Linear Regression - Simple Gradient Descent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readfile(filename):\n",
    "    data = pd.read_csv('salary_data.csv')\n",
    "    X = data.iloc[:,:1].values.reshape(-1,1) # seprating the independent variable\n",
    "    y = data.iloc[:, 1].values.reshape(-1,1) # slicing the dependent variable\n",
    "    plt.title('Salary VS Experience Data( Training Set )')\n",
    "    plt.scatter(X, y)\n",
    "    plt.show()\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def buildModel(X,Y):\n",
    "    \n",
    "    m = 0 # we can do something like this as well --> np.random.random(x.shape[1])\n",
    "    c = 0\n",
    "\n",
    "    L = 0.0001  # The learning Rate\n",
    "    epochs = 1000  # The number of iterations to perform gradient descent\n",
    "    n = float(len(X)) # Number of elements in X\n",
    "\n",
    "# Performing Gradient Descent \n",
    "# Loss function - do partial derivative of 1/2n(sum((c+mX(i) - y(i)))**2) wrt to m and c\n",
    "    for i in range(epochs): \n",
    "        Y_pred = m*X + c  # The current predicted value of Y\n",
    "        D_m = (-2/n) * sum(X * (Y - Y_pred))  # Partial Derivative of loss function wrt m\n",
    "        D_c = (-2/n) * sum(Y - Y_pred)  # Partial Derivative of Loss function wrt c\n",
    "        m = m - L * D_m  # Update m\n",
    "        c = c - L * D_c  # Update c\n",
    "    return (m, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bestFitLine(X,slope,coeff):\n",
    "    Y_pred = slope*X + coeff\n",
    "    return Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot graph\n",
    "def plotGraph(X,Y,Y_pred):\n",
    "    plt.scatter(X, Y) # actual\n",
    "    plt.plot([min(X), max(X)], [min(Y_pred), max(Y_pred)], color='red') # predicted\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(x, slope,coeff):\n",
    "    y_pred = coeff + slope*x\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1741868c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = readfile('salary_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>X_train</th>\n",
       "      <th>y_train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X_train  y_train\n",
       "0       2.9    56642\n",
       "1       5.1    66029\n",
       "2       3.2    64445\n",
       "3       4.5    61111\n",
       "4       8.2   113812\n",
       "5       6.8    91738\n",
       "6       1.3    46205\n",
       "7      10.5   121872\n",
       "8       3.0    60150\n",
       "9       2.2    39891\n",
       "10      5.9    81363\n",
       "11      6.0    93940\n",
       "12      3.7    57189\n",
       "13      3.2    54445\n",
       "14      9.0   105582\n",
       "15      2.0    43525\n",
       "16      1.1    39343\n",
       "17      7.1    98273\n",
       "18      4.9    67938\n",
       "19      4.0    56957"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1/3, random_state=0)\n",
    "df = pd.DataFrame({'X_train': X_train.flatten(), 'y_train': y_train.flatten()})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Years of Experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37731</td>\n",
       "      <td>23075.292849</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122391</td>\n",
       "      <td>139328.758503</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57081</td>\n",
       "      <td>57422.907701</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63218</td>\n",
       "      <td>54780.783482</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116969</td>\n",
       "      <td>128760.261626</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>109431</td>\n",
       "      <td>118191.764748</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>112635</td>\n",
       "      <td>130081.323735</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55794</td>\n",
       "      <td>56101.845591</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>83088</td>\n",
       "      <td>73275.653018</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101302</td>\n",
       "      <td>107623.267870</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual      Predicted  Years of Experience\n",
       "0   37731   23075.292849                  1.5\n",
       "1  122391  139328.758503                 10.3\n",
       "2   57081   57422.907701                  4.1\n",
       "3   63218   54780.783482                  3.9\n",
       "4  116969  128760.261626                  9.5\n",
       "5  109431  118191.764748                  8.7\n",
       "6  112635  130081.323735                  9.6\n",
       "7   55794   56101.845591                  4.0\n",
       "8   83088   73275.653018                  5.3\n",
       "9  101302  107623.267870                  7.9"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope, coeff = buildModel(X_train,y_train)   \n",
    "y_pred=bestFitLine(X_test,slope,coeff)\n",
    "df_result = pd.DataFrame({'Years of Experience': X_test.flatten(),'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
    "df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17418924be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotGraph(X,Y,y_pred)\n",
    "viz_train = plt\n",
    "viz_train.scatter(X_test, y_test, color='red')\n",
    "viz_train.plot(X_test, y_pred, color='blue')\n",
    "viz_train.title('Salary VS Experience( Test Set )')\n",
    "viz_train.xlabel('Year of Experience')\n",
    "viz_train.ylabel('Salary')\n",
    "viz_train.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17418901630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df_result.head()\n",
    "df1.plot(kind='bar',figsize=(8,5))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.015', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.015', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar graph showing the comparison of Actual and Predicted values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 9481.240042649071\n",
      "Mean Squared Error: 122947534.99338603\n",
      "Root Mean Squared Error: 11088.170948961151\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the value of root mean squared error is 11088, which is more than 10% of the mean value of the percentages of all the salary i.e. 76003. This means that our algorithm is not giving good prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the number10\n",
      "[135365.57217407]\n"
     ]
    }
   ],
   "source": [
    "x = input(\"Enter the number\")\n",
    "x = float(x)\n",
    "print(x*slope+coeff)"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}