girlvsdata/Linear_Regression_Salary_YearsExp.IPYNB

## Linear_Regression_Salary_YearsExp.IPYNB
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression - Predicting a new employee's 'Salary' from their 'Years of Experience'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Business Challenge:\n",
    "We need to know which salary to assign to a person depending on how many years of experience they have in the business.\n",
    "We have a prospective employee with 5 years of experience, and we need to know how much we should pay them.\n",
    "\n",
    "Based on the data we have about 30 employees; their Years of Experience and their Salary, can we predict an employee's Salary only given their Years of Experience?\n",
    "\n",
    "#### Import the libraries\n",
    "Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the dataset\n",
    "We are wanting to predict the Salary using the Years of Experience, so:\n",
    "\n",
    "Set the independent variable 'X' as \"Years of Experience\"\n",
    "\n",
    "Set the dependent variable 'y' as \"Salary\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Salary_Data.csv')\n",
    "X = dataset ['YearsExperience'].values.reshape(-1,1)\n",
    "y = dataset ['Salary'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also get a summary of what X and y look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Xsize = 30 \n",
      " Xshape = (30, 1) \n",
      " \n",
      " Xhead =\n",
      " [[ 1.1]\n",
      " [ 1.3]\n",
      " [ 1.5]\n",
      " [ 2. ]\n",
      " [ 2.2]] \n",
      " \n",
      " ysize = 30 \n",
      " yshape = (30, 1) \n",
      " \n",
      " yhead = \n",
      " [[ 39343.]\n",
      " [ 46205.]\n",
      " [ 37731.]\n",
      " [ 43525.]\n",
      " [ 39891.]]\n"
     ]
    }
   ],
   "source": [
    "print ( 'Xsize =',X.size,'\\n',\n",
    "        'Xshape =',X.shape,'\\n','\\n',\n",
    "       \n",
    "        'Xhead =\\n',X[0:5],'\\n','\\n',\n",
    "       \n",
    "        'ysize =',y.size,'\\n',\n",
    "        'yshape =',y.shape,'\\n','\\n',\n",
    "      \n",
    "        'yhead = \\n',y[0:5]\n",
    "      )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like they've been assigned properly, and there is 30 rows of data.\n",
    "\n",
    "Now let's split the data into the Training set and Test set, Train size will be 20 rows, Test will be 10 (1/3 split)\n",
    "\n",
    "#### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 1/3, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 , 20\n"
     ]
    }
   ],
   "source": [
    "print(X_train.size,',', y_train.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 , 10\n"
     ]
    }
   ],
   "source": [
    "print(X_test.size,',', y_test.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting the Linear Regression model to the Training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking the model's R^2 Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97491544077083525"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a very high correlation there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93819000128942776"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also very high. I'm confident we are able to predict the Salary - Let's continue.\n",
    "#### Making the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred_test = regressor.predict(X_test)\n",
    "y_pred_train = regressor.predict(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualising the predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LrVJdObMxs6FjxoxSoGmzZk0qL9CcObDBBinQ3HQTPP54cwUacGZjZkNJAW+g7Mnzz8PG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\nDpw5YULXPdW6u89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2zGXA2O6uSb4OARySy78NnJynrwZOLLteG/d0bf1p3o+Hq7GmEBEv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\nmJlZzTnYmJlZzTnYmJlZzTnYmJlZzf1/D9VzTEMfIqwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x216433aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train,y_train, color ='red')\n",
    "plt.plot(X_train, y_pred_train, color ='blue',linewidth = 1)\n",
    "plt.title('Salary vs Experience (Training set)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+nFWczE08xy78ih/yQ36dFsz1oGZm3YUrGyvM6tVw2WXw8V7PshPPM41dS4kG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\nZmYV9/8DlwbjEH/ubOwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2164628908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test,y_test, color ='red')\n",
    "plt.plot(X_test, regressor.predict(X_test), color ='blue',linewidth = 1)\n",
    "plt.title('Salary vs Experience (Test set)')\n",
    "plt.xlabel('Years of Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, the blue line is our predictions and the red dots are the actual data points.\n",
    "\n",
    "In both the training and test sets, the data fits very well considering the Linear Regression assumptions.\n",
    "\n",
    "\n",
    "This Linear Regression model assumes:\n",
    "\n",
    "- that the relationship is perfectly linear\n",
    "\n",
    "- that the data is normally distributed\n",
    "\n",
    "- that the data is homoscedastic (all random variables have the same finite variance)\n",
    "\n",
    "#### Prediction\n",
    "Lastly, let's get the answer to our initial question:\n",
    "How much should the new employee's salary (who has 5 years of experience) be?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 73545.90445964]]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.predict(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model predicts the employee should be earning $73,545,90 a year."
   ]
  }
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