4-kendi-calismam-lineer-regresyon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc348410",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce971afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bream</td>\n",
       "      <td>242.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bream</td>\n",
       "      <td>290.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>31.2</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bream</td>\n",
       "      <td>340.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>26.5</td>\n",
       "      <td>31.1</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bream</td>\n",
       "      <td>363.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bream</td>\n",
       "      <td>430.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Species  Weight  Length1  Length2  Length3   Height   Width\n",
       "0   Bream   242.0     23.2     25.4     30.0  11.5200  4.0200\n",
       "1   Bream   290.0     24.0     26.3     31.2  12.4800  4.3056\n",
       "2   Bream   340.0     23.9     26.5     31.1  12.3778  4.6961\n",
       "3   Bream   363.0     26.3     29.0     33.5  12.7300  4.4555\n",
       "4   Bream   430.0     26.5     29.0     34.0  12.4440  5.1340"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish=pd.read_csv(\"resources/resources/Fish.csv\")\n",
    "fish.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce654d9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 159 entries, 0 to 158\n",
      "Data columns (total 7 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   Species  159 non-null    object \n",
      " 1   Weight   159 non-null    float64\n",
      " 2   Length1  159 non-null    float64\n",
      " 3   Length2  159 non-null    float64\n",
      " 4   Length3  159 non-null    float64\n",
      " 5   Height   159 non-null    float64\n",
      " 6   Width    159 non-null    float64\n",
      "dtypes: float64(6), object(1)\n",
      "memory usage: 8.8+ KB\n"
     ]
    }
   ],
   "source": [
    "fish.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dec27157",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>159.0</td>\n",
       "      <td>398.326415</td>\n",
       "      <td>357.978317</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>120.00000</td>\n",
       "      <td>273.0000</td>\n",
       "      <td>650.0000</td>\n",
       "      <td>1650.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length1</th>\n",
       "      <td>159.0</td>\n",
       "      <td>26.247170</td>\n",
       "      <td>9.996441</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>19.05000</td>\n",
       "      <td>25.2000</td>\n",
       "      <td>32.7000</td>\n",
       "      <td>59.000</td>\n",
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       "    <tr>\n",
       "      <th>Length2</th>\n",
       "      <td>159.0</td>\n",
       "      <td>28.415723</td>\n",
       "      <td>10.716328</td>\n",
       "      <td>8.4000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>27.3000</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>63.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length3</th>\n",
       "      <td>159.0</td>\n",
       "      <td>31.227044</td>\n",
       "      <td>11.610246</td>\n",
       "      <td>8.8000</td>\n",
       "      <td>23.15000</td>\n",
       "      <td>29.4000</td>\n",
       "      <td>39.6500</td>\n",
       "      <td>68.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>159.0</td>\n",
       "      <td>8.970994</td>\n",
       "      <td>4.286208</td>\n",
       "      <td>1.7284</td>\n",
       "      <td>5.94480</td>\n",
       "      <td>7.7860</td>\n",
       "      <td>12.3659</td>\n",
       "      <td>18.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Width</th>\n",
       "      <td>159.0</td>\n",
       "      <td>4.417486</td>\n",
       "      <td>1.685804</td>\n",
       "      <td>1.0476</td>\n",
       "      <td>3.38565</td>\n",
       "      <td>4.2485</td>\n",
       "      <td>5.5845</td>\n",
       "      <td>8.142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count        mean         std     min        25%       50%       75%  \\\n",
       "Weight   159.0  398.326415  357.978317  0.0000  120.00000  273.0000  650.0000   \n",
       "Length1  159.0   26.247170    9.996441  7.5000   19.05000   25.2000   32.7000   \n",
       "Length2  159.0   28.415723   10.716328  8.4000   21.00000   27.3000   35.5000   \n",
       "Length3  159.0   31.227044   11.610246  8.8000   23.15000   29.4000   39.6500   \n",
       "Height   159.0    8.970994    4.286208  1.7284    5.94480    7.7860   12.3659   \n",
       "Width    159.0    4.417486    1.685804  1.0476    3.38565    4.2485    5.5845   \n",
       "\n",
       "              max  \n",
       "Weight   1650.000  \n",
       "Length1    59.000  \n",
       "Length2    63.400  \n",
       "Length3    68.000  \n",
       "Height     18.957  \n",
       "Width       8.142  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "786a5923",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=fish[fish[\"Weight\"]==0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6210b49",
   "metadata": {},
   "outputs": [],
   "source": [
    "fish.drop(a,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7748eeb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>158.0</td>\n",
       "      <td>400.847468</td>\n",
       "      <td>357.697796</td>\n",
       "      <td>5.9000</td>\n",
       "      <td>121.25000</td>\n",
       "      <td>281.50000</td>\n",
       "      <td>650.00000</td>\n",
       "      <td>1650.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length1</th>\n",
       "      <td>158.0</td>\n",
       "      <td>26.293038</td>\n",
       "      <td>10.011427</td>\n",
       "      <td>7.5000</td>\n",
       "      <td>19.15000</td>\n",
       "      <td>25.30000</td>\n",
       "      <td>32.70000</td>\n",
       "      <td>59.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length2</th>\n",
       "      <td>158.0</td>\n",
       "      <td>28.465823</td>\n",
       "      <td>10.731707</td>\n",
       "      <td>8.4000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>27.40000</td>\n",
       "      <td>35.75000</td>\n",
       "      <td>63.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length3</th>\n",
       "      <td>158.0</td>\n",
       "      <td>31.280380</td>\n",
       "      <td>11.627605</td>\n",
       "      <td>8.8000</td>\n",
       "      <td>23.20000</td>\n",
       "      <td>29.70000</td>\n",
       "      <td>39.67500</td>\n",
       "      <td>68.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>158.0</td>\n",
       "      <td>8.986790</td>\n",
       "      <td>4.295191</td>\n",
       "      <td>1.7284</td>\n",
       "      <td>5.94060</td>\n",
       "      <td>7.78900</td>\n",
       "      <td>12.37185</td>\n",
       "      <td>18.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Width</th>\n",
       "      <td>158.0</td>\n",
       "      <td>4.424232</td>\n",
       "      <td>1.689010</td>\n",
       "      <td>1.0476</td>\n",
       "      <td>3.39865</td>\n",
       "      <td>4.27705</td>\n",
       "      <td>5.58675</td>\n",
       "      <td>8.142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count        mean         std     min        25%        50%  \\\n",
       "Weight   158.0  400.847468  357.697796  5.9000  121.25000  281.50000   \n",
       "Length1  158.0   26.293038   10.011427  7.5000   19.15000   25.30000   \n",
       "Length2  158.0   28.465823   10.731707  8.4000   21.00000   27.40000   \n",
       "Length3  158.0   31.280380   11.627605  8.8000   23.20000   29.70000   \n",
       "Height   158.0    8.986790    4.295191  1.7284    5.94060    7.78900   \n",
       "Width    158.0    4.424232    1.689010  1.0476    3.39865    4.27705   \n",
       "\n",
       "               75%       max  \n",
       "Weight   650.00000  1650.000  \n",
       "Length1   32.70000    59.000  \n",
       "Length2   35.75000    63.400  \n",
       "Length3   39.67500    68.000  \n",
       "Height    12.37185    18.957  \n",
       "Width      5.58675     8.142  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "edd9cff3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0dc81f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Species', 'Weight', 'Length1', 'Length2', 'Length3', 'Height',\n",
       "       'Width'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e88ee5db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bream</td>\n",
       "      <td>23.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bream</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>31.2</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bream</td>\n",
       "      <td>23.9</td>\n",
       "      <td>26.5</td>\n",
       "      <td>31.1</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bream</td>\n",
       "      <td>26.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bream</td>\n",
       "      <td>26.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>11.5</td>\n",
       "      <td>12.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>2.0904</td>\n",
       "      <td>1.3936</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>11.7</td>\n",
       "      <td>12.4</td>\n",
       "      <td>13.5</td>\n",
       "      <td>2.4300</td>\n",
       "      <td>1.2690</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>12.1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>2.2770</td>\n",
       "      <td>1.2558</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>13.2</td>\n",
       "      <td>14.3</td>\n",
       "      <td>15.2</td>\n",
       "      <td>2.8728</td>\n",
       "      <td>2.0672</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>13.8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2.9322</td>\n",
       "      <td>1.8792</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Species  Length1  Length2  Length3   Height   Width  Weight\n",
       "0     Bream     23.2     25.4     30.0  11.5200  4.0200   242.0\n",
       "1     Bream     24.0     26.3     31.2  12.4800  4.3056   290.0\n",
       "2     Bream     23.9     26.5     31.1  12.3778  4.6961   340.0\n",
       "3     Bream     26.3     29.0     33.5  12.7300  4.4555   363.0\n",
       "4     Bream     26.5     29.0     34.0  12.4440  5.1340   430.0\n",
       "..      ...      ...      ...      ...      ...     ...     ...\n",
       "154   Smelt     11.5     12.2     13.4   2.0904  1.3936    12.2\n",
       "155   Smelt     11.7     12.4     13.5   2.4300  1.2690    13.4\n",
       "156   Smelt     12.1     13.0     13.8   2.2770  1.2558    12.2\n",
       "157   Smelt     13.2     14.3     15.2   2.8728  2.0672    19.7\n",
       "158   Smelt     13.8     15.0     16.2   2.9322  1.8792    19.9\n",
       "\n",
       "[158 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X=fish.drop(\"Weight\",axis=1)\n",
    "df_y=fish[\"Weight\"]\n",
    "fish=df_X.join(df_y)\n",
    "fish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3386abdc",
   "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>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Length1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999516</td>\n",
       "      <td>0.992004</td>\n",
       "      <td>0.624409</td>\n",
       "      <td>0.866684</td>\n",
       "      <td>0.915719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length2</th>\n",
       "      <td>0.999516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.994083</td>\n",
       "      <td>0.639503</td>\n",
       "      <td>0.873201</td>\n",
       "      <td>0.918603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length3</th>\n",
       "      <td>0.992004</td>\n",
       "      <td>0.994083</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.702655</td>\n",
       "      <td>0.878189</td>\n",
       "      <td>0.923090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>0.624409</td>\n",
       "      <td>0.639503</td>\n",
       "      <td>0.702655</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792401</td>\n",
       "      <td>0.723857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Width</th>\n",
       "      <td>0.866684</td>\n",
       "      <td>0.873201</td>\n",
       "      <td>0.878189</td>\n",
       "      <td>0.792401</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.886654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.915719</td>\n",
       "      <td>0.918603</td>\n",
       "      <td>0.923090</td>\n",
       "      <td>0.723857</td>\n",
       "      <td>0.886654</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Length1   Length2   Length3    Height     Width    Weight\n",
       "Length1  1.000000  0.999516  0.992004  0.624409  0.866684  0.915719\n",
       "Length2  0.999516  1.000000  0.994083  0.639503  0.873201  0.918603\n",
       "Length3  0.992004  0.994083  1.000000  0.702655  0.878189  0.923090\n",
       "Height   0.624409  0.639503  0.702655  1.000000  0.792401  0.723857\n",
       "Width    0.866684  0.873201  0.878189  0.792401  1.000000  0.886654\n",
       "Weight   0.915719  0.918603  0.923090  0.723857  0.886654  1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4e19dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bfec2336",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.heatmap(fish.corr(),annot=True,cmap='YlGnBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "37df7344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Length1'}>,\n",
       "        <AxesSubplot:title={'center':'Length2'}>],\n",
       "       [<AxesSubplot:title={'center':'Length3'}>,\n",
       "        <AxesSubplot:title={'center':'Height'}>],\n",
       "       [<AxesSubplot:title={'center':'Width'}>,\n",
       "        <AxesSubplot:title={'center':'Weight'}>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fish.hist(figsize=(14,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6823cee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "fish[\"Length1\"]=np.log(fish[\"Length1\"])+1\n",
    "fish[\"Length2\"]=np.log(fish[\"Length2\"])+1\n",
    "fish[\"Length3\"]=np.log(fish[\"Length3\"])+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f044599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Length1'}>,\n",
       "        <AxesSubplot:title={'center':'Length2'}>],\n",
       "       [<AxesSubplot:title={'center':'Length3'}>,\n",
       "        <AxesSubplot:title={'center':'Height'}>],\n",
       "       [<AxesSubplot:title={'center':'Width'}>,\n",
       "        <AxesSubplot:title={'center':'Weight'}>]], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fish.hist(figsize=(14,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a559054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.heatmap(fish.corr(),annot=True,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "90150d21",
   "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>Species</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bream</td>\n",
       "      <td>4.401197</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bream</td>\n",
       "      <td>4.440418</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bream</td>\n",
       "      <td>4.437208</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bream</td>\n",
       "      <td>4.511545</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bream</td>\n",
       "      <td>4.526361</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>3.595255</td>\n",
       "      <td>2.0904</td>\n",
       "      <td>1.3936</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>3.602690</td>\n",
       "      <td>2.4300</td>\n",
       "      <td>1.2690</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>3.624669</td>\n",
       "      <td>2.2770</td>\n",
       "      <td>1.2558</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>3.721295</td>\n",
       "      <td>2.8728</td>\n",
       "      <td>2.0672</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>3.785011</td>\n",
       "      <td>2.9322</td>\n",
       "      <td>1.8792</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Species   Length3   Height   Width  Weight\n",
       "0     Bream  4.401197  11.5200  4.0200   242.0\n",
       "1     Bream  4.440418  12.4800  4.3056   290.0\n",
       "2     Bream  4.437208  12.3778  4.6961   340.0\n",
       "3     Bream  4.511545  12.7300  4.4555   363.0\n",
       "4     Bream  4.526361  12.4440  5.1340   430.0\n",
       "..      ...       ...      ...     ...     ...\n",
       "154   Smelt  3.595255   2.0904  1.3936    12.2\n",
       "155   Smelt  3.602690   2.4300  1.2690    13.4\n",
       "156   Smelt  3.624669   2.2770  1.2558    12.2\n",
       "157   Smelt  3.721295   2.8728  2.0672    19.7\n",
       "158   Smelt  3.785011   2.9322  1.8792    19.9\n",
       "\n",
       "[158 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish.drop([\"Length1\",\"Length2\"],axis=1,inplace=True)\n",
    "fish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b5e14183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bream', 'Roach', 'Whitefish', 'Parkki', 'Perch', 'Pike', 'Smelt'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish[\"Species\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f48831de",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=pd.get_dummies(fish.Species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8eff6f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Species</th>\n",
       "      <th>Length3</th>\n",
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       "      <td>340.0</td>\n",
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       "      <td>4.4555</td>\n",
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       "      <td>Smelt</td>\n",
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       "      <td>Smelt</td>\n",
       "      <td>3.624669</td>\n",
       "      <td>2.2770</td>\n",
       "      <td>1.2558</td>\n",
       "      <td>12.2</td>\n",
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       "<p>158 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Species   Length3   Height   Width  Weight  Bream  Parkki  Perch  Pike  \\\n",
       "0     Bream  4.401197  11.5200  4.0200   242.0      1       0      0     0   \n",
       "1     Bream  4.440418  12.4800  4.3056   290.0      1       0      0     0   \n",
       "2     Bream  4.437208  12.3778  4.6961   340.0      1       0      0     0   \n",
       "3     Bream  4.511545  12.7300  4.4555   363.0      1       0      0     0   \n",
       "4     Bream  4.526361  12.4440  5.1340   430.0      1       0      0     0   \n",
       "..      ...       ...      ...     ...     ...    ...     ...    ...   ...   \n",
       "154   Smelt  3.595255   2.0904  1.3936    12.2      0       0      0     0   \n",
       "155   Smelt  3.602690   2.4300  1.2690    13.4      0       0      0     0   \n",
       "156   Smelt  3.624669   2.2770  1.2558    12.2      0       0      0     0   \n",
       "157   Smelt  3.721295   2.8728  2.0672    19.7      0       0      0     0   \n",
       "158   Smelt  3.785011   2.9322  1.8792    19.9      0       0      0     0   \n",
       "\n",
       "     Roach  Smelt  Whitefish  \n",
       "0        0      0          0  \n",
       "1        0      0          0  \n",
       "2        0      0          0  \n",
       "3        0      0          0  \n",
       "4        0      0          0  \n",
       "..     ...    ...        ...  \n",
       "154      0      1          0  \n",
       "155      0      1          0  \n",
       "156      0      1          0  \n",
       "157      0      1          0  \n",
       "158      0      1          0  \n",
       "\n",
       "[158 rows x 12 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish=fish.join(a)\n",
    "fish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "42241d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>Bream</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>158 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Species   Length3   Height   Width  Bream  Parkki  Perch  Pike  Roach  \\\n",
       "0     Bream  4.401197  11.5200  4.0200      1       0      0     0      0   \n",
       "1     Bream  4.440418  12.4800  4.3056      1       0      0     0      0   \n",
       "2     Bream  4.437208  12.3778  4.6961      1       0      0     0      0   \n",
       "3     Bream  4.511545  12.7300  4.4555      1       0      0     0      0   \n",
       "4     Bream  4.526361  12.4440  5.1340      1       0      0     0      0   \n",
       "..      ...       ...      ...     ...    ...     ...    ...   ...    ...   \n",
       "154   Smelt  3.595255   2.0904  1.3936      0       0      0     0      0   \n",
       "155   Smelt  3.602690   2.4300  1.2690      0       0      0     0      0   \n",
       "156   Smelt  3.624669   2.2770  1.2558      0       0      0     0      0   \n",
       "157   Smelt  3.721295   2.8728  2.0672      0       0      0     0      0   \n",
       "158   Smelt  3.785011   2.9322  1.8792      0       0      0     0      0   \n",
       "\n",
       "     Smelt  Whitefish  Weight  \n",
       "0        0          0   242.0  \n",
       "1        0          0   290.0  \n",
       "2        0          0   340.0  \n",
       "3        0          0   363.0  \n",
       "4        0          0   430.0  \n",
       "..     ...        ...     ...  \n",
       "154      1          0    12.2  \n",
       "155      1          0    13.4  \n",
       "156      1          0    12.2  \n",
       "157      1          0    19.7  \n",
       "158      1          0    19.9  \n",
       "\n",
       "[158 rows x 12 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X=fish.drop(\"Weight\",axis=1)\n",
    "df_y=fish[\"Weight\"]\n",
    "fish=df_X.join(df_y)\n",
    "fish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d6e88279",
   "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>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Bream</th>\n",
       "      <th>Parkki</th>\n",
       "      <th>Perch</th>\n",
       "      <th>Pike</th>\n",
       "      <th>Roach</th>\n",
       "      <th>Smelt</th>\n",
       "      <th>Whitefish</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.401197</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.440418</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.437208</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.511545</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.526361</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>3.595255</td>\n",
       "      <td>2.0904</td>\n",
       "      <td>1.3936</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.602690</td>\n",
       "      <td>2.4300</td>\n",
       "      <td>1.2690</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>3.624669</td>\n",
       "      <td>2.2770</td>\n",
       "      <td>1.2558</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>3.721295</td>\n",
       "      <td>2.8728</td>\n",
       "      <td>2.0672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>3.785011</td>\n",
       "      <td>2.9322</td>\n",
       "      <td>1.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Length3   Height   Width  Bream  Parkki  Perch  Pike  Roach  Smelt  \\\n",
       "0    4.401197  11.5200  4.0200      1       0      0     0      0      0   \n",
       "1    4.440418  12.4800  4.3056      1       0      0     0      0      0   \n",
       "2    4.437208  12.3778  4.6961      1       0      0     0      0      0   \n",
       "3    4.511545  12.7300  4.4555      1       0      0     0      0      0   \n",
       "4    4.526361  12.4440  5.1340      1       0      0     0      0      0   \n",
       "..        ...      ...     ...    ...     ...    ...   ...    ...    ...   \n",
       "154  3.595255   2.0904  1.3936      0       0      0     0      0      1   \n",
       "155  3.602690   2.4300  1.2690      0       0      0     0      0      1   \n",
       "156  3.624669   2.2770  1.2558      0       0      0     0      0      1   \n",
       "157  3.721295   2.8728  2.0672      0       0      0     0      0      1   \n",
       "158  3.785011   2.9322  1.8792      0       0      0     0      0      1   \n",
       "\n",
       "     Whitefish  Weight  \n",
       "0            0   242.0  \n",
       "1            0   290.0  \n",
       "2            0   340.0  \n",
       "3            0   363.0  \n",
       "4            0   430.0  \n",
       "..         ...     ...  \n",
       "154          0    12.2  \n",
       "155          0    13.4  \n",
       "156          0    12.2  \n",
       "157          0    19.7  \n",
       "158          0    19.9  \n",
       "\n",
       "[158 rows x 11 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fish.drop(\"Species\",axis=1,inplace=True)\n",
    "fish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0d5f4e00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.heatmap(fish.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f83308f8",
   "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>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Bream</th>\n",
       "      <th>Parkki</th>\n",
       "      <th>Perch</th>\n",
       "      <th>Pike</th>\n",
       "      <th>Roach</th>\n",
       "      <th>Smelt</th>\n",
       "      <th>Whitefish</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.401197</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.440418</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.437208</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.511545</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.526361</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>3.595255</td>\n",
       "      <td>2.0904</td>\n",
       "      <td>1.3936</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>3.602690</td>\n",
       "      <td>2.4300</td>\n",
       "      <td>1.2690</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>3.624669</td>\n",
       "      <td>2.2770</td>\n",
       "      <td>1.2558</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>3.721295</td>\n",
       "      <td>2.8728</td>\n",
       "      <td>2.0672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>3.785011</td>\n",
       "      <td>2.9322</td>\n",
       "      <td>1.8792</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Length3   Height   Width  Bream  Parkki  Perch  Pike  Roach  Smelt  \\\n",
       "0    4.401197  11.5200  4.0200      1       0      0     0      0      0   \n",
       "1    4.440418  12.4800  4.3056      1       0      0     0      0      0   \n",
       "2    4.437208  12.3778  4.6961      1       0      0     0      0      0   \n",
       "3    4.511545  12.7300  4.4555      1       0      0     0      0      0   \n",
       "4    4.526361  12.4440  5.1340      1       0      0     0      0      0   \n",
       "..        ...      ...     ...    ...     ...    ...   ...    ...    ...   \n",
       "154  3.595255   2.0904  1.3936      0       0      0     0      0      1   \n",
       "155  3.602690   2.4300  1.2690      0       0      0     0      0      1   \n",
       "156  3.624669   2.2770  1.2558      0       0      0     0      0      1   \n",
       "157  3.721295   2.8728  2.0672      0       0      0     0      0      1   \n",
       "158  3.785011   2.9322  1.8792      0       0      0     0      0      1   \n",
       "\n",
       "     Whitefish  Weight  \n",
       "0            0   242.0  \n",
       "1            0   290.0  \n",
       "2            0   340.0  \n",
       "3            0   363.0  \n",
       "4            0   430.0  \n",
       "..         ...     ...  \n",
       "154          0    12.2  \n",
       "155          0    13.4  \n",
       "156          0    12.2  \n",
       "157          0    19.7  \n",
       "158          0    19.9  \n",
       "\n",
       "[158 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X=fish.drop(\"Weight\",axis=1)\n",
    "df_y=fish[\"Weight\"]\n",
    "fish=df_X.join(df_y)\n",
    "fish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "69cfef71",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bb1e131f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(df_X,df_y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0a00554c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dc475d2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ce4e464b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-236.650661  ,   61.16572825,  150.87549098, -296.89360267,\n",
       "       -153.73496737,  -49.63262935,  384.08385861,  -63.50100408,\n",
       "        264.65879128,  -84.98044642])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1b788b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "257.73163139237704"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c6071dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d574c70f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9e1a862f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9497040254336148"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "26f7580f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Weight'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=y_test,y=y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bc11b9c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 522.03851409,   98.1274022 ,  591.91059312,  129.35011896,\n",
       "        124.31908532,  992.05815601,  172.04321514,  145.53645185,\n",
       "        212.80405359, 1144.90724203,  608.48548949,  368.22836812,\n",
       "        654.54726132,  382.21123252,  684.87693765,  882.82538992,\n",
       "        891.91888994,  711.20550714,   80.7481166 ,  142.53302695,\n",
       "          6.03813799,  590.41122696,  374.14592388,  493.26641431,\n",
       "        884.08600835,  119.18116356,  380.49355527,  933.45725204,\n",
       "       1064.7589102 ,  -15.30795636,    1.34217073,    5.38458656])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2905065a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129     300.0\n",
       "46      140.0\n",
       "135     510.0\n",
       "157      19.7\n",
       "91      130.0\n",
       "29     1000.0\n",
       "97      145.0\n",
       "99      180.0\n",
       "96      225.0\n",
       "141    1250.0\n",
       "19      650.0\n",
       "57      306.0\n",
       "15      600.0\n",
       "70      273.0\n",
       "24      700.0\n",
       "30      920.0\n",
       "123    1100.0\n",
       "110     556.0\n",
       "94      150.0\n",
       "66      140.0\n",
       "151      10.0\n",
       "18      610.0\n",
       "12      500.0\n",
       "9       500.0\n",
       "31      955.0\n",
       "82      110.0\n",
       "52      290.0\n",
       "140     950.0\n",
       "126    1000.0\n",
       "147       7.0\n",
       "146       7.5\n",
       "76       70.0\n",
       "Name: Weight, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "810626bd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.15 (main, Nov 24 2022, 14:39:17) [MSC v.1916 64 bit (AMD64)]"
  },
  "vscode": {
   "interpreter": {
    "hash": "0d26fc02ebbad435ad7947a488da048221742f546cd3ca8b16f161c334c35381"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}