Fish Weight prediction.ipynb

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    "**Fish Weight Prediction**\n",
    "\n",
    "With a dataset of fish species, with some of it characteristic like it vertical, diagonal, length, height, and width. We will try to predict the weight of the fish based on their characteristic. We will use Linear Regression Method to see whether the weight of the fish related to their characteristic.\n",
    "\n",
    "Species: Species name of fish\n",
    "\n",
    "Weight: Weight of fish in gram\n",
    "\n",
    "Length1: Vertical length in cm\n",
    "\n",
    "Length2: Diagonal length in cm\n",
    "\n",
    "Length3: Cross length in cm\n",
    "\n",
    "Height: Height in cm\n",
    "\n",
    "Width: Diagonal width in cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "wfLFuDogbbpt"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "bJeKuSbkcL-h"
   },
   "outputs": [],
   "source": [
    "fish = pd.read_csv('Fish.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bream</td>\n",
       "      <td>242.0</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "      <td>23.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>30.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Bream</td>\n",
       "      <td>290.0</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Bream</td>\n",
       "      <td>340.0</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "      <td>23.9</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Bream</td>\n",
       "      <td>363.0</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "      <td>26.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Bream</td>\n",
       "      <td>430.0</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "      <td>26.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Category Species  Weight   Height   Width  Length1  Length2  Length3\n",
       "0         1   Bream   242.0  11.5200  4.0200     23.2     25.4     30.0\n",
       "1         1   Bream   290.0  12.4800  4.3056     24.0     26.3     31.2\n",
       "2         1   Bream   340.0  12.3778  4.6961     23.9     26.5     31.1\n",
       "3         1   Bream   363.0  12.7300  4.4555     26.3     29.0     33.5\n",
       "4         1   Bream   430.0  12.4440  5.1340     26.5     29.0     34.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 159 entries, 0 to 158\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Category  159 non-null    int64  \n",
      " 1   Species   159 non-null    object \n",
      " 2   Weight    159 non-null    float64\n",
      " 3   Height    159 non-null    float64\n",
      " 4   Width     159 non-null    float64\n",
      " 5   Length1   159 non-null    float64\n",
      " 6   Length2   159 non-null    float64\n",
      " 7   Length3   159 non-null    float64\n",
      "dtypes: float64(6), int64(1), object(1)\n",
      "memory usage: 10.1+ KB\n"
     ]
    }
   ],
   "source": [
    "fish.info()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.264151</td>\n",
       "      <td>398.326415</td>\n",
       "      <td>8.970994</td>\n",
       "      <td>4.417486</td>\n",
       "      <td>26.247170</td>\n",
       "      <td>28.415723</td>\n",
       "      <td>31.227044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.704249</td>\n",
       "      <td>357.978317</td>\n",
       "      <td>4.286208</td>\n",
       "      <td>1.685804</td>\n",
       "      <td>9.996441</td>\n",
       "      <td>10.716328</td>\n",
       "      <td>11.610246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.728400</td>\n",
       "      <td>1.047600</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>8.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.944800</td>\n",
       "      <td>3.385650</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>23.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>273.000000</td>\n",
       "      <td>7.786000</td>\n",
       "      <td>4.248500</td>\n",
       "      <td>25.200000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>29.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>12.365900</td>\n",
       "      <td>5.584500</td>\n",
       "      <td>32.700000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>39.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>18.957000</td>\n",
       "      <td>8.142000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>63.400000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Category       Weight      Height       Width     Length1  \\\n",
       "count  159.000000   159.000000  159.000000  159.000000  159.000000   \n",
       "mean     3.264151   398.326415    8.970994    4.417486   26.247170   \n",
       "std      1.704249   357.978317    4.286208    1.685804    9.996441   \n",
       "min      1.000000     0.000000    1.728400    1.047600    7.500000   \n",
       "25%      2.000000   120.000000    5.944800    3.385650   19.050000   \n",
       "50%      3.000000   273.000000    7.786000    4.248500   25.200000   \n",
       "75%      4.500000   650.000000   12.365900    5.584500   32.700000   \n",
       "max      7.000000  1650.000000   18.957000    8.142000   59.000000   \n",
       "\n",
       "          Length2     Length3  \n",
       "count  159.000000  159.000000  \n",
       "mean    28.415723   31.227044  \n",
       "std     10.716328   11.610246  \n",
       "min      8.400000    8.800000  \n",
       "25%     21.000000   23.150000  \n",
       "50%     27.300000   29.400000  \n",
       "75%     35.500000   39.650000  \n",
       "max     63.400000   68.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_UE6ongucwPh",
    "outputId": "7b4c3ddb-7e19-47e4-d9f3-0c1eeb470af4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Category', 'Species', 'Weight', 'Height', 'Width', 'Length1',\n",
       "       'Length2', 'Length3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "CMeYBg3Fc3xR"
   },
   "outputs": [],
   "source": [
    "y = fish['Weight']\n",
    "X = fish[['Category','Height', 'Width', 'Length1',\n",
    "       'Length2', 'Length3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "sMQCPTRwddd0"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, train_size=0.7, random_state=2529)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mAbRQjWzdlFL",
    "outputId": "dd1bf15e-130e-4770-f9cd-9df892f7d053"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((111, 6), (48, 6), (111,), (48,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "pfVOk8bldqtq"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
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    "id": "MxbpEJ12dy6d",
    "outputId": "9122ba38-2e2a-450b-8182-51de7b8c597a"
   },
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       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jDMDGErid5Vb",
    "outputId": "98490f8c-4c5b-4faa-d6f5-fd9dd80fba1d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-684.4235918478512"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "akl2dYOId_rU",
    "outputId": "85777495-3241-41e4-f3f7-9c254f7ee8dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 35.19634977,  52.19372157, -37.13869125,  11.2218449 ,\n",
       "        78.11233002, -59.11783139])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "FbJ6fzdzeEcs"
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NCLD9YbmeNBy",
    "outputId": "4fb1e358-573f-4070-f19a-af5799fbde96"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 475.93351307,  525.81910195,   77.63275849,  881.10235121,\n",
       "        160.9685664 ,  255.94371856,  361.87029932,  358.87068094,\n",
       "        499.83411068, -150.07834151, -115.91810869,  428.65470115,\n",
       "        114.67533404,  812.51385122,  586.5071178 ,  273.38510858,\n",
       "        579.63900729,  225.18126845,  639.26068037,   85.00820599,\n",
       "        136.92159041,  -87.7778087 ,  629.97231046,  732.63097812,\n",
       "        859.8720695 , -166.76928607,  342.04209934,  722.92198147,\n",
       "        321.44827179,  787.98248357,  486.93194673,  541.89982795,\n",
       "        376.74813045,  624.81211202, -170.11945033,  917.76513801,\n",
       "        792.26439518,  -21.15655005,  300.24921659,  914.07325473,\n",
       "        621.05636286,  934.17373986,  676.85479574,  653.92304403,\n",
       "        615.51226767,  336.61090622,  505.75519147,  -33.53283763])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "iRCBuKx9ePA7"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OhrErMp0eWFm",
    "outputId": "696608b4-4a09-44fb-aec8-ae091c37a8dd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.58910366731824"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MPG8l84zeaGL",
    "outputId": "f190fb66-27a7-42db-81b0-10b0605ce1ed"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8398246159944498"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}