creep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rupture time (hour)</th>\n",
       "      <th>Ti</th>\n",
       "      <th>Al</th>\n",
       "      <th>V</th>\n",
       "      <th>Fe</th>\n",
       "      <th>C</th>\n",
       "      <th>N</th>\n",
       "      <th>H</th>\n",
       "      <th>O</th>\n",
       "      <th>Sn</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 16374</th>\n",
       "      <th>Unnamed: 16375</th>\n",
       "      <th>Unnamed: 16376</th>\n",
       "      <th>Unnamed: 16377</th>\n",
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       "      <th>Unnamed: 16379</th>\n",
       "      <th>Unnamed: 16380</th>\n",
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       "      <th>Unnamed: 16382</th>\n",
       "      <th>Unnamed: 16383</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.01000</td>\n",
       "      <td>87.875</td>\n",
       "      <td>6.75</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01167</td>\n",
       "      <td>87.810</td>\n",
       "      <td>6.61</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03050</td>\n",
       "      <td>87.810</td>\n",
       "      <td>6.61</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1.18</td>\n",
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       "      <td>0.0</td>\n",
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.04620</td>\n",
       "      <td>87.810</td>\n",
       "      <td>6.61</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.05000</td>\n",
       "      <td>87.810</td>\n",
       "      <td>6.61</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 16384 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rupture time (hour)      Ti    Al     V    Fe      C      N      H     O  \\\n",
       "0              0.01000  87.875  6.75  4.50  0.40  0.100  0.050  0.125  0.20   \n",
       "1              0.01167  87.810  6.61  4.23  1.18  0.026  0.011  0.003  0.13   \n",
       "2              0.03050  87.810  6.61  4.23  1.18  0.026  0.011  0.003  0.13   \n",
       "3              0.04620  87.810  6.61  4.23  1.18  0.026  0.011  0.003  0.13   \n",
       "4              0.05000  87.810  6.61  4.23  1.18  0.026  0.011  0.003  0.13   \n",
       "\n",
       "    Sn  ...  Unnamed: 16374  Unnamed: 16375  Unnamed: 16376  Unnamed: 16377  \\\n",
       "0  0.0  ...             NaN             NaN             NaN             NaN   \n",
       "1  0.0  ...             NaN             NaN             NaN             NaN   \n",
       "2  0.0  ...             NaN             NaN             NaN             NaN   \n",
       "3  0.0  ...             NaN             NaN             NaN             NaN   \n",
       "4  0.0  ...             NaN             NaN             NaN             NaN   \n",
       "\n",
       "   Unnamed: 16378  Unnamed: 16379  Unnamed: 16380  Unnamed: 16381  \\\n",
       "0             NaN             NaN             NaN             NaN   \n",
       "1             NaN             NaN             NaN             NaN   \n",
       "2             NaN             NaN             NaN             NaN   \n",
       "3             NaN             NaN             NaN             NaN   \n",
       "4             NaN             NaN             NaN             NaN   \n",
       "\n",
       "   Unnamed: 16382  Unnamed: 16383  \n",
       "0             NaN             NaN  \n",
       "1             NaN             NaN  \n",
       "2             NaN             NaN  \n",
       "3             NaN             NaN  \n",
       "4             NaN             NaN  \n",
       "\n",
       "[5 rows x 16384 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data= pd.read_csv(r'D:\\CODS\\creep_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Rupture time (hour)</th>\n",
       "      <th>Ti</th>\n",
       "      <th>Al</th>\n",
       "      <th>V</th>\n",
       "      <th>Fe</th>\n",
       "      <th>C</th>\n",
       "      <th>N</th>\n",
       "      <th>H</th>\n",
       "      <th>O</th>\n",
       "      <th>Sn</th>\n",
       "      <th>...</th>\n",
       "      <th>B</th>\n",
       "      <th>Cr</th>\n",
       "      <th>Solution treated temp(cel)</th>\n",
       "      <th>ST time (h)</th>\n",
       "      <th>Anneal temp (cel)</th>\n",
       "      <th>Annealing Time (hour)</th>\n",
       "      <th>Temperature of creep test (cel)</th>\n",
       "      <th>Stress (Mpa)</th>\n",
       "      <th>steady state strain rate (1/s)</th>\n",
       "      <th>Strain to rupture (%) (Efc)</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.01000</td>\n",
       "      <td>87.875</td>\n",
       "      <td>6.75</td>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01167</td>\n",
       "      <td>87.810</td>\n",
       "      <td>6.61</td>\n",
       "      <td>4.23</td>\n",
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       "      <td>0.011</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>600</td>\n",
       "      <td>319.0</td>\n",
       "      <td>0.000003</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03050</td>\n",
       "      <td>87.810</td>\n",
       "      <td>6.61</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1.18</td>\n",
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       "      <td>0.011</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.04620</td>\n",
       "      <td>87.810</td>\n",
       "      <td>6.61</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1.18</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>500</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.05000</td>\n",
       "      <td>87.810</td>\n",
       "      <td>6.61</td>\n",
       "      <td>4.23</td>\n",
       "      <td>1.18</td>\n",
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       "      <td>0.011</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700</td>\n",
       "      <td>319.0</td>\n",
       "      <td>0.002130</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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       "<p>5 rows × 24 columns</p>\n",
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      ],
      "text/plain": [
       "   Rupture time (hour)      Ti    Al     V    Fe      C      N      H     O  \\\n",
       "0              0.01000  87.875  6.75  4.50  0.40  0.100  0.050  0.125  0.20   \n",
       "1              0.01167  87.810  6.61  4.23  1.18  0.026  0.011  0.003  0.13   \n",
       "2              0.03050  87.810  6.61  4.23  1.18  0.026  0.011  0.003  0.13   \n",
       "3              0.04620  87.810  6.61  4.23  1.18  0.026  0.011  0.003  0.13   \n",
       "4              0.05000  87.810  6.61  4.23  1.18  0.026  0.011  0.003  0.13   \n",
       "\n",
       "    Sn  ...    B   Cr  Solution treated temp(cel)  ST time (h)  \\\n",
       "0  0.0  ...  0.0  0.0                         690          4.0   \n",
       "1  0.0  ...  0.0  0.0                           0          0.0   \n",
       "2  0.0  ...  0.0  0.0                           0          0.0   \n",
       "3  0.0  ...  0.0  0.0                           0          0.0   \n",
       "4  0.0  ...  0.0  0.0                           0          0.0   \n",
       "\n",
       "   Anneal temp (cel)  Annealing Time (hour)  Temperature of creep test (cel)  \\\n",
       "0               1050                    0.5                              700   \n",
       "1                  0                    0.0                              600   \n",
       "2                  0                    0.0                              600   \n",
       "3                  0                    0.0                              500   \n",
       "4                  0                    0.0                              700   \n",
       "\n",
       "   Stress (Mpa)  steady state strain rate (1/s)  Strain to rupture (%) (Efc)  \n",
       "0         319.0                        0.002090                         15.8  \n",
       "1         319.0                        0.000003                          0.0  \n",
       "2         250.0                        0.000044                          0.0  \n",
       "3         520.0                        0.000044                          0.0  \n",
       "4         319.0                        0.002130                          0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=data[['Rupture time (hour)','Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr','Solution treated temp(cel)','ST time (h)','Anneal temp (cel)','Annealing Time (hour)','Temperature of creep test (cel)','Stress (Mpa)','steady state strain rate (1/s)','Strain to rupture (%) (Efc)']]\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rupture time (hour)</th>\n",
       "      <th>Ti</th>\n",
       "      <th>Al</th>\n",
       "      <th>V</th>\n",
       "      <th>Fe</th>\n",
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       "      <th>Anneal temp (cel)</th>\n",
       "      <th>Annealing Time (hour)</th>\n",
       "      <th>Temperature of creep test (cel)</th>\n",
       "      <th>Stress (Mpa)</th>\n",
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       "      <th>Strain to rupture (%) (Efc)</th>\n",
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       "      <th>count</th>\n",
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       "      <td>8.030904</td>\n",
       "      <td>3.027062</td>\n",
       "      <td>0.386475</td>\n",
       "      <td>0.03202</td>\n",
       "      <td>0.015807</td>\n",
       "      <td>0.031502</td>\n",
       "      <td>0.113559</td>\n",
       "      <td>0.518757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023333</td>\n",
       "      <td>0.154802</td>\n",
       "      <td>403.559322</td>\n",
       "      <td>0.485537</td>\n",
       "      <td>551.169492</td>\n",
       "      <td>5.009266</td>\n",
       "      <td>567.389831</td>\n",
       "      <td>270.468362</td>\n",
       "      <td>5.837199e-05</td>\n",
       "      <td>14.757985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>546.648565</td>\n",
       "      <td>8.545178</td>\n",
       "      <td>8.728054</td>\n",
       "      <td>1.898388</td>\n",
       "      <td>0.437286</td>\n",
       "      <td>0.02917</td>\n",
       "      <td>0.018953</td>\n",
       "      <td>0.094951</td>\n",
       "      <td>0.077959</td>\n",
       "      <td>1.370419</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079399</td>\n",
       "      <td>0.468244</td>\n",
       "      <td>487.484601</td>\n",
       "      <td>0.859763</td>\n",
       "      <td>422.378135</td>\n",
       "      <td>13.277593</td>\n",
       "      <td>101.840955</td>\n",
       "      <td>134.613480</td>\n",
       "      <td>2.415170e-04</td>\n",
       "      <td>17.946175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.190000</td>\n",
       "      <td>87.750000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.00550</td>\n",
       "      <td>0.003100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>8.360000e-08</td>\n",
       "      <td>0.042300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>88.250000</td>\n",
       "      <td>6.290000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.02600</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>9.000000e-07</td>\n",
       "      <td>7.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>154.720000</td>\n",
       "      <td>89.256000</td>\n",
       "      <td>6.610000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.06000</td>\n",
       "      <td>0.011000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>980.000000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>2.020000e-05</td>\n",
       "      <td>22.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4722.220000</td>\n",
       "      <td>89.980000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.246000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1373.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>773.000000</td>\n",
       "      <td>673.000000</td>\n",
       "      <td>2.130000e-03</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rupture time (hour)          Ti          Al           V          Fe  \\\n",
       "count           177.000000  177.000000  177.000000  177.000000  177.000000   \n",
       "mean            200.839091   85.995553    8.030904    3.027062    0.386475   \n",
       "std             546.648565    8.545178    8.728054    1.898388    0.437286   \n",
       "min               0.010000   48.000000    5.500000    0.000000    0.000000   \n",
       "25%               2.190000   87.750000    5.800000    0.000000    0.050000   \n",
       "50%              50.000000   88.250000    6.290000    4.000000    0.200000   \n",
       "75%             154.720000   89.256000    6.610000    4.230000    0.400000   \n",
       "max            4722.220000   89.980000   48.000000    5.080000    1.180000   \n",
       "\n",
       "               C           N           H           O          Sn  ...  \\\n",
       "count  177.00000  177.000000  177.000000  177.000000  177.000000  ...   \n",
       "mean     0.03202    0.015807    0.031502    0.113559    0.518757  ...   \n",
       "std      0.02917    0.018953    0.094951    0.077959    1.370419  ...   \n",
       "min      0.00000    0.000000    0.000000    0.000000    0.000000  ...   \n",
       "25%      0.00550    0.003100    0.000000    0.030000    0.000000  ...   \n",
       "50%      0.02600    0.010000    0.003000    0.130000    0.000000  ...   \n",
       "75%      0.06000    0.011000    0.008000    0.170000    0.000000  ...   \n",
       "max      0.10000    0.050000    0.400000    0.246000    4.800000  ...   \n",
       "\n",
       "                B          Cr  Solution treated temp(cel)  ST time (h)  \\\n",
       "count  177.000000  177.000000                  177.000000   177.000000   \n",
       "mean     0.023333    0.154802                  403.559322     0.485537   \n",
       "std      0.079399    0.468244                  487.484601     0.859763   \n",
       "min      0.000000    0.000000                    0.000000     0.000000   \n",
       "25%      0.000000    0.000000                    0.000000     0.000000   \n",
       "50%      0.000000    0.000000                    0.000000     0.000000   \n",
       "75%      0.000000    0.000000                  980.000000     0.670000   \n",
       "max      0.500000    2.000000                 1100.000000     4.000000   \n",
       "\n",
       "       Anneal temp (cel)  Annealing Time (hour)  \\\n",
       "count         177.000000             177.000000   \n",
       "mean          551.169492               5.009266   \n",
       "std           422.378135              13.277593   \n",
       "min             0.000000               0.000000   \n",
       "25%             0.000000               0.000000   \n",
       "50%           700.000000               1.000000   \n",
       "75%           900.000000               4.000000   \n",
       "max          1373.000000             150.000000   \n",
       "\n",
       "       Temperature of creep test (cel)  Stress (Mpa)  \\\n",
       "count                       177.000000    177.000000   \n",
       "mean                        567.389831    270.468362   \n",
       "std                         101.840955    134.613480   \n",
       "min                         315.000000     14.000000   \n",
       "25%                         500.000000    150.000000   \n",
       "50%                         600.000000    300.000000   \n",
       "75%                         600.000000    319.000000   \n",
       "max                         773.000000    673.000000   \n",
       "\n",
       "       steady state strain rate (1/s)  Strain to rupture (%) (Efc)  \n",
       "count                    1.770000e+02                   177.000000  \n",
       "mean                     5.837199e-05                    14.757985  \n",
       "std                      2.415170e-04                    17.946175  \n",
       "min                      0.000000e+00                     0.000000  \n",
       "25%                      8.360000e-08                     0.042300  \n",
       "50%                      9.000000e-07                     7.480000  \n",
       "75%                      2.020000e-05                    22.300000  \n",
       "max                      2.130000e-03                    95.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz_data2=data2[['Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr']]\n",
    "viz_data2.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hp\\AppData\\Local\\Temp\\ipykernel_27624\\262062881.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2[['Rupture time (hour)']]=np.log10(data2[['Rupture time (hour)']])\n"
     ]
    }
   ],
   "source": [
    "data2[['Rupture time (hour)']]=np.log10(data2[['Rupture time (hour)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data2['Temperature of creep test (cel)'],data2['Rupture time (hour)'],color='red')\n",
    "plt.xlabel('Temperature of creep test (cel)')\n",
    "plt.ylabel('Rupture time (hour)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True  True False  True  True  True\n",
      "  True  True  True False  True  True  True False  True  True  True  True\n",
      "  True  True  True  True False  True  True  True  True False False  True\n",
      " False  True  True  True  True  True False  True  True  True  True  True\n",
      "  True False False  True  True  True  True  True False  True  True  True\n",
      "  True  True  True  True False False  True False  True  True  True  True\n",
      "  True  True  True  True  True  True  True False  True  True  True False\n",
      " False  True False  True False  True  True  True  True  True False  True\n",
      "  True  True False  True False False  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True False  True  True  True  True  True\n",
      "  True  True  True  True  True  True  True False  True  True  True  True\n",
      "  True  True  True  True  True  True False False  True  True  True  True\n",
      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True  True  True False  True  True  True False  True\n",
      " False  True  True  True  True  True False  True  True]\n",
      "[False False False False False False False False  True False False False\n",
      " False False False  True False False False  True False False False False\n",
      " False False False False  True False False False False  True  True False\n",
      "  True False False False False False  True False False False False False\n",
      " False  True  True False False False False False  True False False False\n",
      " False False False False  True  True False  True False False False False\n",
      " False False False False False False False  True False False False  True\n",
      "  True False  True False  True False False False False False  True False\n",
      " False False  True False  True  True False False False False False False\n",
      " False False False False False False  True False False False False False\n",
      " False False False False False False False  True False False False False\n",
      " False False False False False False  True  True False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False  True False False False  True False\n",
      "  True False False False False False  True False False]\n",
      "     Rupture time (hour)       Ti    Al     V    Fe      C      N       H  \\\n",
      "0              -2.000000  87.8750  6.75  4.50  0.40  0.100  0.050  0.1250   \n",
      "1              -1.932929  87.8100  6.61  4.23  1.18  0.026  0.011  0.0030   \n",
      "2              -1.515700  87.8100  6.61  4.23  1.18  0.026  0.011  0.0030   \n",
      "3              -1.335358  87.8100  6.61  4.23  1.18  0.026  0.011  0.0030   \n",
      "4              -1.301030  87.8100  6.61  4.23  1.18  0.026  0.011  0.0030   \n",
      "..                   ...      ...   ...   ...   ...    ...    ...     ...   \n",
      "172             3.064458  85.9870  6.20  0.00  0.00  0.021  0.004  0.0080   \n",
      "173             3.209247  89.0434  6.51  4.08  0.16  0.010  0.005  0.0016   \n",
      "174             3.241546  89.0434  6.51  4.08  0.16  0.010  0.005  0.0016   \n",
      "175             3.670339  89.4100  6.00  4.00  0.25  0.080  0.050  0.0100   \n",
      "176             3.674146  89.3000  6.00  4.00  0.25  0.080  0.050  0.0100   \n",
      "\n",
      "        O    Sn  ...     B   Cr  Solution treated temp(cel)  ST time (h)  \\\n",
      "0    0.20  0.00  ...  0.00  0.0                         690          4.0   \n",
      "1    0.13  0.00  ...  0.00  0.0                           0          0.0   \n",
      "2    0.13  0.00  ...  0.00  0.0                           0          0.0   \n",
      "3    0.13  0.00  ...  0.00  0.0                           0          0.0   \n",
      "4    0.13  0.00  ...  0.00  0.0                           0          0.0   \n",
      "..    ...   ...  ...   ...  ...                         ...          ...   \n",
      "172  0.03  1.95  ...  0.00  0.0                         900          1.0   \n",
      "173  0.19  0.00  ...  0.00  0.0                           0          0.0   \n",
      "174  0.19  0.00  ...  0.00  0.0                           0          0.0   \n",
      "175  0.20  0.00  ...  0.00  0.0                           0          0.0   \n",
      "176  0.20  0.00  ...  0.11  0.0                           0          0.0   \n",
      "\n",
      "     Anneal temp (cel)  Annealing Time (hour)  \\\n",
      "0                 1050                    0.5   \n",
      "1                    0                    0.0   \n",
      "2                    0                    0.0   \n",
      "3                    0                    0.0   \n",
      "4                    0                    0.0   \n",
      "..                 ...                    ...   \n",
      "172                580                    8.0   \n",
      "173                940                    4.0   \n",
      "174               1030                    0.5   \n",
      "175                  0                    0.0   \n",
      "176                900                    2.0   \n",
      "\n",
      "     Temperature of creep test (cel)  Stress (Mpa)  \\\n",
      "0                                700         319.0   \n",
      "1                                600         319.0   \n",
      "2                                600         250.0   \n",
      "3                                500         520.0   \n",
      "4                                700         319.0   \n",
      "..                               ...           ...   \n",
      "172                              520         310.0   \n",
      "173                              455         379.0   \n",
      "174                              455         379.0   \n",
      "175                              538         103.0   \n",
      "176                              475         300.0   \n",
      "\n",
      "     steady state strain rate (1/s)  Strain to rupture (%) (Efc)  \n",
      "0                      2.090000e-03                        15.80  \n",
      "1                      3.240000e-06                         0.00  \n",
      "2                      4.400000e-05                         0.00  \n",
      "3                      4.430000e-05                         0.00  \n",
      "4                      2.130000e-03                         0.00  \n",
      "..                              ...                          ...  \n",
      "172                    1.200000e-04                         0.00  \n",
      "173                    9.160000e-07                         0.00  \n",
      "174                    1.270000e-06                         0.00  \n",
      "175                    9.000000e-07                        22.30  \n",
      "176                    4.440000e-09                        16.31  \n",
      "\n",
      "[177 rows x 24 columns]\n",
      "     Rupture time (hour)       Ti    Al     V    Fe      C      N       H  \\\n",
      "0              -2.000000  87.8750  6.75  4.50  0.40  0.100  0.050  0.1250   \n",
      "1              -1.932929  87.8100  6.61  4.23  1.18  0.026  0.011  0.0030   \n",
      "2              -1.515700  87.8100  6.61  4.23  1.18  0.026  0.011  0.0030   \n",
      "3              -1.335358  87.8100  6.61  4.23  1.18  0.026  0.011  0.0030   \n",
      "4              -1.301030  87.8100  6.61  4.23  1.18  0.026  0.011  0.0030   \n",
      "..                   ...      ...   ...   ...   ...    ...    ...     ...   \n",
      "171             2.875061  85.9870  6.20  0.00  0.00  0.021  0.004  0.0080   \n",
      "172             3.064458  85.9870  6.20  0.00  0.00  0.021  0.004  0.0080   \n",
      "173             3.209247  89.0434  6.51  4.08  0.16  0.010  0.005  0.0016   \n",
      "175             3.670339  89.4100  6.00  4.00  0.25  0.080  0.050  0.0100   \n",
      "176             3.674146  89.3000  6.00  4.00  0.25  0.080  0.050  0.0100   \n",
      "\n",
      "        O    Sn  ...     B   Cr  Solution treated temp(cel)  ST time (h)  \\\n",
      "0    0.20  0.00  ...  0.00  0.0                         690          4.0   \n",
      "1    0.13  0.00  ...  0.00  0.0                           0          0.0   \n",
      "2    0.13  0.00  ...  0.00  0.0                           0          0.0   \n",
      "3    0.13  0.00  ...  0.00  0.0                           0          0.0   \n",
      "4    0.13  0.00  ...  0.00  0.0                           0          0.0   \n",
      "..    ...   ...  ...   ...  ...                         ...          ...   \n",
      "171  0.03  1.95  ...  0.00  0.0                        1050          1.0   \n",
      "172  0.03  1.95  ...  0.00  0.0                         900          1.0   \n",
      "173  0.19  0.00  ...  0.00  0.0                           0          0.0   \n",
      "175  0.20  0.00  ...  0.00  0.0                           0          0.0   \n",
      "176  0.20  0.00  ...  0.11  0.0                           0          0.0   \n",
      "\n",
      "     Anneal temp (cel)  Annealing Time (hour)  \\\n",
      "0                 1050                    0.5   \n",
      "1                    0                    0.0   \n",
      "2                    0                    0.0   \n",
      "3                    0                    0.0   \n",
      "4                    0                    0.0   \n",
      "..                 ...                    ...   \n",
      "171                550                   24.0   \n",
      "172                580                    8.0   \n",
      "173                940                    4.0   \n",
      "175                  0                    0.0   \n",
      "176                900                    2.0   \n",
      "\n",
      "     Temperature of creep test (cel)  Stress (Mpa)  \\\n",
      "0                                700         319.0   \n",
      "1                                600         319.0   \n",
      "2                                600         250.0   \n",
      "3                                500         520.0   \n",
      "4                                700         319.0   \n",
      "..                               ...           ...   \n",
      "171                              520         310.0   \n",
      "172                              520         310.0   \n",
      "173                              455         379.0   \n",
      "175                              538         103.0   \n",
      "176                              475         300.0   \n",
      "\n",
      "     steady state strain rate (1/s)  Strain to rupture (%) (Efc)  \n",
      "0                      2.090000e-03                        15.80  \n",
      "1                      3.240000e-06                         0.00  \n",
      "2                      4.400000e-05                         0.00  \n",
      "3                      4.430000e-05                         0.00  \n",
      "4                      2.130000e-03                         0.00  \n",
      "..                              ...                          ...  \n",
      "171                    1.000000e-05                         0.00  \n",
      "172                    1.200000e-04                         0.00  \n",
      "173                    9.160000e-07                         0.00  \n",
      "175                    9.000000e-07                        22.30  \n",
      "176                    4.440000e-09                        16.31  \n",
      "\n",
      "[146 rows x 24 columns]\n",
      "     Rupture time (hour)       Ti     Al     V     Fe       C       N       H  \\\n",
      "8              -0.850781  87.8100   6.61  4.23  1.180  0.0260  0.0110  0.0030   \n",
      "15             -0.522879  87.8100   6.61  4.23  1.180  0.0260  0.0110  0.0030   \n",
      "19             -0.468521  89.9800   5.79  3.86  0.070  0.0300  0.0000  0.0000   \n",
      "28             -0.207608  87.8750   6.75  4.50  0.400  0.1000  0.0500  0.1250   \n",
      "33             -0.058986  88.9100   5.98  4.25  0.230  0.0000  0.0000  0.4000   \n",
      "34              0.000000  89.9800   5.79  3.86  0.070  0.0300  0.0000  0.0000   \n",
      "36              0.000000  89.9800   5.79  3.86  0.070  0.0300  0.0000  0.0000   \n",
      "42              0.283301  87.8100   6.61  4.23  1.180  0.0260  0.0110  0.0030   \n",
      "49              0.746634  89.2550   6.00  4.00  0.400  0.0800  0.0500  0.0150   \n",
      "50              0.837588  89.3500   6.00  4.00  0.250  0.0800  0.0500  0.0100   \n",
      "56              1.096910  87.8100   6.61  4.23  1.180  0.0260  0.0110  0.0030   \n",
      "64              1.343802  89.2560   6.33  4.00  0.200  0.0300  0.0100  0.0040   \n",
      "65              1.348889  48.0000  48.00  0.00  0.000  0.0000  0.0000  0.0000   \n",
      "67              1.393751  87.8100   6.61  4.23  1.180  0.0260  0.0110  0.0030   \n",
      "79              1.680336  89.6800   5.50  3.87  0.220  0.0800  0.0500  0.4000   \n",
      "83              1.692318  89.6800   5.50  3.87  0.220  0.0800  0.0500  0.4000   \n",
      "84              1.698970  89.4100   6.00  4.00  0.250  0.0800  0.0500  0.0100   \n",
      "86              1.698970  88.2600   6.29  0.00  0.050  0.0100  0.0500  0.0100   \n",
      "88              1.698970  88.2600   6.29  0.00  0.050  0.0100  0.0500  0.0100   \n",
      "94              1.848805  48.0000  48.00  0.00  0.000  0.0000  0.0000  0.0000   \n",
      "98              1.857332  82.3400   5.60  5.08  0.950  0.0000  0.0000  0.0000   \n",
      "100             1.857332  82.3400   5.60  5.08  0.950  0.0000  0.0000  0.0000   \n",
      "101             1.857332  82.3400   5.60  5.08  0.950  0.0000  0.0000  0.0000   \n",
      "114             2.000000  84.7730   5.78  0.00  0.000  0.0600  0.0070  0.0000   \n",
      "127             2.132996  87.7500   6.00  0.00  0.000  0.0000  0.0000  0.0000   \n",
      "138             2.284115  48.0000  48.00  0.00  0.000  0.0000  0.0000  0.0000   \n",
      "139             2.320146  89.0434   6.51  4.08  0.160  0.0100  0.0050  0.0016   \n",
      "162             2.738781  89.2560   6.33  4.00  0.200  0.0300  0.0100  0.0040   \n",
      "166             2.829278  89.2024   6.29  4.13  0.194  0.0055  0.0077  0.0044   \n",
      "168             2.848515  84.5820   5.80  0.00  0.000  0.0600  0.0080  0.0000   \n",
      "174             3.241546  89.0434   6.51  4.08  0.160  0.0100  0.0050  0.0016   \n",
      "\n",
      "         O    Sn  ...     B    Cr  Solution treated temp(cel)  ST time (h)  \\\n",
      "8    0.130  0.00  ...  0.00  0.00                           0         0.00   \n",
      "15   0.130  0.00  ...  0.00  0.00                           0         0.00   \n",
      "19   0.120  0.00  ...  0.00  0.00                         800         0.67   \n",
      "28   0.200  0.00  ...  0.00  0.00                         690         4.00   \n",
      "33   0.000  0.00  ...  0.00  0.00                           0         0.00   \n",
      "34   0.120  0.00  ...  0.00  0.00                         800         0.67   \n",
      "36   0.120  0.00  ...  0.00  0.00                         800         0.67   \n",
      "42   0.130  0.00  ...  0.00  0.00                           0         0.00   \n",
      "49   0.200  0.00  ...  0.00  0.00                           0         0.00   \n",
      "50   0.200  0.00  ...  0.06  0.00                           0         0.00   \n",
      "56   0.130  0.00  ...  0.00  0.00                           0         0.00   \n",
      "64   0.170  0.00  ...  0.00  0.00                         950         1.00   \n",
      "65   0.000  0.00  ...  0.00  2.00                           0         0.00   \n",
      "67   0.130  0.00  ...  0.00  0.00                        1050         0.50   \n",
      "79   0.200  0.00  ...  0.00  0.00                        1040         1.00   \n",
      "83   0.200  0.00  ...  0.00  0.00                        1040         1.00   \n",
      "84   0.200  0.00  ...  0.00  0.00                           0         0.00   \n",
      "86   0.030  0.00  ...  0.00  0.00                         950         1.00   \n",
      "88   0.030  0.00  ...  0.00  0.00                         970         1.00   \n",
      "94   0.000  0.00  ...  0.00  2.00                           0         0.00   \n",
      "98   0.000  0.00  ...  0.00  0.95                           0         0.00   \n",
      "100  0.000  0.00  ...  0.00  0.95                           0         0.00   \n",
      "101  0.000  0.00  ...  0.00  0.95                           0         0.00   \n",
      "114  0.090  4.01  ...  0.00  0.00                         995         2.00   \n",
      "127  0.000  0.00  ...  0.50  0.00                        1050         0.50   \n",
      "138  0.000  0.00  ...  0.00  2.00                           0         0.00   \n",
      "139  0.190  0.00  ...  0.00  0.00                           0         0.00   \n",
      "162  0.170  0.00  ...  0.00  0.00                         940         0.42   \n",
      "166  0.166  0.00  ...  0.00  0.00                           0         0.00   \n",
      "168  0.080  3.98  ...  0.19  0.00                        1055         2.00   \n",
      "174  0.190  0.00  ...  0.00  0.00                           0         0.00   \n",
      "\n",
      "     Anneal temp (cel)  Annealing Time (hour)  \\\n",
      "8                    0                   0.00   \n",
      "15                   0                   0.00   \n",
      "19                 800                   0.66   \n",
      "28                1050                   0.50   \n",
      "33                 750                   1.00   \n",
      "34                 800                   0.66   \n",
      "36                 800                   0.66   \n",
      "42                   0                   0.00   \n",
      "49                   0                   0.00   \n",
      "50                 900                   2.00   \n",
      "56                   0                   0.00   \n",
      "64                 600                  24.00   \n",
      "65                1260                   4.00   \n",
      "67                 950                   1.00   \n",
      "79                   0                   0.00   \n",
      "83                   0                   0.00   \n",
      "84                   0                   0.00   \n",
      "86                 530                   6.00   \n",
      "88                 530                  24.00   \n",
      "94                1260                   4.00   \n",
      "98                   0                   0.00   \n",
      "100                  0                   0.00   \n",
      "101                  0                   0.00   \n",
      "114                700                   2.00   \n",
      "127                550                  24.00   \n",
      "138               1260                   4.00   \n",
      "139               1030                   0.50   \n",
      "162                480                  24.00   \n",
      "166               1373                   1.00   \n",
      "168                700                   2.00   \n",
      "174               1030                   0.50   \n",
      "\n",
      "     Temperature of creep test (cel)  Stress (Mpa)  \\\n",
      "8                                700         56.00   \n",
      "15                               500        465.00   \n",
      "19                               700        126.00   \n",
      "28                               600        319.00   \n",
      "33                               650        100.00   \n",
      "34                               700         18.00   \n",
      "36                               700         81.00   \n",
      "42                               500        465.00   \n",
      "49                               600        125.00   \n",
      "50                               550        300.00   \n",
      "56                               700         14.00   \n",
      "64                               600        125.00   \n",
      "65                               750        150.00   \n",
      "67                               500        403.00   \n",
      "79                               500        100.00   \n",
      "83                               500         80.00   \n",
      "84                               538        241.00   \n",
      "86                               500        300.00   \n",
      "88                               500        300.00   \n",
      "94                               750        200.00   \n",
      "98                               400        200.00   \n",
      "100                              400        300.00   \n",
      "101                              500        200.00   \n",
      "114                              600        150.00   \n",
      "127                              500        300.00   \n",
      "138                              750        325.00   \n",
      "139                              455        489.00   \n",
      "162                              400        600.00   \n",
      "166                              773        311.75   \n",
      "168                              600        150.00   \n",
      "174                              455        379.00   \n",
      "\n",
      "     steady state strain rate (1/s)  Strain to rupture (%) (Efc)  \n",
      "8                      4.330000e-05                        0.000  \n",
      "15                     1.190000e-05                        0.000  \n",
      "19                     1.100000e-04                        3.620  \n",
      "28                     4.440000e-05                       23.700  \n",
      "33                     1.180000e-04                        1.273  \n",
      "34                     2.600000e-06                        1.000  \n",
      "36                     3.500000e-05                       16.450  \n",
      "42                     1.190000e-05                        0.000  \n",
      "49                     1.130000e-05                       40.170  \n",
      "50                     2.550000e-06                       14.850  \n",
      "56                     1.100000e-06                        0.000  \n",
      "64                     2.050000e-06                       36.800  \n",
      "65                     4.830000e-07                        1.400  \n",
      "67                     3.980000e-07                        6.614  \n",
      "79                     8.550000e-08                        0.057  \n",
      "83                     7.120000e-08                        0.050  \n",
      "84                     5.900000e-05                       13.500  \n",
      "86                     1.920000e-07                       44.200  \n",
      "88                     2.400000e-07                       45.500  \n",
      "94                     5.720000e-07                        2.180  \n",
      "98                     5.340000e-09                        0.000  \n",
      "100                    1.080000e-08                        0.000  \n",
      "101                    9.270000e-08                        0.000  \n",
      "114                    2.400000e-05                       39.000  \n",
      "127                    8.360000e-08                        7.640  \n",
      "138                    9.060000e-07                        1.760  \n",
      "139                    1.030000e-05                        0.000  \n",
      "162                    1.510000e-07                        0.000  \n",
      "166                    0.000000e+00                        0.000  \n",
      "168                    0.000000e+00                       26.400  \n",
      "174                    1.270000e-06                        0.000  \n",
      "\n",
      "[31 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "msk= np.random.rand(len(data2))<0.8\n",
    "train=data2[msk]\n",
    "test=data2[~msk]\n",
    "print(msk)\n",
    "print(~msk)\n",
    "print(data2)\n",
    "print(train)\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rupture time (hour)</th>\n",
       "      <th>Ti</th>\n",
       "      <th>Al</th>\n",
       "      <th>V</th>\n",
       "      <th>Fe</th>\n",
       "      <th>C</th>\n",
       "      <th>N</th>\n",
       "      <th>H</th>\n",
       "      <th>O</th>\n",
       "      <th>Sn</th>\n",
       "      <th>...</th>\n",
       "      <th>B</th>\n",
       "      <th>Cr</th>\n",
       "      <th>Solution treated temp(cel)</th>\n",
       "      <th>ST time (h)</th>\n",
       "      <th>Anneal temp (cel)</th>\n",
       "      <th>Annealing Time (hour)</th>\n",
       "      <th>Temperature of creep test (cel)</th>\n",
       "      <th>Stress (Mpa)</th>\n",
       "      <th>steady state strain rate (1/s)</th>\n",
       "      <th>Strain to rupture (%) (Efc)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>1.460000e+02</td>\n",
       "      <td>146.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.367156</td>\n",
       "      <td>86.422032</td>\n",
       "      <td>7.578014</td>\n",
       "      <td>3.007123</td>\n",
       "      <td>0.386726</td>\n",
       "      <td>0.032342</td>\n",
       "      <td>0.015686</td>\n",
       "      <td>0.028529</td>\n",
       "      <td>0.114890</td>\n",
       "      <td>0.574178</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023151</td>\n",
       "      <td>0.127055</td>\n",
       "      <td>399.315068</td>\n",
       "      <td>0.476096</td>\n",
       "      <td>549.342466</td>\n",
       "      <td>5.206575</td>\n",
       "      <td>565.458904</td>\n",
       "      <td>277.398288</td>\n",
       "      <td>6.739644e-05</td>\n",
       "      <td>15.657529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.255036</td>\n",
       "      <td>7.543050</td>\n",
       "      <td>7.646214</td>\n",
       "      <td>1.904090</td>\n",
       "      <td>0.437892</td>\n",
       "      <td>0.028888</td>\n",
       "      <td>0.018662</td>\n",
       "      <td>0.088949</td>\n",
       "      <td>0.078057</td>\n",
       "      <td>1.433864</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076020</td>\n",
       "      <td>0.421538</td>\n",
       "      <td>490.419817</td>\n",
       "      <td>0.861868</td>\n",
       "      <td>411.959396</td>\n",
       "      <td>14.168044</td>\n",
       "      <td>99.346494</td>\n",
       "      <td>130.811061</td>\n",
       "      <td>2.648407e-04</td>\n",
       "      <td>18.358161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.369425</td>\n",
       "      <td>87.765000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.005500</td>\n",
       "      <td>0.003100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>5.342500e-08</td>\n",
       "      <td>0.240250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.767309</td>\n",
       "      <td>88.250000</td>\n",
       "      <td>6.245000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.026000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>8.850000e-07</td>\n",
       "      <td>10.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.210696</td>\n",
       "      <td>89.256000</td>\n",
       "      <td>6.510000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.011000</td>\n",
       "      <td>0.007100</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>995.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>2.162500e-05</td>\n",
       "      <td>22.877500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.674146</td>\n",
       "      <td>89.980000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.246000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1373.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>773.000000</td>\n",
       "      <td>673.000000</td>\n",
       "      <td>2.130000e-03</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rupture time (hour)          Ti          Al           V          Fe  \\\n",
       "count           146.000000  146.000000  146.000000  146.000000  146.000000   \n",
       "mean              1.367156   86.422032    7.578014    3.007123    0.386726   \n",
       "std               1.255036    7.543050    7.646214    1.904090    0.437892   \n",
       "min              -2.000000   48.000000    5.500000    0.000000    0.000000   \n",
       "25%               0.369425   87.765000    5.900000    0.000000    0.050000   \n",
       "50%               1.767309   88.250000    6.245000    4.000000    0.200000   \n",
       "75%               2.210696   89.256000    6.510000    4.230000    0.400000   \n",
       "max               3.674146   89.980000   48.000000    5.080000    1.180000   \n",
       "\n",
       "                C           N           H           O          Sn  ...  \\\n",
       "count  146.000000  146.000000  146.000000  146.000000  146.000000  ...   \n",
       "mean     0.032342    0.015686    0.028529    0.114890    0.574178  ...   \n",
       "std      0.028888    0.018662    0.088949    0.078057    1.433864  ...   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000  ...   \n",
       "25%      0.005500    0.003100    0.000000    0.030000    0.000000  ...   \n",
       "50%      0.026000    0.010000    0.003000    0.130000    0.000000  ...   \n",
       "75%      0.060000    0.011000    0.007100    0.170000    0.000000  ...   \n",
       "max      0.100000    0.050000    0.400000    0.246000    4.800000  ...   \n",
       "\n",
       "                B          Cr  Solution treated temp(cel)  ST time (h)  \\\n",
       "count  146.000000  146.000000                  146.000000   146.000000   \n",
       "mean     0.023151    0.127055                  399.315068     0.476096   \n",
       "std      0.076020    0.421538                  490.419817     0.861868   \n",
       "min      0.000000    0.000000                    0.000000     0.000000   \n",
       "25%      0.000000    0.000000                    0.000000     0.000000   \n",
       "50%      0.000000    0.000000                    0.000000     0.000000   \n",
       "75%      0.000000    0.000000                  995.000000     0.500000   \n",
       "max      0.500000    2.000000                 1100.000000     4.000000   \n",
       "\n",
       "       Anneal temp (cel)  Annealing Time (hour)  \\\n",
       "count         146.000000             146.000000   \n",
       "mean          549.342466               5.206575   \n",
       "std           411.959396              14.168044   \n",
       "min             0.000000               0.000000   \n",
       "25%             0.000000               0.000000   \n",
       "50%           700.000000               2.000000   \n",
       "75%           900.000000               4.000000   \n",
       "max          1373.000000             150.000000   \n",
       "\n",
       "       Temperature of creep test (cel)  Stress (Mpa)  \\\n",
       "count                       146.000000    146.000000   \n",
       "mean                        565.458904    277.398288   \n",
       "std                          99.346494    130.811061   \n",
       "min                         315.000000     42.000000   \n",
       "25%                         500.000000    150.000000   \n",
       "50%                         600.000000    300.000000   \n",
       "75%                         600.000000    319.000000   \n",
       "max                         773.000000    673.000000   \n",
       "\n",
       "       steady state strain rate (1/s)  Strain to rupture (%) (Efc)  \n",
       "count                    1.460000e+02                   146.000000  \n",
       "mean                     6.739644e-05                    15.657529  \n",
       "std                      2.648407e-04                    18.358161  \n",
       "min                      0.000000e+00                     0.000000  \n",
       "25%                      5.342500e-08                     0.240250  \n",
       "50%                      8.850000e-07                    10.800000  \n",
       "75%                      2.162500e-05                    22.877500  \n",
       "max                      2.130000e-03                    95.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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>Rupture time (hour)</th>\n",
       "      <th>Ti</th>\n",
       "      <th>Al</th>\n",
       "      <th>V</th>\n",
       "      <th>Fe</th>\n",
       "      <th>C</th>\n",
       "      <th>N</th>\n",
       "      <th>H</th>\n",
       "      <th>O</th>\n",
       "      <th>Sn</th>\n",
       "      <th>...</th>\n",
       "      <th>B</th>\n",
       "      <th>Cr</th>\n",
       "      <th>Solution treated temp(cel)</th>\n",
       "      <th>ST time (h)</th>\n",
       "      <th>Anneal temp (cel)</th>\n",
       "      <th>Annealing Time (hour)</th>\n",
       "      <th>Temperature of creep test (cel)</th>\n",
       "      <th>Stress (Mpa)</th>\n",
       "      <th>steady state strain rate (1/s)</th>\n",
       "      <th>Strain to rupture (%) (Efc)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.329930</td>\n",
       "      <td>83.986974</td>\n",
       "      <td>10.163871</td>\n",
       "      <td>3.120968</td>\n",
       "      <td>0.385290</td>\n",
       "      <td>0.030500</td>\n",
       "      <td>0.016377</td>\n",
       "      <td>0.045503</td>\n",
       "      <td>0.107290</td>\n",
       "      <td>0.257742</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024194</td>\n",
       "      <td>0.285484</td>\n",
       "      <td>423.548387</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>559.774194</td>\n",
       "      <td>4.080000</td>\n",
       "      <td>576.483871</td>\n",
       "      <td>237.830645</td>\n",
       "      <td>1.586971e-05</td>\n",
       "      <td>10.521419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.094454</td>\n",
       "      <td>12.178888</td>\n",
       "      <td>12.595160</td>\n",
       "      <td>1.899516</td>\n",
       "      <td>0.441619</td>\n",
       "      <td>0.030915</td>\n",
       "      <td>0.020585</td>\n",
       "      <td>0.120023</td>\n",
       "      <td>0.078466</td>\n",
       "      <td>0.997683</td>\n",
       "      <td>...</td>\n",
       "      <td>0.095141</td>\n",
       "      <td>0.637207</td>\n",
       "      <td>480.820815</td>\n",
       "      <td>0.862462</td>\n",
       "      <td>475.680825</td>\n",
       "      <td>7.935192</td>\n",
       "      <td>114.189863</td>\n",
       "      <td>149.226134</td>\n",
       "      <td>3.048712e-05</td>\n",
       "      <td>15.428038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.850781</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.514968</td>\n",
       "      <td>86.261500</td>\n",
       "      <td>5.790000</td>\n",
       "      <td>1.930000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.002750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>1.218500e-07</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.692318</td>\n",
       "      <td>88.260000</td>\n",
       "      <td>6.290000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.026000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1.100000e-06</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.928666</td>\n",
       "      <td>89.256000</td>\n",
       "      <td>6.610000</td>\n",
       "      <td>4.230000</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.045000</td>\n",
       "      <td>0.030500</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>0.835000</td>\n",
       "      <td>925.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>315.375000</td>\n",
       "      <td>1.190000e-05</td>\n",
       "      <td>15.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.241546</td>\n",
       "      <td>89.980000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.080000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1055.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1373.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>773.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>1.180000e-04</td>\n",
       "      <td>45.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rupture time (hour)         Ti         Al          V         Fe  \\\n",
       "count            31.000000  31.000000  31.000000  31.000000  31.000000   \n",
       "mean              1.329930  83.986974  10.163871   3.120968   0.385290   \n",
       "std               1.094454  12.178888  12.595160   1.899516   0.441619   \n",
       "min              -0.850781  48.000000   5.500000   0.000000   0.000000   \n",
       "25%               0.514968  86.261500   5.790000   1.930000   0.060000   \n",
       "50%               1.692318  88.260000   6.290000   4.000000   0.200000   \n",
       "75%               1.928666  89.256000   6.610000   4.230000   0.675000   \n",
       "max               3.241546  89.980000  48.000000   5.080000   1.180000   \n",
       "\n",
       "               C          N          H          O         Sn  ...          B  \\\n",
       "count  31.000000  31.000000  31.000000  31.000000  31.000000  ...  31.000000   \n",
       "mean    0.030500   0.016377   0.045503   0.107290   0.257742  ...   0.024194   \n",
       "std     0.030915   0.020585   0.120023   0.078466   0.997683  ...   0.095141   \n",
       "min     0.000000   0.000000   0.000000   0.000000   0.000000  ...   0.000000   \n",
       "25%     0.002750   0.000000   0.000000   0.015000   0.000000  ...   0.000000   \n",
       "50%     0.026000   0.008000   0.003000   0.130000   0.000000  ...   0.000000   \n",
       "75%     0.045000   0.030500   0.010000   0.180000   0.000000  ...   0.000000   \n",
       "max     0.100000   0.050000   0.400000   0.200000   4.010000  ...   0.500000   \n",
       "\n",
       "              Cr  Solution treated temp(cel)  ST time (h)  Anneal temp (cel)  \\\n",
       "count  31.000000                   31.000000    31.000000          31.000000   \n",
       "mean    0.285484                  423.548387     0.530000         559.774194   \n",
       "std     0.637207                  480.820815     0.862462         475.680825   \n",
       "min     0.000000                    0.000000     0.000000           0.000000   \n",
       "25%     0.000000                    0.000000     0.000000           0.000000   \n",
       "50%     0.000000                    0.000000     0.000000         600.000000   \n",
       "75%     0.000000                  950.000000     0.835000         925.000000   \n",
       "max     2.000000                 1055.000000     4.000000        1373.000000   \n",
       "\n",
       "       Annealing Time (hour)  Temperature of creep test (cel)  Stress (Mpa)  \\\n",
       "count              31.000000                        31.000000     31.000000   \n",
       "mean                4.080000                       576.483871    237.830645   \n",
       "std                 7.935192                       114.189863    149.226134   \n",
       "min                 0.000000                       400.000000     14.000000   \n",
       "25%                 0.000000                       500.000000    125.000000   \n",
       "50%                 0.660000                       550.000000    200.000000   \n",
       "75%                 3.000000                       700.000000    315.375000   \n",
       "max                24.000000                       773.000000    600.000000   \n",
       "\n",
       "       steady state strain rate (1/s)  Strain to rupture (%) (Efc)  \n",
       "count                    3.100000e+01                    31.000000  \n",
       "mean                     1.586971e-05                    10.521419  \n",
       "std                      3.048712e-05                    15.428038  \n",
       "min                      0.000000e+00                     0.000000  \n",
       "25%                      1.218500e-07                     0.000000  \n",
       "50%                      1.100000e-06                     1.400000  \n",
       "75%                      1.190000e-05                    15.650000  \n",
       "max                      1.180000e-04                    45.500000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[-3.97452498e+00 -3.61021847e+00 -2.75851666e+00 -4.50265062e+00\n",
      "  -1.06655686e+02  1.41312601e+02 -6.53004868e+00  1.12043214e+01\n",
      "  -1.01461839e+00 -1.42317207e+01 -4.90727245e+00 -2.89719435e+00\n",
      "  -5.70516932e-01 -3.28135146e+00  2.41739710e+00  1.28680517e-03\n",
      "  -2.26758991e-01  1.15356368e-03  5.52437423e-03 -6.92552523e-03\n",
      "  -3.35350673e-03 -4.63194766e+02 -5.88887687e-03]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "regr = linear_model.LinearRegression()\n",
    "x = np.asanyarray(train[['Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr','Solution treated temp(cel)','ST time (h)','Anneal temp (cel)','Annealing Time (hour)','Temperature of creep test (cel)','Stress (Mpa)','steady state strain rate (1/s)','Strain to rupture (%) (Efc)']])\n",
    "y = np.asanyarray(train[['Rupture time (hour)']])\n",
    "regr.fit (x, y)\n",
    "print ('Coefficients: ', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residual sum of squares: 0.33\n",
      "Variance score: 0.72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:432: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y_hat= regr.predict(test[['Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr','Solution treated temp(cel)','ST time (h)','Anneal temp (cel)','Annealing Time (hour)','Temperature of creep test (cel)','Stress (Mpa)','steady state strain rate (1/s)','Strain to rupture (%) (Efc)']])\n",
    "x = np.asanyarray(test[['Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr','Solution treated temp(cel)','ST time (h)','Anneal temp (cel)','Annealing Time (hour)','Temperature of creep test (cel)','Stress (Mpa)','steady state strain rate (1/s)','Strain to rupture (%) (Efc)']])\n",
    "y = np.asanyarray(test[['Rupture time (hour)']])\n",
    "print(\"Residual sum of squares: %.2f\"\n",
    "      % np.mean((y_hat - y) ** 2))\n",
    "\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % regr.score(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:432: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig= plt.figure()\n",
    "plt.plot(test[['Temperature of creep test (cel)']],regr.predict(test[['Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr','Solution treated temp(cel)','ST time (h)','Anneal temp (cel)','Annealing Time (hour)','Temperature of creep test (cel)','Stress (Mpa)','steady state strain rate (1/s)','Strain to rupture (%) (Efc)']]),color='black')\n",
    "plt.scatter(test[['Temperature of creep test (cel)']],test[['Rupture time (hour)']],color='red')\n",
    "plt.legend(('Pridicted', 'Real'))\n",
    "plt.xlabel('Temperature of creep test (cel)')\n",
    "plt.ylabel('Log(Rupture time (hour))')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Hp\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\ensemble\\_gb.py:437: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2_test: 0.8136589463199511\n",
      "R2_train: 0.9787348114773828\n",
      "rmse_test: 0.4647632065288014\n",
      "rmse_train: 0.18238887300078582\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import datasets, metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "reg = GradientBoostingRegressor(n_estimators=1100, learning_rate=0.01, max_depth=3, subsample=1.0) \n",
    "                 \n",
    "reg.fit(train[['Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr','Solution treated temp(cel)','ST time (h)','Anneal temp (cel)','Annealing Time (hour)','Temperature of creep test (cel)','Stress (Mpa)','steady state strain rate (1/s)','Strain to rupture (%) (Efc)']]\n",
    "        ,train[['Rupture time (hour)']])       \n",
    "predicted_test=reg.predict(test[['Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr','Solution treated temp(cel)','ST time (h)','Anneal temp (cel)','Annealing Time (hour)','Temperature of creep test (cel)','Stress (Mpa)','steady state strain rate (1/s)','Strain to rupture (%) (Efc)']])\n",
    "predicted_train=reg.predict(train[['Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr','Solution treated temp(cel)','ST time (h)','Anneal temp (cel)','Annealing Time (hour)','Temperature of creep test (cel)','Stress (Mpa)','steady state strain rate (1/s)','Strain to rupture (%) (Efc)']])\n",
    "R2_test=metrics.r2_score(test[['Rupture time (hour)']],predicted_test)\n",
    "R2_train=metrics.r2_score(train[['Rupture time (hour)']],predicted_train)\n",
    "rmse_test=np.sqrt(metrics.mean_squared_error(test[['Rupture time (hour)']],predicted_test))\n",
    "rmse_train=np.sqrt(metrics.mean_squared_error(train[['Rupture time (hour)']],predicted_train))\n",
    "print('R2_test:',R2_test)\n",
    "print('R2_train:',R2_train)\n",
    "print('rmse_test:',rmse_test)\n",
    "print('rmse_train:',rmse_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code block\n",
    "fig,ax=plt.subplots(figsize=(10,10))\n",
    "plt.scatter(train[['Rupture time (hour)']],\n",
    "             reg.predict(train[['Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr','Solution treated temp(cel)','ST time (h)','Anneal temp (cel)','Annealing Time (hour)','Temperature of creep test (cel)','Stress (Mpa)','steady state strain rate (1/s)','Strain to rupture (%) (Efc)']]),\n",
    "             s = 400, marker='s',linewidth=2,edgecolors=\"blue\", label = 'Train data',color = \"lavender\")  #, marker='o',color = \"grey\", edgecolors=\"white\", linewidth='2', s = 600, ) \n",
    "plt.scatter( test[['Rupture time (hour)']],\n",
    "             reg.predict(test[['Ti','Al','V','Fe','C','N','H','O','Sn','Nb','Mo','Zr','Si','B','Cr','Solution treated temp(cel)','ST time (h)','Anneal temp (cel)','Annealing Time (hour)','Temperature of creep test (cel)','Stress (Mpa)','steady state strain rate (1/s)','Strain to rupture (%) (Efc)']]),\n",
    "            s = 500, marker='s', linewidth=2,edgecolors=\"white\",label = 'Test data',color = \"red\")  #,color = \"navy\", edgecolors=\"white\",linewidth='1', s = 600, label = 'Test data') \n",
    "xl=[-2,-1,0,1,2,3,4]\n",
    "yl=[-2,-1,0,1,2,3,4]\n",
    "plt.plot(yl,xl,linestyle='dashed',color='black', linewidth=3.5) \n",
    "plt.legend(loc = 'upper left',fontsize='xx-large',facecolor='w',edgecolor='black') \n",
    "plt.tick_params(axis='both', which='major', labelsize=30)\n",
    "plt.xlabel('Actual',fontsize=45)\n",
    "plt.ylabel(\"Predicted\",fontsize=45)\n",
    "plt.show()\n"
   ]
  }
 ],
 "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": "3.11.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}