ML Project SVR 6033657523.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"ML Project SVR.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"authorship_tag":"ABX9TyPbxtxFg2Wkb+I9+Btdi+VI"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"slXGp-CekuPx","colab_type":"text"},"source":["# ML Project 6033657523 - Support Vector Regression"]},{"cell_type":"markdown","metadata":{"id":"53n4JsZZky25","colab_type":"text"},"source":["## Importing the libraries"]},{"cell_type":"code","metadata":{"id":"1UcAad-7kYr8","colab_type":"code","colab":{}},"source":["from sklearn.metrics import mean_absolute_error\n","from sklearn.svm import SVR\n","from sklearn.model_selection import KFold, train_test_split\n","from math import sqrt\n","import pandas as pd\n","import numpy as np\n","from sklearn.metrics import mean_squared_error, mean_absolute_error\n","import matplotlib.pyplot as plt"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"fD5qaIfUlv9f","colab_type":"text"},"source":["## Importing the cleaned dataset"]},{"cell_type":"code","metadata":{"id":"ZfX6YuZrly4O","colab_type":"code","colab":{}},"source":["dataset = pd.read_csv('cleanData_Final.csv')\n","X = dataset[['PrevAVGCost', 'PrevAssignedCost', 'AVGCost', 'LatestDateCost', 'A', 'B', 'C', 'D', 'E', 'F', 'G']]\n","y = dataset['GenPrice']"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YRZGyOzxl3Y1","colab_type":"text"},"source":["## Splitting the dataset into the Training set and Test set"]},{"cell_type":"code","metadata":{"id":"AFkdyFpLl3EB","colab_type":"code","colab":{}},"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OiWyzTsBmAgP","colab_type":"text"},"source":["## Support Vector Regression"]},{"cell_type":"markdown","metadata":{"id":"r16hHjVQmIK3","colab_type":"text"},"source":["### Fitting Support Vector Regression to the Training Set"]},{"cell_type":"code","metadata":{"id":"qHTxnFMImAO7","colab_type":"code","outputId":"720c33b6-d57c-4467-d0e3-5efaf90b31be","executionInfo":{"status":"ok","timestamp":1587306452043,"user_tz":-420,"elapsed":60751,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["regressor = SVR(C = 1000)\n","regressor.fit(X_train, y_train)"],"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVR(C=1000, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n","    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"id":"S59fAabYmcbq","colab_type":"code","outputId":"6b626314-a885-4473-82ed-31de52ecf1df","executionInfo":{"status":"ok","timestamp":1587306452046,"user_tz":-420,"elapsed":60741,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["SVR (C = 100.0, cache_size = 200, coef0 = 0.0, degree = 3, epsilon = 0.1, gamma = 'scale', kernel = 'rbf',\n","     max_iter = -1, shrinking = True, tol = 0.001, verbose = False)"],"execution_count":42,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SVR(C=100.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n","    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"code","metadata":{"id":"r_peOOznmv8n","colab_type":"code","outputId":"044539de-0c79-4b11-eff8-f65a800167ee","executionInfo":{"status":"ok","timestamp":1587306452048,"user_tz":-420,"elapsed":60732,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["trainSet = pd.concat([X_train, y_train], axis = 1)\n","trainSet.head()"],"execution_count":43,"outputs":[{"output_type":"execute_result","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>PrevAVGCost</th>\n","      <th>PrevAssignedCost</th>\n","      <th>AVGCost</th>\n","      <th>LatestDateCost</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","      <th>E</th>\n","      <th>F</th>\n","      <th>G</th>\n","      <th>GenPrice</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>17651</th>\n","      <td>12.049180</td>\n","      <td>30.0</td>\n","      <td>18.196721</td>\n","      <td>22.967213</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>42.0</td>\n","    </tr>\n","    <tr>\n","      <th>22606</th>\n","      <td>124.793814</td>\n","      <td>130.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>169.0</td>\n","    </tr>\n","    <tr>\n","      <th>14124</th>\n","      <td>28.343434</td>\n","      <td>29.0</td>\n","      <td>32.959596</td>\n","      <td>40.949495</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>60.0</td>\n","    </tr>\n","    <tr>\n","      <th>5777</th>\n","      <td>21.506024</td>\n","      <td>24.0</td>\n","      <td>22.963855</td>\n","      <td>29.000000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>36.0</td>\n","    </tr>\n","    <tr>\n","      <th>15500</th>\n","      <td>12.557895</td>\n","      <td>13.0</td>\n","      <td>14.452632</td>\n","      <td>16.463158</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>25.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       PrevAVGCost  PrevAssignedCost    AVGCost  ...  F  G  GenPrice\n","17651    12.049180              30.0  18.196721  ...  0  0      42.0\n","22606   124.793814             130.0   0.000000  ...  0  0     169.0\n","14124    28.343434              29.0  32.959596  ...  0  0      60.0\n","5777     21.506024              24.0  22.963855  ...  0  0      36.0\n","15500    12.557895              13.0  14.452632  ...  0  0      25.0\n","\n","[5 rows x 12 columns]"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"markdown","metadata":{"id":"9tR4HkJmbyfx","colab_type":"text"},"source":["## Applying Grid Search to find the best model and the best parameter"]},{"cell_type":"code","metadata":{"id":"A2g33TTabw6b","colab_type":"code","colab":{}},"source":["# from sklearn.model_selection import GridSearchCV\n","# parameters = [{'C': [1, 10, 100, 1000], 'kernel': ['linear']},\n","#               {'C': [1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]\n","\n","# grid_search = GridSearchCV(estimator = regressor,\n","#                            param_grid = parameters,\n","#                            scoring = 'accuracy',\n","#                            cv = 10,\n","#                            n_jobs = -1)\n","\n","# grid_search = grid_search.fit(X_train, y_train)\n","# best_accuracy = grid_search.best_score_\n","# best_parameters = grid_search.best_params_\n","# print(\"Best Accuracy: {:.2f} %\".format(best_accuracy*100))\n","# print(\"Best Parameters:\", best_parameters)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"s7R5otcBP1gw","colab_type":"text"},"source":["## Evaluate model accuracy"]},{"cell_type":"code","metadata":{"id":"MEX79D0aL0y4","colab_type":"code","outputId":"1a1423f6-604f-4f1a-887e-ffb6f8d7483e","executionInfo":{"status":"ok","timestamp":1587306456124,"user_tz":-420,"elapsed":64789,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["y_pred = regressor.predict(X_test)\n","y_pred"],"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 29.46638669, 108.02588748,  85.14855899, ...,  98.43355054,\n","        27.1104581 ,  39.60803558])"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"code","metadata":{"id":"oR1K5HWeM9XB","colab_type":"code","outputId":"baa97f2a-140f-4cc6-8f61-63c1de7c3e00","executionInfo":{"status":"ok","timestamp":1587306456128,"user_tz":-420,"elapsed":64783,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["testSet = pd.concat([X_test, y_test], axis = 1)\n","testSet.head()"],"execution_count":46,"outputs":[{"output_type":"execute_result","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>PrevAVGCost</th>\n","      <th>PrevAssignedCost</th>\n","      <th>AVGCost</th>\n","      <th>LatestDateCost</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","      <th>E</th>\n","      <th>F</th>\n","      <th>G</th>\n","      <th>GenPrice</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>12301</th>\n","      <td>18.919192</td>\n","      <td>20.0</td>\n","      <td>17.707071</td>\n","      <td>17.757576</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>27.0</td>\n","    </tr>\n","    <tr>\n","      <th>13317</th>\n","      <td>62.808081</td>\n","      <td>67.0</td>\n","      <td>73.484848</td>\n","      <td>74.656566</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>105.0</td>\n","    </tr>\n","    <tr>\n","      <th>12320</th>\n","      <td>0.000000</td>\n","      <td>61.0</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>88.0</td>\n","    </tr>\n","    <tr>\n","      <th>4013</th>\n","      <td>107.747475</td>\n","      <td>110.0</td>\n","      <td>80.808081</td>\n","      <td>80.808081</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>142.0</td>\n","    </tr>\n","    <tr>\n","      <th>7807</th>\n","      <td>40.606061</td>\n","      <td>50.0</td>\n","      <td>61.505051</td>\n","      <td>66.222222</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>99.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       PrevAVGCost  PrevAssignedCost    AVGCost  ...  F  G  GenPrice\n","12301    18.919192              20.0  17.707071  ...  0  0      27.0\n","13317    62.808081              67.0  73.484848  ...  0  0     105.0\n","12320     0.000000              61.0   0.000000  ...  0  0      88.0\n","4013    107.747475             110.0  80.808081  ...  0  1     142.0\n","7807     40.606061              50.0  61.505051  ...  0  0      99.0\n","\n","[5 rows x 12 columns]"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"markdown","metadata":{"id":"qU37_B-SP6TS","colab_type":"text"},"source":["Compare GenPrice with PredictedGenPrice"]},{"cell_type":"code","metadata":{"id":"4pjuV0IfNHrj","colab_type":"code","outputId":"29597585-368f-4264-c9ff-6acdd228d18e","executionInfo":{"status":"ok","timestamp":1587306456130,"user_tz":-420,"elapsed":64775,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":359}},"source":["datasetPredict = pd.concat([testSet.reset_index(), pd.Series(y_pred, name = 'PredictedGenPrice')], axis = 1).round(2)\n","datasetPredict.head(10)"],"execution_count":47,"outputs":[{"output_type":"execute_result","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>index</th>\n","      <th>PrevAVGCost</th>\n","      <th>PrevAssignedCost</th>\n","      <th>AVGCost</th>\n","      <th>LatestDateCost</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","      <th>E</th>\n","      <th>F</th>\n","      <th>G</th>\n","      <th>GenPrice</th>\n","      <th>PredictedGenPrice</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>12301</td>\n","      <td>18.92</td>\n","      <td>20.0</td>\n","      <td>17.71</td>\n","      <td>17.76</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>27.0</td>\n","      <td>29.47</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>13317</td>\n","      <td>62.81</td>\n","      <td>67.0</td>\n","      <td>73.48</td>\n","      <td>74.66</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>105.0</td>\n","      <td>108.03</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>12320</td>\n","      <td>0.00</td>\n","      <td>61.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>88.0</td>\n","      <td>85.15</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4013</td>\n","      <td>107.75</td>\n","      <td>110.0</td>\n","      <td>80.81</td>\n","      <td>80.81</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>142.0</td>\n","      <td>124.81</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7807</td>\n","      <td>40.61</td>\n","      <td>50.0</td>\n","      <td>61.51</td>\n","      <td>66.22</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>99.0</td>\n","      <td>102.58</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>439</td>\n","      <td>0.00</td>\n","      <td>13.0</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>33.0</td>\n","      <td>28.68</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>9995</td>\n","      <td>33.55</td>\n","      <td>33.0</td>\n","      <td>33.18</td>\n","      <td>33.94</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>54.0</td>\n","      <td>51.63</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>14270</td>\n","      <td>48.82</td>\n","      <td>84.0</td>\n","      <td>50.51</td>\n","      <td>50.51</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>117.0</td>\n","      <td>104.13</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>11358</td>\n","      <td>15.15</td>\n","      <td>15.0</td>\n","      <td>15.15</td>\n","      <td>15.15</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>30.0</td>\n","      <td>31.25</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>8320</td>\n","      <td>22.69</td>\n","      <td>25.0</td>\n","      <td>22.88</td>\n","      <td>22.47</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>52.0</td>\n","      <td>37.72</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   index  PrevAVGCost  PrevAssignedCost  ...  G  GenPrice  PredictedGenPrice\n","0  12301        18.92              20.0  ...  0      27.0              29.47\n","1  13317        62.81              67.0  ...  0     105.0             108.03\n","2  12320         0.00              61.0  ...  0      88.0              85.15\n","3   4013       107.75             110.0  ...  1     142.0             124.81\n","4   7807        40.61              50.0  ...  0      99.0             102.58\n","5    439         0.00              13.0  ...  0      33.0              28.68\n","6   9995        33.55              33.0  ...  1      54.0              51.63\n","7  14270        48.82              84.0  ...  0     117.0             104.13\n","8  11358        15.15              15.0  ...  0      30.0              31.25\n","9   8320        22.69              25.0  ...  1      52.0              37.72\n","\n","[10 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":47}]},{"cell_type":"code","metadata":{"id":"lXfwo7PdPkpe","colab_type":"code","outputId":"f4cfe5f6-b0ae-4090-c57a-4514970adf81","executionInfo":{"status":"ok","timestamp":1587306456132,"user_tz":-420,"elapsed":64766,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":503}},"source":["datasetPredict.corr()"],"execution_count":48,"outputs":[{"output_type":"execute_result","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>index</th>\n","      <th>PrevAVGCost</th>\n","      <th>PrevAssignedCost</th>\n","      <th>AVGCost</th>\n","      <th>LatestDateCost</th>\n","      <th>A</th>\n","      <th>B</th>\n","      <th>C</th>\n","      <th>D</th>\n","      <th>E</th>\n","      <th>F</th>\n","      <th>G</th>\n","      <th>GenPrice</th>\n","      <th>PredictedGenPrice</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>index</th>\n","      <td>1.000000</td>\n","      <td>-0.005980</td>\n","      <td>0.003228</td>\n","      <td>-0.004309</td>\n","      <td>0.006373</td>\n","      <td>-0.018983</td>\n","      <td>-0.007056</td>\n","      <td>0.006269</td>\n","      <td>0.018399</td>\n","      <td>0.012576</td>\n","      <td>0.002493</td>\n","      <td>0.012633</td>\n","      <td>0.007801</td>\n","      <td>0.008008</td>\n","    </tr>\n","    <tr>\n","      <th>PrevAVGCost</th>\n","      <td>-0.005980</td>\n","      <td>1.000000</td>\n","      <td>0.525551</td>\n","      <td>0.784302</td>\n","      <td>0.764423</td>\n","      <td>-0.059053</td>\n","      <td>0.124087</td>\n","      <td>0.148206</td>\n","      <td>-0.056017</td>\n","      <td>-0.079911</td>\n","      <td>0.080937</td>\n","      <td>-0.101463</td>\n","      <td>0.494235</td>\n","      <td>0.494815</td>\n","    </tr>\n","    <tr>\n","      <th>PrevAssignedCost</th>\n","      <td>0.003228</td>\n","      <td>0.525551</td>\n","      <td>1.000000</td>\n","      <td>0.524154</td>\n","      <td>0.462267</td>\n","      <td>-0.117272</td>\n","      <td>0.105027</td>\n","      <td>0.170726</td>\n","      <td>-0.068318</td>\n","      <td>-0.094067</td>\n","      <td>0.037458</td>\n","      <td>-0.028785</td>\n","      <td>0.981192</td>\n","      <td>0.984813</td>\n","    </tr>\n","    <tr>\n","      <th>AVGCost</th>\n","      <td>-0.004309</td>\n","      <td>0.784302</td>\n","      <td>0.524154</td>\n","      <td>1.000000</td>\n","      <td>0.878765</td>\n","      <td>-0.055066</td>\n","      <td>0.126399</td>\n","      <td>0.132833</td>\n","      <td>-0.054911</td>\n","      <td>-0.079057</td>\n","      <td>0.076166</td>\n","      <td>-0.094352</td>\n","      <td>0.529382</td>\n","      <td>0.532406</td>\n","    </tr>\n","    <tr>\n","      <th>LatestDateCost</th>\n","      <td>0.006373</td>\n","      <td>0.764423</td>\n","      <td>0.462267</td>\n","      <td>0.878765</td>\n","      <td>1.000000</td>\n","      <td>-0.050260</td>\n","      <td>0.149702</td>\n","      <td>0.146129</td>\n","      <td>-0.053841</td>\n","      <td>-0.079573</td>\n","      <td>0.085198</td>\n","      <td>-0.125042</td>\n","      <td>0.473442</td>\n","      <td>0.473455</td>\n","    </tr>\n","    <tr>\n","      <th>A</th>\n","      <td>-0.018983</td>\n","      <td>-0.059053</td>\n","      <td>-0.117272</td>\n","      <td>-0.055066</td>\n","      <td>-0.050260</td>\n","      <td>1.000000</td>\n","      <td>-0.254609</td>\n","      <td>-0.424238</td>\n","      <td>-0.097037</td>\n","      <td>-0.119567</td>\n","      <td>-0.077771</td>\n","      <td>-0.593428</td>\n","      <td>-0.130243</td>\n","      <td>-0.131958</td>\n","    </tr>\n","    <tr>\n","      <th>B</th>\n","      <td>-0.007056</td>\n","      <td>0.124087</td>\n","      <td>0.105027</td>\n","      <td>0.126399</td>\n","      <td>0.149702</td>\n","      <td>-0.254609</td>\n","      <td>1.000000</td>\n","      <td>-0.105758</td>\n","      <td>-0.024190</td>\n","      <td>-0.029807</td>\n","      <td>-0.019388</td>\n","      <td>-0.147936</td>\n","      <td>0.099396</td>\n","      <td>0.103240</td>\n","    </tr>\n","    <tr>\n","      <th>C</th>\n","      <td>0.006269</td>\n","      <td>0.148206</td>\n","      <td>0.170726</td>\n","      <td>0.132833</td>\n","      <td>0.146129</td>\n","      <td>-0.424238</td>\n","      <td>-0.105758</td>\n","      <td>1.000000</td>\n","      <td>-0.040307</td>\n","      <td>-0.049665</td>\n","      <td>-0.032304</td>\n","      <td>-0.246495</td>\n","      <td>0.195184</td>\n","      <td>0.196793</td>\n","    </tr>\n","    <tr>\n","      <th>D</th>\n","      <td>0.018399</td>\n","      <td>-0.056017</td>\n","      <td>-0.068318</td>\n","      <td>-0.054911</td>\n","      <td>-0.053841</td>\n","      <td>-0.097037</td>\n","      <td>-0.024190</td>\n","      <td>-0.040307</td>\n","      <td>1.000000</td>\n","      <td>-0.011360</td>\n","      <td>-0.007389</td>\n","      <td>-0.056381</td>\n","      <td>-0.073099</td>\n","      <td>-0.072498</td>\n","    </tr>\n","    <tr>\n","      <th>E</th>\n","      <td>0.012576</td>\n","      <td>-0.079911</td>\n","      <td>-0.094067</td>\n","      <td>-0.079057</td>\n","      <td>-0.079573</td>\n","      <td>-0.119567</td>\n","      <td>-0.029807</td>\n","      <td>-0.049665</td>\n","      <td>-0.011360</td>\n","      <td>1.000000</td>\n","      <td>-0.009105</td>\n","      <td>-0.069472</td>\n","      <td>-0.101863</td>\n","      <td>-0.101920</td>\n","    </tr>\n","    <tr>\n","      <th>F</th>\n","      <td>0.002493</td>\n","      <td>0.080937</td>\n","      <td>0.037458</td>\n","      <td>0.076166</td>\n","      <td>0.085198</td>\n","      <td>-0.077771</td>\n","      <td>-0.019388</td>\n","      <td>-0.032304</td>\n","      <td>-0.007389</td>\n","      <td>-0.009105</td>\n","      <td>1.000000</td>\n","      <td>-0.045187</td>\n","      <td>0.037332</td>\n","      <td>0.037199</td>\n","    </tr>\n","    <tr>\n","      <th>G</th>\n","      <td>0.012633</td>\n","      <td>-0.101463</td>\n","      <td>-0.028785</td>\n","      <td>-0.094352</td>\n","      <td>-0.125042</td>\n","      <td>-0.593428</td>\n","      <td>-0.147936</td>\n","      <td>-0.246495</td>\n","      <td>-0.056381</td>\n","      <td>-0.069472</td>\n","      <td>-0.045187</td>\n","      <td>1.000000</td>\n","      <td>-0.027723</td>\n","      <td>-0.029253</td>\n","    </tr>\n","    <tr>\n","      <th>GenPrice</th>\n","      <td>0.007801</td>\n","      <td>0.494235</td>\n","      <td>0.981192</td>\n","      <td>0.529382</td>\n","      <td>0.473442</td>\n","      <td>-0.130243</td>\n","      <td>0.099396</td>\n","      <td>0.195184</td>\n","      <td>-0.073099</td>\n","      <td>-0.101863</td>\n","      <td>0.037332</td>\n","      <td>-0.027723</td>\n","      <td>1.000000</td>\n","      <td>0.991964</td>\n","    </tr>\n","    <tr>\n","      <th>PredictedGenPrice</th>\n","      <td>0.008008</td>\n","      <td>0.494815</td>\n","      <td>0.984813</td>\n","      <td>0.532406</td>\n","      <td>0.473455</td>\n","      <td>-0.131958</td>\n","      <td>0.103240</td>\n","      <td>0.196793</td>\n","      <td>-0.072498</td>\n","      <td>-0.101920</td>\n","      <td>0.037199</td>\n","      <td>-0.029253</td>\n","      <td>0.991964</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                      index  PrevAVGCost  ...  GenPrice  PredictedGenPrice\n","index              1.000000    -0.005980  ...  0.007801           0.008008\n","PrevAVGCost       -0.005980     1.000000  ...  0.494235           0.494815\n","PrevAssignedCost   0.003228     0.525551  ...  0.981192           0.984813\n","AVGCost           -0.004309     0.784302  ...  0.529382           0.532406\n","LatestDateCost     0.006373     0.764423  ...  0.473442           0.473455\n","A                 -0.018983    -0.059053  ... -0.130243          -0.131958\n","B                 -0.007056     0.124087  ...  0.099396           0.103240\n","C                  0.006269     0.148206  ...  0.195184           0.196793\n","D                  0.018399    -0.056017  ... -0.073099          -0.072498\n","E                  0.012576    -0.079911  ... -0.101863          -0.101920\n","F                  0.002493     0.080937  ...  0.037332           0.037199\n","G                  0.012633    -0.101463  ... -0.027723          -0.029253\n","GenPrice           0.007801     0.494235  ...  1.000000           0.991964\n","PredictedGenPrice  0.008008     0.494815  ...  0.991964           1.000000\n","\n","[14 rows x 14 columns]"]},"metadata":{"tags":[]},"execution_count":48}]},{"cell_type":"code","metadata":{"id":"V3iu6Sn7PoQQ","colab_type":"code","outputId":"2a996df2-7bdb-4c29-aac1-1c20e3da7d93","executionInfo":{"status":"ok","timestamp":1587306472401,"user_tz":-420,"elapsed":81023,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["print(\"Training set accuracy = \" + str(regressor.score(X_train, y_train)))\n","print(\"Test set accuracy = \" + str(regressor.score(X_test, y_test)))"],"execution_count":49,"outputs":[{"output_type":"stream","text":["Training set accuracy = 0.9932171490653403\n","Test set accuracy = 0.9839647896372504\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GZ3MNuvfP-sF","colab_type":"text"},"source":["### MSE"]},{"cell_type":"code","metadata":{"id":"kw1iRsnlQD3A","colab_type":"code","outputId":"62320671-055d-44c2-8a6f-fe0f3f62fbaf","executionInfo":{"status":"ok","timestamp":1587306472403,"user_tz":-420,"elapsed":81014,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from sklearn import metrics\n","print('MSE:', metrics.mean_squared_error(y_test, y_pred))"],"execution_count":50,"outputs":[{"output_type":"stream","text":["MSE: 162.3914396460034\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"lOD30X8_st56","colab_type":"text"},"source":["MSE v1: 166.24065925082212"]},{"cell_type":"markdown","metadata":{"id":"2-OGsiV-QJJG","colab_type":"text"},"source":["### MAPE"]},{"cell_type":"code","metadata":{"id":"TuyHS9lNQKTz","colab_type":"code","outputId":"37c5a7f1-bcfd-45cc-8b40-236251b7902b","executionInfo":{"status":"ok","timestamp":1587306472404,"user_tz":-420,"elapsed":81005,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["def mean_absolute_percentage_error(y_test, y_pred):\n","  y_test, y_pred = np.array(y_test), np.array(y_pred)\n","  return np.mean(np.abs((y_test - y_pred)/y_test)) * 100\n","\n","print('MAPE:', mean_absolute_percentage_error(y_test, y_pred))"],"execution_count":51,"outputs":[{"output_type":"stream","text":["MAPE: 6.457370539718754\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"DoozplnOsw2A","colab_type":"text"},"source":["MAPE v1: 6.8783985972082045"]},{"cell_type":"markdown","metadata":{"id":"GtQ7_le_QhN-","colab_type":"text"},"source":["### Visualize Results"]},{"cell_type":"code","metadata":{"id":"qnUTkchjQj1s","colab_type":"code","outputId":"19ba2f93-e0e2-42e3-9846-345cd7383489","executionInfo":{"status":"ok","timestamp":1587306472405,"user_tz":-420,"elapsed":80994,"user":{"displayName":"Wutipat Khamnuansin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhMufONefW7RhVaq3BPhpMxL-V398Ht54OCT4fTFQ=s64","userId":"07746324893032953154"}},"colab":{"base_uri":"https://localhost:8080/","height":279}},"source":["import matplotlib.pyplot as plt\n","plt.plot([i for i in range(len(y_pred))], y_pred, color = 'r')\n","plt.scatter([i for i in range(len(y_pred))], y_test, color = 'b')\n","plt.ylabel('Price')\n","plt.xlabel('Index')\n","plt.legend(['Predict', 'True'], loc = 'best')\n","plt.show()"],"execution_count":52,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}