50_startups_prj3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dec87a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "87fbb38e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the data set\n",
    "def read_data(path):\n",
    "    data = pd.read_csv(path)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f33e3735",
   "metadata": {},
   "outputs": [],
   "source": [
    "url=r\"D:\\Topmentor\\Oct\\29th oct\\b88assignments\\Project - 3&4_Multiple_Reg\\Project - 3&4_Multiple_Reg\\50_Startups.csv\"\n",
    "df = read_data(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "99164ea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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",
      "---------------------------------\n",
      "<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",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Display the first few rows of the dataset\n",
    "print(df.head())\n",
    "print(\"---------------------------------\")\n",
    "\n",
    "# Display information about the dataset\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "47ebff51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary Statistics:\n",
      "           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\n"
     ]
    }
   ],
   "source": [
    "# Summary statistics\n",
    "print(\"\\nSummary Statistics:\")\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6e584dd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Missing Values:\n",
      "R&D Spend          0\n",
      "Administration     0\n",
      "Marketing Spend    0\n",
      "State              0\n",
      "Profit             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(\"\\nMissing Values:\")\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fcc43d43",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    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"
     ]
    }
   ],
   "source": [
    "### Preprocessing and Feature Engineering\n",
    "df_copy = df.copy()\n",
    "print(df_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8bd439bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\swenc\\AppData\\Local\\Temp\\ipykernel_7444\\4042109939.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  correlation_matrix=df.corr()\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the correlation matrix using a heatmap\n",
    "correlation_matrix=df.corr()\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(correlation_matrix, annot=True,cmap='coolwarm',fmt='.2f')\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "8a6b7b2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1316.38x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the relationship between Profit and numerical features\n",
    "\n",
    "sns.pairplot(df, x_vars=['R&D Spend', 'Administration', 'Marketing Spend'], y_vars='Profit',hue ='State', height=4, aspect=1)\n",
    "plt.suptitle('Scatterplots between Profit and Numerical Features', y=1.02)\n",
    "plt.show()\n",
    "plt.savefig(\"D:/Topmentor/Oct/29th oct/50_Startup/pairplot_50starup1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f5c5c40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the relationship between Profit and State (categorical feature)\n",
    "sns.barplot(x='State', y='Profit', data=df)\n",
    "plt.title('Boxplot of Profit by State')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ef2a541c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New York', 'California', 'Florida'], dtype=object)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df['State'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "99e71a43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'New York': 0, 'California': 1, 'Florida': 2}\n"
     ]
    }
   ],
   "source": [
    "### Converting the Textual Column to Numerical Colum is called as Encoding\n",
    "d={'New York':0,'California':1,'Florida':2}\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b9da3f5e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>131876.90</td>\n",
       "      <td>99814.71</td>\n",
       "      <td>362861.36</td>\n",
       "      <td>New York</td>\n",
       "      <td>156991.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>134615.46</td>\n",
       "      <td>147198.87</td>\n",
       "      <td>127716.82</td>\n",
       "      <td>California</td>\n",
       "      <td>156122.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>130298.13</td>\n",
       "      <td>145530.06</td>\n",
       "      <td>323876.68</td>\n",
       "      <td>Florida</td>\n",
       "      <td>155752.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>120542.52</td>\n",
       "      <td>148718.95</td>\n",
       "      <td>311613.29</td>\n",
       "      <td>New York</td>\n",
       "      <td>152211.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>123334.88</td>\n",
       "      <td>108679.17</td>\n",
       "      <td>304981.62</td>\n",
       "      <td>California</td>\n",
       "      <td>149759.96</td>\n",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mapping in new column name\n",
    "df_copy['count_map'] = df_copy['State'].map(d)\n",
    "df_copy.drop('count_map', axis = 1, inplace = True)\n",
    "df_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a660edb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit',\n",
       "       'California', 'Florida', 'New York'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### One hot Encoding\n",
    "dumm = pd.get_dummies(df_copy['State']).astype('int')\n",
    "final_data = pd.concat([df_copy,dumm], axis = 1)\n",
    "final_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "815367cd",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>California</th>\n",
       "      <th>Florida</th>\n",
       "      <th>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>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>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>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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend  California  Florida  New York\n",
       "0  165349.20       136897.80        471784.10           0        0         1\n",
       "1  162597.70       151377.59        443898.53           1        0         0\n",
       "2  153441.51       101145.55        407934.54           0        1         0\n",
       "3  144372.41       118671.85        383199.62           0        0         1\n",
       "4  142107.34        91391.77        366168.42           0        1         0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = final_data[['R&D Spend','Administration', 'Marketing Spend','California', 'Florida', 'New York']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "377e6623",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Profit\n",
       "0  192261.83\n",
       "1  191792.06\n",
       "2  191050.39\n",
       "3  182901.99\n",
       "4  166187.94"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = final_data[['Profit']]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "9426b7c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now  Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "dd3ac1a6",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 92531.10641376],\n",
       "       [ 46482.82452022],\n",
       "       [160474.57456196],\n",
       "       [ 66496.45002329],\n",
       "       [129631.90736555],\n",
       "       [ 74004.41170643],\n",
       "       [150477.63298555],\n",
       "       [100479.61020034],\n",
       "       [171564.92610869],\n",
       "       [ 50391.73836456],\n",
       "       [115822.61015533],\n",
       "       [128047.43403092],\n",
       "       [134706.64446954]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,train_size=0.75,random_state=1234)\n",
    "X_train.head(2)\n",
    "\n",
    "model=LinearRegression()\n",
    "model_train = model.fit(X_train,y_train)\n",
    "pred1 = model_train.predict(X_test)\n",
    "pred1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "3876101a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Predctor     Actual\n",
      "0   92531.106414   89949.14\n",
      "1   46482.824520   64926.08\n",
      "2  160474.574562  156122.51\n",
      "3   66496.450023   65200.33\n"
     ]
    }
   ],
   "source": [
    "### Create a predictor column\n",
    "dd = pd.DataFrame(pred1,columns=['Predctor'])\n",
    "dd['Actual'] =y_test.values\n",
    "print(dd.head(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "4c00cb0f",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8.10267499e-01 -3.77279914e-02  2.60256860e-02  2.20473649e+03\n",
      "  -1.08669509e+03 -1.11804140e+03]]\n",
      "------------------------------------------------\n",
      "R&D Spend Coefficient (0.8103):\n",
      "\tThe positive coefficient indicates that an increase in R&D spending is associated with an increase in profit.\n",
      "       \n",
      "Administration Coefficient (-0.0377):\n",
      "\tThe negative coefficient suggests that higher administration costs are associated with a decrease in profit.\n",
      "       \n",
      "Marketing Spend Coefficient (0.0260):\n",
      "\tThe positive coefficient implies that an increase in marketing spending is associated with a rise in profit.\n",
      "       \n",
      "California Coefficient (2204.74):\n",
      "\tThe positive coefficient for California indicates that, on average, companies based in California have a higher profit compared to the baseline .\n",
      "       \n",
      "Florida Coefficient (-1086.70):\n",
      "\tThe negative coefficient for Florida suggests that, on average, companies based in Florida have lower profit compared to the baseline.\n",
      "      \n",
      "New York Coefficient (-1118.04):\n",
      "\tThe negative coefficient for New York suggests that, on average, companies based in New York have lower profit compared to the baseline.\n"
     ]
    }
   ],
   "source": [
    "coef = model_train.coef_\n",
    "print(coef)\n",
    "print(\"------------------------------------------------\")\n",
    "print(\"R&D Spend Coefficient (0.8103):\\n\\tThe positive coefficient indicates that an increase in R&D spending is associated with an increase in profit.\")\n",
    "print(\"       \")\n",
    "print(\"Administration Coefficient (-0.0377):\\n\\tThe negative coefficient suggests that higher administration costs are associated with a decrease in profit.\")\n",
    "print(\"       \")\n",
    "print(\"Marketing Spend Coefficient (0.0260):\\n\\tThe positive coefficient implies that an increase in marketing spending is associated with a rise in profit.\")\n",
    "print(\"       \")\n",
    "print(\"California Coefficient (2204.74):\\n\\tThe positive coefficient for California indicates that, on average, companies based in California have a higher profit compared to the baseline .\")\n",
    "print(\"       \")\n",
    "print(\"Florida Coefficient (-1086.70):\\n\\tThe negative coefficient for Florida suggests that, on average, companies based in Florida have lower profit compared to the baseline.\")\n",
    "print(\"      \")\n",
    "print(\"New York Coefficient (-1118.04):\\n\\tThe negative coefficient for New York suggests that, on average, companies based in New York have lower profit compared to the baseline.\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "a09ed69b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1.Focus on increasing R&D spending, as it has a positive impact on profit.\n",
      "\t2.Implement cost-cutting measures in administration to optimize overall expenses.\n",
      "\t3.Strategically invest in marketing activities to drive profitability.\n",
      "\t4.Explore opportunities to leverage the positive business environment in California for increased profitability.\n",
      "\t5.Analyze and address challenges in Florida and New York to improve profit margins in these regions.\n"
     ]
    }
   ],
   "source": [
    "#Insight:\n",
    "print(\"\\t1.Focus on increasing R&D spending, as it has a positive impact on profit.\")\n",
    "print(\"\\t2.Implement cost-cutting measures in administration to optimize overall expenses.\")\n",
    "print(\"\\t3.Strategically invest in marketing activities to drive profitability.\")\n",
    "print(\"\\t4.Explore opportunities to leverage the positive business environment in California for increased profitability.\")\n",
    "print(\"\\t5.Analyze and address challenges in Florida and New York to improve profit margins in these regions.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "9ec32c5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept value(baseline ref value):  [51425.]\n",
      "                 \n",
      "The intercept in a linear regression model represents the predicted value of the dependent variable (profit, in this case) when all independent variables are zero. In this context, the intercept of 51425 suggests that, with zero spending on R&D, administration, and marketing, and in the baseline state (reference state), the predicted profit would be $51,425.\n"
     ]
    }
   ],
   "source": [
    "intercept=model_train.intercept_\n",
    "print(\"Intercept value(baseline ref value): \",np.round(intercept))\n",
    "print(\"                 \")\n",
    "print(\"The intercept in a linear regression model represents the predicted value of the dependent variable (profit, in this case) when all independent variables are zero. In this context, the intercept of 51425 suggests that, with zero spending on R&D, administration, and marketing, and in the baseline state (reference state), the predicted profit would be $51,425.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "2f7a91b9",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error = 8280.2\n",
      "---------------------\n",
      "\tWith an RMSE of 8280.23, the company can have confidence that the model provides reasonably reliable predictions of profit, allowing for better planning and decision-making.\n"
     ]
    }
   ],
   "source": [
    "sqrt_value=sqrt(mean_squared_error(dd['Actual'],dd['Predctor']))\n",
    "print(\"Root Mean Squared Error =\",round(sqrt_value, 1))\n",
    "print(\"---------------------\")\n",
    "print(\"\\tWith an RMSE of 8280.23, the company can have confidence that the model provides reasonably reliable predictions of profit, allowing for better planning and decision-making.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "86020884",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strength of model:  0.96\n",
      "\tWith a high R^2 score, the company can identify optimization opportunities in resource allocation (R&D, Administration, Marketing) to maximize profit.\n",
      "For example, if increasing R&D spending is associated with higher profits, the company might consider allocating more resources to research and development.\n"
     ]
    }
   ],
   "source": [
    "Strength_of_model = r2_score(dd['Actual'],dd['Predctor'])   ### Strength of the model\n",
    "print(\"Strength of model: \", round(Strength_of_model,2))\n",
    "# Evaluate the model\n",
    "print(\"\\tWith a high R^2 score, the company can identify optimization opportunities in resource allocation (R&D, Administration, Marketing) to maximize profit.\\nFor example, if increasing R&D spending is associated with higher profits, the company might consider allocating more resources to research and development.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "bd681c21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "baf26094",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 92531.10641376],\n",
       "       [ 46482.82452022],\n",
       "       [160474.57456196],\n",
       "       [ 66496.45002329],\n",
       "       [129631.90736555],\n",
       "       [ 74004.41170643],\n",
       "       [150477.63298555],\n",
       "       [100479.61020034],\n",
       "       [171564.92610869],\n",
       "       [ 50391.73836456],\n",
       "       [115822.61015533],\n",
       "       [128047.43403092],\n",
       "       [134706.64446954]])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #### Saiving the model Using a Joblib\n",
    "import joblib\n",
    "filename = 'joblip_reg_model.sav'\n",
    "joblib.dump(model_train,filename)\n",
    "#### Loading the Model --\n",
    "loaded_model = joblib.load(filename)\n",
    "print(loaded_model)\n",
    "\n",
    "y_predict = loaded_model.predict(X_test)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "161941d7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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