Day-2/Day2_06oct2020.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "### Today concept\n",
    "1. Working with csv file\n",
    "2. Applying Linear Regression \n",
    "    * for predicting the salary of employee with his experience\n",
    "    * predicting the revenue of reliance industries with number of jio subscribers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### working with csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How to read csv file in python**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "      <th>Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>10000</td>\n",
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       "      <th>1</th>\n",
       "      <td>16000</td>\n",
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       "      <th>2</th>\n",
       "      <td>20000</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>27000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35000</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>apssdc</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Salary  Experience\n",
       "0   10000           1\n",
       "1   16000           2\n",
       "2   20000           3\n",
       "3   27000           4\n",
       "4     NaN           5\n",
       "5   35000           6\n",
       "6   70000           7\n",
       "7  apssdc          10"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('employee.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many rows and How many columns available?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape # (rows count, columns count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Basic Information about dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 2 columns):\n",
      "Salary        7 non-null object\n",
      "Experience    8 non-null int64\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 136.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**is there any missing values?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "      <th>Experience</th>\n",
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       "      <td>True</td>\n",
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       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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      "text/plain": [
       "   Salary  Experience\n",
       "0   False       False\n",
       "1   False       False\n",
       "2   False       False\n",
       "3   False       False\n",
       "4    True       False\n",
       "5   False       False\n",
       "6   False       False\n",
       "7   False       False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To count missing values in dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Salary        1\n",
       "Experience    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Replacing missing value with default value**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "   Salary  Experience\n",
       "0   10000           1\n",
       "1   16000           2\n",
       "2   20000           3\n",
       "3   27000           4\n",
       "4     NaN           5\n",
       "5   35000           6\n",
       "6   70000           7\n",
       "7  apssdc          10"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Salary  Experience\n",
       "0   10000           1\n",
       "1   16000           2\n",
       "2   20000           3\n",
       "3   27000           4\n",
       "4    1000           5\n",
       "5   35000           6\n",
       "6   70000           7\n",
       "7  apssdc          10"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.fillna(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df = df.fillna(1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**removing the rows related to the missing values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "   Salary  Experience\n",
       "0   10000           1\n",
       "1   16000           2\n",
       "2   20000           3\n",
       "3   27000           4\n",
       "4     NaN           5\n",
       "5   35000           6\n",
       "6   70000           7\n",
       "7  apssdc          10"
      ]
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     "execution_count": 13,
     "metadata": {},
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Salary  Experience\n",
       "0   10000           1\n",
       "1   16000           2\n",
       "2   20000           3\n",
       "3   27000           4\n",
       "5   35000           6\n",
       "6   70000           7\n",
       "7  apssdc          10"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Is there any invalid values?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 2 columns):\n",
      "Salary        7 non-null object\n",
      "Experience    8 non-null int64\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 136.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How to remove particular row in dataframe**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```df.drop(rowindex)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Salary  Experience\n",
       "0  10000           1\n",
       "1  16000           2\n",
       "2  20000           3\n",
       "3  27000           4\n",
       "4    NaN           5\n",
       "5  35000           6\n",
       "6  70000           7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![img](8_simpleLinear.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Linear Regression is a example of Linear Model**\n",
    "\n",
    "**A linear model is a sum of weighted variables that predicts the target value for given input data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Positive Linearity\n",
    "    * Experience Vs Salary\n",
    "    * Temperature Increases Vs Sales of Icecream increases\n",
    "    * Land Area increases VS cost of land price increases\n",
    "* Negative Linearity\n",
    "    * GDP decreases VS unemploybility increases\n",
    "    * price increases vs demand\n",
    "\n",
    "* No change/No linearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X - input/independent variable\n",
    "y - output/dependent variable\n",
    "\n",
    "y   =  mX + c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying LinearRegression for predicting the salary of employee with his experience**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Salary_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
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       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
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       "      <td>5.3</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</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\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Pre-process the data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is there any missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "basic information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 2 columns):\n",
      "YearsExperience    30 non-null float64\n",
      "Salary             30 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 520.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</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\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train the model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Experience ---> Feature**\n",
    "\n",
    "**Salary ---> Target**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Salary has continuous values then it is going to regression problem**\n",
    "\n",
    "**Now we have to select the regression related algorithm**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**we are using linear regression algorithm**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**before directly taking we can observe is there any relation between feature(Experience) and target(salary)  by matplotlib visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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.figure()\n",
    "\n",
    "plt.scatter( [1,2,3]   , [4,5,6] ,c='green' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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.figure()\n",
    "\n",
    "plt.plot(   [1,2,3]   ,  [3,4,5], c='red' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**in ML we have to seperate the features and target**\n",
    "\n",
    "we save the features in  X(upper case) variable\n",
    "\n",
    "and for target we use y (lower case) variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[ 'YearsExperience'  ]]\n",
    "\n",
    "y = df['Salary']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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.figure()\n",
    "\n",
    "plt.scatter(df['YearsExperience'] , df['Salary'],c='green')\n",
    "plt.xlabel('Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.title('Experience VS Salary')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. import the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Create object for the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. we have to train the model\n",
    "model.fit(features , target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Test the model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for experience 5 years how much salary an employee expect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([73042.01180594])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(  [   [ 5  ]    ]  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for experience 7 and 11 years how much salary they may expect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 91941.93644885, 129741.78573467])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(  [  [7],  [11]   ]    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**training score**\n",
    "\n",
    "model.score(feature,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score 95.69566641435084\n"
     ]
    }
   ],
   "source": [
    "print('training score',model.score(X,y) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Nxt dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### prediction of revenue of reliance industries with jio subscribers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get data\n",
    "    1. search from the google and other search engines\n",
    "    2. gathering data from official websites\n",
    "    3. by conducting lotorry/survey\n",
    "    4. kaggle and uci\n",
    "    5. we can collect manually for required data\n",
    "    6. we can create data by using formula (y=mx+c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>JioSubscribers</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>160.1</td>\n",
       "      <td>8136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186.6</td>\n",
       "      <td>8421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215.3</td>\n",
       "      <td>10023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>252.3</td>\n",
       "      <td>11416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>280.1</td>\n",
       "      <td>12893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>306.7</td>\n",
       "      <td>14328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>331.3</td>\n",
       "      <td>15741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>355.2</td>\n",
       "      <td>16534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>370.0</td>\n",
       "      <td>17555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>387.5</td>\n",
       "      <td>18632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   JioSubscribers  Revenue\n",
       "0           160.1     8136\n",
       "1           186.6     8421\n",
       "2           215.3    10023\n",
       "3           252.3    11416\n",
       "4           280.1    12893\n",
       "5           306.7    14328\n",
       "6           331.3    15741\n",
       "7           355.2    16534\n",
       "8           370.0    17555\n",
       "9           387.5    18632"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('reliance.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. pre-process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JioSubscribers    0\n",
       "Revenue           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 2 columns):\n",
      "JioSubscribers    10 non-null float64\n",
      "Revenue           10 non-null int64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 200.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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.figure()\n",
    "\n",
    "plt.scatter(df['JioSubscribers'] ,df['Revenue']  )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X =  df[['JioSubscribers']]\n",
    "y = df['Revenue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**how much revenue for 400million subscribers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18861.7845469])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[400]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "training score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.20788182785108"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X,y) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**metrics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "r2_score(y actual,y predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.20788182785108"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y, model.predict(X)    ) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}