Task on LR & MLR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as pl\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "data = sns.load_dataset('geyser')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>duration</th>\n",
       "      <th>waiting</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.600</td>\n",
       "      <td>79</td>\n",
       "      <td>long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.800</td>\n",
       "      <td>54</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.333</td>\n",
       "      <td>74</td>\n",
       "      <td>long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.283</td>\n",
       "      <td>62</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.533</td>\n",
       "      <td>85</td>\n",
       "      <td>long</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  waiting   kind\n",
       "0     3.600       79   long\n",
       "1     1.800       54  short\n",
       "2     3.333       74   long\n",
       "3     2.283       62  short\n",
       "4     4.533       85   long"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>duration</th>\n",
       "      <th>waiting</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>4.117</td>\n",
       "      <td>81</td>\n",
       "      <td>long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>2.150</td>\n",
       "      <td>46</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>4.417</td>\n",
       "      <td>90</td>\n",
       "      <td>long</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1.817</td>\n",
       "      <td>46</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>4.467</td>\n",
       "      <td>74</td>\n",
       "      <td>long</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     duration  waiting   kind\n",
       "267     4.117       81   long\n",
       "268     2.150       46  short\n",
       "269     4.417       90   long\n",
       "270     1.817       46  short\n",
       "271     4.467       74   long"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows:  272 \n",
      "Columns:  3\n",
      "Size:  816\n"
     ]
    }
   ],
   "source": [
    "rows, col = data.shape\n",
    "print('Rows: ', rows, '\\nColumns: ', col)\n",
    "print('Size: ',data.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 272 entries, 0 to 271\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   duration  272 non-null    float64\n",
      " 1   waiting   272 non-null    int64  \n",
      " 2   kind      272 non-null    object \n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 6.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>duration</th>\n",
       "      <th>waiting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>272.000000</td>\n",
       "      <td>272.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.487783</td>\n",
       "      <td>70.897059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.141371</td>\n",
       "      <td>13.594974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.162750</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.454250</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration     waiting\n",
       "count  272.000000  272.000000\n",
       "mean     3.487783   70.897059\n",
       "std      1.141371   13.594974\n",
       "min      1.600000   43.000000\n",
       "25%      2.162750   58.000000\n",
       "50%      4.000000   76.000000\n",
       "75%      4.454250   82.000000\n",
       "max      5.100000   96.000000"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration    126\n",
       "waiting      51\n",
       "kind          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration    0\n",
       "waiting     0\n",
       "kind        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing  Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>3.600</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.800</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.333</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.283</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.533</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   duration  waiting  kind\n",
       "0     3.600       79     0\n",
       "1     1.800       54     1\n",
       "2     3.333       74     0\n",
       "3     2.283       62     1\n",
       "4     4.533       85     0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = LabelEncoder()\n",
    "data['kind'] = label.fit_transform(data['kind'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
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       "      <td>0.900811</td>\n",
       "      <td>-0.932612</td>\n",
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       "      <th>waiting</th>\n",
       "      <td>0.900811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.907299</td>\n",
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      "text/plain": [
       "          duration   waiting      kind\n",
       "duration  1.000000  0.900811 -0.932612\n",
       "waiting   0.900811  1.000000 -0.907299\n",
       "kind     -0.932612 -0.907299  1.000000"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x23735f02470>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "legendgroup": "0",
         "marker": {
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          "pattern": {
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         },
         "name": "0",
         "offsetgroup": "0",
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              "solidity": 0.2
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            "type": "histogram"
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          "histogram2d": [
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             [
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             [
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            ],
            "type": "histogram2d"
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          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
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            "colorscale": [
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            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
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            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
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            },
            "type": "scattergeo"
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          "scattergl": [
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            "marker": {
             "colorbar": {
              "outlinewidth": 0,
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            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
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            },
            "type": "scattermapbox"
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          ],
          "scatterpolar": [
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            "marker": {
             "colorbar": {
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              "ticks": ""
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            },
            "type": "scatterpolar"
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          ],
          "scatterpolargl": [
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             "colorbar": {
              "outlinewidth": 0,
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            "type": "scatterpolargl"
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          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
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            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
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            "colorscale": [
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            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
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             "line": {
              "color": "white"
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            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
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             "line": {
              "color": "white"
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            },
            "type": "table"
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          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
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          "autotypenumbers": "strict",
          "coloraxis": {
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            "ticks": ""
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          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
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            [
             0.2,
             "#de77ae"
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            [
             0.3,
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            [
             0.4,
             "#fde0ef"
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            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
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             0.7,
             "#b8e186"
            ],
            [
             0.8,
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            ],
            [
             0.9,
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             1,
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           ],
           "sequential": [
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            ],
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             0.4444444444444444,
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           ],
           "sequentialminus": [
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             0,
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             "#fdca26"
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             1,
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          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "duration of  each kind"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "duration"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "waiting"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.scatter(data, x='duration', y='waiting', color='kind',  title = 'duration of  each kind')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data[['duration', 'waiting']]\n",
    "y=data['kind']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is splited Successfully...\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=0)\n",
    "print('Data is splited Successfully...')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model is Trained Successfully...\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "print('Model is Trained Successfully...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.26382408,  0.94532799,  0.00235982,  0.82884069,  0.08117415,\n",
       "        0.00685566,  0.04680872,  0.01680147, -0.15180045, -0.09628178,\n",
       "        0.66040861,  0.44395374, -0.04978697,  1.10156635,  0.24752815,\n",
       "        0.94962182,  1.01961431,  0.72904296,  0.02334322,  0.0721503 ,\n",
       "        0.2653095 ,  0.93405622,  0.11114923,  1.02618824, -0.11749936,\n",
       "        0.16554394,  1.0539154 , -0.12182537,  0.86991771, -0.0175318 ,\n",
       "        0.08005019,  0.95748954, -0.04978697, -0.10530563,  0.22971462,\n",
       "       -0.20169932, -0.01432975,  0.66698254,  0.94529582, -0.03646928,\n",
       "       -0.07533056, -0.03329941,  0.9110269 , -0.11525144,  0.14663863,\n",
       "        0.05229086, -0.02767961,  0.00126803,  0.95752172, -0.10190158,\n",
       "        0.96184773,  1.03746001,  1.05956737,  0.89202507,  1.0119486 ])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:  0.10898574097857089\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy Score: ', metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuraccy:  89.47716941301715\n",
      "Testing Accuraccy:  88.91577371961328\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuraccy: ', model.score(x_train, y_train)*100)\n",
    "print('Testing Accuraccy: ', model.score(x_test, y_test)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data[['duration']]\n",
    "y=data[['waiting']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data is splited Successfully...\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=0)\n",
    "print('Data is splited Successfully...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model is Trained Successfully...\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(x_train, y_train)\n",
    "print('Model is Trained Successfully...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression line of Traing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_train, y_train, color='green')\n",
    "plt.plot(x_train, model.predict(x_train), color='red')\n",
    "plt.xlabel('duration')\n",
    "plt.ylabel('waiting')\n",
    "plt.title('Duration VS wainting of geyser')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression line of Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test, y_test, color='green')\n",
    "plt.plot(x_test, model.predict(x_test), color='red')\n",
    "plt.xlabel('duration')\n",
    "plt.ylabel('waiting')\n",
    "plt.title('Duration VS wainting of geyser')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[84.73284668],\n",
       "       [54.88396846],\n",
       "       [79.93570553],\n",
       "       [58.08206255],\n",
       "       [79.22146452],\n",
       "       [77.8036428 ],\n",
       "       [76.20459576],\n",
       "       [78.86967417],\n",
       "       [85.61765271],\n",
       "       [82.41955861],\n",
       "       [58.96686859],\n",
       "       [69.09416655],\n",
       "       [81.53475258],\n",
       "       [53.99916243],\n",
       "       [73.53951734],\n",
       "       [56.66424084],\n",
       "       [54.35095278],\n",
       "       [59.14809392],\n",
       "       [79.58391518],\n",
       "       [81.53475258],\n",
       "       [68.92360153],\n",
       "       [58.26328789],\n",
       "       [74.60554871],\n",
       "       [57.01603119],\n",
       "       [84.73284668],\n",
       "       [71.9404703 ],\n",
       "       [53.46614674],\n",
       "       [81.0017369 ],\n",
       "       [55.94999982],\n",
       "       [77.8036428 ],\n",
       "       [79.7544802 ],\n",
       "       [52.93313106],\n",
       "       [81.53475258],\n",
       "       [84.73284668],\n",
       "       [76.20459576],\n",
       "       [86.15066839],\n",
       "       [82.06776826],\n",
       "       [61.631947  ],\n",
       "       [52.93313106],\n",
       "       [81.0017369 ],\n",
       "       [80.11693087],\n",
       "       [83.31502496],\n",
       "       [55.76877449],\n",
       "       [83.66681531],\n",
       "       [77.08940179],\n",
       "       [81.35352725],\n",
       "       [80.64994655],\n",
       "       [82.41955861],\n",
       "       [54.88396846],\n",
       "       [85.08463703],\n",
       "       [58.61507824],\n",
       "       [53.63671176],\n",
       "       [52.75190573],\n",
       "       [55.06519379],\n",
       "       [54.16972745]])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[94]</td>\n",
       "      <td>84.732847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[56]</td>\n",
       "      <td>54.883968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[82]</td>\n",
       "      <td>79.935706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[59]</td>\n",
       "      <td>58.082063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[77]</td>\n",
       "      <td>79.221465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[86]</td>\n",
       "      <td>77.803643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[86]</td>\n",
       "      <td>76.204596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[83]</td>\n",
       "      <td>78.869674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[83]</td>\n",
       "      <td>85.617653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[85]</td>\n",
       "      <td>82.419559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[71]</td>\n",
       "      <td>58.966869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[68]</td>\n",
       "      <td>69.094167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[83]</td>\n",
       "      <td>81.534753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[45]</td>\n",
       "      <td>53.999162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[75]</td>\n",
       "      <td>73.539517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[52]</td>\n",
       "      <td>56.664241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[51]</td>\n",
       "      <td>54.350953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[65]</td>\n",
       "      <td>59.148094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[81]</td>\n",
       "      <td>79.583915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[73]</td>\n",
       "      <td>81.534753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[83]</td>\n",
       "      <td>68.923602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[50]</td>\n",
       "      <td>58.263288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[84]</td>\n",
       "      <td>74.605549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[45]</td>\n",
       "      <td>57.016031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>[82]</td>\n",
       "      <td>84.732847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>[85]</td>\n",
       "      <td>71.940470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>[50]</td>\n",
       "      <td>53.466147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>[90]</td>\n",
       "      <td>81.001737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>[60]</td>\n",
       "      <td>55.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>[88]</td>\n",
       "      <td>77.803643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>[76]</td>\n",
       "      <td>79.754480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>[59]</td>\n",
       "      <td>52.933131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>[83]</td>\n",
       "      <td>81.534753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>[81]</td>\n",
       "      <td>84.732847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>[71]</td>\n",
       "      <td>76.204596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>[86]</td>\n",
       "      <td>86.150668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>[79]</td>\n",
       "      <td>82.067768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>[65]</td>\n",
       "      <td>61.631947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>[60]</td>\n",
       "      <td>52.933131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>[83]</td>\n",
       "      <td>81.001737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>[88]</td>\n",
       "      <td>80.116931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>[78]</td>\n",
       "      <td>83.315025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>[57]</td>\n",
       "      <td>55.768774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>[84]</td>\n",
       "      <td>83.666815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>[76]</td>\n",
       "      <td>77.089402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>[75]</td>\n",
       "      <td>81.353527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>[83]</td>\n",
       "      <td>80.649947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>[77]</td>\n",
       "      <td>82.419559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>[55]</td>\n",
       "      <td>54.883968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>[80]</td>\n",
       "      <td>85.084637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>[47]</td>\n",
       "      <td>58.615078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>[51]</td>\n",
       "      <td>53.636712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>[51]</td>\n",
       "      <td>52.751906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>[60]</td>\n",
       "      <td>55.065194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>[52]</td>\n",
       "      <td>54.169727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0    [94]  84.732847\n",
       "1    [56]  54.883968\n",
       "2    [82]  79.935706\n",
       "3    [59]  58.082063\n",
       "4    [77]  79.221465\n",
       "5    [86]  77.803643\n",
       "6    [86]  76.204596\n",
       "7    [83]  78.869674\n",
       "8    [83]  85.617653\n",
       "9    [85]  82.419559\n",
       "10   [71]  58.966869\n",
       "11   [68]  69.094167\n",
       "12   [83]  81.534753\n",
       "13   [45]  53.999162\n",
       "14   [75]  73.539517\n",
       "15   [52]  56.664241\n",
       "16   [51]  54.350953\n",
       "17   [65]  59.148094\n",
       "18   [81]  79.583915\n",
       "19   [73]  81.534753\n",
       "20   [83]  68.923602\n",
       "21   [50]  58.263288\n",
       "22   [84]  74.605549\n",
       "23   [45]  57.016031\n",
       "24   [82]  84.732847\n",
       "25   [85]  71.940470\n",
       "26   [50]  53.466147\n",
       "27   [90]  81.001737\n",
       "28   [60]  55.950000\n",
       "29   [88]  77.803643\n",
       "30   [76]  79.754480\n",
       "31   [59]  52.933131\n",
       "32   [83]  81.534753\n",
       "33   [81]  84.732847\n",
       "34   [71]  76.204596\n",
       "35   [86]  86.150668\n",
       "36   [79]  82.067768\n",
       "37   [65]  61.631947\n",
       "38   [60]  52.933131\n",
       "39   [83]  81.001737\n",
       "40   [88]  80.116931\n",
       "41   [78]  83.315025\n",
       "42   [57]  55.768774\n",
       "43   [84]  83.666815\n",
       "44   [76]  77.089402\n",
       "45   [75]  81.353527\n",
       "46   [83]  80.649947\n",
       "47   [77]  82.419559\n",
       "48   [55]  54.883968\n",
       "49   [80]  85.084637\n",
       "50   [47]  58.615078\n",
       "51   [51]  53.636712\n",
       "52   [51]  52.751906\n",
       "53   [60]  55.065194\n",
       "54   [52]  54.169727"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'Actual': y_test.values.tolist(), 'Predicted': y_pred.reshape(55,)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Absolute Error 4.948911030572998\n"
     ]
    }
   ],
   "source": [
    "print('Absolute Error', metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Training Data:  81.25470430516121\n",
      "Accuracy on Training Data:  80.64187451434306\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on Training Data: ', model.score(x_train, y_train)*100)\n",
    "print('Accuracy on Training Data: ', model.score(x_test, y_test)*100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient:  [[10.66031365]] \n",
      "Intercept:  [33.56334116]\n"
     ]
    }
   ],
   "source": [
    "print('Coefficient: ', model.coef_, '\\nIntercept: ', model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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