Train_test_salary_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First lets import the pandas library so that we can load the csv into a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"salary_data.csv\")\n",
    "# See the lenght ie: number of rows of data\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Then view the top 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearsExperience  Salary\n",
       "0              1.1   39343\n",
       "1              1.3   46205\n",
       "2              1.5   37731\n",
       "3              2.0   43525\n",
       "4              2.2   39891\n",
       "5              2.9   56642\n",
       "6              3.0   60150\n",
       "7              3.2   54445\n",
       "8              3.2   64445\n",
       "9              3.7   57189"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the matplotlib library to visualize the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "#above line is required sometimes to render the graphs in Jupyter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will plot the data on the X(YearsExperience) and Y(Salary) axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24cfb3f3a00>"
      ]
     },
     "execution_count": 29,
     "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(df['YearsExperience'],df['Salary'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets load the data into the X(independent) and Y(dependent) variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['YearsExperience']]\n",
    "y = df['Salary']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next we will split the data into train and test.We will keep the test size 30%.\n",
    "> The rando, state of 10 ensures that the data split always contains the same data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>2</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience\n",
       "2               1.5\n",
       "28             10.3\n",
       "16              5.1\n",
       "22              7.9\n",
       "26              9.5\n",
       "17              5.3\n",
       "19              6.0\n",
       "4               2.2\n",
       "12              4.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)\n",
    "\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=10)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets view the lenght on the X train data and also see whats in X train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)\n",
    "#X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now lets view the X train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)\n",
    "#X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets create our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "clf = LinearRegression()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model is easy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets see what X_test has"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>YearsExperience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience\n",
       "22              7.9\n",
       "0               1.1\n",
       "4               2.2\n",
       "8               3.2\n",
       "9               3.7\n",
       "25              9.0\n",
       "26              9.5\n",
       "23              8.2\n",
       "21              7.1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we ask our model to predict the salary(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 99265.63460776,  36238.21299838,  46433.82531754,  55702.56378951,\n",
       "        60336.93302549, 109461.24692692, 114095.61616291, 102046.25614935,\n",
       "        91850.64383018])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now lets see what Y_test actually is.This is why we split the data into train and test so that we can check our model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22    101302\n",
       "0      39343\n",
       "4      39891\n",
       "8      64445\n",
       "9      57189\n",
       "25    105582\n",
       "26    116969\n",
       "23    113812\n",
       "21     98273\n",
       "Name: Salary, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We have a method to score the model. This takes the predictions from our model and comprares them to the Y_test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9514192825302265"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "clf.score(X_test, y_test)"
   ]
  }
 ],
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