{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e24c6ee-fc89-4aea-afef-0a6bccb71f48",
   "metadata": {},
   "source": [
    "### Importing dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "233bcb25-0ac2-44a3-bcda-c9b3a3264399",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score,mean_absolute_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d671bf5-1b1f-466e-9d54-1d103daf3831",
   "metadata": {},
   "source": [
    "### Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "921f3f5f-0cd8-4f83-8501-24350f5fbe7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('fish.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "83de8bbb-f13f-4636-8be2-c71b311e3f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d91ac1c8-c7b1-463e-94b7-73281c191037",
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "82efddd2-58f9-4c66-a023-301f16659992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>398.326415</td>\n",
       "      <td>26.247170</td>\n",
       "      <td>28.415723</td>\n",
       "      <td>31.227044</td>\n",
       "      <td>8.970994</td>\n",
       "      <td>4.417486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>357.978317</td>\n",
       "      <td>9.996441</td>\n",
       "      <td>10.716328</td>\n",
       "      <td>11.610246</td>\n",
       "      <td>4.286208</td>\n",
       "      <td>1.685804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>1.728400</td>\n",
       "      <td>1.047600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>23.150000</td>\n",
       "      <td>5.944800</td>\n",
       "      <td>3.385650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>273.000000</td>\n",
       "      <td>25.200000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>29.400000</td>\n",
       "      <td>7.786000</td>\n",
       "      <td>4.248500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>650.000000</td>\n",
       "      <td>32.700000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>39.650000</td>\n",
       "      <td>12.365900</td>\n",
       "      <td>5.584500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1650.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>63.400000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>18.957000</td>\n",
       "      <td>8.142000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Weight     Length1     Length2     Length3      Height       Width\n",
       "count   159.000000  159.000000  159.000000  159.000000  159.000000  159.000000\n",
       "mean    398.326415   26.247170   28.415723   31.227044    8.970994    4.417486\n",
       "std     357.978317    9.996441   10.716328   11.610246    4.286208    1.685804\n",
       "min       0.000000    7.500000    8.400000    8.800000    1.728400    1.047600\n",
       "25%     120.000000   19.050000   21.000000   23.150000    5.944800    3.385650\n",
       "50%     273.000000   25.200000   27.300000   29.400000    7.786000    4.248500\n",
       "75%     650.000000   32.700000   35.500000   39.650000   12.365900    5.584500\n",
       "max    1650.000000   59.000000   63.400000   68.000000   18.957000    8.142000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07b51c6d-8404-44ee-8e52-8be32599a619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Species    0\n",
       "Weight     0\n",
       "Length1    0\n",
       "Length2    0\n",
       "Length3    0\n",
       "Height     0\n",
       "Width      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cbc3f7fc-1d38-4d99-a90d-b348663ceaa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f00c6ab-54e2-4693-963c-d73f78e6b316",
   "metadata": {},
   "source": [
    "### Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ac4a2a52-27b6-4853-913f-ff895763dfcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=\"Weight\",y=\"Length1\",data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e425ed02-e25c-47b1-a109-4486a1d01161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=\"Weight\",y=\"Length2\",data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac87680e-300c-4140-873e-219629055151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=\"Weight\",y=\"Length3\",data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a687a992-645a-4cdc-9ec3-5ad2a37c7c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=\"Weight\",y=\"Width\",data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2b55340e-3672-4692-a801-b99406dd426c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=\"Weight\",y=\"Height\",data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e198172b-2f3a-4815-864e-e40b2e2c457f",
   "metadata": {},
   "source": [
    "### Handling the object column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d8e77be-92b6-4388-8d2b-cd453fd45408",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "df['Species_encoded'] = le.fit_transform(df['Species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c385e78-d625-44cd-8325-69c9e737f03f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bream', 'Roach', 'Whitefish', 'Parkki', 'Perch', 'Pike', 'Smelt'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Species'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "297abab3-ff7e-4cef-9834-143d037aec5a",
   "metadata": {},
   "outputs": [
    {
     "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   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",
      " 7   Species_encoded  159 non-null    int32  \n",
      "dtypes: float64(6), int32(1), object(1)\n",
      "memory usage: 9.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f3e91466-1c11-4262-9707-15bf94e090e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['Species'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c8c04c9a-bc4d-4bf2-a151-b3f18b237f94",
   "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   Weight           159 non-null    float64\n",
      " 1   Length1          159 non-null    float64\n",
      " 2   Length2          159 non-null    float64\n",
      " 3   Length3          159 non-null    float64\n",
      " 4   Height           159 non-null    float64\n",
      " 5   Width            159 non-null    float64\n",
      " 6   Species_encoded  159 non-null    int32  \n",
      "dtypes: float64(6), int32(1)\n",
      "memory usage: 8.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee3e50f6-13bd-48af-ba6d-707e35ad4a87",
   "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>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Species_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight  Length1  Length2  Length3   Height   Width  Species_encoded\n",
       "0   242.0     23.2     25.4     30.0  11.5200  4.0200                0\n",
       "1   290.0     24.0     26.3     31.2  12.4800  4.3056                0\n",
       "2   340.0     23.9     26.5     31.1  12.3778  4.6961                0\n",
       "3   363.0     26.3     29.0     33.5  12.7300  4.4555                0\n",
       "4   430.0     26.5     29.0     34.0  12.4440  5.1340                0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91799aa-e9fc-4301-b08f-526d7f9cf450",
   "metadata": {},
   "source": [
    "### Removing the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0f621d0-9983-4da4-99c8-446327e55cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "\n",
    "z_scores = np.abs(stats.zscore(df))\n",
    "threshold = 3\n",
    "df_clean = df[(z_scores < threshold).all(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f384d47-e9d5-42a9-b95b-46fb2625602c",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "db281b95-243c-4f91-8350-c089128815b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df_clean.drop(['Weight'],axis=1)\n",
    "Y=df_clean['Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bb5d3c4f-34eb-4cdd-8e09-1881b462c0ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c634151a-24c0-4045-8d88-8cd4ef866376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4b8d880-6792-4139-a9f7-9a258c58cd36",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a9e9178-1282-4fd1-bd50-a4bb428676a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 8336.192904502052\n",
      "R-squared: 0.9202567948572947\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ab378bff-2401-41a4-b51b-f1134780ee2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 73.33138348038102\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(\"Mean Absolute Error:\", mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c5e1f3-d65e-477a-b80d-103feac5ed7b",
   "metadata": {},
   "source": [
    "### Standardizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d17551ab-0ded-4bc6-8da8-3e8ecd0fb63d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard scaling the data\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d0b3213b-20a2-42de-9f12-57b53474d9c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Weight', 'Length1', 'Length2', 'Length3', 'Height', 'Width',\n",
       "       'Species_encoded'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ebeea689-8989-4512-bade-cdabf5a5dcf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['Weight', 'Length1', 'Length2', 'Length3', 'Height', 'Width','Species_encoded']]=scaler.fit_transform(df[['Weight', 'Length1', 'Length2', 'Length3', 'Height', 'Width','Species_encoded']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d04d5b0c-93ab-4fae-b837-5dd6bdaa5f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "      <th>Species_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.438072</td>\n",
       "      <td>-0.305789</td>\n",
       "      <td>-0.282303</td>\n",
       "      <td>-0.106020</td>\n",
       "      <td>0.596579</td>\n",
       "      <td>-0.236529</td>\n",
       "      <td>-1.33273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.303562</td>\n",
       "      <td>-0.225507</td>\n",
       "      <td>-0.198054</td>\n",
       "      <td>-0.002337</td>\n",
       "      <td>0.821261</td>\n",
       "      <td>-0.066579</td>\n",
       "      <td>-1.33273</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.163448</td>\n",
       "      <td>-0.235542</td>\n",
       "      <td>-0.179332</td>\n",
       "      <td>-0.010977</td>\n",
       "      <td>0.797341</td>\n",
       "      <td>0.165793</td>\n",
       "      <td>-1.33273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.098995</td>\n",
       "      <td>0.005302</td>\n",
       "      <td>0.054694</td>\n",
       "      <td>0.196390</td>\n",
       "      <td>0.879771</td>\n",
       "      <td>0.022621</td>\n",
       "      <td>-1.33273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.088759</td>\n",
       "      <td>0.025372</td>\n",
       "      <td>0.054694</td>\n",
       "      <td>0.239592</td>\n",
       "      <td>0.812835</td>\n",
       "      <td>0.426371</td>\n",
       "      <td>-1.33273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Weight   Length1   Length2   Length3    Height     Width  Species_encoded\n",
       "0 -0.438072 -0.305789 -0.282303 -0.106020  0.596579 -0.236529         -1.33273\n",
       "1 -0.303562 -0.225507 -0.198054 -0.002337  0.821261 -0.066579         -1.33273\n",
       "2 -0.163448 -0.235542 -0.179332 -0.010977  0.797341  0.165793         -1.33273\n",
       "3 -0.098995  0.005302  0.054694  0.196390  0.879771  0.022621         -1.33273\n",
       "4  0.088759  0.025372  0.054694  0.239592  0.812835  0.426371         -1.33273"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b91a679f-e77b-4cb9-a907-7fa5fe725d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df.drop(['Weight'],axis=1)\n",
    "Y=df['Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7594f823-e926-421f-8a21-42a0763b438b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "528f7313-986b-4154-b097-ff4868c3314c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c3e6f503-ecb8-40d2-ae0f-818903bb8687",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "11bdbf11-e589-4f62-82cc-8e023262fa7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.10780230864937543\n",
      "R-squared: 0.9034878699241474\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "745f7c0a-cf8e-444b-bfe8-b32768b45126",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.27265710707403384\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(\"Mean Absolute Error:\", mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6500f89d-43b7-46bd-aa6c-e21b09dc0379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FishWeightPrediction.joblib']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model,'FishWeightPrediction.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35983525-fcce-48fb-bcdd-688f9db1f420",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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