Simple Linear Regression Employee Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libarary\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read CSV File\n",
    "Emp_data = pd.read_csv(\"~/Downloads/Data Science/data set/emp_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split dataset\n",
    "x = Emp_data.iloc[:, :-1].values \n",
    "y = Emp_data.iloc[:,-1:].values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1580],\n",
       "       [1600],\n",
       "       [1610],\n",
       "       [1640],\n",
       "       [1660],\n",
       "       [1690],\n",
       "       [1706],\n",
       "       [1730],\n",
       "       [1800],\n",
       "       [1870]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[92],\n",
       "       [85],\n",
       "       [80],\n",
       "       [75],\n",
       "       [72],\n",
       "       [70],\n",
       "       [68],\n",
       "       [65],\n",
       "       [62],\n",
       "       [60]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Salary_hike</th>\n",
       "      <th>Churn_out_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1580</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1600</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1610</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1640</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1660</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1690</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1706</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1730</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1800</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1870</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Salary_hike  Churn_out_rate\n",
       "0         1580              92\n",
       "1         1600              85\n",
       "2         1610              80\n",
       "3         1640              75\n",
       "4         1660              72\n",
       "5         1690              70\n",
       "6         1706              68\n",
       "7         1730              65\n",
       "8         1800              62\n",
       "9         1870              60"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Emp_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "#QQ Plot\n",
    "stats.probplot(Emp_data.Churn_out_rate, dist=\"norm\", plot=plt)\n",
    "plt.title(\"Normal Q-Q plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "stats.probplot(Emp_data.Salary_hike, dist=\"norm\", plot=plt)\n",
    "plt.title(\"Normal Q-Q plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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.plot(Emp_data.Churn_out_rate,Emp_data.Salary_hike) \n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_c737d9e2_8967_11ea_879d_98460a9ba302row0_col0 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_c737d9e2_8967_11ea_879d_98460a9ba302row0_col1 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_c737d9e2_8967_11ea_879d_98460a9ba302row1_col0 {\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_c737d9e2_8967_11ea_879d_98460a9ba302row1_col1 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_c737d9e2_8967_11ea_879d_98460a9ba302\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Salary_hike</th>        <th class=\"col_heading level0 col1\" >Churn_out_rate</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c737d9e2_8967_11ea_879d_98460a9ba302level0_row0\" class=\"row_heading level0 row0\" >Salary_hike</th>\n",
       "                        <td id=\"T_c737d9e2_8967_11ea_879d_98460a9ba302row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_c737d9e2_8967_11ea_879d_98460a9ba302row0_col1\" class=\"data row0 col1\" >-0.911722</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c737d9e2_8967_11ea_879d_98460a9ba302level0_row1\" class=\"row_heading level0 row1\" >Churn_out_rate</th>\n",
       "                        <td id=\"T_c737d9e2_8967_11ea_879d_98460a9ba302row1_col0\" class=\"data row1 col0\" >-0.911722</td>\n",
       "                        <td id=\"T_c737d9e2_8967_11ea_879d_98460a9ba302row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x121e63a90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Multicollinearity check\n",
    "corr = Emp_data.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bulid model\n",
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8312363099883749\n"
     ]
    }
   ],
   "source": [
    "#fit model\n",
    "reg.fit(x,y)\n",
    "print(reg.score(x, y)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8486236840445497\n"
     ]
    }
   ],
   "source": [
    "#fit model using transform model\n",
    "reg.fit(np.log(x),y)\n",
    "print(reg.score(np.log(x), y)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8890925507825578\n"
     ]
    }
   ],
   "source": [
    "reg.fit(np.log(x),np.log(y))\n",
    "print(reg.score(np.log(x),np.log(y)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}