Linear Regression - Using Sklearn Algorithm.ipynb

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  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       YearsExperience         Salary\n",
      "count        30.000000      30.000000\n",
      "mean          5.313333   76003.000000\n",
      "std           2.837888   27414.429785\n",
      "min           1.100000   37731.000000\n",
      "25%           3.200000   56720.750000\n",
      "50%           4.700000   65237.000000\n",
      "75%           7.700000  100544.750000\n",
      "max          10.500000  122391.000000\n"
     ]
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('salary_data.csv')\n",
    "X = dataset.iloc[:, :1].values #get a copy of dataset exclude last column\n",
    "y = dataset.iloc[:, 1].values #get array of dataset in column 1st\n",
    "print(dataset.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting the dataset into training and testing, usually we do 80-20/70-30 train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>X_train</th>\n",
       "      <th>y_train</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738</td>\n",
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       "      <th>6</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343</td>\n",
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       "    <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",
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      ],
      "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"
      ]
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     "execution_count": 12,
     "metadata": {},
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   "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": 16,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X_test</th>\n",
       "      <th>y_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391</td>\n",
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       "      <th>2</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969</td>\n",
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       "      <td>8.7</td>\n",
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       "      <td>9.6</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>55794</td>\n",
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       "      <th>8</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.9</td>\n",
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      "text/plain": [
       "   X_test  y_test\n",
       "0     1.5   37731\n",
       "1    10.3  122391\n",
       "2     4.1   57081\n",
       "3     3.9   63218\n",
       "4     9.5  116969\n",
       "5     8.7  109431\n",
       "6     9.6  112635\n",
       "7     4.0   55794\n",
       "8     5.3   83088\n",
       "9     7.9  101302"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'X_test':X_test.flatten(),'y_test':y_test.flatten()})\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the intercept and coffecient value using regressior object, as shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression() # Using Sklearn pre-defined model\n",
    "regressor.fit(X_train, y_train) # Passing the X_Train and Y_Train values to train the model and get the best fit line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26816.192244031183\n",
      "[9345.94244312]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.intercept_)\n",
    "print(regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37731</td>\n",
       "      <td>40835.105909</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122391</td>\n",
       "      <td>123079.399408</td>\n",
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       "      <th>2</th>\n",
       "      <td>57081</td>\n",
       "      <td>65134.556261</td>\n",
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       "      <th>3</th>\n",
       "      <td>63218</td>\n",
       "      <td>63265.367772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116969</td>\n",
       "      <td>115602.645454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>109431</td>\n",
       "      <td>108125.891499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>112635</td>\n",
       "      <td>116537.239698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>55794</td>\n",
       "      <td>64199.962017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>83088</td>\n",
       "      <td>76349.687193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>101302</td>\n",
       "      <td>100649.137545</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Actual      Predicted\n",
       "0   37731   40835.105909\n",
       "1  122391  123079.399408\n",
       "2   57081   65134.556261\n",
       "3   63218   63265.367772\n",
       "4  116969  115602.645454\n",
       "5  109431  108125.891499\n",
       "6  112635  116537.239698\n",
       "7   55794   64199.962017\n",
       "8   83088   76349.687193\n",
       "9  101302  100649.137545"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X_test)\n",
    "df_result = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
    "df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c27fdac208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c27fdf1908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz_train = plt\n",
    "viz_train.scatter(X_train, y_train, color='red')\n",
    "viz_train.plot(X_train, regressor.predict(X_train), color='blue')\n",
    "viz_train.title('Salary VS Experience (Training set)')\n",
    "viz_train.xlabel('Year of Experience')\n",
    "viz_train.ylabel('Salary')\n",
    "viz_train.show()\n",
    "\n",
    "# Visualizing the Test set results\n",
    "viz_test = plt\n",
    "viz_test.scatter(X_test, y_test, color='blue')\n",
    "viz_test.plot(X_test, regressor.predict(X_test), color='red')\n",
    "viz_test.title('Salary VS Experience (Test set)')\n",
    "viz_test.xlabel('Year of Experience')\n",
    "viz_test.ylabel('Salary')\n",
    "viz_test.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c20206e4e0>"
      ]
     },
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c202714048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test, y_test,  color='gray')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The straight line in the above graph shows our algorithm is correct. Strainght line covers most of data points, so it's shows this line is best fit line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 3426.4269374307078\n",
      "Mean Squared Error: 21026037.329511296\n",
      "Root Mean Squared Error: 4585.4157204675885\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 4585, which is less than 10% of the mean value of the percentages of all the salary i.e. 76003. This means that our algorithm did a decent job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[120275.61667525]\n"
     ]
    }
   ],
   "source": [
    "value = [10] #Pass the value for which you want to get the salary prediction\n",
    "y_pred_single = regressor.predict([value])\n",
    "print(y_pred_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}