MLR(50 START UPS)-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f41297f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b7142eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"50_Startups.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5ec2ddaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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",
       "</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"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "01dcf2e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 5)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75b8aec",
   "metadata": {},
   "source": [
    "# EDA and Visualization of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4c3df0ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "60542230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicate values\n",
    "data.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8e0965",
   "metadata": {},
   "source": [
    "There are no duplicate values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3f91aa8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R&D Spend          False\n",
       "Administration     False\n",
       "Marketing Spend    False\n",
       "State              False\n",
       "Profit             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null values\n",
    "data.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce064bf",
   "metadata": {},
   "source": [
    "There are no null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5f59c446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>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",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking The statics of the Data\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d6b917d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_states=data.State.value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "62fef89a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State', ylabel='Profit'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking Profit For Each States\n",
    "sn.barplot(data.State,data.Profit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff88178",
   "metadata": {},
   "source": [
    "1.Florida makes most profit\n",
    "\n",
    "2.California makes Least profit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0e03d2f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking spend od Each states \n",
    "for i in data.iloc[:,[0,1,2]]:\n",
    "    sn.barplot(data.State,data[i])\n",
    "    plt.ylabel(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833b3407",
   "metadata": {},
   "source": [
    "##### OBSERVATIONS:\n",
    "1. The Administration spend of Each States is almost Same.\n",
    "2. RandD spend and Marketing Spend is done more in Florida.\n",
    "3. Where as, RandD spend and Marketing Spend is done least in California state.\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cd30e47d",
   "metadata": {},
   "outputs": [],
   "source": [
    "spend=data['R&D Spend']+data['Administration']+data['Marketing Spend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3c279c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='State'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.barplot(data.State,spend)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daf2810f",
   "metadata": {},
   "source": [
    "#### Total spend is more in Florida And Profit is also more in florida "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "67d03564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Q5P8Bn8uSXFGLywcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+NsZ49rLrXDw/Uj7w/rxtjHFJrdE/eOX45c/QAJrYBQHQRIABmggwQBMBBmgiwABNBBigiQADNBFggCb/BzIH+KnvnzyMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking Outliers using boxplot \n",
    "for i in data.iloc[:,[0,1,2,4]]:\n",
    "    plt.figure(figsize=(6,5))\n",
    "    sn.boxplot(data[i])\n",
    "    plt.xlabel(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68325ded",
   "metadata": {},
   "source": [
    "#### Except Profit no other column has outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8e933ae1",
   "metadata": {},
   "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>index</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>49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116983.8</td>\n",
       "      <td>45173.06</td>\n",
       "      <td>California</td>\n",
       "      <td>14681.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  R&D Spend  Administration  Marketing Spend       State   Profit\n",
       "0     49        0.0        116983.8         45173.06  California  14681.4"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the outlier\n",
    "lower_fence=data.Profit.quantile(0.25)-1.5*(data.Profit.quantile(0.75)-data.Profit.quantile(0.25))\n",
    "data[data['Profit']<lower_fence].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e4f4f36f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the outlier with lower fence\n",
    "data['Profit']=np.where(data['Profit']<lower_fence,lower_fence,data['Profit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a8acc120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'profit')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Again plotting boxplot for profit\n",
    "sn.boxplot(data[\"Profit\"])\n",
    "plt.xlabel(\"profit\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc40c228",
   "metadata": {},
   "source": [
    "#### Outlier is replaced now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f3534e17",
   "metadata": {},
   "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>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.241955</td>\n",
       "      <td>0.724248</td>\n",
       "      <td>0.973278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administration</th>\n",
       "      <td>0.241955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>0.200885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing Spend</th>\n",
       "      <td>0.724248</td>\n",
       "      <td>-0.032154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.747993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>0.973278</td>\n",
       "      <td>0.200885</td>\n",
       "      <td>0.747993</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 R&D Spend  Administration  Marketing Spend    Profit\n",
       "R&D Spend         1.000000        0.241955         0.724248  0.973278\n",
       "Administration    0.241955        1.000000        -0.032154  0.200885\n",
       "Marketing Spend   0.724248       -0.032154         1.000000  0.747993\n",
       "Profit            0.973278        0.200885         0.747993  1.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking The corelation of all variables \n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24998b37",
   "metadata": {},
   "source": [
    "### Plotting corelations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7ac9d161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x199abad60a0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pair plot\n",
    "sn.pairplot(data,corner=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "518a8775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heat map\n",
    "sn.heatmap(data.iloc[:,:3].corr(),annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7df4e04d",
   "metadata": {},
   "source": [
    "### Clearly RandD spend and Marketing Spend are colinear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "81b08ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking The Distribution of Each Feature\n",
    "for i in data.iloc[:,[0,1,2,4]]:\n",
    "    plt.figure(figsize=(6,5))\n",
    "    sn.distplot(data[i],kde=True)\n",
    "    plt.xlabel(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ba899a6",
   "metadata": {},
   "source": [
    "Observations:\n",
    "1. Profit is Normally Distributed\n",
    "2. R and D spend and marketing spend is almost normal distribution\n",
    "3. administration looks a little negetively skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "655f8d8a",
   "metadata": {},
   "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>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>165349.2</td>\n",
       "      <td>136897.8</td>\n",
       "      <td>471784.1</td>\n",
       "      <td>192261.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend     Profit  State_California  \\\n",
       "0   165349.2        136897.8         471784.1  192261.83                 0   \n",
       "\n",
       "   State_Florida  State_New York  \n",
       "0              0               1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dummification of state feature\n",
    "data_1=pd.get_dummies(data)\n",
    "data_1.head(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f36d70aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating Depenpent(y) and independent variable(X)\n",
    "x=data_1[['R&D Spend','Administration','Marketing Spend','State_California','State_Florida','State_New York']]\n",
    "y=data_1[\"Profit\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a73c6f8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5376d38b",
   "metadata": {},
   "source": [
    "## Model Building And Subset selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "957570d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "model1=sm.OLS(y,x).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "dfa18510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   172.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 10 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>9.68e-28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:24:37</td>     <th>  Log-Likelihood:    </th> <td> -524.95</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1062.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    44</td>      <th>  BIC:               </th> <td>   1073.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R&D Spend</th>        <td>    0.8054</td> <td>    0.046</td> <td>   17.506</td> <td> 0.000</td> <td>    0.713</td> <td>    0.898</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>   <td>   -0.0269</td> <td>    0.052</td> <td>   -0.519</td> <td> 0.606</td> <td>   -0.131</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Marketing Spend</th>  <td>    0.0269</td> <td>    0.017</td> <td>    1.586</td> <td> 0.120</td> <td>   -0.007</td> <td>    0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_California</th> <td> 5.021e+04</td> <td> 6825.548</td> <td>    7.357</td> <td> 0.000</td> <td> 3.65e+04</td> <td>  6.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_Florida</th>    <td> 5.036e+04</td> <td> 7189.336</td> <td>    7.005</td> <td> 0.000</td> <td> 3.59e+04</td> <td> 6.49e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_New York</th>   <td> 5.012e+04</td> <td> 6892.732</td> <td>    7.271</td> <td> 0.000</td> <td> 3.62e+04</td> <td>  6.4e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13.298</td> <th>  Durbin-Watson:     </th> <td>   1.294</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  17.729</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.887</td> <th>  Prob(JB):          </th> <td>0.000141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.315</td> <th>  Cond. No.          </th> <td>2.45e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.45e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.951\n",
       "Model:                            OLS   Adj. R-squared:                  0.946\n",
       "Method:                 Least Squares   F-statistic:                     172.6\n",
       "Date:                Tue, 10 Jan 2023   Prob (F-statistic):           9.68e-28\n",
       "Time:                        23:24:37   Log-Likelihood:                -524.95\n",
       "No. Observations:                  50   AIC:                             1062.\n",
       "Df Residuals:                      44   BIC:                             1073.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "R&D Spend            0.8054      0.046     17.506      0.000       0.713       0.898\n",
       "Administration      -0.0269      0.052     -0.519      0.606      -0.131       0.077\n",
       "Marketing Spend      0.0269      0.017      1.586      0.120      -0.007       0.061\n",
       "State_California  5.021e+04   6825.548      7.357      0.000    3.65e+04     6.4e+04\n",
       "State_Florida     5.036e+04   7189.336      7.005      0.000    3.59e+04    6.49e+04\n",
       "State_New York    5.012e+04   6892.732      7.271      0.000    3.62e+04     6.4e+04\n",
       "==============================================================================\n",
       "Omnibus:                       13.298   Durbin-Watson:                   1.294\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               17.729\n",
       "Skew:                          -0.887   Prob(JB):                     0.000141\n",
       "Kurtosis:                       5.315   Cond. No.                     2.45e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.45e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea9a602a",
   "metadata": {},
   "source": [
    "### Eventhough accuracy is Quite high, pvalues for Administration and marketing spend is not good. so check for multilcilinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0d11fa29",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculation cooks distance\n",
    "from statsmodels.graphics.regressionplots import influence_plot\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0205e177",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cooks diatnce')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model1_inf= model1.get_influence()\n",
    "(c,_)=model1_inf.cooks_distance\n",
    "# Plotting Cooks distance\n",
    "plt.stem(np.arange(len(data_1)),np.round(c,3))\n",
    "plt.xlabel(\"index\")\n",
    "plt.ylabel(\"cooks diatnce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f1007fb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 0.25411526588586847)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.argmax(c),np.max(c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "35cc4e96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3\n"
     ]
    }
   ],
   "source": [
    "levarage_cutoff=3*(data.shape[1])/data.shape[0]\n",
    "print(levarage_cutoff)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "013a0344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x199ac60aaf0>]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting influence plot\n",
    "influence_plot(model1)\n",
    "x=[i for i in range(-4,6)]\n",
    "y=[levarage_cutoff for i in range(10)]\n",
    "plt.plot(y,x,\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f22dfa82",
   "metadata": {},
   "source": [
    "Every point seem to fall inside leverage score.No need to drop any rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ba228de5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating VIF For Every Features\n",
    "vif=pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(x.values, i) for i in range(x.shape[1])]\n",
    "vif[\"features\"]=x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10f65f87",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "0e32744a",
   "metadata": {},
   "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>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.495511</td>\n",
       "      <td>R&amp;D Spend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.177766</td>\n",
       "      <td>Administration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.416797</td>\n",
       "      <td>Marketing Spend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.044052</td>\n",
       "      <td>State_California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.443578</td>\n",
       "      <td>State_Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.222969</td>\n",
       "      <td>State_New York</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VIF Factor          features\n",
       "0    2.495511         R&D Spend\n",
       "1    1.177766    Administration\n",
       "2    2.416797   Marketing Spend\n",
       "3    9.044052  State_California\n",
       "4    9.443578     State_Florida\n",
       "5    9.222969    State_New York"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6353458c",
   "metadata": {},
   "source": [
    "## Subset selection\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "ff9f86b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Without R and D\n",
    "a=data_1[['Administration','Marketing Spend','State_California','State_Florida','State_New York']]\n",
    "model2=sm.OLS(y,a).fit()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4e1869a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# without Marketing spend\n",
    "\n",
    "b=data_1[['R&D Spend','Administration','State_California','State_Florida','State_New York']]\n",
    "model3=sm.OLS(y,b).fit()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "357d36a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'model2_AIC:1163.6536705807039,model3_AIC:1062.6789358921965'"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking AIC of both model\n",
    "f'model2_AIC:{model2.aic},model3_AIC:{model3.aic}'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a98742f",
   "metadata": {},
   "source": [
    "### The model3 Where Marketing spend was not taken has less AIC value, Hence Marketing spend can be discarded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "eaaaa6fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Again changing X as\n",
    "x=b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a29f2671",
   "metadata": {},
   "source": [
    "## 1. Model without any Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "97d41843",
   "metadata": {},
   "outputs": [],
   "source": [
    "model3=sm.OLS(y,x).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ff8ebf6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.949</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.944</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   208.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 10 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>2.12e-28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:26:54</td>     <th>  Log-Likelihood:    </th> <td> -526.34</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1063.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th> <td>   1072.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R&D Spend</th>        <td>    0.8602</td> <td>    0.031</td> <td>   27.873</td> <td> 0.000</td> <td>    0.798</td> <td>    0.922</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>   <td>   -0.0525</td> <td>    0.050</td> <td>   -1.051</td> <td> 0.299</td> <td>   -0.153</td> <td>    0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_California</th> <td> 5.469e+04</td> <td> 6318.746</td> <td>    8.655</td> <td> 0.000</td> <td>  4.2e+04</td> <td> 6.74e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_Florida</th>    <td> 5.573e+04</td> <td> 6449.138</td> <td>    8.642</td> <td> 0.000</td> <td> 4.27e+04</td> <td> 6.87e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_New York</th>   <td>  5.46e+04</td> <td> 6392.954</td> <td>    8.540</td> <td> 0.000</td> <td> 4.17e+04</td> <td> 6.75e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>12.901</td> <th>  Durbin-Watson:     </th> <td>   1.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.002</td> <th>  Jarque-Bera (JB):  </th> <td>  16.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.898</td> <th>  Prob(JB):          </th> <td>0.000303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.134</td> <th>  Cond. No.          </th> <td>1.15e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.15e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.949\n",
       "Model:                            OLS   Adj. R-squared:                  0.944\n",
       "Method:                 Least Squares   F-statistic:                     208.1\n",
       "Date:                Tue, 10 Jan 2023   Prob (F-statistic):           2.12e-28\n",
       "Time:                        23:26:54   Log-Likelihood:                -526.34\n",
       "No. Observations:                  50   AIC:                             1063.\n",
       "Df Residuals:                      45   BIC:                             1072.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "R&D Spend            0.8602      0.031     27.873      0.000       0.798       0.922\n",
       "Administration      -0.0525      0.050     -1.051      0.299      -0.153       0.048\n",
       "State_California  5.469e+04   6318.746      8.655      0.000     4.2e+04    6.74e+04\n",
       "State_Florida     5.573e+04   6449.138      8.642      0.000    4.27e+04    6.87e+04\n",
       "State_New York     5.46e+04   6392.954      8.540      0.000    4.17e+04    6.75e+04\n",
       "==============================================================================\n",
       "Omnibus:                       12.901   Durbin-Watson:                   1.208\n",
       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               16.205\n",
       "Skew:                          -0.898   Prob(JB):                     0.000303\n",
       "Kurtosis:                       5.134   Cond. No.                     1.15e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.15e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b29a27",
   "metadata": {},
   "source": [
    "### Has 94.4% accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c2f8859",
   "metadata": {},
   "source": [
    "## 2. With LOG transformation on y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8b42f5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since in X \"0\" is there we cannot apply log transformation to that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "dabc06fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.773</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   38.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 10 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>5.78e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:26:55</td>     <th>  Log-Likelihood:    </th> <td>  5.7653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>  -1.531</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th> <td>   8.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R&D Spend</th>        <td>  8.64e-06</td> <td> 7.37e-07</td> <td>   11.718</td> <td> 0.000</td> <td> 7.15e-06</td> <td> 1.01e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>   <td> 3.403e-08</td> <td> 1.19e-06</td> <td>    0.028</td> <td> 0.977</td> <td>-2.37e-06</td> <td> 2.44e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_California</th> <td>   10.8650</td> <td>    0.151</td> <td>   71.978</td> <td> 0.000</td> <td>   10.561</td> <td>   11.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_Florida</th>    <td>   10.9370</td> <td>    0.154</td> <td>   70.990</td> <td> 0.000</td> <td>   10.627</td> <td>   11.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_New York</th>   <td>   10.9080</td> <td>    0.153</td> <td>   71.424</td> <td> 0.000</td> <td>   10.600</td> <td>   11.216</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>69.400</td> <th>  Durbin-Watson:     </th> <td>   0.528</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 722.777</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-3.590</td> <th>  Prob(JB):          </th> <td>1.12e-157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>20.187</td> <th>  Cond. No.          </th> <td>1.15e+06</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.15e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.773\n",
       "Model:                            OLS   Adj. R-squared:                  0.753\n",
       "Method:                 Least Squares   F-statistic:                     38.38\n",
       "Date:                Tue, 10 Jan 2023   Prob (F-statistic):           5.78e-14\n",
       "Time:                        23:26:55   Log-Likelihood:                 5.7653\n",
       "No. Observations:                  50   AIC:                            -1.531\n",
       "Df Residuals:                      45   BIC:                             8.030\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "R&D Spend          8.64e-06   7.37e-07     11.718      0.000    7.15e-06    1.01e-05\n",
       "Administration    3.403e-08   1.19e-06      0.028      0.977   -2.37e-06    2.44e-06\n",
       "State_California    10.8650      0.151     71.978      0.000      10.561      11.169\n",
       "State_Florida       10.9370      0.154     70.990      0.000      10.627      11.247\n",
       "State_New York      10.9080      0.153     71.424      0.000      10.600      11.216\n",
       "==============================================================================\n",
       "Omnibus:                       69.400   Durbin-Watson:                   0.528\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              722.777\n",
       "Skew:                          -3.590   Prob(JB):                    1.12e-157\n",
       "Kurtosis:                      20.187   Cond. No.                     1.15e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.15e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4=sm.OLS(np.log(y),x).fit()\n",
    "model4.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c8b4a4",
   "metadata": {},
   "source": [
    "### Has acuuracy 75.3%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3bdbe8d",
   "metadata": {},
   "source": [
    "## 3. With square root transformation to both x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "68282074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   154.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 10 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>1.16e-25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:26:56</td>     <th>  Log-Likelihood:    </th> <td> -211.63</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   433.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th> <td>   442.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R&D Spend</th>        <td>    0.5916</td> <td>    0.025</td> <td>   23.747</td> <td> 0.000</td> <td>    0.541</td> <td>    0.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>   <td>   -0.0409</td> <td>    0.061</td> <td>   -0.670</td> <td> 0.507</td> <td>   -0.164</td> <td>    0.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_California</th> <td>  192.3816</td> <td>   20.786</td> <td>    9.255</td> <td> 0.000</td> <td>  150.516</td> <td>  234.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_Florida</th>    <td>  195.0989</td> <td>   20.985</td> <td>    9.297</td> <td> 0.000</td> <td>  152.834</td> <td>  237.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_New York</th>   <td>  196.0499</td> <td>   20.829</td> <td>    9.413</td> <td> 0.000</td> <td>  154.099</td> <td>  238.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.585</td> <th>  Durbin-Watson:     </th> <td>   1.187</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.037</td> <th>  Jarque-Bera (JB):  </th> <td>   9.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.230</td> <th>  Prob(JB):          </th> <td> 0.00828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.095</td> <th>  Cond. No.          </th> <td>6.22e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 6.22e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.932\n",
       "Model:                            OLS   Adj. R-squared:                  0.926\n",
       "Method:                 Least Squares   F-statistic:                     154.4\n",
       "Date:                Tue, 10 Jan 2023   Prob (F-statistic):           1.16e-25\n",
       "Time:                        23:26:56   Log-Likelihood:                -211.63\n",
       "No. Observations:                  50   AIC:                             433.3\n",
       "Df Residuals:                      45   BIC:                             442.8\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "R&D Spend            0.5916      0.025     23.747      0.000       0.541       0.642\n",
       "Administration      -0.0409      0.061     -0.670      0.507      -0.164       0.082\n",
       "State_California   192.3816     20.786      9.255      0.000     150.516     234.247\n",
       "State_Florida      195.0989     20.985      9.297      0.000     152.834     237.364\n",
       "State_New York     196.0499     20.829      9.413      0.000     154.099     238.001\n",
       "==============================================================================\n",
       "Omnibus:                        6.585   Durbin-Watson:                   1.187\n",
       "Prob(Omnibus):                  0.037   Jarque-Bera (JB):                9.587\n",
       "Skew:                           0.230   Prob(JB):                      0.00828\n",
       "Kurtosis:                       5.095   Cond. No.                     6.22e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 6.22e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5=sm.OLS(np.sqrt(y),np.sqrt(x)).fit()\n",
    "model5.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7107885e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(1,1)\n",
    "plt.suptitle(\"X\")\n",
    "sn.distplot(x)\n",
    "sn.distplot(np.sqrt(x))\n",
    "labels=(\"Original\",\"Square root Transformed\")\n",
    "plt.legend(labels)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db520013",
   "metadata": {},
   "source": [
    "### Has Accuracy Around 92.6%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c94654",
   "metadata": {},
   "source": [
    "## 4. With Boxcox transformation to y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bc7a79cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   197.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 10 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>6.41e-28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:26:57</td>     <th>  Log-Likelihood:    </th> <td> -497.83</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   1006.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th> <td>   1015.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R&D Spend</th>        <td>    0.4735</td> <td>    0.017</td> <td>   27.135</td> <td> 0.000</td> <td>    0.438</td> <td>    0.509</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>   <td>   -0.0274</td> <td>    0.028</td> <td>   -0.967</td> <td> 0.338</td> <td>   -0.084</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_California</th> <td> 3.286e+04</td> <td> 3572.741</td> <td>    9.199</td> <td> 0.000</td> <td> 2.57e+04</td> <td> 4.01e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_Florida</th>    <td> 3.356e+04</td> <td> 3646.467</td> <td>    9.204</td> <td> 0.000</td> <td> 2.62e+04</td> <td> 4.09e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_New York</th>   <td> 3.289e+04</td> <td> 3614.700</td> <td>    9.099</td> <td> 0.000</td> <td> 2.56e+04</td> <td> 4.02e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>17.343</td> <th>  Durbin-Watson:     </th> <td>   1.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  26.994</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.081</td> <th>  Prob(JB):          </th> <td>1.37e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.878</td> <th>  Cond. No.          </th> <td>1.15e+06</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.15e+06. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.946\n",
       "Model:                            OLS   Adj. R-squared:                  0.941\n",
       "Method:                 Least Squares   F-statistic:                     197.5\n",
       "Date:                Tue, 10 Jan 2023   Prob (F-statistic):           6.41e-28\n",
       "Time:                        23:26:57   Log-Likelihood:                -497.83\n",
       "No. Observations:                  50   AIC:                             1006.\n",
       "Df Residuals:                      45   BIC:                             1015.\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "R&D Spend            0.4735      0.017     27.135      0.000       0.438       0.509\n",
       "Administration      -0.0274      0.028     -0.967      0.338      -0.084       0.030\n",
       "State_California  3.286e+04   3572.741      9.199      0.000    2.57e+04    4.01e+04\n",
       "State_Florida     3.356e+04   3646.467      9.204      0.000    2.62e+04    4.09e+04\n",
       "State_New York    3.289e+04   3614.700      9.099      0.000    2.56e+04    4.02e+04\n",
       "==============================================================================\n",
       "Omnibus:                       17.343   Durbin-Watson:                   1.158\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               26.994\n",
       "Skew:                          -1.081   Prob(JB):                     1.37e-06\n",
       "Kurtosis:                       5.878   Cond. No.                     1.15e+06\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.15e+06. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "bc_y=stats.boxcox(y)\n",
    "bc_y=list(bc_y)\n",
    "bc_y=bc_y[0]\n",
    "model6=sm.OLS(bc_y,x).fit()\n",
    "model6.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8247c6bf",
   "metadata": {},
   "source": [
    "### Has Accuracy 94.1%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00b90582",
   "metadata": {},
   "source": [
    "## 5. With Cuberoot Transformation both x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "fd1b4bc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.911</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.903</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   114.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 10 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>5.56e-23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:26:58</td>     <th>  Log-Likelihood:    </th> <td> -103.54</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   217.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th> <td>   226.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R&D Spend</th>        <td>    0.4615</td> <td>    0.023</td> <td>   20.367</td> <td> 0.000</td> <td>    0.416</td> <td>    0.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>   <td>   -0.0021</td> <td>    0.072</td> <td>   -0.030</td> <td> 0.977</td> <td>   -0.147</td> <td>    0.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_California</th> <td>   29.5460</td> <td>    3.467</td> <td>    8.521</td> <td> 0.000</td> <td>   22.562</td> <td>   36.530</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_Florida</th>    <td>   29.7912</td> <td>    3.488</td> <td>    8.541</td> <td> 0.000</td> <td>   22.766</td> <td>   36.816</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_New York</th>   <td>   29.9392</td> <td>    3.469</td> <td>    8.632</td> <td> 0.000</td> <td>   22.953</td> <td>   36.925</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.730</td> <th>  Durbin-Watson:     </th> <td>   1.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.008</td> <th>  Jarque-Bera (JB):  </th> <td>   9.274</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.877</td> <th>  Prob(JB):          </th> <td> 0.00969</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.172</td> <th>  Cond. No.          </th> <td>1.32e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.32e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.911\n",
       "Model:                            OLS   Adj. R-squared:                  0.903\n",
       "Method:                 Least Squares   F-statistic:                     114.5\n",
       "Date:                Tue, 10 Jan 2023   Prob (F-statistic):           5.56e-23\n",
       "Time:                        23:26:58   Log-Likelihood:                -103.54\n",
       "No. Observations:                  50   AIC:                             217.1\n",
       "Df Residuals:                      45   BIC:                             226.6\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "R&D Spend            0.4615      0.023     20.367      0.000       0.416       0.507\n",
       "Administration      -0.0021      0.072     -0.030      0.977      -0.147       0.143\n",
       "State_California    29.5460      3.467      8.521      0.000      22.562      36.530\n",
       "State_Florida       29.7912      3.488      8.541      0.000      22.766      36.816\n",
       "State_New York      29.9392      3.469      8.632      0.000      22.953      36.925\n",
       "==============================================================================\n",
       "Omnibus:                        9.730   Durbin-Watson:                   1.062\n",
       "Prob(Omnibus):                  0.008   Jarque-Bera (JB):                9.274\n",
       "Skew:                           0.877   Prob(JB):                      0.00969\n",
       "Kurtosis:                       4.172   Cond. No.                     1.32e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.32e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model7=sm.OLS(np.cbrt(y),np.cbrt(x)).fit()\n",
    "model7.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e681dc",
   "metadata": {},
   "source": [
    "### Has Accuray around 90.3%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "196a0bc2",
   "metadata": {},
   "source": [
    "## 6. With squareroot and Cuberoot Transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7503c91c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Profit</td>      <th>  R-squared:         </th> <td>   0.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   141.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 10 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>6.92e-25</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:26:59</td>     <th>  Log-Likelihood:    </th> <td> -98.651</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    50</td>      <th>  AIC:               </th> <td>   207.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    45</td>      <th>  BIC:               </th> <td>   216.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>R&D Spend</th>        <td>    0.0591</td> <td>    0.003</td> <td>   22.704</td> <td> 0.000</td> <td>    0.054</td> <td>    0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Administration</th>   <td>   -0.0038</td> <td>    0.006</td> <td>   -0.589</td> <td> 0.559</td> <td>   -0.017</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_California</th> <td>   33.6201</td> <td>    2.170</td> <td>   15.493</td> <td> 0.000</td> <td>   29.250</td> <td>   37.991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_Florida</th>    <td>   34.0067</td> <td>    2.191</td> <td>   15.523</td> <td> 0.000</td> <td>   29.594</td> <td>   38.419</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>State_New York</th>   <td>   34.0728</td> <td>    2.174</td> <td>   15.670</td> <td> 0.000</td> <td>   29.693</td> <td>   38.452</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>19.268</td> <th>  Durbin-Watson:     </th> <td>   1.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  72.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.709</td> <th>  Prob(JB):          </th> <td>1.70e-16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.731</td> <th>  Cond. No.          </th> <td>6.22e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 6.22e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 Profit   R-squared:                       0.926\n",
       "Model:                            OLS   Adj. R-squared:                  0.920\n",
       "Method:                 Least Squares   F-statistic:                     141.7\n",
       "Date:                Tue, 10 Jan 2023   Prob (F-statistic):           6.92e-25\n",
       "Time:                        23:26:59   Log-Likelihood:                -98.651\n",
       "No. Observations:                  50   AIC:                             207.3\n",
       "Df Residuals:                      45   BIC:                             216.9\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "R&D Spend            0.0591      0.003     22.704      0.000       0.054       0.064\n",
       "Administration      -0.0038      0.006     -0.589      0.559      -0.017       0.009\n",
       "State_California    33.6201      2.170     15.493      0.000      29.250      37.991\n",
       "State_Florida       34.0067      2.191     15.523      0.000      29.594      38.419\n",
       "State_New York      34.0728      2.174     15.670      0.000      29.693      38.452\n",
       "==============================================================================\n",
       "Omnibus:                       19.268   Durbin-Watson:                   1.201\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               72.625\n",
       "Skew:                          -0.709   Prob(JB):                     1.70e-16\n",
       "Kurtosis:                       8.731   Cond. No.                     6.22e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 6.22e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model8=sm.OLS(np.cbrt(y),np.sqrt(x)).fit()\n",
    "model8.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b440d5",
   "metadata": {},
   "source": [
    "### Has accuracy 92%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c34ce0",
   "metadata": {},
   "source": [
    "### SUMMARISING ACCURACY OF ALL MODELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "f9475d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "performance_matrix=pd.DataFrame() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "55d73fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sqr=[]\n",
    "adj_r=[]\n",
    "for i in [model3,model4,model5,model6,model7,model8]:\n",
    "    r=np.round(i.rsquared,4)*100\n",
    "    a_r=np.round(i.rsquared_adj,4)*100\n",
    "    rr=sqr.append(r)\n",
    "    arr=adj_r.append(a_r)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "da7e05fc",
   "metadata": {},
   "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_square_value(in%)</th>\n",
       "      <th>Adjusted_R_Square_value(in%)</th>\n",
       "      <th>Models</th>\n",
       "      <th>Transformations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>94.87</td>\n",
       "      <td>94.41</td>\n",
       "      <td>model3</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>77.33</td>\n",
       "      <td>75.32</td>\n",
       "      <td>model4</td>\n",
       "      <td>log(to Y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>93.21</td>\n",
       "      <td>92.60</td>\n",
       "      <td>model5</td>\n",
       "      <td>square root(to x and y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94.61</td>\n",
       "      <td>94.13</td>\n",
       "      <td>model6</td>\n",
       "      <td>boxcox(to y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>91.05</td>\n",
       "      <td>90.26</td>\n",
       "      <td>model7</td>\n",
       "      <td>Cuberoot(to x and y)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>92.64</td>\n",
       "      <td>91.99</td>\n",
       "      <td>model8</td>\n",
       "      <td>squareroot and cuberoot</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R_square_value(in%)  Adjusted_R_Square_value(in%)  Models  \\\n",
       "0                94.87                         94.41  model3   \n",
       "1                77.33                         75.32  model4   \n",
       "2                93.21                         92.60  model5   \n",
       "3                94.61                         94.13  model6   \n",
       "4                91.05                         90.26  model7   \n",
       "5                92.64                         91.99  model8   \n",
       "\n",
       "           Transformations  \n",
       "0                       No  \n",
       "1                log(to Y)  \n",
       "2  square root(to x and y)  \n",
       "3             boxcox(to y)  \n",
       "4     Cuberoot(to x and y)  \n",
       "5  squareroot and cuberoot  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_matrix[\"R_square_value(in%)\"]=sqr\n",
    "performance_matrix[\"Adjusted_R_Square_value(in%)\"] = adj_r\n",
    "performance_matrix[\"Models\"]=['model3','model4','model5','model6','model7','model8']\n",
    "performance_matrix['Transformations']=[\"No\",\"log(to Y)\",\"square root(to x and y)\",\"boxcox(to y)\",\"Cuberoot(to x and y)\",\"squareroot and cuberoot\"]\n",
    "performance_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c81e5f1",
   "metadata": {},
   "source": [
    "## Thus, the Model With no Transformation is more accurate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdb17936",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be3b164c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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