datascience_tutorial_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn import metrics \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data\n",
    "dataset = pd.read_csv('neat_crude_cut_properties.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SampleID</th>\n",
       "      <th>VR CCR Lab</th>\n",
       "      <th>VR Asp Lab</th>\n",
       "      <th>W_IBP-15 Lab</th>\n",
       "      <th>W_C5-105 Lab</th>\n",
       "      <th>W_105-165 Lab</th>\n",
       "      <th>W_165-227 Lab</th>\n",
       "      <th>W_227-270 lab</th>\n",
       "      <th>W_270-370 lab</th>\n",
       "      <th>W_370-390 Lab</th>\n",
       "      <th>...</th>\n",
       "      <th>4032</th>\n",
       "      <th>4025</th>\n",
       "      <th>4020</th>\n",
       "      <th>4016</th>\n",
       "      <th>4015</th>\n",
       "      <th>4013</th>\n",
       "      <th>4009</th>\n",
       "      <th>4005</th>\n",
       "      <th>4001</th>\n",
       "      <th>4000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AF_001_1010_120107_LP</td>\n",
       "      <td>30.10</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0.61</td>\n",
       "      <td>5.77</td>\n",
       "      <td>6.27</td>\n",
       "      <td>7.72</td>\n",
       "      <td>5.50</td>\n",
       "      <td>13.66</td>\n",
       "      <td>0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005060</td>\n",
       "      <td>0.004777</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>0.004538</td>\n",
       "      <td>0.004518</td>\n",
       "      <td>0.004478</td>\n",
       "      <td>0.004399</td>\n",
       "      <td>0.004317</td>\n",
       "      <td>0.004231</td>\n",
       "      <td>0.004210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF_001_2013_141227_TBP</td>\n",
       "      <td>31.40</td>\n",
       "      <td>21.9</td>\n",
       "      <td>0.56</td>\n",
       "      <td>6.30</td>\n",
       "      <td>5.57</td>\n",
       "      <td>6.81</td>\n",
       "      <td>5.03</td>\n",
       "      <td>13.49</td>\n",
       "      <td>0.95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005215</td>\n",
       "      <td>0.004953</td>\n",
       "      <td>0.004817</td>\n",
       "      <td>0.004729</td>\n",
       "      <td>0.004710</td>\n",
       "      <td>0.004671</td>\n",
       "      <td>0.004594</td>\n",
       "      <td>0.004511</td>\n",
       "      <td>0.004428</td>\n",
       "      <td>0.004407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AF_001_2083_150309_STC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005197</td>\n",
       "      <td>0.004927</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>0.004696</td>\n",
       "      <td>0.004676</td>\n",
       "      <td>0.004636</td>\n",
       "      <td>0.004554</td>\n",
       "      <td>0.004473</td>\n",
       "      <td>0.004391</td>\n",
       "      <td>0.004371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AF_001_2111_150409_STC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005155</td>\n",
       "      <td>0.004882</td>\n",
       "      <td>0.004738</td>\n",
       "      <td>0.004646</td>\n",
       "      <td>0.004625</td>\n",
       "      <td>0.004584</td>\n",
       "      <td>0.004501</td>\n",
       "      <td>0.004416</td>\n",
       "      <td>0.004330</td>\n",
       "      <td>0.004309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF_001_2145_150504_TBP</td>\n",
       "      <td>30.70</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.61</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.72</td>\n",
       "      <td>6.42</td>\n",
       "      <td>5.20</td>\n",
       "      <td>13.38</td>\n",
       "      <td>1.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005194</td>\n",
       "      <td>0.004911</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>0.004675</td>\n",
       "      <td>0.004656</td>\n",
       "      <td>0.004619</td>\n",
       "      <td>0.004544</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>UN_029_1065_111225_LP</td>\n",
       "      <td>10.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.30</td>\n",
       "      <td>6.00</td>\n",
       "      <td>8.20</td>\n",
       "      <td>9.60</td>\n",
       "      <td>8.60</td>\n",
       "      <td>24.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005324</td>\n",
       "      <td>0.005030</td>\n",
       "      <td>0.004891</td>\n",
       "      <td>0.004814</td>\n",
       "      <td>0.004796</td>\n",
       "      <td>0.004761</td>\n",
       "      <td>0.004681</td>\n",
       "      <td>0.004586</td>\n",
       "      <td>0.004479</td>\n",
       "      <td>0.004453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>UN_029_1664_140124_TBP</td>\n",
       "      <td>11.29</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.76</td>\n",
       "      <td>7.99</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.98</td>\n",
       "      <td>9.84</td>\n",
       "      <td>21.31</td>\n",
       "      <td>0.04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005386</td>\n",
       "      <td>0.005043</td>\n",
       "      <td>0.004888</td>\n",
       "      <td>0.004808</td>\n",
       "      <td>0.004789</td>\n",
       "      <td>0.004749</td>\n",
       "      <td>0.004650</td>\n",
       "      <td>0.004553</td>\n",
       "      <td>0.004434</td>\n",
       "      <td>0.004405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>UN_029_2619_160920_TBP</td>\n",
       "      <td>12.21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.87</td>\n",
       "      <td>8.27</td>\n",
       "      <td>10.74</td>\n",
       "      <td>11.85</td>\n",
       "      <td>9.33</td>\n",
       "      <td>22.48</td>\n",
       "      <td>1.32</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005307</td>\n",
       "      <td>0.004997</td>\n",
       "      <td>0.004851</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>0.004745</td>\n",
       "      <td>0.004702</td>\n",
       "      <td>0.004607</td>\n",
       "      <td>0.004493</td>\n",
       "      <td>0.004368</td>\n",
       "      <td>0.004338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>UN_029_9073_190325_TBP</td>\n",
       "      <td>11.90</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.62</td>\n",
       "      <td>7.92</td>\n",
       "      <td>11.84</td>\n",
       "      <td>12.21</td>\n",
       "      <td>9.75</td>\n",
       "      <td>22.47</td>\n",
       "      <td>0.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005296</td>\n",
       "      <td>0.004982</td>\n",
       "      <td>0.004842</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>0.004747</td>\n",
       "      <td>0.004708</td>\n",
       "      <td>0.004613</td>\n",
       "      <td>0.004492</td>\n",
       "      <td>0.004355</td>\n",
       "      <td>0.004323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2963</th>\n",
       "      <td>UN_031_9309_161009_TBP</td>\n",
       "      <td>23.30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.48</td>\n",
       "      <td>3.79</td>\n",
       "      <td>5.62</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.09</td>\n",
       "      <td>20.38</td>\n",
       "      <td>1.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005363</td>\n",
       "      <td>0.005020</td>\n",
       "      <td>0.004830</td>\n",
       "      <td>0.004712</td>\n",
       "      <td>0.004685</td>\n",
       "      <td>0.004632</td>\n",
       "      <td>0.004531</td>\n",
       "      <td>0.004425</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.004287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2964 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SampleID  VR CCR Lab  VR Asp Lab  W_IBP-15 Lab  \\\n",
       "0      AF_001_1010_120107_LP       30.10        21.6          0.61   \n",
       "1     AF_001_2013_141227_TBP       31.40        21.9          0.56   \n",
       "2     AF_001_2083_150309_STC         NaN         NaN           NaN   \n",
       "3     AF_001_2111_150409_STC         NaN         NaN           NaN   \n",
       "4     AF_001_2145_150504_TBP       30.70        23.8          0.61   \n",
       "...                      ...         ...         ...           ...   \n",
       "2959   UN_029_1065_111225_LP       10.90         NaN          0.30   \n",
       "2960  UN_029_1664_140124_TBP       11.29         1.3          0.76   \n",
       "2961  UN_029_2619_160920_TBP       12.21         1.0          0.87   \n",
       "2962  UN_029_9073_190325_TBP       11.90         1.5          0.62   \n",
       "2963  UN_031_9309_161009_TBP       23.30        12.2          0.48   \n",
       "\n",
       "      W_C5-105 Lab  W_105-165 Lab  W_165-227 Lab  W_227-270 lab  \\\n",
       "0             5.77           6.27           7.72           5.50   \n",
       "1             6.30           5.57           6.81           5.03   \n",
       "2              NaN            NaN            NaN            NaN   \n",
       "3              NaN            NaN            NaN            NaN   \n",
       "4             4.45           4.72           6.42           5.20   \n",
       "...            ...            ...            ...            ...   \n",
       "2959          6.00           8.20           9.60           8.60   \n",
       "2960          7.99          10.75          11.98           9.84   \n",
       "2961          8.27          10.74          11.85           9.33   \n",
       "2962          7.92          11.84          12.21           9.75   \n",
       "2963          3.79           5.62           9.96           8.09   \n",
       "\n",
       "      W_270-370 lab  W_370-390 Lab  ...      4032      4025      4020  \\\n",
       "0             13.66           0.05  ...  0.005060  0.004777  0.004628   \n",
       "1             13.49           0.95  ...  0.005215  0.004953  0.004817   \n",
       "2               NaN            NaN  ...  0.005197  0.004927  0.004785   \n",
       "3               NaN            NaN  ...  0.005155  0.004882  0.004738   \n",
       "4             13.38           1.35  ...  0.005194  0.004911  0.004765   \n",
       "...             ...            ...  ...       ...       ...       ...   \n",
       "2959          24.30           0.00  ...  0.005324  0.005030  0.004891   \n",
       "2960          21.31           0.04  ...  0.005386  0.005043  0.004888   \n",
       "2961          22.48           1.32  ...  0.005307  0.004997  0.004851   \n",
       "2962          22.47           0.29  ...  0.005296  0.004982  0.004842   \n",
       "2963          20.38           1.30  ...  0.005363  0.005020  0.004830   \n",
       "\n",
       "          4016      4015      4013      4009      4005      4001      4000  \n",
       "0     0.004538  0.004518  0.004478  0.004399  0.004317  0.004231  0.004210  \n",
       "1     0.004729  0.004710  0.004671  0.004594  0.004511  0.004428  0.004407  \n",
       "2     0.004696  0.004676  0.004636  0.004554  0.004473  0.004391  0.004371  \n",
       "3     0.004646  0.004625  0.004584  0.004501  0.004416  0.004330  0.004309  \n",
       "4     0.004675  0.004656  0.004619  0.004544  0.004463  0.004378  0.004357  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "2959  0.004814  0.004796  0.004761  0.004681  0.004586  0.004479  0.004453  \n",
       "2960  0.004808  0.004789  0.004749  0.004650  0.004553  0.004434  0.004405  \n",
       "2961  0.004765  0.004745  0.004702  0.004607  0.004493  0.004368  0.004338  \n",
       "2962  0.004765  0.004747  0.004708  0.004613  0.004492  0.004355  0.004323  \n",
       "2963  0.004712  0.004685  0.004632  0.004531  0.004425  0.004314  0.004287  \n",
       "\n",
       "[2964 rows x 200 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show dataset \n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SampleID</th>\n",
       "      <th>VR CCR Lab</th>\n",
       "      <th>VR Asp Lab</th>\n",
       "      <th>W_IBP-15 Lab</th>\n",
       "      <th>W_C5-105 Lab</th>\n",
       "      <th>W_105-165 Lab</th>\n",
       "      <th>W_165-227 Lab</th>\n",
       "      <th>W_227-270 lab</th>\n",
       "      <th>W_270-370 lab</th>\n",
       "      <th>W_370-390 Lab</th>\n",
       "      <th>...</th>\n",
       "      <th>4032</th>\n",
       "      <th>4025</th>\n",
       "      <th>4020</th>\n",
       "      <th>4016</th>\n",
       "      <th>4015</th>\n",
       "      <th>4013</th>\n",
       "      <th>4009</th>\n",
       "      <th>4005</th>\n",
       "      <th>4001</th>\n",
       "      <th>4000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AF_001_1010_120107_LP</td>\n",
       "      <td>30.10</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0.61</td>\n",
       "      <td>5.77</td>\n",
       "      <td>6.27</td>\n",
       "      <td>7.72</td>\n",
       "      <td>5.50</td>\n",
       "      <td>13.66</td>\n",
       "      <td>0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005060</td>\n",
       "      <td>0.004777</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>0.004538</td>\n",
       "      <td>0.004518</td>\n",
       "      <td>0.004478</td>\n",
       "      <td>0.004399</td>\n",
       "      <td>0.004317</td>\n",
       "      <td>0.004231</td>\n",
       "      <td>0.004210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AF_001_2013_141227_TBP</td>\n",
       "      <td>31.40</td>\n",
       "      <td>21.9</td>\n",
       "      <td>0.56</td>\n",
       "      <td>6.30</td>\n",
       "      <td>5.57</td>\n",
       "      <td>6.81</td>\n",
       "      <td>5.03</td>\n",
       "      <td>13.49</td>\n",
       "      <td>0.95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005215</td>\n",
       "      <td>0.004953</td>\n",
       "      <td>0.004817</td>\n",
       "      <td>0.004729</td>\n",
       "      <td>0.004710</td>\n",
       "      <td>0.004671</td>\n",
       "      <td>0.004594</td>\n",
       "      <td>0.004511</td>\n",
       "      <td>0.004428</td>\n",
       "      <td>0.004407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AF_001_2145_150504_TBP</td>\n",
       "      <td>30.70</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.61</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.72</td>\n",
       "      <td>6.42</td>\n",
       "      <td>5.20</td>\n",
       "      <td>13.38</td>\n",
       "      <td>1.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005194</td>\n",
       "      <td>0.004911</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>0.004675</td>\n",
       "      <td>0.004656</td>\n",
       "      <td>0.004619</td>\n",
       "      <td>0.004544</td>\n",
       "      <td>0.004463</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AF_001_2430_160325_TBP</td>\n",
       "      <td>27.66</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>5.63</td>\n",
       "      <td>4.59</td>\n",
       "      <td>5.96</td>\n",
       "      <td>5.41</td>\n",
       "      <td>13.68</td>\n",
       "      <td>1.28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005234</td>\n",
       "      <td>0.004961</td>\n",
       "      <td>0.004818</td>\n",
       "      <td>0.004730</td>\n",
       "      <td>0.004710</td>\n",
       "      <td>0.004672</td>\n",
       "      <td>0.004597</td>\n",
       "      <td>0.004520</td>\n",
       "      <td>0.004439</td>\n",
       "      <td>0.004419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AF_001_2727_161214_TBP</td>\n",
       "      <td>29.60</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4.32</td>\n",
       "      <td>5.29</td>\n",
       "      <td>6.51</td>\n",
       "      <td>5.17</td>\n",
       "      <td>13.76</td>\n",
       "      <td>1.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005051</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>0.004619</td>\n",
       "      <td>0.004529</td>\n",
       "      <td>0.004509</td>\n",
       "      <td>0.004467</td>\n",
       "      <td>0.004383</td>\n",
       "      <td>0.004298</td>\n",
       "      <td>0.004208</td>\n",
       "      <td>0.004186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>UN_028_9425_170919_TBP</td>\n",
       "      <td>23.60</td>\n",
       "      <td>21.7</td>\n",
       "      <td>0.29</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.91</td>\n",
       "      <td>5.57</td>\n",
       "      <td>2.96</td>\n",
       "      <td>13.47</td>\n",
       "      <td>0.23</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005362</td>\n",
       "      <td>0.005127</td>\n",
       "      <td>0.004987</td>\n",
       "      <td>0.004894</td>\n",
       "      <td>0.004872</td>\n",
       "      <td>0.004828</td>\n",
       "      <td>0.004739</td>\n",
       "      <td>0.004668</td>\n",
       "      <td>0.004593</td>\n",
       "      <td>0.004574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>UN_029_1664_140124_TBP</td>\n",
       "      <td>11.29</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.76</td>\n",
       "      <td>7.99</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.98</td>\n",
       "      <td>9.84</td>\n",
       "      <td>21.31</td>\n",
       "      <td>0.04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005386</td>\n",
       "      <td>0.005043</td>\n",
       "      <td>0.004888</td>\n",
       "      <td>0.004808</td>\n",
       "      <td>0.004789</td>\n",
       "      <td>0.004749</td>\n",
       "      <td>0.004650</td>\n",
       "      <td>0.004553</td>\n",
       "      <td>0.004434</td>\n",
       "      <td>0.004405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>UN_029_2619_160920_TBP</td>\n",
       "      <td>12.21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.87</td>\n",
       "      <td>8.27</td>\n",
       "      <td>10.74</td>\n",
       "      <td>11.85</td>\n",
       "      <td>9.33</td>\n",
       "      <td>22.48</td>\n",
       "      <td>1.32</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005307</td>\n",
       "      <td>0.004997</td>\n",
       "      <td>0.004851</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>0.004745</td>\n",
       "      <td>0.004702</td>\n",
       "      <td>0.004607</td>\n",
       "      <td>0.004493</td>\n",
       "      <td>0.004368</td>\n",
       "      <td>0.004338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>UN_029_9073_190325_TBP</td>\n",
       "      <td>11.90</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.62</td>\n",
       "      <td>7.92</td>\n",
       "      <td>11.84</td>\n",
       "      <td>12.21</td>\n",
       "      <td>9.75</td>\n",
       "      <td>22.47</td>\n",
       "      <td>0.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005296</td>\n",
       "      <td>0.004982</td>\n",
       "      <td>0.004842</td>\n",
       "      <td>0.004765</td>\n",
       "      <td>0.004747</td>\n",
       "      <td>0.004708</td>\n",
       "      <td>0.004613</td>\n",
       "      <td>0.004492</td>\n",
       "      <td>0.004355</td>\n",
       "      <td>0.004323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2963</th>\n",
       "      <td>UN_031_9309_161009_TBP</td>\n",
       "      <td>23.30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.48</td>\n",
       "      <td>3.79</td>\n",
       "      <td>5.62</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.09</td>\n",
       "      <td>20.38</td>\n",
       "      <td>1.30</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005363</td>\n",
       "      <td>0.005020</td>\n",
       "      <td>0.004830</td>\n",
       "      <td>0.004712</td>\n",
       "      <td>0.004685</td>\n",
       "      <td>0.004632</td>\n",
       "      <td>0.004531</td>\n",
       "      <td>0.004425</td>\n",
       "      <td>0.004314</td>\n",
       "      <td>0.004287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>578 rows × 200 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    SampleID  VR CCR Lab  VR Asp Lab  W_IBP-15 Lab  \\\n",
       "0      AF_001_1010_120107_LP       30.10        21.6          0.61   \n",
       "1     AF_001_2013_141227_TBP       31.40        21.9          0.56   \n",
       "4     AF_001_2145_150504_TBP       30.70        23.8          0.61   \n",
       "11    AF_001_2430_160325_TBP       27.66        21.4          0.50   \n",
       "16    AF_001_2727_161214_TBP       29.60        19.4          0.50   \n",
       "...                      ...         ...         ...           ...   \n",
       "2958  UN_028_9425_170919_TBP       23.60        21.7          0.29   \n",
       "2960  UN_029_1664_140124_TBP       11.29         1.3          0.76   \n",
       "2961  UN_029_2619_160920_TBP       12.21         1.0          0.87   \n",
       "2962  UN_029_9073_190325_TBP       11.90         1.5          0.62   \n",
       "2963  UN_031_9309_161009_TBP       23.30        12.2          0.48   \n",
       "\n",
       "      W_C5-105 Lab  W_105-165 Lab  W_165-227 Lab  W_227-270 lab  \\\n",
       "0             5.77           6.27           7.72           5.50   \n",
       "1             6.30           5.57           6.81           5.03   \n",
       "4             4.45           4.72           6.42           5.20   \n",
       "11            5.63           4.59           5.96           5.41   \n",
       "16            4.32           5.29           6.51           5.17   \n",
       "...            ...            ...            ...            ...   \n",
       "2958          3.33           2.91           5.57           2.96   \n",
       "2960          7.99          10.75          11.98           9.84   \n",
       "2961          8.27          10.74          11.85           9.33   \n",
       "2962          7.92          11.84          12.21           9.75   \n",
       "2963          3.79           5.62           9.96           8.09   \n",
       "\n",
       "      W_270-370 lab  W_370-390 Lab  ...      4032      4025      4020  \\\n",
       "0             13.66           0.05  ...  0.005060  0.004777  0.004628   \n",
       "1             13.49           0.95  ...  0.005215  0.004953  0.004817   \n",
       "4             13.38           1.35  ...  0.005194  0.004911  0.004765   \n",
       "11            13.68           1.28  ...  0.005234  0.004961  0.004818   \n",
       "16            13.76           1.03  ...  0.005051  0.004765  0.004619   \n",
       "...             ...            ...  ...       ...       ...       ...   \n",
       "2958          13.47           0.23  ...  0.005362  0.005127  0.004987   \n",
       "2960          21.31           0.04  ...  0.005386  0.005043  0.004888   \n",
       "2961          22.48           1.32  ...  0.005307  0.004997  0.004851   \n",
       "2962          22.47           0.29  ...  0.005296  0.004982  0.004842   \n",
       "2963          20.38           1.30  ...  0.005363  0.005020  0.004830   \n",
       "\n",
       "          4016      4015      4013      4009      4005      4001      4000  \n",
       "0     0.004538  0.004518  0.004478  0.004399  0.004317  0.004231  0.004210  \n",
       "1     0.004729  0.004710  0.004671  0.004594  0.004511  0.004428  0.004407  \n",
       "4     0.004675  0.004656  0.004619  0.004544  0.004463  0.004378  0.004357  \n",
       "11    0.004730  0.004710  0.004672  0.004597  0.004520  0.004439  0.004419  \n",
       "16    0.004529  0.004509  0.004467  0.004383  0.004298  0.004208  0.004186  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "2958  0.004894  0.004872  0.004828  0.004739  0.004668  0.004593  0.004574  \n",
       "2960  0.004808  0.004789  0.004749  0.004650  0.004553  0.004434  0.004405  \n",
       "2961  0.004765  0.004745  0.004702  0.004607  0.004493  0.004368  0.004338  \n",
       "2962  0.004765  0.004747  0.004708  0.004613  0.004492  0.004355  0.004323  \n",
       "2963  0.004712  0.004685  0.004632  0.004531  0.004425  0.004314  0.004287  \n",
       "\n",
       "[578 rows x 200 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop all the raw with even one NaN number \n",
    "dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset.colums gives you columns: 3rd and from 13th: to the end]\n",
    "\n",
    "feature_colums = list (dataset.columns[14:])\n",
    "output_colums = dataset.columns[3]\n",
    "feature_colums.append(output_colums)\n",
    "# feature_colums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list(dataset.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaned dataset\n",
    "cleaned_dataset = dataset[feature_colums].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take \"cleaned_dataset\" and randomly split it into 2 dataframes: train(70% of original data) and test(30% of original data) dataframe using (a) normal code \n",
    "\n",
    "type(cleaned_dataset)\n",
    "\n",
    "msk = np.random.rand(len(cleaned_dataset)) < 0.7\n",
    "\n",
    "train = cleaned_dataset[msk]\n",
    "\n",
    "test = cleaned_dataset[~msk]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = cleaned_dataset.sample(frac=1).reset_index(drop = True)\n",
    "\n",
    "train = df[int(len(df) * 0.7):]\n",
    "test = df[:int(len(df) * 0.7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample2 (dataframe):\n",
    "    dataframe = dataframe.copy()\n",
    "    dataframe['hello'] = 0\n",
    "    return dataframe\n",
    "\n",
    "\n",
    "sample2(cleaned_dataset)\n",
    "\n",
    "train = df.sample(frac=0.7,random_state=200) #random state is a seed value\n",
    "test = df.drop(train.index)\n",
    "\n",
    "X_train = train[feature_colums[:-1]]\n",
    "y_train = test[feature_colums[-1]]\n",
    "\n",
    "X_test = test[feature_colums[:-1]]\n",
    "y_test = test[feature_colums[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.39\n",
       "2      7.27\n",
       "6      0.69\n",
       "7      0.21\n",
       "10     0.81\n",
       "       ... \n",
       "747    0.27\n",
       "754    0.48\n",
       "761    0.58\n",
       "763    0.72\n",
       "766    0.65\n",
       "Name: W_IBP-15 Lab, Length: 231, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = cleaned_dataset[feature_colums[:-1]]\n",
    "y = cleaned_dataset[feature_colums[-1]]\n",
    "\n",
    "X_test = test[feature_colums[:-1]]\n",
    "y_test = test[feature_colums[-1]]\n",
    "\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cleaned_dataset[feature_colums[:-1]]\n",
    "y = cleaned_dataset[feature_colums[-1]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1670    0.68\n",
       "2004    0.52\n",
       "1885    0.32\n",
       "1530    0.71\n",
       "1056    0.44\n",
       "        ... \n",
       "2772    0.42\n",
       "2153    0.45\n",
       "1492    0.89\n",
       "1408    0.68\n",
       "1       0.56\n",
       "Name: W_IBP-15 Lab, Length: 231, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_LinearRegression (X, y):\n",
    "    return LinearRegression().fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = train_LinearRegression(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.3618534761889801\n",
      "Mean Squared Error: 0.3357685220885545\n",
      "Root Mean Squared Error: 0.5794553667786282\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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,\n",
       "         0.,  1.,  1.,  2.,  2.,  3.,  4.,  6., 13.,  7., 10., 15., 15.,\n",
       "        15., 13., 25., 18., 11., 14.,  8., 12.,  7.,  3.,  1.,  0.,  5.,\n",
       "         0.,  1.,  1.,  2.,  1.,  0.,  2.,  0.,  1.,  0.,  1.,  0.,  0.,\n",
       "         0.,  0.,  1.,  2.,  0.,  1.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,\n",
       "         0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
       " array([-1.62092196, -1.54030258, -1.45968319, -1.37906381, -1.29844443,\n",
       "        -1.21782505, -1.13720566, -1.05658628, -0.9759669 , -0.89534751,\n",
       "        -0.81472813, -0.73410875, -0.65348936, -0.57286998, -0.4922506 ,\n",
       "        -0.41163122, -0.33101183, -0.25039245, -0.16977307, -0.08915368,\n",
       "        -0.0085343 ,  0.07208508,  0.15270446,  0.23332385,  0.31394323,\n",
       "         0.39456261,  0.475182  ,  0.55580138,  0.63642076,  0.71704014,\n",
       "         0.79765953,  0.87827891,  0.95889829,  1.03951768,  1.12013706,\n",
       "         1.20075644,  1.28137582,  1.36199521,  1.44261459,  1.52323397,\n",
       "         1.60385336,  1.68447274,  1.76509212,  1.8457115 ,  1.92633089,\n",
       "         2.00695027,  2.08756965,  2.16818904,  2.24880842,  2.3294278 ,\n",
       "         2.41004718,  2.49066657,  2.57128595,  2.65190533,  2.73252472,\n",
       "         2.8131441 ,  2.89376348,  2.97438287,  3.05500225,  3.13562163,\n",
       "         3.21624101,  3.2968604 ,  3.37747978,  3.45809916,  3.53871855,\n",
       "         3.61933793,  3.69995731,  3.78057669,  3.86119608,  3.94181546,\n",
       "         4.02243484,  4.10305423,  4.18367361,  4.26429299,  4.34491237,\n",
       "         4.42553176,  4.50615114,  4.58677052,  4.66738991,  4.74800929,\n",
       "         4.82862867,  4.90924805,  4.98986744,  5.07048682,  5.1511062 ,\n",
       "         5.23172559,  5.31234497,  5.39296435,  5.47358373,  5.55420312,\n",
       "         5.6348225 ,  5.71544188,  5.79606127,  5.87668065,  5.95730003,\n",
       "         6.03791942,  6.1185388 ,  6.19915818,  6.27977756,  6.36039695,\n",
       "         6.44101633]),\n",
       " <a list of 100 Patch objects>)"
      ]
     },
     "execution_count": 87,
     "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.hist(y_test, bins = 100)\n",
    "plt.hist(y_pred, bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    231.000000\n",
       "mean       0.772208\n",
       "std        0.780505\n",
       "min        0.000000\n",
       "25%        0.305000\n",
       "50%        0.550000\n",
       "75%        0.920000\n",
       "max        7.270000\n",
       "Name: W_IBP-15 Lab, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    231.000000\n",
       "mean       0.753260\n",
       "std        0.816260\n",
       "min       -1.620922\n",
       "25%        0.338025\n",
       "50%        0.661408\n",
       "75%        0.933995\n",
       "max        6.441016\n",
       "dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_pred).describe()"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}