",
              "      <td>New York</td>\n",
              "      <td>97483.56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>63408.86</td>\n",
              "      <td>129219.61</td>\n",
              "      <td>46085.25</td>\n",
              "      <td>California</td>\n",
              "      <td>97427.84</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>55493.95</td>\n",
              "      <td>103057.49</td>\n",
              "      <td>214634.81</td>\n",
              "      <td>Florida</td>\n",
              "      <td>96778.92</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>46426.07</td>\n",
              "      <td>157693.92</td>\n",
              "      <td>210797.67</td>\n",
              "      <td>California</td>\n",
              "      <td>96712.80</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>46014.02</td>\n",
              "      <td>85047.44</td>\n",
              "      <td>205517.64</td>\n",
              "      <td>New York</td>\n",
              "      <td>96479.51</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>28663.76</td>\n",
              "      <td>127056.21</td>\n",
              "      <td>201126.82</td>\n",
              "      <td>Florida</td>\n",
              "      <td>90708.19</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>44069.95</td>\n",
              "      <td>51283.14</td>\n",
              "      <td>197029.42</td>\n",
              "      <td>California</td>\n",
              "      <td>89949.14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>20229.59</td>\n",
              "      <td>65947.93</td>\n",
              "      <td>185265.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>81229.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>38558.51</td>\n",
              "      <td>82982.09</td>\n",
              "      <td>174999.30</td>\n",
              "      <td>California</td>\n",
              "      <td>81005.76</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>28754.33</td>\n",
              "      <td>118546.05</td>\n",
              "      <td>172795.67</td>\n",
              "      <td>California</td>\n",
              "      <td>78239.91</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>27892.92</td>\n",
              "      <td>84710.77</td>\n",
              "      <td>164470.71</td>\n",
              "      <td>Florida</td>\n",
              "      <td>77798.83</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>23640.93</td>\n",
              "      <td>96189.63</td>\n",
              "      <td>148001.11</td>\n",
              "      <td>California</td>\n",
              "      <td>71498.49</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>15505.73</td>\n",
              "      <td>127382.30</td>\n",
              "      <td>35534.17</td>\n",
              "      <td>New York</td>\n",
              "      <td>69758.98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>22177.74</td>\n",
              "      <td>154806.14</td>\n",
              "      <td>28334.72</td>\n",
              "      <td>California</td>\n",
              "      <td>65200.33</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1000.23</td>\n",
              "      <td>124153.04</td>\n",
              "      <td>1903.93</td>\n",
              "      <td>New York</td>\n",
              "      <td>64926.08</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1315.46</td>\n",
              "      <td>115816.21</td>\n",
              "      <td>297114.46</td>\n",
              "      <td>Florida</td>\n",
              "      <td>49490.75</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.00</td>\n",
              "      <td>135426.92</td>\n",
              "      <td>0.00</td>\n",
              "      <td>California</td>\n",
              "      <td>42559.73</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>542.05</td>\n",
              "      <td>51743.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>New York</td>\n",
              "      <td>35673.41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.00</td>\n",
              "      <td>116983.80</td>\n",
              "      <td>45173.06</td>\n",
              "      <td>California</td>\n",
              "      <td>14681.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-36e10b1f-db4d-45d6-bf9d-db475067dde9')\"\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",
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            "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   \n",
              "10  101913.08       110594.11        229160.95     Florida  146121.95   \n",
              "11  100671.96        91790.61        249744.55  California  144259.40   \n",
              "12   93863.75       127320.38        249839.44     Florida  141585.52   \n",
              "13   91992.39       135495.07        252664.93  California  134307.35   \n",
              "14  119943.24       156547.42        256512.92     Florida  132602.65   \n",
              "15  114523.61       122616.84        261776.23    New York  129917.04   \n",
              "16   78013.11       121597.55        264346.06  California  126992.93   \n",
              "17   94657.16       145077.58        282574.31    New York  125370.37   \n",
              "18   91749.16       114175.79        294919.57     Florida  124266.90   \n",
              "19   86419.70       153514.11             0.00    New York  122776.86   \n",
              "20   76253.86       113867.30        298664.47  California  118474.03   \n",
              "21   78389.47       153773.43        299737.29    New York  111313.02   \n",
              "22   73994.56       122782.75        303319.26     Florida  110352.25   \n",
              "23   67532.53       105751.03        304768.73     Florida  108733.99   \n",
              "24   77044.01        99281.34        140574.81    New York  108552.04   \n",
              "25   64664.71       139553.16        137962.62  California  107404.34   \n",
              "26   75328.87       144135.98        134050.07     Florida  105733.54   \n",
              "27   72107.60       127864.55        353183.81    New York  105008.31   \n",
              "28   66051.52       182645.56        118148.20     Florida  103282.38   \n",
              "29   65605.48       153032.06        107138.38    New York  101004.64   \n",
              "30   61994.48       115641.28         91131.24     Florida   99937.59   \n",
              "31   61136.38       152701.92         88218.23    New York   97483.56   \n",
              "32   63408.86       129219.61         46085.25  California   97427.84   \n",
              "33   55493.95       103057.49        214634.81     Florida   96778.92   \n",
              "34   46426.07       157693.92        210797.67  California   96712.80   \n",
              "35   46014.02        85047.44        205517.64    New York   96479.51   \n",
              "36   28663.76       127056.21        201126.82     Florida   90708.19   \n",
              "37   44069.95        51283.14        197029.42  California   89949.14   \n",
              "38   20229.59        65947.93        185265.10    New York   81229.06   \n",
              "39   38558.51        82982.09        174999.30  California   81005.76   \n",
              "40   28754.33       118546.05        172795.67  California   78239.91   \n",
              "41   27892.92        84710.77        164470.71     Florida   77798.83   \n",
              "42   23640.93        96189.63        148001.11  California   71498.49   \n",
              "43   15505.73       127382.30         35534.17    New York   69758.98   \n",
              "44   22177.74       154806.14         28334.72  California   65200.33   \n",
              "45    1000.23       124153.04          1903.93    New York   64926.08   \n",
              "46    1315.46       115816.21        297114.46     Florida   49490.75   \n",
              "47       0.00       135426.92             0.00  California   42559.73   \n",
              "48     542.05        51743.15             0.00    New York   35673.41   \n",
              "49       0.00       116983.80         45173.06  California   14681.40   \n",
              "\n",
              "    State_California  State_Florida  State_New York  \n",
              "0                  0              0               1  \n",
              "1                  1              0               0  \n",
              "2                  0              1               0  \n",
              "3                  0              0               1  \n",
              "4                  0              1               0  \n",
              "5                  0              0               1  \n",
              "6                  1              0               0  \n",
              "7                  0              1               0  \n",
              "8                  0              0               1  \n",
              "9                  1              0               0  \n",
              "10                 0              1               0  \n",
              "11                 1              0               0  \n",
              "12                 0              1               0  \n",
              "13                 1              0               0  \n",
              "14                 0              1               0  \n",
              "15                 0              0               1  \n",
              "16                 1              0               0  \n",
              "17                 0              0               1  \n",
              "18                 0              1               0  \n",
              "19                 0              0               1  \n",
              "20                 1              0               0  \n",
              "21                 0              0               1  \n",
              "22                 0              1               0  \n",
              "23                 0              1               0  \n",
              "24                 0              0               1  \n",
              "25                 1              0               0  \n",
              "26                 0              1               0  \n",
              "27                 0              0               1  \n",
              "28                 0              1               0  \n",
              "29                 0              0               1  \n",
              "30                 0              1               0  \n",
              "31                 0              0               1  \n",
              "32                 1              0               0  \n",
              "33                 0              1               0  \n",
              "34                 1              0               0  \n",
              "35                 0              0               1  \n",
              "36                 0              1               0  \n",
              "37                 1              0               0  \n",
              "38                 0              0               1  \n",
              "39                 1              0               0  \n",
              "40                 1              0               0  \n",
              "41                 0              1               0  \n",
              "42                 1              0               0  \n",
              "43                 0              0               1  \n",
              "44                 1              0               0  \n",
              "45                 0              0               1  \n",
              "46                 0              1               0  \n",
              "47                 1              0               0  \n",
              "48                 0              0               1  \n",
              "49                 1              0               0  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newdata=mergeddata.drop(['State'],axis='columns')\n",
        "newdata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DGDE3wKqF9nh",
        "outputId": "e179dea2-61f2-42a6-f28a-695f21055a82"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <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>Profit</th>\n",
              "      <th>State_California</th>\n",
              "      <th>State_Florida</th>\n",
              "      <th>State_New York</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>192261.83</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>191792.06</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>191050.39</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>182901.99</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>166187.94</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>156991.12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>156122.51</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>155752.60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>152211.77</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>149759.96</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>101913.08</td>\n",
              "      <td>110594.11</td>\n",
              "      <td>229160.95</td>\n",
              "      <td>146121.95</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>100671.96</td>\n",
              "      <td>91790.61</td>\n",
              "      <td>249744.55</td>\n",
              "      <td>144259.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>93863.75</td>\n",
              "      <td>127320.38</td>\n",
              "      <td>249839.44</td>\n",
              "      <td>141585.52</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>91992.39</td>\n",
              "      <td>135495.07</td>\n",
              "      <td>252664.93</td>\n",
              "      <td>134307.35</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>119943.24</td>\n",
              "      <td>156547.42</td>\n",
              "      <td>256512.92</td>\n",
              "      <td>132602.65</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>114523.61</td>\n",
              "      <td>122616.84</td>\n",
              "      <td>261776.23</td>\n",
              "      <td>129917.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>78013.11</td>\n",
              "      <td>121597.55</td>\n",
              "      <td>264346.06</td>\n",
              "      <td>126992.93</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>94657.16</td>\n",
              "      <td>145077.58</td>\n",
              "      <td>282574.31</td>\n",
              "      <td>125370.37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>91749.16</td>\n",
              "      <td>114175.79</td>\n",
              "      <td>294919.57</td>\n",
              "      <td>124266.90</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>86419.70</td>\n",
              "      <td>153514.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>122776.86</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>76253.86</td>\n",
              "      <td>113867.30</td>\n",
              "      <td>298664.47</td>\n",
              "      <td>118474.03</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>78389.47</td>\n",
              "      <td>153773.43</td>\n",
              "      <td>299737.29</td>\n",
              "      <td>111313.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>73994.56</td>\n",
              "      <td>122782.75</td>\n",
              "      <td>303319.26</td>\n",
              "      <td>110352.25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>67532.53</td>\n",
              "      <td>105751.03</td>\n",
              "      <td>304768.73</td>\n",
              "      <td>108733.99</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>77044.01</td>\n",
              "      <td>99281.34</td>\n",
              "      <td>140574.81</td>\n",
              "      <td>108552.04</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>64664.71</td>\n",
              "      <td>139553.16</td>\n",
              "      <td>137962.62</td>\n",
              "      <td>107404.34</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>75328.87</td>\n",
              "      <td>144135.98</td>\n",
              "      <td>134050.07</td>\n",
              "      <td>105733.54</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>72107.60</td>\n",
              "      <td>127864.55</td>\n",
              "      <td>353183.81</td>\n",
              "      <td>105008.31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>66051.52</td>\n",
              "      <td>182645.56</td>\n",
              "      <td>118148.20</td>\n",
              "      <td>103282.38</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>65605.48</td>\n",
              "      <td>153032.06</td>\n",
              "      <td>107138.38</td>\n",
              "      <td>101004.64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>61994.48</td>\n",
              "      <td>115641.28</td>\n",
              "      <td>91131.24</td>\n",
              "      <td>99937.59</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>61136.38</td>\n",
              "      <td>152701.92</td>\n",
              "      <td>88218.23</td>\n",
              "      <td>97483.56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>63408.86</td>\n",
              "      <td>129219.61</td>\n",
              "      <td>46085.25</td>\n",
              "      <td>97427.84</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>55493.95</td>\n",
              "      <td>103057.49</td>\n",
              "      <td>214634.81</td>\n",
              "      <td>96778.92</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>46426.07</td>\n",
              "      <td>157693.92</td>\n",
              "      <td>210797.67</td>\n",
              "      <td>96712.80</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>46014.02</td>\n",
              "      <td>85047.44</td>\n",
              "      <td>205517.64</td>\n",
              "      <td>96479.51</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>28663.76</td>\n",
              "      <td>127056.21</td>\n",
              "      <td>201126.82</td>\n",
              "      <td>90708.19</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>44069.95</td>\n",
              "      <td>51283.14</td>\n",
              "      <td>197029.42</td>\n",
              "      <td>89949.14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>20229.59</td>\n",
              "      <td>65947.93</td>\n",
              "      <td>185265.10</td>\n",
              "      <td>81229.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>38558.51</td>\n",
              "      <td>82982.09</td>\n",
              "      <td>174999.30</td>\n",
              "      <td>81005.76</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>28754.33</td>\n",
              "      <td>118546.05</td>\n",
              "      <td>172795.67</td>\n",
              "      <td>78239.91</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>27892.92</td>\n",
              "      <td>84710.77</td>\n",
              "      <td>164470.71</td>\n",
              "      <td>77798.83</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>23640.93</td>\n",
              "      <td>96189.63</td>\n",
              "      <td>148001.11</td>\n",
              "      <td>71498.49</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>15505.73</td>\n",
              "      <td>127382.30</td>\n",
              "      <td>35534.17</td>\n",
              "      <td>69758.98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>22177.74</td>\n",
              "      <td>154806.14</td>\n",
              "      <td>28334.72</td>\n",
              "      <td>65200.33</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>1000.23</td>\n",
              "      <td>124153.04</td>\n",
              "      <td>1903.93</td>\n",
              "      <td>64926.08</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1315.46</td>\n",
              "      <td>115816.21</td>\n",
              "      <td>297114.46</td>\n",
              "      <td>49490.75</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>0.00</td>\n",
              "      <td>135426.92</td>\n",
              "      <td>0.00</td>\n",
              "      <td>42559.73</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>542.05</td>\n",
              "      <td>51743.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>35673.41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>0.00</td>\n",
              "      <td>116983.80</td>\n",
              "      <td>45173.06</td>\n",
              "      <td>14681.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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            "text/plain": [
              "    R&D Spend  Administration  Marketing Spend     Profit  State_California  \\\n",
              "0   165349.20       136897.80        471784.10  192261.83                 0   \n",
              "1   162597.70       151377.59        443898.53  191792.06                 1   \n",
              "2   153441.51       101145.55        407934.54  191050.39                 0   \n",
              "3   144372.41       118671.85        383199.62  182901.99                 0   \n",
              "4   142107.34        91391.77        366168.42  166187.94                 0   \n",
              "5   131876.90        99814.71        362861.36  156991.12                 0   \n",
              "6   134615.46       147198.87        127716.82  156122.51                 1   \n",
              "7   130298.13       145530.06        323876.68  155752.60                 0   \n",
              "8   120542.52       148718.95        311613.29  152211.77                 0   \n",
              "9   123334.88       108679.17        304981.62  149759.96                 1   \n",
              "10  101913.08       110594.11        229160.95  146121.95                 0   \n",
              "11  100671.96        91790.61        249744.55  144259.40                 1   \n",
              "12   93863.75       127320.38        249839.44  141585.52                 0   \n",
              "13   91992.39       135495.07        252664.93  134307.35                 1   \n",
              "14  119943.24       156547.42        256512.92  132602.65                 0   \n",
              "15  114523.61       122616.84        261776.23  129917.04                 0   \n",
              "16   78013.11       121597.55        264346.06  126992.93                 1   \n",
              "17   94657.16       145077.58        282574.31  125370.37                 0   \n",
              "18   91749.16       114175.79        294919.57  124266.90                 0   \n",
              "19   86419.70       153514.11             0.00  122776.86                 0   \n",
              "20   76253.86       113867.30        298664.47  118474.03                 1   \n",
              "21   78389.47       153773.43        299737.29  111313.02                 0   \n",
              "22   73994.56       122782.75        303319.26  110352.25                 0   \n",
              "23   67532.53       105751.03        304768.73  108733.99                 0   \n",
              "24   77044.01        99281.34        140574.81  108552.04                 0   \n",
              "25   64664.71       139553.16        137962.62  107404.34                 1   \n",
              "26   75328.87       144135.98        134050.07  105733.54                 0   \n",
              "27   72107.60       127864.55        353183.81  105008.31                 0   \n",
              "28   66051.52       182645.56        118148.20  103282.38                 0   \n",
              "29   65605.48       153032.06        107138.38  101004.64                 0   \n",
              "30   61994.48       115641.28         91131.24   99937.59                 0   \n",
              "31   61136.38       152701.92         88218.23   97483.56                 0   \n",
              "32   63408.86       129219.61         46085.25   97427.84                 1   \n",
              "33   55493.95       103057.49        214634.81   96778.92                 0   \n",
              "34   46426.07       157693.92        210797.67   96712.80                 1   \n",
              "35   46014.02        85047.44        205517.64   96479.51                 0   \n",
              "36   28663.76       127056.21        201126.82   90708.19                 0   \n",
              "37   44069.95        51283.14        197029.42   89949.14                 1   \n",
              "38   20229.59        65947.93        185265.10   81229.06                 0   \n",
              "39   38558.51        82982.09        174999.30   81005.76                 1   \n",
              "40   28754.33       118546.05        172795.67   78239.91                 1   \n",
              "41   27892.92        84710.77        164470.71   77798.83                 0   \n",
              "42   23640.93        96189.63        148001.11   71498.49                 1   \n",
              "43   15505.73       127382.30         35534.17   69758.98                 0   \n",
              "44   22177.74       154806.14         28334.72   65200.33                 1   \n",
              "45    1000.23       124153.04          1903.93   64926.08                 0   \n",
              "46    1315.46       115816.21        297114.46   49490.75                 0   \n",
              "47       0.00       135426.92             0.00   42559.73                 1   \n",
              "48     542.05        51743.15             0.00   35673.41                 0   \n",
              "49       0.00       116983.80         45173.06   14681.40                 1   \n",
              "\n",
              "    State_Florida  State_New York  \n",
              "0               0               1  \n",
              "1               0               0  \n",
              "2               1               0  \n",
              "3               0               1  \n",
              "4               1               0  \n",
              "5               0               1  \n",
              "6               0               0  \n",
              "7               1               0  \n",
              "8               0               1  \n",
              "9               0               0  \n",
              "10              1               0  \n",
              "11              0               0  \n",
              "12              1               0  \n",
              "13              0               0  \n",
              "14              1               0  \n",
              "15              0               1  \n",
              "16              0               0  \n",
              "17              0               1  \n",
              "18              1               0  \n",
              "19              0               1  \n",
              "20              0               0  \n",
              "21              0               1  \n",
              "22              1               0  \n",
              "23              1               0  \n",
              "24              0               1  \n",
              "25              0               0  \n",
              "26              1               0  \n",
              "27              0               1  \n",
              "28              1               0  \n",
              "29              0               1  \n",
              "30              1               0  \n",
              "31              0               1  \n",
              "32              0               0  \n",
              "33              1               0  \n",
              "34              0               0  \n",
              "35              0               1  \n",
              "36              1               0  \n",
              "37              0               0  \n",
              "38              0               1  \n",
              "39              0               0  \n",
              "40              0               0  \n",
              "41              1               0  \n",
              "42              0               0  \n",
              "43              0               1  \n",
              "44              0               0  \n",
              "45              0               1  \n",
              "46              1               0  \n",
              "47              0               0  \n",
              "48              0               1  \n",
              "49              0               0  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converting data into int datatype to avoid errors below.\n",
        "prepareddata=newdata.astype(int)\n",
        "prepareddata.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pMCvSC7OCIVo",
        "outputId": "d5a7d6a9-c7b5-456a-878b-8ab71ef0c9f5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Marketing Spend</th>\n",
              "      <th>Profit</th>\n",
              "      <th>State_California</th>\n",
              "      <th>State_Florida</th>\n",
              "      <th>State_New York</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349</td>\n",
              "      <td>136897</td>\n",
              "      <td>471784</td>\n",
              "      <td>192261</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597</td>\n",
              "      <td>151377</td>\n",
              "      <td>443898</td>\n",
              "      <td>191792</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441</td>\n",
              "      <td>101145</td>\n",
              "      <td>407934</td>\n",
              "      <td>191050</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372</td>\n",
              "      <td>118671</td>\n",
              "      <td>383199</td>\n",
              "      <td>182901</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107</td>\n",
              "      <td>91391</td>\n",
              "      <td>366168</td>\n",
              "      <td>166187</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b95f92e-dc8f-45f6-88c8-5e00c924a0e6')\"\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-1b95f92e-dc8f-45f6-88c8-5e00c924a0e6 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-1b95f92e-dc8f-45f6-88c8-5e00c924a0e6');\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",
              "  "
            ],
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend  Profit  State_California  \\\n",
              "0     165349          136897           471784  192261                 0   \n",
              "1     162597          151377           443898  191792                 1   \n",
              "2     153441          101145           407934  191050                 0   \n",
              "3     144372          118671           383199  182901                 0   \n",
              "4     142107           91391           366168  166187                 0   \n",
              "\n",
              "   State_Florida  State_New York  \n",
              "0              0               1  \n",
              "1              0               0  \n",
              "2              1               0  \n",
              "3              0               1  \n",
              "4              1               0  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepareddata.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwVA8SgXC977",
        "outputId": "54fdd233-b40a-4b6b-c889-1a3eaf83608f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'Profit',\n",
              "       'State_California', 'State_Florida', 'State_New York'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prepareddata.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sDm8iXbGUY7",
        "outputId": "e3d7cc94-84bc-49cc-b7a2-ef73bdfcb608"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50 entries, 0 to 49\n",
            "Data columns (total 7 columns):\n",
            " #   Column            Non-Null Count  Dtype\n",
            "---  ------            --------------  -----\n",
            " 0   R&D Spend         50 non-null     int64\n",
            " 1   Administration    50 non-null     int64\n",
            " 2   Marketing Spend   50 non-null     int64\n",
            " 3   Profit            50 non-null     int64\n",
            " 4   State_California  50 non-null     int64\n",
            " 5   State_Florida     50 non-null     int64\n",
            " 6   State_New York    50 non-null     int64\n",
            "dtypes: int64(7)\n",
            "memory usage: 2.9 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Training Our Data**\n",
        "\n"
      ],
      "metadata": {
        "id": "P41kwXp4JBH5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import train_test_split from sklearn.model_selection\n",
        "from sklearn.model_selection import train_test_split\n",
        "# Here, X is the data which will have features and y will have our target.\n",
        "x=prepareddata[['R&D Spend', 'Administration', 'Marketing Spend','State_California', 'State_Florida', 'State_New York']]  \n",
        "y=prepareddata['Profit']"
      ],
      "metadata": {
        "id": "MJa5GnxPCZaE"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into training data and testing data\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2) \n",
        "#Ratio used for splitting training and testing data is 8:2 respectively"
      ],
      "metadata": {
        "id": "fDQcGTJXDAOX"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Creation**"
      ],
      "metadata": {
        "id": "l6vk_SdZGwPv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Linear Regression"
      ],
      "metadata": {
        "id": "vD9AVlk4GsMm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing linear regression model\n",
        "from sklearn.linear_model import LinearRegression \n",
        "reg1 = LinearRegression()"
      ],
      "metadata": {
        "id": "bc5Dk-TbDEAm"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting data into the model.\n",
        "reg1.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ntbFAnND5Yc",
        "outputId": "aab48579-aad5-4dd2-ee8f-cd2ff0978c98"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions \n",
        "pred1 = reg1.predict(x_test)"
      ],
      "metadata": {
        "id": "w11EfqAYD8Ae"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrGObL-WD-9x",
        "outputId": "02a3e327-4c5f-464d-9595-4eec1ad37cb1"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 98484.89210481,  44026.08455385, 157308.22696881, 164609.14315337,\n",
              "       151943.20929796,  56973.06829596,  84210.34971486, 116881.35386378,\n",
              "       184161.16574319, 129863.57642281])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lasso Regression"
      ],
      "metadata": {
        "id": "7sbXj2IRG7Sy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing model\n",
        "from sklearn.linear_model import Lasso\n",
        "reg2 = Lasso()"
      ],
      "metadata": {
        "id": "QccbhNhLEEtq"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting data into the model.\n",
        "reg2.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYNgs9leEFkb",
        "outputId": "0564c43b-2bf9-4efb-8850-e1fe79d42191"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lasso()"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions \n",
        "pred2 = reg2.predict(x_test)"
      ],
      "metadata": {
        "id": "ul8NvlADEHxP"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3BxHt-RPEKGM",
        "outputId": "df944e4e-8f51-4b9b-97ca-c1c20c9caa9a"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 98481.63268423,  44030.60188057, 157304.38045446, 164612.05593362,\n",
              "       151947.12473384,  56977.5034078 ,  84207.08582321, 116886.10440022,\n",
              "       184159.91077455, 129867.79195108])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ridge Regression"
      ],
      "metadata": {
        "id": "6TjhWEG0HCQ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing model\n",
        "from sklearn.linear_model import Ridge\n",
        "reg3 = Ridge()"
      ],
      "metadata": {
        "id": "eiuI077XEMF2"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fitting data into the model.\n",
        "reg3.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Do_IVkEKEO9i",
        "outputId": "df33f62c-5e0f-4dca-be69-876662879aa2"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge()"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions \n",
        "pred3= reg3.predict(x_test)\n"
      ],
      "metadata": {
        "id": "DL4ooUm3ETQn"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQ_UYvYvEYfs",
        "outputId": "2451ab9f-0d32-4a56-c2c9-06efc590f7db"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 98402.18973791,  44116.59986663, 157204.03565538, 164655.26881854,\n",
              "       152009.91085477,  57060.68872438,  84122.19767986, 116963.88574768,\n",
              "       184158.12254095, 129936.98380266])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Performance Check**"
      ],
      "metadata": {
        "id": "AqY21cEQHNqg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import mean_squared_error\n",
        "print(\"Model\\t\\t\\t RootMeanSquareError \\t\\t Accuracy of the model\") \n",
        "print(\"\"\"Linear Regression \\t\\t {:.4f} \\t \\t\\t {:.4f}\"\"\".format(  np.sqrt(mean_squared_error(y_test, pred1)), reg1.score(x_train,y_train)))\n",
        "print(\"\"\"Lasso Regression \\t\\t {:.4f} \\t \\t\\t {:.4f}\"\"\".format(  np.sqrt(mean_squared_error(y_test, pred2)), reg2.score(x_train,y_train)))\n",
        "print(\"\"\"Ridge Regression \\t\\t {:.4f} \\t \\t\\t {:.4f}\"\"\".format(  np.sqrt(mean_squared_error(y_test, pred3)), reg3.score(x_train,y_train)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1b5N9rlEZVB",
        "outputId": "d5ccb24d-e358-4437-cf7e-ab7d690e9466"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model\t\t\t RootMeanSquareError \t\t Accuracy of the model\n",
            "Linear Regression \t\t 9085.1958 \t \t\t 0.9487\n",
            "Lasso Regression \t\t 9083.8880 \t \t\t 0.9487\n",
            "Ridge Regression \t\t 9052.1743 \t \t\t 0.9487\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Conclusion**\n",
        "* All 3 regression algorithms used in this project are equally efficient for the given dataset.\n",
        "* RMSE for Ridge Regression is least."
      ],
      "metadata": {
        "id": "I4INbffZHWFz"
      }
    }
  ]
}