Salary_SLR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "935788b4",
   "metadata": {},
   "source": [
    "# Build a simple linear regression model predicting salary hike. Performe EDA and do necessary transformations and select the best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf55c88e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:49.500620Z",
     "start_time": "2022-10-23T10:08:47.185093Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf \n",
    "import statsmodels.api as sm \n",
    "from sklearn.metrics import mean_squared_error "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8f4ebbb6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:49.521241Z",
     "start_time": "2022-10-23T10:08:49.502206Z"
    }
   },
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience   Salary\n",
       "0              1.1  39343.0\n",
       "1              1.3  46205.0\n",
       "2              1.5  37731.0\n",
       "3              2.0  43525.0\n",
       "4              2.2  39891.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary= pd.read_csv(\"Salary_Data.csv\")\n",
    "salary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c59042",
   "metadata": {},
   "source": [
    "Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d334e16e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:49.540826Z",
     "start_time": "2022-10-23T10:08:49.521241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c11563",
   "metadata": {},
   "source": [
    "Checking for duplicate entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef433ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:49.556588Z",
     "start_time": "2022-10-23T10:08:49.540826Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d627475d",
   "metadata": {},
   "source": [
    "Glance at pattern and structure of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "993c3f3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:49.588267Z",
     "start_time": "2022-10-23T10:08:49.558238Z"
    }
   },
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.313333</td>\n",
       "      <td>76003.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>37731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.200000</td>\n",
       "      <td>56720.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>65237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.700000</td>\n",
       "      <td>100544.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.500000</td>\n",
       "      <td>122391.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       YearsExperience         Salary\n",
       "count        30.000000      30.000000\n",
       "mean          5.313333   76003.000000\n",
       "std           2.837888   27414.429785\n",
       "min           1.100000   37731.000000\n",
       "25%           3.200000   56720.750000\n",
       "50%           4.700000   65237.000000\n",
       "75%           7.700000  100544.750000\n",
       "max          10.500000  122391.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8a6d52a",
   "metadata": {},
   "source": [
    "Outlier detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e364da9c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:49.867263Z",
     "start_time": "2022-10-23T10:08:49.588267Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.hist(salary[\"YearsExperience\"])\n",
    " \n",
    "plt.subplot(1,2,2)\n",
    "plt.boxplot(salary[\"YearsExperience\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "807ffbef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:50.147473Z",
     "start_time": "2022-10-23T10:08:49.867263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.hist(salary[\"Salary\"])\n",
    " \n",
    "plt.subplot(1,2,2)\n",
    "plt.boxplot(salary[\"Salary\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd7c177",
   "metadata": {},
   "source": [
    "Checking co relation between input and target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa097ad8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:50.164303Z",
     "start_time": "2022-10-23T10:08:50.147473Z"
    }
   },
   "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>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YearsExperience</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>0.978242</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 YearsExperience    Salary\n",
       "YearsExperience         1.000000  0.978242\n",
       "Salary                  0.978242  1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc7bae18",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:50.255999Z",
     "start_time": "2022-10-23T10:08:50.167048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20aa7f01df0>"
      ]
     },
     "execution_count": 9,
     "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": [
    "plt.scatter(salary[\"YearsExperience\"], salary[\"Salary\"]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1b3513",
   "metadata": {},
   "source": [
    "Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "af4384ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:50.274977Z",
     "start_time": "2022-10-23T10:08:50.257676Z"
    }
   },
   "outputs": [],
   "source": [
    "model = smf.ols(\"Salary~YearsExperience\",data =salary).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "083263a7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:50.439493Z",
     "start_time": "2022-10-23T10:08:50.277451Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='YearsExperience', ylabel='Salary'>"
      ]
     },
     "execution_count": 11,
     "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": [
    "sns.regplot(x=\"YearsExperience\", y=\"Salary\", data=salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "455b38d5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:50.455473Z",
     "start_time": "2022-10-23T10:08:50.439493Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept          11.346940\n",
      "YearsExperience    24.950094\n",
      "dtype: float64 \n",
      " Intercept          5.511950e-12\n",
      "YearsExperience    1.143068e-20\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#t and p-Values\n",
    "print(model.tvalues, '\\n', model.pvalues)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb184d3f",
   "metadata": {},
   "source": [
    "H0 = Two variables are indep. ie B1 = 0\n",
    "\n",
    "H1 = Two variables are dependent. ie B1 != 0\n",
    "\n",
    "p value (1.143068e-20) < alpha so H1 accepted ie two variables are dependent, B1!=0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ec3de394",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:50.474648Z",
     "start_time": "2022-10-23T10:08:50.457587Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept          25792.200199\n",
       "YearsExperience     9449.962321\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.params \n",
    "#Y(salary)=B0(25792) + 9449(B1)*X(Years of exp.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f1c6a371",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:50.490295Z",
     "start_time": "2022-10-23T10:08:50.476655Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9569566641435086, 0.9554194021486339)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#R squared values\n",
    "(model.rsquared,model.rsquared_adj)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c63749",
   "metadata": {},
   "source": [
    "Lets perform some transformations on data to see whether we get improved r square value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3465515",
   "metadata": {},
   "source": [
    "## Transforming Years of Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bde0b26b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:51.537161Z",
     "start_time": "2022-10-23T10:08:50.490989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(salary[\"YearsExperience\"], kde=True) \n",
    "plt.title('No transformation')\n",
    "\n",
    "sns.displot(np.sqrt(salary[\"YearsExperience\"]), kde=True)\n",
    "plt.title('Square root transformation')\n",
    "\n",
    "sns.displot(np.cbrt(salary[\"YearsExperience\"]), kde=True)\n",
    "plt.title('Cube root transformation')\n",
    "\n",
    "sns.displot(np.log(salary[\"YearsExperience\"]), kde=True)\n",
    "plt.title('Log transformation')\n",
    "\n",
    "\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a874fb0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:51.933713Z",
     "start_time": "2022-10-23T10:08:51.540906Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Log transformation')"
      ]
     },
     "execution_count": 16,
     "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"
    },
    {
     "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": [
    "sm.qqplot(salary[\"YearsExperience\"], line = 'r')\n",
    "plt.title('No transformation')\n",
    "\n",
    "sm.qqplot(np.sqrt(salary[\"YearsExperience\"]), line = 'r')\n",
    "plt.title('Square root transformation')\n",
    "\n",
    "sm.qqplot(np.cbrt(salary[\"YearsExperience\"]), line = 'r')\n",
    "plt.title('Cube root transformation')\n",
    "\n",
    "sm.qqplot(np.log(salary[\"YearsExperience\"]), line = 'r')\n",
    "plt.title('Log transformation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9da521",
   "metadata": {},
   "source": [
    "## Transforming salary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "044600b6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:52.711890Z",
     "start_time": "2022-10-23T10:08:51.933713Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T57FznbBNLRzPDz64jF0nWvjyb17VTjkZE8IO4Audc8uAq4C/NLO3DJ7AzG4ys/Vmtr6mpuasGimvmIKZxeVtOAfr2lh/sIEFE3OYU5p9Vu9NqCxpVN+LS+YUU/fUXfzPq8fJrXxnqO97ecUU3++eSLg74Zxzx4K/1Wb2W2A58OygaW4HbgeorKw8q9WeI4cP8f3Hdr7Jav24efWcUw7v7Onj8ddOMGF8GhfPLopyVSPk+kf9fXfO8YdXj5O0+jN87h++SUlOxqjO/6Sh3neRaAptDdjMxptZ9sn7wGpga1jtJZqnd1bT0dPHFQtKEnK771DMjNXzSxifnsJDW47T1dPnuySR0IT5zS4BnjezzcBa4H+cc4+E2F7C2HWihV0nWjl/WgHF2eGsAcayjNRkrlpYSmtXL49vP6HtwZKwQtsE4ZzbBywOa/6JqrWrl6d3VFOak0HllHzf5XhTlpvJhTMLeW53LZsON7K0Yuy+F5K4xs5v2zjgnOOJ7Sfo7Xesnl9CUgx3LxkNS8vzmFY4nuf31FLV3Om7HJFRpwCOIVuPNXOwrp1VMwvJj+N+HkbLye3B49JSeGRrlTrtkYSjAI4RLZ09PLe7hvL8TBZNzvVdTszISE3migUlNHX08MyusztMUSRWKYBjgHOOp3fW4BxcPq8koU41Hg2T88dx3tR8XjvezC5dSUMSiAI4BuyubmV/bRsrZhSQk5nqu5yYdP60AkpzMnhyRzXN6sRdEoQC2LOkjCzW7KyhODudJZPzfJcTs5KTjCsWlOCc49FtVfTr0DRJAApgz/Lf+nE6e/u4fJ6OejidvHFpvHVOMceaOll3oN53OSJvmgLYo8P17WQtWs2yinyKstN9lxMX5pZmM6ckm1f213O8SRf1lPimAPakt6+fJ3dU09NwjAumTfBdTtwwM946t4js9MihaV29OlVZ4pcC2JP1Bxto6uih/tHb4uKS8rEkPSWZKxeW0tLVy9M7dWiaxC998z1o6uhh/cEGZhVn0Xlws+9y4lJZbibnT53AzqoWdhxv9l2OyFlRAHvw7K4akgwumqXrn70Z502dwMS8DJ7eWUNje7fvckTOmAI4yvbXtrGvto3l0yaQnaFjft+MpCTjigWlYPDothP09evQNIkvCuAo6u3r55ldNeSPS2VpuXr3Gg05GalcNreYquZOXtlf57sckTOiAI6iDYciO94umVNMso75HTWzS7KZX5bDugMNHGnQVZUlfiiAo6S5o4d1BxqYWZxFxYRxvstJOBfPLiJvXCqPbjtBp66iIXFCARwlz+6uwYC3aMdbKNJSkrhyQSnt3b08oatoSJxQAEfBgbo29tZox1vYSnIyWDmjkL01bWw7pkPTJPYpgEPW1+94ZmcNeeNSWabL6oRuWUUe5RMyeWZXDfVtOjRNYpsCOGSbjzTS2NHDW2YVacdbFJgZV8wvJTU5iUe2VtHbr6toSOxSAIeoo7uPtfvrmTJhHFMLtOMtWsanp3D5/GJqWrt4YbcOTZPYpQAO0cv76+ju6+eiWYW6ykWUTS/MYkl5HpuONLK7WlfRkNikAA5JXWsXW442cc7EXAqy1NWkD6tmFlKSk84Tr1XrVGWJSQrgkDy3p5bU5CQumF7gu5QxKznJuHphGWbw0NYqevu0PVhiiwI4BAdq2zhY18750yaQmZbsu5wxLSczldULSqhp6eLZ3bW+yxF5AwXwKOvrdzy3u5bczFQW6xpvMWF6YRbnVuSz5WgTO6u0PVhihwJ4lG092kR9ezcXzSrUYWcxZMWMAspyM3hi+wlqWrp8lyMCKIBHVWdPHy/vq2NyfibTC8f7LkcGSE4y3n5OGempSTz46jGSMnN8lySiAB5Naw/U09nbz1tmFemwsxg0Pj2Fa86ZSFt3H4Xv/HvtlBPvFMCjpLmjh1cPNzG/LEdXOI5hpbkZXDq3mMypi/nWQzt8lyNjnAJ4lLy8rw4MLpiuKxzHuvllOTSv/x13vrCf+zcc8V2OjGEK4FFQ09LF9qoWlpTnqbezONHw9J2snFHALQ9sYd2Bet/lyBilAB4FL+6tJT0licop6u0sbvT3cdsHlzE5P5NP3bOevTWtvitKOH39jsb2bg7VtXOorp3jTR0kZeaor+YBUsJuwMySgfXAUefcNWG3F21HGto5UNfOqpmFZKTqpIt4kj8+jbtvXM57/uMFbrxrHQ98biWFOm38jHX39vPa8WY2HmxgR1Uz+2vb2F/bRm3rn5/+Xf5XP+e2p/eSlZFCbmYqxdnplOVmUJaXSeYY/P6EHsDAXwPbgYQ77sc5x/N7aslKT2Hx5Fzf5chZqCgYxx0fq+T6H7/MJ+5ex72fuoCs9Gh8LeLbwbo2ntxezdM7q1m7v56u3sgRJYVZ6UwvGs/l80ooyckgNzOVnMxUDOjp6+emz32et33iK7R09NDY0cPGQw30OzBgUl4mM4uzmFmcxfgx8hmE+irNbDLwduCbwM1htuXDnppWTjR3cfm8YlKStTUnXi2tyOcH1y/jMz/bwKd+up67bjxPv2ZOoaali99vPsZv/3iErUcjVxyZUTSeD55fwXlTJ7CsIp/S3Ixh53H9hj+w6l++96fHPX39VDd3cai+nT01razZVcOzu2uYWZzF0vLTzy/ehf1v5lbg74DskNuJur5+x4t76igYn8a8soRbuR9z3ja/hO+9fxFf+tVmPv/zjfzww+eSqn+q9Pb18/hrJ/jV+sM8t7uWvn7HOZNy+ce3z2P1/FIq3mQ/16nJSUzKz2RSfiYrZhRQ39bN1qNNbDvWzK4TrUzKy2TVzMKEDeLQAtjMrgGqnXMbzOySYaa7CbgJoKKiIqxyRt22Y000dvTwjkVlJOmki4TwnqWTae3s5Z9+t40v/moTt/7FkpgM4fKKKRw5fCjUNpIysshadAXZy95OSm4xvc01tG17mtZtT3Gw7ggPhtTuhPFpvGV2ERdML2DbsSbWHWjgV+sPM6s4i1UzC8nJTKyjjEYUwGZ2oXPuhdMNG+RC4J1mdjWQAeSY2c+ccx8eOJFz7nbgdoDKysq42D3a3dvPK/vrmZibwTSdcpxQPrJiKh09fXzroR309Tn+3/VLSUuJrRA+cvgQ339sZyjzburoYf3BenYcb6G33zE5P5Ml5XlMK5xJ0ntWAl99U/O/efWcEU2XlpLE0op8FkzMZeOhBjYeauBAXRsrZxSyeHJuwpxpOtI14B8Ay0Yw7E+cc7cAtwAEa8B/Ozh849Wmw420d/dxzaKyhFkQ5HU3vWUGKUlJ/PODr/G5ezdw24eWkZ6S2NuEG9q6WXewnh1VLSRhzC3LZkl5nvejQtJSIn1qL5iYw5M7qnlmVw27T7SwekEpuQmwNjxsAJvZCmAlUGRmA3ei5QCJvUQOob27lw0HG5hRNJ6y3Ezf5UhIPr5qGmkpSfzjf2/lY3eu5UcfqUyIL/xgta1drDtQz64TraQkGUsm57FsSn7MHQmSnZHKuxZPZEdVC2t21fCLtYdYPb+E6UVZvkt7U073LqcBWcF0A3ekNQPXjrQR59waYM0Z1haT1h1ooKevn5UzCn2XIiH78AVTyEpP4cu/2cy1P3yRuz++nEl5ifFPt7q5k7UH6tlb00ZqsnHulHyWVeQxLi22gncgM2NeWQ4T8zJ5aMtx/vDqcSqn5LNiRkHc7ocZ9t12zj0DPGNmdzvnDkapppjV1NHDq0caWTAxhwnj03yXI1Hw7qWTKM5J59P/tYH33PYCP/rIuSytiN8zHo83dbB2fz0H6tpJS0li+bQJLCnPi6uTIHIzU3n/uZN5ZlcN6w820NDezRULSmNyh+npjLTidDO73cweM7OnTt5CrSwGvbSvjiQzzp+m67yNJStnFHL/Z1eSlpLEdT96if966UBcnU7rnONwfTsPbDzCfeuPUNXcyYrpBXz8wqmsmF4QV+F7UkpyEpfNK+Hi2UXsrWnjgY1Hae/u9V3WGRvp741fA/8J3AH0hVdO7Kpu6WRnVQuVU/LJyojdn2kSjtkl2Tz4hVXcfN9m/ul329hwsIH//e6FMd35knOO/XVtrD/QwPGmTsalJbNqZiHnTMqNuSM7ztaS8jyy0lN4ZFsV960/wnuXTSInhj+TwUaaJL3OuR+GWkmMe3FPHRnqcGdMyxuXxh0freS2p/fwf5/Yxdr99fzL+xZx8ewi36W9Qb9z7KluZd2Bempbu8nOSOGSOUUsKMtJyDM2ZxZn8b70Sfz3pmPcv+EI71s2OW6OFx7pp/EHM/ucmZWZ2YSTt1AriyGH6ts5WN/OedMmkB6HP9dk9CQlGV+4bBb3f3Yl49JT+Nida/nyrzfHxHXmunr72HiogXteOsjDW6vo7Xe8bV4JH1sxlcWT8xIyfE8qy83kvUsn0dXbz282HqGpo8d3SSMy0jXgjwV/vzxgmAOmj245scc5xwt7asnOSGHRJHW4IxFLK/J58AuruPWJ3dzx3D4e3lrFZy+ZwSdWTYt6PxL1bd1sPtLI9uPN9PQ5ynIzuHBGATOKs+L26ICzUZKTwXuXTuKBPx7l/o1HeP+5k2N6ExGMMICdc9PCLiRW7a5upbqli9XzSxJ6DULOXEZqMl+5ai7XVU7mXx7ewXcf3cndLx7ghpVT+dD5FeSNC+9ImfbuXnadaGX78WaqW7pINmN2SRaLy/MoyUnMfhNGojgng/csncQDG4/yu03HuPbcyTHdsdJIT0X+6KmGO+fuGd1yYktfv+PFvXUUZKUxpzTh+hOSUTK9KIsff7SSV/bVcduavXz30Z38+1N7uGZRGe9YPJGVMwpG5Z/30cYOnt5RTdG1/4s7nt+Pc1CUlc5FswqZU5I9ZrpwPJ2SnAyuWVTG7zYd4/ebj/GepZNi9hC1kX5i5w24nwFcBmwEEjqAtx5toqmjh3ctnjimfsrJ2Tl/egHnTy9gZ1ULP3l+Hw9tqeLXG44wYXwaq2YWUjk1n3On5DOjKOu0a2VtXb3sr21j27EmNh5sZMOhBvZUR67akVZQzrKKfOaWZns/VThWlU8YxxULS3hoSxUPb63imhjtNGukmyC+MPCxmeUC/xVKRTHiZIc7k/MymfImu9yTsWVOaTbfuXYx//yuhTyzq4aHtxznpX11/H7zsT9NU5ydzqT8TManpZCekoSZ0drVQ0tnLzUtXVQP2KmXNy6VpeV5XFc5mUvnFjOrJIdVIXXGk0hmFWdzyew+1uyq4YU9tVw0K7aOVoGz746yHZg1moXEmo2HGujo6ePCmYXqcEfOSkZqMlcsKOWKBaU45zjS0MHGQw0crGvncH07x5o66Ojpo7Gjm94+R05GKqU5Gcwvy2Fq4XimFY5nTmk20wvHaxk8S4vL86hv72bjoUYmjE9jwcTY2pE+0m3AfyBy1ANEOuGZB9wXVlG+tXX1svFQAzOLsxK2I2iJLjOjfMI4yifo11S0XTyriMb2Hp7aUU1eZhqT8mOnP4+RrgF/b8D9XuCgc+5ICPXEhHUH6untd6ycoVOOReJdUpJx9cJSfrX+MA9tPc71yytipre3Ee0aDDrl2UGkR7R84M8vd5ogGtu72XK0iYUTc8kP8TAiEYme9NRk3n5OGT19/Ty85Th9/bHRl8eIAtjMrgPWAu8HrgNeMbMRd0cZT17vcGfMnOgnMiYUZKVz2dwSjjV18sLeWt/lACPfBPFV4DznXDWAmRUBTwC/CaswH040d7LrRCvLp07QMZUiCWhOaTbHmzr446FGJuZmMrPYb4fuIz06Oelk+AbqzuC5ceOFvbVkpCaxbEqe71JEJCQXzSqiJCedJ7afoKXTb58RIw3RR8zsUTO7wcxuAP4HeCi8sqLvYF0bh+s7WD51QsJf/0tkLEtOMq5YUEpfv+Ox10547dt52AA2s5nB1Y+/DPwIWAQsBl4iuJJxInDO8cLeOnIyUjhncmwdJygioy9/XBoXzykKjs1u9FbH6daAbwVaAJxzDzjnbnbOfYnI2u+t4ZYWPTtPtFDT0sWKGQWkJCXclhUROYUFZTnMKBrPi3trqW7u9FLD6dJmqnPu1cEDnXPrgamhVBRlvf39vLS3jqKsdOaUqMMdkbHCzLhsXgmZack8sq2Knr7+qNdwugAe7jSw2Dmd5E3YerSZ5s5eLpxZoNM9RcaYzNRkVs8vpaG9h+d2R//QtNMF8Doz+9TggWb2CWBDOCVFT1dvH2v311Oen0mFThEVGZMqJoxjSXkeW442caShPaptn+5g1y8CvzWzD/F64FYCacB7QqwrKjYebFSHOyLCyhkF7K9t44nt1Xzo/Iqo9R88bCvOuRPOuZXAN4ADwe0bzrkVzrmq8MsLz8kOd2YXZ43pKwiICKQmJ3HZ3GKaOnp4aV9d1NodaX/ATwNPh1xLVL2yv55+51ihDndEhEgn7udMyuWPhxqZVZxFWW74u7nG5DFXDe3dbD3WxMJJuaFet0tE4suFMwvISk/hideq6e0P/6iIMRnAL+2tIyXJWD5VHe6IyOvSU5K5bG4x9e3drN1fH3p7Yy6Aq5o62V3dyrKKfHW4IyJ/ZmrheOaVZbPhYAO1rV2nf8KbMKYC2DnHC3tqyUxNZllFvu9yRCRGXTSziLTkJJ7eWR1qXxFjKoAP1rdzpLGD86dNIC1lTL10ETkDmWnJXDirkGONnWw/3hJaO2MmhU6u/eZmprJwkjrcEZHhLSjLoSw3g+f31NLR0xdKG2MmgHdUtVDb2s3KGQUkJ+mkCxEZnpnx1jnFdPb28eKecE5TDi2AzSzDzNaa2WYz22Zm3wirrdPp7e/npX11FGenM8tzD/giEj+KstNZUp7H1mPNpE2cO+rzD3MNuAu41Dm3GFgCXGlmF4TY3pBePdJES2evTjkWkTN2wbTIscEFqz9H7yj3mBZaALuI1uBhanCLetfznT2RDnemTBinDndE5IylpSTxltmFpBZPHfVjg0PdBmxmyWa2CagGHnfOvXKKaW4ys/Vmtr6mpmbUa1h/sIGu3n4unFk46vMWkbFhZlEWx+74HCtHOUdCDWDnXJ9zbgkwGVhuZgtPMc3tzrlK51xlUVHRqLbf0tnDpsONzCvNpig7fVTnLSJjh5nRW39k1OcblaMgnHONwBrgymi0d9LJXo0uUIc7IhKDwjwKosjM8oL7mcDlwI6w2hustrWL7cdbWDw5l5yM1Gg1KyIyYmF2hlAG/NTMkokE/X3OuQdDbO8Nnt9TS3pKEuepwx0RiVGhBXBwMc+lYc1/OIfr2zlY186qmYVkpCb7KEFE5LQS7kw45xzP76klOyOFxZN1yrGIxK6EC+Dd1a1Ut3SxYnoBKVG6rpOIyNlIqITq63e8uLeOwqw05pRm+y5HRGRYCRXAW4420dTRw6qZhSTplGMRiXEJE8BdvZFTjsvzM3XKsYjEhYQJ4A0HG+jo6VOHOyISNxIigJOzCvjjoUbmlGRTkpPhuxwRkRFJiADOXfVBnIMVOuVYROJI3Afw7hMtZJ1zOedMziU3U6cci0j8iPsA/vYjO3A9nSzXKcciEmfiOoCPNXbw/J5aml76NZlpOuVYROJLXAfwxLxM1vztW2nZ8AffpYiInLG4DmCA0twMXG+X7zJERM5Y3AewiEi8UgCLiHiiABYR8UQBLCLiiQJYRMQTBbCIiCcKYBERTxTAIiKeKIBFRDxRAIuIeKIAFhHxRAEsIuKJAlhExBMFsIiIJwpgERFPFMAiIp4ogEVEPFEAi4h4ogAWEfFEASwi4kloAWxm5Wb2tJltN7NtZvbXYbUlIhKPUkKcdy/wN865jWaWDWwws8edc6+F2KaISNwIbQ3YOXfcObcxuN8CbAcmhdWeiEi8CXMN+E/MbCqwFHjlFONuAm4CqKioiEY5ImBJmJnvKmSMCz2AzSwLuB/4onOuefB459ztwO0AlZWVLux6RABw/Xz/sZ2+qzgrN6+e47sEGSWhHgVhZqlEwvde59wDYbYlIhJvwjwKwoCfANudc98Pqx0RkXgV5hrwhcBHgEvNbFNwuzrE9kRE4kpo24Cdc88D2sshIjIEnQknIuKJAlhExBMFsIiIJwpgERFPFMAiIp4ogEVEPFEAi4h4ogAWEfFEASwi4okCWETEEwWwiIgnCmAREU8UwCIiniiARUQ8UQCLiHiiABYR8UQBLCLiiQJYRMQTBbCIiCcKYBERTxTAIiKeKIBFRDxRAIuIeKIAFhHxRAEsIuKJAlhExBMFsIiIJwpgERFPFMAiIp4ogEVEPFEAi4h4ogAWEfFEASwi4kloAWxmd5pZtZltDasNEZF4FuYa8N3AlSHOX0QkroUWwM65Z4H6sOYvIhLvvG8DNrObzGy9ma2vqanxXY6ISNR4D2Dn3O3OuUrnXGVRUZHvckREosZ7AIuIjFUKYBERT8I8DO0XwEvAHDM7YmafCKstEZF4lBLWjJ1z14c1bxGRRKBNECIiniiARUQ8UQCLiHiiABYR8UQBLCLiiQJYRMQTBbCIiCcKYBERTxTAIiKeKIBFRDxRAIuIeKIAFhHxRAEsIuKJAlhExBMFsIiIJwpgERFPFMAiIp4ogEVEPFEAi4h4ogAWEfFEASwi4okCWETEEwWwiIgnCmAREU8UwCIiniiARUQ8UQCLiHiiABYR8UQBLCLiiQJYRMQTBbCIiCcKYBERTxTAIiKehBrAZnalme00sz1m9pUw2xIRiTehBbCZJQO3AVcB84HrzWx+WO2JiMSbMNeAlwN7nHP7nHPdwC+Bd4XYnohIXDHnXDgzNrsWuNI598ng8UeA851znx803U3ATcHDOcDOUAqKKARqQ5z/2Yi1mlTP6cVaTarn9HzXVOucu3LwwJQQG7RTDPuztHfO3Q7cHmIdf2Jm651zldFoa6RirSbVc3qxVpPqOb1YrAnC3QRxBCgf8HgycCzE9kRE4kqYAbwOmGVm08wsDfgA8PsQ2xMRiSuhbYJwzvWa2eeBR4Fk4E7n3Law2huhqGzqOEOxVpPqOb1Yq0n1nF4s1hTeTjgRERmezoQTEfFEASwi4knCBLCZlZvZ02a23cy2mdlfB8MnmNnjZrY7+Js/4Dm3BKdJ7zSzK6JY03fNbIeZvWpmvzWzvGjUNFQ9A8b/rZk5Myv0XY+ZfSFoc5uZfSca9QxXk5ktMbOXzWyTma03s+XRqMnMMsxsrZltDur5RjDcy3I9TD1elunhahowPqrL9RlxziXEDSgDlgX3s4FdRE6B/g7wlWD4V4BvB/fnA5uBdGAasBdIjlJNq4GUYPi3o1XTUPUEj8uJ7DA9CBT6rAd4K/AEkB6MK46Bz+wx4Kpg+NXAmii9RwZkBfdTgVeAC3wt18PU42WZHq4mX8v1mdwSZg3YOXfcObcxuN8CbAcmETn9+afBZD8F3h3cfxfwS+dcl3NuP7CHyOnTodfknHvMOdcbTPYykWOkQ69pmPcI4P8Cf8cbT5bxVc9ngX91znUF46qjUc9panJATjBZLq8f0x72e+Scc63Bw9Tg5vC0XA9Vj69leriagsdRX67PRMIE8EBmNhVYSuQ/YYlz7jhEvlxAcTDZJODwgKcd4fUwCrumgT4OPBztmgbWY2bvBI465zYPmsxLPcBs4CIze8XMnjGz86Jdzylq+iLwXTM7DHwPuCVaNZlZspltAqqBx51zXpfrIeoZKOrL9KlqioXl+nQSLoDNLAu4H/iic655uElPMSyUY/KGqsnMvgr0AvdGs6aB9QTtfxX42qkmjXY9wfuTAuQT+Wn7ZeA+M7No1TNETZ8FvuScKwe+BPzk5KRh1+Sc63POLSGyVrnczBYOM7nXenwt06eoaRGel+uRSKgANrNUIl+ae51zDwSDT5hZWTC+jMh/SIjSqdJD1ISZfQy4BviQCzZMRaOmU9Qzg8h2sM1mdiBoc6OZlXqqh6DdB4KflmuBfiKdqfj8zD4GnLz/a17/yRq1U+6dc43AGuBKPC/Xp6jH2zI9RE3vwuNyPWI+NjyHcSPyX+0e4NZBw7/LG3dWfCe4v4A3bojfRzg7B05V05XAa0DRoOGh1jRUPYOmOcDrOyu81AN8Bvjn4P5sIj8XzfNnth24JLh/GbAhSu9REZAX3M8EniMScl6W62Hq8bJMD1eTr+X6jGr30WgoLwRWEfkZ8SqwKbhdDRQATwK7g78TBjznq0T2gO4k2MMdpZr2BKFycth/RqOmoeoZakH1VQ+QBvwM2ApsBC6Ngc9sFbAh+OK+ApwbpfdoEfDHoJ6twNeC4V6W62Hq8bJMD1eTr+X6TG46FVlExJOE2gYsIhJPFMAiIp4ogEVEPFEAi4h4ogAWEfFEASxxzcy+GvSA9WrQU9n5w0x7t0Wu1i0SE8K8KrJIqMxsBZGTAJY557qC7gbTRnH+Ke71DmZERp3WgCWelQG17vVe02qdc8fM7Gtmts7MtprZ7UE/Em8w1DRmtsbMvmVmzwBfNbP9wanJmFmOmR04+VjkzVIASzx7DCg3s11m9h9mdnEw/N+dc+c55xYSOTX1mlM8d7hp8pxzFzvnvkGkX4G3B8M/ANzvnOsJ5dXImKMAlrjlIn3AngvcBNQAvzKzG4C3Bl1ZbgEuJXLu/2DDTfOrAffvAG4M7t8I3DW6r0LGMm0DlrjmnOsjspa6JgjTTxPpG6DSOXfYzL4OZAx8jpllAP8xzDRtA+b/gplNDdauk51zW8N8PTK2aA1Y4paZzTGzWQMGLSHSuQpAbdCn76mOesgYwTQD3QP8Aq39yijTGrDEsyzgB8EFIHuJ9Mh1E9AIbCHSA9a6wU9yzjWa2Y+Hm2aQe4H/QySERUaNekMTOY3g2OF3Oec+4rsWSSxaAxYZhpn9ALiKSJ/AIqNKa8AiIp5oJ5yIiCcKYBERTxTAIiKeKIBFRDxRAIuIePL/ATnQ63bQpnIGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(salary[\"Salary\"], kde=True) \n",
    "plt.title('No transformation')\n",
    "\n",
    "sns.displot(np.sqrt(salary[\"Salary\"]), kde=True)\n",
    "plt.title('Square root transformation')\n",
    "\n",
    "sns.displot(np.cbrt(salary[\"Salary\"]), kde=True)\n",
    "plt.title('Cube root transformation')\n",
    "\n",
    "sns.displot(np.log(salary[\"Salary\"]), kde=True)\n",
    "plt.title('Log transformation')\n",
    "\n",
    "\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "914b21cd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:53.212527Z",
     "start_time": "2022-10-23T10:08:52.711890Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Log transformation')"
      ]
     },
     "execution_count": 18,
     "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"
    },
    {
     "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": [
    "sm.qqplot(salary[\"Salary\"], line = 'r')\n",
    "plt.title('No transformation')\n",
    "\n",
    "sm.qqplot(np.sqrt(salary[\"Salary\"]), line = 'r')\n",
    "plt.title('Square root transformation')\n",
    "\n",
    "sm.qqplot(np.cbrt(salary[\"Salary\"]), line = 'r')\n",
    "plt.title('Cube root transformation')\n",
    "\n",
    "sm.qqplot(np.log(salary[\"Salary\"]), line = 'r')\n",
    "plt.title('Log transformation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "690aa834",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:53.239859Z",
     "start_time": "2022-10-23T10:08:53.214057Z"
    }
   },
   "outputs": [],
   "source": [
    "model_sqrt = smf.ols(\"np.sqrt(Salary)~np.sqrt(YearsExperience)\",data =salary).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a8a19940",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:53.255854Z",
     "start_time": "2022-10-23T10:08:53.242848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9419490138976826, 0.9398757643940284)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(model_sqrt.rsquared, model_sqrt.rsquared_adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2ce70c4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:53.290096Z",
     "start_time": "2022-10-23T10:08:53.255854Z"
    }
   },
   "outputs": [],
   "source": [
    "model_cbrt = smf.ols(\"np.cbrt(Salary)~np.cbrt(YearsExperience)\",data =salary).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d76b5b65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:53.306213Z",
     "start_time": "2022-10-23T10:08:53.290681Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9324434036761005, 0.9300306680931041)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(model_cbrt.rsquared, model_cbrt.rsquared_adj) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6af55e0a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:53.339804Z",
     "start_time": "2022-10-23T10:08:53.306213Z"
    }
   },
   "outputs": [],
   "source": [
    "model_log = smf.ols(\"np.log(Salary)~np.log(YearsExperience)\",data =salary).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9d15153c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:53.371975Z",
     "start_time": "2022-10-23T10:08:53.341530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.905215072581715, 0.9018298966024905)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(model_log.rsquared, model_log.rsquared_adj) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bda3992",
   "metadata": {},
   "source": [
    "Among all the transformations, Sqrt transformation has highest R square adj value, but not higher than model with No transformation\n",
    "\n",
    "Model with no transformation has highest r square adj value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29f7c9f",
   "metadata": {},
   "source": [
    "## Residual Analysis  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ca6909",
   "metadata": {},
   "source": [
    "### Test for Normality of Residuals (Q-Q Plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "abc864ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:53.924092Z",
     "start_time": "2022-10-23T10:08:53.374004Z"
    }
   },
   "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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Dk0rNfwxoGW2rUXHWT/g9bK2Slm1VQnSWP4mQBB5Lcd1LCAfuDu6+MMEyQwkX+2+oaKwihcBCRw5Pu/uj2Y5Fqo6s36RYmdx9UVTFcaaZ1YkuCCe77j/NbC2haXXcxCYiItlXpRIbgLvPIBR3t2bdeA0HREQkh1SpqkgRESl8ed+7v4iISKyCq4ps2LChN23aNNthiIjklalTpy5z93I7B8gHBZfYmjZtypQpU7IdhohIXjGzpHsKynWqihQRkYKixCYiIgVFiU1ERAqKEpuIiBQUJTYRESkoBdcqUkRENjdq+mIGjZ3LkqJiGtWvTc9OLejctqJDy+UuJTYRkQI2avpi+rwwg+K1YUSbxUXF9Hkh9CpYqMlNVZEiIgVs0Ni5G5NaieK16xk0dm6WIso8JTYRkQK2pKg4pemFQIlNRKSANapfO6XphUCJTUSkgPXs1ILaNaptNq12jWr07JTsQO35R41HREQKWEkDEbWKFBGRgtG5beOCTmSlqSpSREQKihKbiIgUFCU2EREpKEpsIiJSUJTYRESkoCixiYhIQVFiExGpKn74IdsRVAolNhGRQvf113DppdCiBRQVZTuajFNiExEpVKtXw1//CvvtB08+Cb/7HWxT+Id99TwiIlJo3GH0aLj2Wpg3D047De66KyS4KqDwU7eISFUyYwaccAJ06QI1a8LYsfDii1UmqYESm4hIYVi2DK64Atq0gWnT4P774aOP4MQTsx1ZpVNVpIhIPlu7Fh54AG65BVasCMmtXz/YaadsR5Y1OVFiM7M9zewNM5ttZrPMrFs0vYGZjTOzz6L/O2Y7VhGRnPHKK3DQQdC9Oxx2WCih3X9/lU5qkCOJDVgHXOPuBwBHAH82s5ZAb2C8uzcHxkfPRUSqtjlz4JRTwt+GDeEa2quvQqtW2Y4sJ+REYnP3r9x9WvR4BTAbaAycCTwRLfYE0DkrAYqI5ILly+Hqq0Mp7d13Q0vHmTNDq0ezbEeXM3LuGpuZNQXaAh8Au7r7VxCSn5ntkmCdy4HLAZo0aVJJkYqIVJJ162DIELjpJvj+e7jsMrjtNtgl7iGxysuJElsJM6sDPA9c7e4/Jrueuw9x93bu3m7nnXfOXIAiIpVt/Hho2xb+/OdQUps+HR5+WEmtDDmT2MysBiGpDXP3F6LJS81s92j+7sA32YpPRKRSff45dO4Mxx8Pq1bB88/DhAnQunW2I8t5OZHYzMyAx4DZ7n53zKwxwIXR4wuB0ZUdm4hIpfrxR+jVKzQEef116N8fPvkEunbVdbQk5co1tg7A+cAMM/swmtYXGACMNLNLgYXA2dkJT0Qkw9avh6FD4frrYelSuOiikNR23z3bkeWdnEhs7j4RSHQqclxlxiIiUuneeQe6dQvXz9q3h5degnbtsh1V3sqJqkgRkSppwQL4zW/g6KPh229hxAiYOFFJrYJyosQmIlJVjJq+mPtf/JDOrz7F5f/9N9Wqb0P1fv2gZ0/Ybrtsh1cQlNhERCrJqKlf8t5t9zNs/GPstvJ7Rh9wDPcefyndzvgVnZXU0kaJTUQkCaOmL2bQ2LksKSqmUf3a9OzUgs5tGyf/Ah98QPOzL6bzl7P5aLfmXHFmH6btcQAAg8bOTe21pExKbCIi5Rg1fTF9XphB8dr1ACwuKqbPCzMAyk9IixdD797w9NM0rNOAa07pzgsHHovbpiYOS4qKMxZ7VaTEJiJSjkFj525MaiWK164vu6RVXAyDB8OAAaEpf9++/Hbb9nweJ4c1ql87A1FXXWoVKSJSjkQlqrjT3WHkSDjggNC348knw+zZcMcdXHlGG2rXqLbZ4rVrVKNnpxaZCLvKUmITESlHohLVFtOnTYNjjglN+OvXhzfegOeeg2bNgFBteWfXg2hcvzYGNK5fmzu7HqTra2mmqkgRkXL07NRis2tsUKqk9fXXoceQxx+Hhg1DJ8WXXgrVqm3xWp3bNlYiyzAlNhGRcpQkoi1aRbZsCH/9K9x+O/z8M/ToATfeCPXqZTniqk2JTUSqjIo02d+spOUOo0fD2dfCvHlw+ulh0M/mzTMYvSRL19hEpEooabK/uKgYZ1OT/VHTF6f2QjNmwAknQJcuULMmjB0LY8YoqeUQJTYRyUujpi+mw4AJNOv9HzoMmFBugiqryX5Sli2DK66ANm1CZ8X33w8ffQQnnriVn0AyRVWRIpJ3tuaG6ZSa7MdauxYeeABuuQVWrAgjWffrBw0abHX8klkqsYlI3tma0lfSTfZjvfIKHHQQdO8Ohx0GH38M992npJbjlNhEJO9sTemrZ6cWyd8cPWcOnHJK+NuwAV58EV59FVq2rFDcUjmU2EQk72xN6Supm6OXL4errw6ltPfeCy0dZ86E004DSzQWsuQaXWMTkbxT7g3TCSS8OXrdOnjkkXAP2vLlcNllcNttsPPO6Q5dKoESm4jknYQ3TG9Njx7jx4dS2syZ0LEj3HsvtG6dznClkimxiUheqnDXVPPmwbXXwqhRoS/H558P96apyjHv6RqbiFQtP/4IvXqFhiCvvw79+8Mnn0DXrkpqBUIlNhGpGtavhyeegL59YelSuOiikNR23z3bkUmaKbGJSOF7551wHW3aNGjfHl56Cdq1y3ZUkiGqihSRwrVgQRgb7eij4ZtvYMQImDhRSa3AqcQmIoVn1SoYMAAGDw7Xzfr1g549Ybvtsh2ZVAIlNhEpHBs2wPDhoXHIkiVw3nkhwe25Z7Yjk0qkqkgRKQwffBCun51/PjRqBO++C8OGKalVQUpsIpLfFi8OyeyII2DhQhg6dFOSkypJVZEikp+Ki0NfjnfeGZry9+0LffpAnTrZjkyyTIlNRHLKqOmLy+4qyx2efRauuy60ejzrLBg0KPQeIkKKic3M9gEWuftqM+sIHAw86e5F6Q9NRApJuQmLJAYQnTYt3I/2zjuhP8ehQ0P/jiIxUr3G9jyw3sz2BR4DmgHD0x6ViBSUkoS1uKgYZ1PCGjV98WbLJRpA9LHn3odLLw33n82ZAw8/DFOnKqlJXKkmtg3uvg7oAtzr7t2BCvdHY2b/NLNvzGxmzLQGZjbOzD6L/u9Y0fcRkexIdsTr0gOFbrtuLX/44DmGD74AnnwSevSAzz6Dyy+HapsPGipSItXEttbMzgUuBF6KptVIQxxDgZNKTesNjHf35sD46LmI5KFkR7zeOFCoOyd++j6vPXYFfd4cykf7tIFZs8IN1/XqZThayXepJraLgSOBO9z9CzNrBjxd0SDc/W3g+1KTzwSeiB4/AXSu6PuISHYkO+J1z04tOHj5Qp5+5gaG/PsO1lSrwaXn3s6yYc/CfvtVRqhSAFJKbO7+CdALmBY9/8LdB2QiMGBXd/8qep+vgF0SLWhml5vZFDOb8u2332YoHBHZWj07taB2jc2rDrcY8XrZMjo/cgejH72Sg7/5H/2O/wO/7/4op/e8qGLjrkmVk2qryNOBwcC2QDMzawPc6u5nZCC2pLn7EGAIQLt27TybsYjIlsoc8XrtWnjgAbjlFlixArviCnbo149+O+1Ev+yGLXkq1fvY+gGHAW8CuPuHUXVkJiw1s93d/Ssz2x34JkPvIyKVIO6I16+8At27w9y5cOKJcPfd0KpVdgKUgpHqNbZ17v5DqWmZKiGNITRSIfo/OkPvIyKVbc4cOOWU8LdhA7z4Irz6qpKapEWqJbaZZnYeUM3MmgN/Ad6raBBmNgLoCDQ0s0XAzcAAYKSZXQosBM6u6PuISGYkc/M1AMuXhyrHBx4IQ8gMHgxXXQXbblv5QUvBSjWxXQVcD6wGRgBjgdsqGoS7n5tg1nEVfW0RyaxyewsBWLcOHnkEbrwRvv8eLrsMbrsNdknYJkxkq6XaKvInd7/e3Q9193bR458zFZyI5L5yb74ePx7atoUrroADDwzdYj38sJKaZExSJTYze5EyrqVlu1WkiGRPopuva3wxDzrfB6NHQ9Om8Nxz0LVrGNFaJIOSrYocnNEoRCRvNapfm8Uxya3O6p+48v1nuGTKGKi1LfTvH1o+1qqVxSilKkkqsbn7W5kORETyU89OLejzwgxWr17D/80YT893nmTnVUUsPP1smjx0bxjNWqQSJVsVOdLdf21mM4hTJenuB6c9MhHJC53bNmanaR+w80292X/JZ3y8VytmPfQ0HX93arZDkyoq2arIbtH/0zIViIjkoQUL4LrrOGrkSNhjDxg2jIPPPVfX0SSrkmoVWdJnI3CFuy+I/QOuyFx4IpKTVq0KTff33z/cXH3zzaH3kPPOU1KTrEu155ET4kw7OR2BiEge2LABnn469LR/++3QpUtIaP36hRuuRXJAstfY/kQome1tZh/HzKoLvJuJwEQkx3zwAXTrFv63awfPPgvt22c7KpEtJHuNbTjwCnAnmw/4ucLdS4+jJiIF5NWxU/A+fTl5+jiW1W3Aglvu4ZAb/gLbpFrhI1I5km3u/wPwA3CumVUDdo3WrWNmddx9YQZjFJEMStjPY3Exn1x7M0c/ch/VNmzggSPO5sEjzmbD2rrc+dFXGiNNcpa5J985v5ldSRi6ZimwIZrsudTcv127dj5lypRshyGSF0r38whQu/o2PFF3AYf9YwAsWMDL+7Wn/7GXsKj+bhuXaVy/Nu/2/lU2QpYMMbOp7t4u23GkQ6qdIF8NtHD37zIQi4hUstL9PLb6+nNuGv8Ihy2aBa1bc077PzCpyZbnrYm60RLJBalWkn9JqJIUkQJQkqB2XrmcgS//jRef6M4+3y+ib6crYepUvjz48LjrNapfuzLDFElJqiW2/wFvmtl/CEPXAODud6c1KhGpFHvVqUan8c9w5XvPUGvdGh49tDP3dziHHXZtCNWqbewua7OqyhphukiuSjWxLYz+to3+RCQPbNFA5MT96LxwCi89fDV1Fi1g3L6H0f/YS/miQePNEldJA5GkBhEVyREpJTZ3vyVTgYhIZpRuIFLns9ns+vA1MP9D6rRsybsPDKPfj7uxpKiYxnESV+e2jZXIJK+klNjMbGfgOqAVsHEMCndX8yiRHFXSQGTHn36gx8RhnPfhq6youR13n34VPV64mw7Vq6uXBSkoqVZFDgOeIXSG/EfgQuDbdAclIqlJeC8a8M13K7h4+n+4euJwtl9TzFNtT+HeX57HD7V3oEf1VA8BIrkv1b16J3d/zMy6RWO0vWVmGqtNJItKVzUuLiqmzwszAOj89ceMe+IvNP12IW83bcttv/o9n+28FxDuRRMpRKkmtrXR/6/M7FRgCbBHekMSkVSUvhcNoNHX89n1nJvh08k0bNKMP/2mH6/sdcjGnvfVslEKWaqJ7XYzqwdcA9wP7AB0T3tUIpK02Juld/h5Jd3eHcEF016iuHpNuOsu6lx5JZ1mfcvHatkoVUSqrSJfih7+AByb/nBEJFWN6tfm6+9Xcs5HY+nxztPsWLyCf7XuxLDTfs9/epwFqGWjVC2ptop8HNiic0l3vyRtEYlIXIkaiAzccRk739OLFt/M5/0mB3HrcZcxv3Fz7ux6ULZDFsmKVKsiX4p5XAvoQrjOJiJpkCh5xWsg8uCjYzls9kh++cZYVjVuQt/f9mNE40NotON23KmqRqnCUq2KfD72uZmNAF5Pa0QiVVRZrRtjG4jUWf0TV77/DBdPGc36atWhf3+2796d/rVq0T9r0YvkjorexNIcaJKOQESqunitG4vXrt9Ygttmw3rOmjme695+kp1XFfHsgccz6JgLmNzn/CxFLJKbUr3GtoJwjc2i/18DvTIQl0iVk2gomCVFxZy8/DP+NPrvHLR0HlMb7c+lZ93Ex7vvp3vRROJItSqybqYCEanqGtWvzeJSya3xD99w67tPctyMN/mqbkP+cnpPxhxwNJjpXjSRBJJObGZWG/gt0DKaNAV4zt3XZCIwkaomdoiY2mt+5k+TnuXy//6b6tW3gZtvZsqJv2Pq2wsx3YsmUqakEpuZHQS8CLwFTCVURXYCupvZCcC17n5DxqIUKSCJWj52btsYNmzgo78+yOUvP8LuK7/jy5POZM8h98Oee3I6cHr7fbMdvkjOS7bEdh9wmbuPi51oZscDM4FZ6Q5MpBCV2a/jmkV07taNzh98AO3awd/GsGf79tkMVyQvbZPkcruXTmoA7v46of/ILmmNKoaZnWRmc83sczPrnan3EakM8Vo+7vD9UmpdchEccQQsXAhDh8IHH4CSmshWSbbEto2Z1XT31bETzawWsNbdf0p/aGBm1YAHgBOARcB/zWyMu3+SifcTybTYlo81167m8skv8KcPnqPahg3Qty/06QN16mQxQpH8l2yJ7UngeTNrWjIhejwSeCr9YW10GPC5u/8vaqTyL+DMDL6fSEY1ql8b3Dl19juMf/RPXDNxGG82O4Tf9ngc7rhDSU0kDZIqsbn77WZ2JfC2mW0XTV4FDHb3+zMWHTQGvox5vgg4vPRCZnY5cDlAkya6X1xy1+1N1lD3wT60+3Imn+zSjHNO7c9H+7RVv44iaZR0c393/zvwdzOrGz1fkbGoNrF4oWwxwX0IMASgXbt2W8wXybqvv4brr+fYxx9ndf0GDOzSnSH7dmS3BnXUr6NImqXcpVYlJbQSi4A9Y57vgTpdlnyyejX87W9w++3w88/Qowc1b7yRXvXqqcsekQypaF+RmfZfoLmZNQMWA+cA52U3JJHyjZq2iPfvfZw/vfgPmhZ9xVdHn8Dujz4AzZtnOzSRgpds45GscPd1wJXAWGA2MNLddc+c5LQJI19n17NOZ+BTN7GmWg3O//Wt/OroHoxauV35K4tIhaXaCfJ2wDVAE3e/zMyaAy1iRtZOO3d/GXg5U68vVVuiXkCSmV963vWH7cwpz/2DYx56mBU1t+Om4//A8DYns65adYh66de1NJHMS7Uq8nFCl1pHRs8XAc+y+QCkIjkllcE7N/YCUs58YOO86uvX0Wncv+hwywg2rC3m6bancM8vz6Oo9g6bxZGo934RSa9UE9s+7v4bMzsXwN2LzSxey0WRnJDs4J0limNKVmXNL3nccd4UbpjwKPt+v4i3m7ZlSOcr+WLXphTFSWKNNMSMSKVINbGtiXr5dwAz2wdYXfYqItlT3uCd8ZRML2v+3t99yeMTHuXY/03lfzs24pKzbmLCPodiZtwT00t/CQ0xI1J5Uk1sNwOvAnua2TCgA3BRuoMSSZeyklO88c9gU8kq3vwdfl7JDf8dSddJo/mpek1uP/YSnjjkdNZWq7FxnZLraGVduxORzEl1oNFxZjYNOIJw83Q3d1+WkchE0qCs5NWznJJV7PxqG9Zz7kdjueadp6n/8wrmdzmP3+11Gou3rRt33Y3D0IhIpUt2PLZflJr0VfS/iZk1cfdp6Q1LJD3KSl7llaxK/o9/8F/8edT97L9sAcsOOQJ79B80a9OGnuW0qBSR7DD38nugMrM3ypjt7v6r9IVUMe3atfMpU6ZkOwzJIeU16U9o3jy49loYNQqaNoXBg6FrV1B7KSlAZjbV3dtlO450SLYT5GMzHYhIpqRcLfjjj6Gn/XvvhRo1wuMePaBWrYzFKCLpk+oN2rWAK4BfElpGvgM85O4/ZyA2kcq1fn0Y5PP662HpUrjwQujfHxo1ynZkIpKCVFtFPgmsAEqGqjmXMB7b2ekMSqTSvfMOXH01TJsGRx4JL74Ihx6a7ahEZCukmthauHvrmOdvmNlH6QxIpFItWADXXQcjR8Iee8CwYXDuubqOJpLHUu0EebqZHVHyxMwOB95Nb0gilWDVKrjpJth//1A6u/lmmDMHzjtPSU0kz6VaYjscuMDMFkbPmwCzzWwGoXXkwWmNTiTdNmyA4cOhd29YvDiUzgYOhD33LH9dEckLqSa2kzIShUhlmDwZunWDSZPgkEPgmWegQ4dsRyUiaZZqzyMLzGxHwqjW1WOm6wZtyZiKDC0DhJJZnz7w1FOw227w+ONwwQWwTU4PRygiWynV5v63EfqGnEfUEXL0P2du0JbCUpGhZTrv3wDuugvuvBPWrQvVj337Qt268d9MRApCqlWRvyYMXbMmE8GIlLZVQ8usWceUwUPo/O4TodVj164waBDsvXdlhi4iWZJqYpsJ1Ae+SX8oIltKdWiZVkvncdPrQzh80Sw4+GB44w3o2DHTYYpIDkk1sd1JaPI/k5hx2Nz9jLRGJRJJdmiZhquWc+3bT/Hrj8fx/XY7MLBLd3o9OwiqVavskEUky1JNbE8AA4EZwIb0hyOyufKGlul1bFM+7XsHf5g4glrr1vDooZ155Jjfcv15RyipiVRRqSa2Ze5+X0YiEYkj4dAybRrB6NGccc01MG8eE/c/gpuOupjVe+/L9Ro+RqRKSzWxTTWzO4ExbF4Vqeb+kjFb9M4/cyaccCGMHw8tW8LYsfzyxBOZkL0QRSSHpJrY2kb/j4iZpub+UjmWLQtdXz30ENSrB/fdB3/8YxhaRkQkkuoN2hqXTSrf2rXw4IPQrx+sWAFXXBEe77RTtiMTkRyUaokNMzsVaAVsHHXR3W9NZ1AiG736KnTvHjooPv74MPhnq1bZjkpEclhKfQqZ2UPAb4CrACOMw7ZXBuKSqm7uXDj1VDj55NBryJgx8NprSmoiUq5UO8tr7+4XAMvd/RbgSEK/kSLpUVQEPXrAgQfCxIkweDDMmgWnn67hZEQkKalWRZbcKfuTmTUCvgOapTckqZLWr4dHHoEbb4TvvoPLLoPbboNddsl2ZCKSZ1JNbC+ZWX1gEDCN0CLykXQHJVXMhAnhOtrHH8Mxx4TraG3aZDsqEclTqbaKvC16+LyZvQTUcvcf0h+WVAnz5sG118KoUdC0KTz3XOiwWFWOIlIBSV1jM7NDzWy3mOcXACOB28ysQaaCkwK1YkUYQqZlSxg3Dvr3h9mz4ayzlNREpMKSbTzyMLAGwMyOBgYATwI/AEMyE5oUnA0b4J//hObNYeBAOPdc+PTTMAhorVrlry8ikoRkE1s1d/8+evwbYIi7P+/uNwL7ViQAMzvbzGaZ2QYza1dqXh8z+9zM5ppZp4q8j2TZxIlw6KFw6aVhXLTJk2HoUGjUiFHTF9NhwASa9f4PHQZMYNT0xdmOVkTyWNKJzcxKrscdB5t1y5fyTd6lzAS6Am/HTjSzlsA5hJvBTwIeNDN1155nxr48mddbHwtHHcXSeV8y5Y774d13Q5Jj0wjZi4uKcTaNgK3kJiJbK9nENgJ4y8xGE5r8vwNgZvsSqiO3mrvPdve5cWadCfzL3Ve7+xfA58BhFXkvqUSrVjHn8qs55syj6PDJ+9zb4Vw6XvIPzi/el1EfLtm4WFkjZIuIbI2kSlvufoeZjQd2B15zd49mbUPohSQTGgOTYp4viqZtwcwuBy4HaNKkSYbCkaS4w/Dh0KsX+y9ezOgDjmFgxwtZskN0P1qUtEp66y9vhGwRkVQlXY3o7pPiTPs0mXXN7HVgtzizrnf30YlWixdGgtiGEDViadeuXdxlpBJMngzdusGkSXDIIfxfx25M2aPlFovFJq3yRsgWEUlVql1qbRV3P97dD4zzlyipQSihxXbXtQewJMGykk1LlsCFF8Lhh8P8+fD44zB5Ml8deEjcxWOTVs9OLahdY/NLp7EjZIuIpKpSEttWGgOcY2Y1zawZ0ByYnOWYJFZxMdxxB+y3HzzzTGi2/+mncNFFsM02SSWtzm0bc2fXg2hcvzYGNK5fmzu7HqQRsEVkq1W0RWOFmVkX4H5gZ+A/Zvahu3dy91lmNhL4BFgH/Nnd15f1WlJJ3EMvIT17woIF4cbqQYOg2ebdhpYkp0Fj57KkqJhG9WvTs1OLLZLWFiNki4hUgG1qB1IY2rVr51OmTMl2GIVr+nS4+mp4+21o3RruuQeO1fizIvnOzKa6e7vyl8x9uVwVKblk6VL4/e/hkENC91cPPwxTpyqpiUjOyXpVpOS41avhvvvCEDLFxWGstBtvhHr1sh2ZiEhcSmwSn3sYtfqaa0Iv/KedBnfdFRqKiIjkMFVFypZmzIATToDOnaFmTRg7Fl58UUlNRPKCEptssmwZXHFFGORz2rRQBfnhh3DiidmOTEQkaaqKFFi7Fh58EPr1C2OlXXFFeLzTTtmOTEQkZUpsVd0rr4QGIXPmhJLZ3XdDq1bZjkpEZKupKrKqmjMHTjkl/K1fH66hvfqqkpqI5D0ltqpm+fJwg/VBB4Vx0QYPhpkzQ6tHi9fvtIhIflFVZFWxbh088ki4B+377+Gyy8K9abvsku3IRETSSiW2qmD8eGjbNjQKOfDA0OLx4YeV1ESkICmxFbJ586BLFzj+eFi5MnRc/MYboTm/iEiBUmIrRD/+CL16QcuWMG5cGFpm9uzQC7+uo4lIgdM1tkKyYQMMHQp9+4ZOiy+8EPr3h0aNsh2ZiEilUWIrFBMnQrdu4frZkUeG5vuHHprtqEREKp2qIvPdwoVwzjlw1FHwzTcwbFhoxq+kJiJVlEps+WrVKhg4MIxcbQY33xxGtN5++61+yVHTF5c72vXWLCsiUpmU2PLNhg0wfDj07g2LF8O558KAAdCkSYVedtT0xfR5YQbFa9cDsLiomD4vzADYImGlsqyISGVTVWQ+mTwZOnSA88+H3XYL19WGD69wUgMYNHbuxkRVonjtegaNnVuhZUVEKptKbPlgyZJQQnvqqZDQHn8cLrgAtin7vCSV6sIlRcVJT09lWRGRyqYSWy4rLg73oDVvDiNHQp8+8OmncNFFSSW1Pi/MYHFRMc6m6sJR0xfHXb5R/dpJT09lWRGRyqbElovc4dln4YAD4IYbeLPZIRx90QN02OF4Rn3+Y1IvkWp1Yc9OLahdo9pm02rXqEbPTi0qtKyISGVTVWSumT499L7/9tv80PwArvrdQN5uHA0lk0IjjVSrC0teL5mqy1SWFRGpbEpsGbBVTeGXLoUbboDHHgsjVz/8MKcta8aXP67ZbLGSUld5r9eofm0Wx0liZVUXdm7bOOnklMqyIiKVSVWRaZbqtS1Wrw73ojVvHrrD6t4dPvsMLr+cRaWSWolkGmmoulBEqioltjRL+tqWO4weHUasvu46OOYYmDUL7roL6tcHKtZIo3PbxtzZ9SAa16+NAY3r1+bOrgeplCUiBa/gqyIru4eMpK5tzZwZSmavvx4aiLz6KnTqtMU6PTu12OxGaEit1KXqQhGpigq6xJZytWAalFnKWrYM/vxnaN0apk6F++6Djz6Km9RApS4Rka1R0CW2sqoFM5Uc4pWy6m7j/OO7d6D5WbBiRRjJul+/0EikHCp1iYikpqATWzZ6yCjdFL7L0hnc8uZj1J3/OZxwAtxzT7iuJiIiGVHQiW1rmrynQ+e2jem83Uro0QNefhn23RfGjIHTTtMI1iIiGVbQ19iy0uS9qCgktAMPDJ0UDx4cWjuefrqSmohIJch6YjOzQWY2x8w+NrN/m1n9mHl9zOxzM5trZvFbWJShUhtfrFsHDz0U7ke79164+OJwP9o118C226b//UREJC5z9+wGYHYiMMHd15nZQAB372VmLYERwGFAI+B1YD93X5/41aBdu3Y+ZcqUTIe9uQkTQjdYM2aE+9HuvRfatKncGEREKsDMprp7u2zHkQ5ZL7G5+2vuvi56OgnYI3p8JvAvd1/t7l8AnxOSXO6YNw+6dIHjjgutHZ97Dt54Q0lNRCSLsp7YSrkEeCV63Bj4MmbeomjaFszscjObYmZTvv322wyHSEhivXtDy5YwblwYWmb2bDjrLF1HExHJskppFWlmrwO7xZl1vbuPjpa5HlgHDCtZLc7ycetN3X0IMARCVWSFA05kw4bQn2PfvqHT4gsvhP79oVGjjL2liIikplISm7sfX9Z8M7sQOA04zjdd9FsE7Bmz2B7AksxEmISJE6FbN5g2DY48MjTfPyy3akZFRCQHqiLN7CSgF3CGu/8UM2sMcI6Z1TSzZkBzYHKlB7hwIZxzDhx1FHzzDQwbBu++q6QmIpKjcuEG7b8DNYFxFq5PTXL3P7r7LDMbCXxCqKL8c3ktItNq1SoYODAMKWMGN98MPXvC9ttXWggiIpK6rCc2d9+3jHl3AHdUYjhhOJnhw6FXL1i8OJTWBg6EJk0qNQwREdk6Wa+KzCmTJ0P79vC738Fuu4XraiNGKKmJiOQRJTYIJbMLLoDDD4f58+Gf/wxJrkOHbEcmIiIpynpVZFYVF8Pdd4cm++vWhXvT+vaFunWzHZmIiGylqpnY3EMvIT17woIF0LVraCSy997ZjkxERCqo6lVFTp8OHTvCr38N9eqFfh6ff15JTUSkQFSdxLZ0KVx2GRxyCHzySeiJf9o0OPbYbEcmIiJpVDWqIp97Di65JFxT694dbrwR6tfPdlQiIpIBVSOxNW8eqh8HDYIWGRxkVEREsq5qJLbWrUPfjiIiUvCqzjU2ERGpEpTYRESkoCixiYhIQVFiExGRgqLEJiIiBUWJTURECooSm4iIFBQlNhERKSjm7tmOIa3M7FtgQbbjKKUhsCzbQSQpn2KF/Io3n2KF/Io3n2KF3Ix3L3ffOdtBpEPBJbZcZGZT3L1dtuNIRj7FCvkVbz7FCvkVbz7FCvkXb75RVaSIiBQUJTYRESkoSmyVY0i2A0hBPsUK+RVvPsUK+RVvPsUK+RdvXtE1NhERKSgqsYmISEFRYhMRkYKixJYBZna2mc0ysw1mlrBJr5nNN7MZZvahmU2pzBhjYkg21pPMbK6ZfW5mvSszxlJxNDCzcWb2WfR/xwTLZW3blretLLgvmv+xmf2iMuMrFUt5sXY0sx+i7fihmd2UjThj4vmnmX1jZjMTzM+lbVterDm1bQuKu+svzX/AAUAL4E2gXRnLzQca5nqsQDVgHrA3sC3wEdAyS/H+FegdPe4NDMylbZvMtgJOAV4BDDgC+CBL2zKZWDsCL2UjvgQxHw38ApiZYH5ObNskY82pbVtIfyqxZYC7z3b3udmOIxlJxnoY8Lm7/8/d1wD/As7MfHRxnQk8ET1+AuicpTgSSWZbnQk86cEkoL6Z7V7ZgZJb32tS3P1t4PsyFsmVbZtMrJIhSmzZ5cBrZjbVzC7PdjBlaAx8GfN8UTQtG3Z1968Aov+7JFguW9s2mW2VK9sz2TiONLOPzOwVM2tVOaFttVzZtsnKp22bN6pnO4B8ZWavA7vFmXW9u49O8mU6uPsSM9sFGGdmc6KzvLRKQ6wWZ1rG7hMpK94UXqZStm0cyWyrSt2eZUgmjmmEPgRXmtkpwCigeaYDq4Bc2bbJyLdtmzeU2LaSux+fhtdYEv3/xsz+TagaSvvBNw2xLgL2jHm+B7Ckgq+ZUFnxmtlSM9vd3b+Kqpi+SfAalbJt40hmW1Xq9ixDuXG4+48xj182swfNrKG751oHviVyZduWKw+3bd5QVWSWmNn2Zla35DFwIhC39VQO+C/Q3Myamdm2wDnAmCzFMga4MHp8IbBFiTPL2zaZbTUGuCBqwXcE8ENJ9WolKzdWM9vNzCx6fBjhmPFdpUeavFzZtuXKw22bP7LdeqUQ/4AuhDPH1cBSYGw0vRHwcvR4b0IrtI+AWYRqwZyMNXp+CvApoRVdVmKN4tgJGA98Fv1vkGvbNt62Av4I/DF6bMAD0fwZlNFyNgdivTLahh8Bk4D22Yo1imcE8BWwNtpvL83hbVterDm1bQvpT11qiYhIQVFVpIiIFBQlNhERKShKbCIiUlCU2EREpKAosYmISEFRYpOcZGY7xfR6/rWZLY4eF5nZJ5UcS2czaxnz/FYzS/mmdzNrWkZP763MbIKZfWpm88zsFjNL+++zrM9iZm9aGSM8iOQLJTbJSe7+nbu3cfc2wEPAPdHjNsCGdL+fmZXVC09nYGMycPeb3P31NL53bcKNxQPcfT/gIEJPKd3S9R4xOpPBzyKSC5TYJB9VM7NHLIwj91qUGDCzfczs1ajj43fMbP9o+l5mNj4an2u8mTWJpg81s7vN7A1gYLz1zaw9cAYwKCox7hOt93/RaxxqZu9FHdlONrO6UcnsHTObFv21L+fznAe86+6vAbj7T4Sbd3tG79HPzK4tWdjMZppZ0+jxqCjeWRbT2bOZrTSzO6K4JpnZruV9llhmdqKZvR/F/6yZ1YmmDzCzT6JtOTj1r04k85TYJB81Bx5w91ZAEXBWNH0IcJW7HwJcCzwYTf87YSiTg4FhwH0xr7UfcLy7XxNvfXd/j1Ca6hmVIOeVrBh1Q/UM0M3dWwPHA8WE/itPcPdfAL8p9X7xtAKmxk6I3qe2mdUvZ91LonjbAX8xs52i6dsDk6K43gYuK+uzxDKzhsAN0Xb5BTAF6GFmDQg91bSKtuXt5cQmkhXqBFny0Rfu/mH0eCrQNCpRtAeejbrfA6gZ/T8S6Bo9foowWGmJZ919fTnrJ9IC+Mrd/wubOrW10D/l382sDbCekDzLYsTvgT5eT/Wl/cXMukSP9yQk/e+ANcBL0fSpwAlJvFaJIwjVle9G22Jb4H3gR+Bn4FEz+0/M64vkFCU2yUerYx6vB2oTah+Koutw5YlNIqui/6msXyJRQupO6HezdfS6P5fzOrMIoy1vemGzvYFl7l5kZuvYvHalVrRMR0Ip8Uh3/8nM3iyZB6z1Tf3lrSe137oB49z93C1mhM56jyN0mHwl8KsUXlekUqgqUgpCVFr6wszOBoh6d28dzX6PcCAG+C0wMcX1VwB147ztHKCRmR0arVM3aoRSj1CS2wCcD1QrJ/xhwC9jWifWJlRf3hzNnw/8Ipr3C6BZNL0esDxKavsTSlrlSfRZYk0COpjZvtF7bmdm+0Wl2nru/jJwNaEhj0jOUWKTQvJb4FIzK+nV/8xo+l+Ai83sY0KiSdTaMNH6/wJ6mtl0M9unZGF3X0O4hnZ/tM44QonpQeBCM5tEqIZcRRncvZjQqON6M/sUWEZoTDIsWuR5oIGZfQj8idAbP8CrQPXoc91GSEjliftZSsXzLXARMCJ67UnA/oSE+FI07S1CyVQk56h3f5EcY2adgbuBY919QZbDEck7SmwiIlJQVBUpIiIFRYlNREQKihKbiIgUFCU2EREpKEpsIiJSUJTYRESkoPw/SE9vaSeKZv4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.qqplot(model.resid, line = 'q')\n",
    "plt.title('Normal Q-Q plot of residuals of Model with NO transformation')\n",
    "plt.show()\n",
    "\n",
    "sm.qqplot(model_sqrt.resid, line = 'q')\n",
    "plt.title('Normal Q-Q plot of residuals of Model with Square root transformation')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "886fd51b",
   "metadata": {},
   "source": [
    "Model with NO transformation follows near normal distributon with less skewness than model with sqrt transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25fa1fc",
   "metadata": {},
   "source": [
    "### Residual Plot to check Homoscedasticity  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3e31df0e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:53.937156Z",
     "start_time": "2022-10-23T10:08:53.926864Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_standardized_values( vals ):\n",
    "    return (vals - vals.mean())/vals.std() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d50a67d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.259230Z",
     "start_time": "2022-10-23T10:08:53.941475Z"
    }
   },
   "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.scatter(get_standardized_values(model.fittedvalues),\n",
    "            get_standardized_values(model.resid))\n",
    "\n",
    "plt.title('Residual Plot for model with NO transformation')\n",
    "plt.xlabel('Standardized Fitted values')\n",
    "plt.ylabel('Standardized residual values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "108bfff8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.599435Z",
     "start_time": "2022-10-23T10:08:54.265472Z"
    },
    "scrolled": true
   },
   "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.scatter(get_standardized_values(model_sqrt.fittedvalues),\n",
    "            get_standardized_values(model_sqrt.resid))\n",
    "\n",
    "plt.title('Residual Plot for model with Sqrt transformation')\n",
    "plt.xlabel('Standardized Fitted values')\n",
    "plt.ylabel('Standardized residual values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87d968c1",
   "metadata": {},
   "source": [
    "Homoscedasticity refers to a condition in which the variance of the residual or error term, in a regression model is constant "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca6f773",
   "metadata": {},
   "source": [
    "### Some errors are positive, some are negative. Hence mean of errors should be zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4d876b7d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.615480Z",
     "start_time": "2022-10-23T10:08:54.599435Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.1230426480372746e-12"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors=model.resid \n",
    "errors.mean() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "324903d8",
   "metadata": {},
   "source": [
    "## Model Validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bea80517",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.631804Z",
     "start_time": "2022-10-23T10:08:54.618856Z"
    }
   },
   "outputs": [],
   "source": [
    "model_rmse=np.sqrt(model.mse_resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d90dea60",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.655500Z",
     "start_time": "2022-10-23T10:08:54.631804Z"
    }
   },
   "outputs": [],
   "source": [
    "model_sqrt_pred_y =np.square(model_sqrt.predict(salary.YearsExperience))\n",
    "model_sqrt_rmse =np.sqrt(mean_squared_error(salary.Salary, model_sqrt_pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ec97f140",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.687133Z",
     "start_time": "2022-10-23T10:08:54.659891Z"
    }
   },
   "outputs": [],
   "source": [
    "model_cbrt_pred_y =pow(model_cbrt.predict(salary.YearsExperience),3)\n",
    "model_cbrt_rmse =np.sqrt(mean_squared_error(salary.Salary, model_cbrt_pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eb8b4fa1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.703636Z",
     "start_time": "2022-10-23T10:08:54.690547Z"
    }
   },
   "outputs": [],
   "source": [
    "model_log_pred_y =np.exp(model_log.predict(salary.YearsExperience))\n",
    "model_log_rmse =np.sqrt(mean_squared_error(salary.Salary, model_log_pred_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ec7056df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.730952Z",
     "start_time": "2022-10-23T10:08:54.707644Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model= 5788.315051119395 \n",
      "model_sqrt= 5960.647096174306 \n",
      "model_cbrt= 6232.815455835857 \n",
      "model_log= 7219.716974372746\n"
     ]
    }
   ],
   "source": [
    "print('model=', model_rmse,'\\n' 'model_sqrt=', model_sqrt_rmse,'\\n' 'model_cbrt=', model_cbrt_rmse,'\\n' 'model_log=', model_log_rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8ef9cd",
   "metadata": {},
   "source": [
    "### Model with No transformation has minimum RMSE and highest R square adjusted score. Hence model with NO transformation will be our final model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbaebf32",
   "metadata": {},
   "source": [
    "# Predicting Salary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2be090be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.772577Z",
     "start_time": "2022-10-23T10:08:54.730952Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Salary</th>\n",
       "      <th>YearsExperience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39343.0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46205.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37731.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43525.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39891.0</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56642.0</td>\n",
       "      <td>2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60150.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>54445.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64445.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57189.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Salary  YearsExperience\n",
       "0  39343.0              1.1\n",
       "1  46205.0              1.3\n",
       "2  37731.0              1.5\n",
       "3  43525.0              2.0\n",
       "4  39891.0              2.2\n",
       "5  56642.0              2.9\n",
       "6  60150.0              3.0\n",
       "7  54445.0              3.2\n",
       "8  64445.0              3.2\n",
       "9  57189.0              3.7"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.DataFrame()\n",
    "train_data['Salary'] = salary.Salary.head(10)\n",
    "train_data['YearsExperience'] = salary.YearsExperience.head(10) \n",
    "train_data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f752d007",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.804193Z",
     "start_time": "2022-10-23T10:08:54.773728Z"
    }
   },
   "outputs": [],
   "source": [
    "train_data['predicted_delivery_normal'] = model.predict(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "60b754fe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.823886Z",
     "start_time": "2022-10-23T10:08:54.807835Z"
    }
   },
   "outputs": [],
   "source": [
    "train_data['predicted_delivery_log'] = np.exp(model_log.predict(train_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "25f1addf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:08:54.855439Z",
     "start_time": "2022-10-23T10:08:54.823886Z"
    }
   },
   "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>Salary</th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>predicted_delivery_normal</th>\n",
       "      <th>predicted_delivery_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39343.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>36187.158752</td>\n",
       "      <td>32261.052293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46205.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>38077.151217</td>\n",
       "      <td>35437.122429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37731.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>39967.143681</td>\n",
       "      <td>38405.293942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43525.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44692.124842</td>\n",
       "      <td>45145.842502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39891.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>46582.117306</td>\n",
       "      <td>47630.388981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56642.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>53197.090931</td>\n",
       "      <td>55631.518478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60150.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54142.087163</td>\n",
       "      <td>56701.779991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>54445.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>56032.079627</td>\n",
       "      <td>58796.479782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64445.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>56032.079627</td>\n",
       "      <td>58796.479782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57189.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>60757.060788</td>\n",
       "      <td>63795.788170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Salary  YearsExperience  predicted_delivery_normal  predicted_delivery_log\n",
       "0  39343.0              1.1               36187.158752            32261.052293\n",
       "1  46205.0              1.3               38077.151217            35437.122429\n",
       "2  37731.0              1.5               39967.143681            38405.293942\n",
       "3  43525.0              2.0               44692.124842            45145.842502\n",
       "4  39891.0              2.2               46582.117306            47630.388981\n",
       "5  56642.0              2.9               53197.090931            55631.518478\n",
       "6  60150.0              3.0               54142.087163            56701.779991\n",
       "7  54445.0              3.2               56032.079627            58796.479782\n",
       "8  64445.0              3.2               56032.079627            58796.479782\n",
       "9  57189.0              3.7               60757.060788            63795.788170"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3541b30e",
   "metadata": {},
   "source": [
    "# Taking input from user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8b8060d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:11:52.271845Z",
     "start_time": "2022-10-23T10:08:54.857218Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your work experience: 6\n"
     ]
    }
   ],
   "source": [
    "workXp = float(input(\"Enter your work experience: \")) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c7125a1c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:11:52.308014Z",
     "start_time": "2022-10-23T10:11:52.284357Z"
    }
   },
   "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>YearsExperience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience\n",
       "0              6.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata=pd.DataFrame([workXp],columns=['YearsExperience']) \n",
    "newdata "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1fb39160",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-23T10:11:52.336915Z",
     "start_time": "2022-10-23T10:11:52.309220Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    82491.974127\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(newdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe32191",
   "metadata": {},
   "source": [
    "# Thank You "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
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