data_analysis_and_model_selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fish Weight Prediction - A regression implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 1;\n",
       "                var nbb_unformatted_code = \"# importing dependencies here\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport seaborn as sns\\n\\n############################################# scikit Learn ###############################\\n\\n# data split and transformation\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.preprocessing import FunctionTransformer\\nfrom sklearn.compose import ColumnTransformer\\nfrom sklearn.preprocessing import OneHotEncoder\\nfrom sklearn.compose import TransformedTargetRegressor\\nfrom sklearn.preprocessing import StandardScaler\\n\\n# pipeline\\nfrom sklearn.pipeline import make_pipeline\\n\\n# model and its evaluation\\nfrom sklearn.linear_model import LinearRegression\\nfrom sklearn.linear_model import Lasso\\nfrom sklearn.model_selection import GridSearchCV\\nfrom sklearn.ensemble import RandomForestRegressor\\n\\nfrom sklearn.metrics import mean_squared_error\\nfrom sklearn.metrics import r2_score\\nfrom sklearn.model_selection import cross_val_score\\n\\n###########################################################################################\\n\\n# plot\\nfrom scipy import stats\\n\\n# saving the model\\nfrom joblib import dump\\n\\n# formatting\\n%load_ext nb_black\";\n",
       "                var nbb_formatted_code = \"# importing dependencies here\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport seaborn as sns\\n\\n############################################# scikit Learn ###############################\\n\\n# data split and transformation\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.preprocessing import FunctionTransformer\\nfrom sklearn.compose import ColumnTransformer\\nfrom sklearn.preprocessing import OneHotEncoder\\nfrom sklearn.compose import TransformedTargetRegressor\\nfrom sklearn.preprocessing import StandardScaler\\n\\n# pipeline\\nfrom sklearn.pipeline import make_pipeline\\n\\n# model and its evaluation\\nfrom sklearn.linear_model import LinearRegression\\nfrom sklearn.linear_model import Lasso\\nfrom sklearn.model_selection import GridSearchCV\\nfrom sklearn.ensemble import RandomForestRegressor\\n\\nfrom sklearn.metrics import mean_squared_error\\nfrom sklearn.metrics import r2_score\\nfrom sklearn.model_selection import cross_val_score\\n\\n###########################################################################################\\n\\n# plot\\nfrom scipy import stats\\n\\n# saving the model\\nfrom joblib import dump\\n\\n# formatting\\n%load_ext nb_black\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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   "source": [
    "# importing dependencies here\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "############################################# scikit Learn ###############################\n",
    "\n",
    "# data split and transformation\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import TransformedTargetRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# pipeline\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# model and its evaluation\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "###########################################################################################\n",
    "\n",
    "# plot\n",
    "from scipy import stats\n",
    "\n",
    "# saving the model\n",
    "from joblib import dump\n",
    "\n",
    "# formatting\n",
    "%load_ext nb_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"# reading the input file\\nfish_data = pd.read_csv(\\\"fish_participant.csv\\\")\";\n",
       "                var nbb_formatted_code = \"# reading the input file\\nfish_data = pd.read_csv(\\\"fish_participant.csv\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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   "source": [
    "# reading the input file\n",
    "fish_data = pd.read_csv(\"fish_participant.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>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",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Perch</td>\n",
       "      <td>110.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>5.5225</td>\n",
       "      <td>3.9950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Roach</td>\n",
       "      <td>160.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>25.3</td>\n",
       "      <td>7.0334</td>\n",
       "      <td>3.8203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parkki</td>\n",
       "      <td>60.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>6.5772</td>\n",
       "      <td>2.3142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bream</td>\n",
       "      <td>700.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>14.8604</td>\n",
       "      <td>5.2854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Species  Weight  Length1  Length2  Length3   Height   Width\n",
       "0   Bream   430.0     26.5     29.0     34.0  12.4440  5.1340\n",
       "1   Perch   110.0     20.0     22.0     23.5   5.5225  3.9950\n",
       "2   Roach   160.0     20.5     22.5     25.3   7.0334  3.8203\n",
       "3  Parkki    60.0     14.3     15.5     17.4   6.5772  2.3142\n",
       "4   Bream   700.0     30.4     33.0     38.3  14.8604  5.2854"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"# checking the first 5 records\\nfish_data.head()\";\n",
       "                var nbb_formatted_code = \"# checking the first 5 records\\nfish_data.head()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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   "source": [
    "# checking the first 5 records\n",
    "fish_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Species     object\n",
       "Weight     float64\n",
       "Length1    float64\n",
       "Length2    float64\n",
       "Length3    float64\n",
       "Height     float64\n",
       "Width      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"# checking the data types\\nfish_data.dtypes\";\n",
       "                var nbb_formatted_code = \"# checking the data types\\nfish_data.dtypes\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
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       "            }, 500);\n",
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   "source": [
    "# checking the data types\n",
    "fish_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data types look okay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"# checking for missing values\\nfish_data.isnull().sum()\";\n",
       "                var nbb_formatted_code = \"# checking for missing values\\nfish_data.isnull().sum()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
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   "source": [
    "# checking for missing values\n",
    "fish_data.isnull().sum()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no missing data present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>111.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>401.676577</td>\n",
       "      <td>26.447748</td>\n",
       "      <td>28.615315</td>\n",
       "      <td>31.422523</td>\n",
       "      <td>9.015701</td>\n",
       "      <td>4.480407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>338.510755</td>\n",
       "      <td>9.795155</td>\n",
       "      <td>10.498781</td>\n",
       "      <td>11.306311</td>\n",
       "      <td>4.225369</td>\n",
       "      <td>1.696240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.900000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>1.738800</td>\n",
       "      <td>1.047600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>142.500000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>6.138850</td>\n",
       "      <td>3.551400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>30.100000</td>\n",
       "      <td>8.145400</td>\n",
       "      <td>4.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>682.500000</td>\n",
       "      <td>33.750000</td>\n",
       "      <td>36.250000</td>\n",
       "      <td>40.150000</td>\n",
       "      <td>12.143400</td>\n",
       "      <td>5.658300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1550.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>64.000000</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   111.000000  111.000000  111.000000  111.000000  111.000000  111.000000\n",
       "mean    401.676577   26.447748   28.615315   31.422523    9.015701    4.480407\n",
       "std     338.510755    9.795155   10.498781   11.306311    4.225369    1.696240\n",
       "min       5.900000    7.500000    8.400000    8.800000    1.738800    1.047600\n",
       "25%     142.500000   20.000000   22.000000   23.500000    6.138850    3.551400\n",
       "50%     300.000000   25.400000   27.500000   30.100000    8.145400    4.335000\n",
       "75%     682.500000   33.750000   36.250000   40.150000   12.143400    5.658300\n",
       "max    1550.000000   56.000000   60.000000   64.000000   18.957000    8.142000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"# checking the stats\\nfish_data.describe()\";\n",
       "                var nbb_formatted_code = \"# checking the stats\\nfish_data.describe()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
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       "                        }\n",
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       "            }, 500);\n",
       "            "
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     "metadata": {},
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   ],
   "source": [
    "# checking the stats\n",
    "fish_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the target variable \"Weight\", the difference between the min and max value seems to be significantly large. <br>\n",
    "There could be some outliers present possibly that I will handle later in this code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "            background-color:  #005b92;\n",
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       "            background-color:  #00246d;\n",
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       "            color:  #000000;\n",
       "        }</style><table id=\"T_f0b59efa_90a7_11ea_b158_380025f3998f\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Weight</th>        <th class=\"col_heading level0 col1\" >Length1</th>        <th class=\"col_heading level0 col2\" >Length2</th>        <th class=\"col_heading level0 col3\" >Length3</th>        <th class=\"col_heading level0 col4\" >Height</th>        <th class=\"col_heading level0 col5\" >Width</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f0b59efa_90a7_11ea_b158_380025f3998flevel0_row0\" class=\"row_heading level0 row0\" >Weight</th>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow0_col1\" class=\"data row0 col1\" >0.900963</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow0_col2\" class=\"data row0 col2\" >0.904090</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow0_col3\" class=\"data row0 col3\" >0.909553</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow0_col4\" class=\"data row0 col4\" >0.738316</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow0_col5\" class=\"data row0 col5\" >0.893379</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f0b59efa_90a7_11ea_b158_380025f3998flevel0_row1\" class=\"row_heading level0 row1\" >Length1</th>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow1_col0\" class=\"data row1 col0\" >0.900963</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow1_col2\" class=\"data row1 col2\" >0.999468</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow1_col3\" class=\"data row1 col3\" >0.991363</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow1_col4\" class=\"data row1 col4\" >0.621648</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow1_col5\" class=\"data row1 col5\" >0.862612</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f0b59efa_90a7_11ea_b158_380025f3998flevel0_row2\" class=\"row_heading level0 row2\" >Length2</th>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow2_col0\" class=\"data row2 col0\" >0.904090</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow2_col1\" class=\"data row2 col1\" >0.999468</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow2_col3\" class=\"data row2 col3\" >0.993695</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow2_col4\" class=\"data row2 col4\" >0.637353</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow2_col5\" class=\"data row2 col5\" >0.869217</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f0b59efa_90a7_11ea_b158_380025f3998flevel0_row3\" class=\"row_heading level0 row3\" >Length3</th>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow3_col0\" class=\"data row3 col0\" >0.909553</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow3_col1\" class=\"data row3 col1\" >0.991363</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow3_col2\" class=\"data row3 col2\" >0.993695</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow3_col4\" class=\"data row3 col4\" >0.702567</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow3_col5\" class=\"data row3 col5\" >0.872672</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f0b59efa_90a7_11ea_b158_380025f3998flevel0_row4\" class=\"row_heading level0 row4\" >Height</th>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow4_col0\" class=\"data row4 col0\" >0.738316</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow4_col1\" class=\"data row4 col1\" >0.621648</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow4_col2\" class=\"data row4 col2\" >0.637353</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow4_col3\" class=\"data row4 col3\" >0.702567</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow4_col5\" class=\"data row4 col5\" >0.782936</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f0b59efa_90a7_11ea_b158_380025f3998flevel0_row5\" class=\"row_heading level0 row5\" >Width</th>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow5_col0\" class=\"data row5 col0\" >0.893379</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow5_col1\" class=\"data row5 col1\" >0.862612</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow5_col2\" class=\"data row5 col2\" >0.869217</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow5_col3\" class=\"data row5 col3\" >0.872672</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow5_col4\" class=\"data row5 col4\" >0.782936</td>\n",
       "                        <td id=\"T_f0b59efa_90a7_11ea_b158_380025f3998frow5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1a5329d1708>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"# checking for correlations\\ncorr = fish_data.corr()\\ncorr.style.background_gradient(cmap=\\\"ocean\\\")\";\n",
       "                var nbb_formatted_code = \"# checking for correlations\\ncorr = fish_data.corr()\\ncorr.style.background_gradient(cmap=\\\"ocean\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for correlations\n",
    "corr = fish_data.corr()\n",
    "corr.style.background_gradient(cmap=\"ocean\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the features are showing strong correlation with the target vaibale weight.<br>\n",
    "The 3 Length variables are showing collinearity. I will handle this later in this code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"# checking for linear relationship between features and the target variable\\n\\nnumerical = [\\\"Length1\\\", \\\"Length2\\\", \\\"Length3\\\", \\\"Height\\\", \\\"Width\\\"]\\n\\nfor feature in numerical:\\n    plt.subplot(1, 2, 1)\\n    plt.scatter(x=fish_data[feature], y=fish_data[\\\"Weight\\\"])\\n    plt.xlabel(feature)\\n    plt.ylabel(\\\"weight\\\")\\n    plt.show()\";\n",
       "                var nbb_formatted_code = \"# checking for linear relationship between features and the target variable\\n\\nnumerical = [\\\"Length1\\\", \\\"Length2\\\", \\\"Length3\\\", \\\"Height\\\", \\\"Width\\\"]\\n\\nfor feature in numerical:\\n    plt.subplot(1, 2, 1)\\n    plt.scatter(x=fish_data[feature], y=fish_data[\\\"Weight\\\"])\\n    plt.xlabel(feature)\\n    plt.ylabel(\\\"weight\\\")\\n    plt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for linear relationship between features and the target variable\n",
    "\n",
    "numerical = [\"Length1\", \"Length2\", \"Length3\", \"Height\", \"Width\"]\n",
    "\n",
    "for feature in numerical:\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.scatter(x=fish_data[feature], y=fish_data[\"Weight\"])\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel(\"weight\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The relationship between the features and the target variable is not linear. I will be fix this by applying log transformations to the features as well as the target variable through the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G9q6gdPP4uzovg/EJsZR8cMiWcjhLlviNMWelqb2LY93eoLb4ARLjY7l2bhbVhzvY22St/rNhid8Yc1aqGjuA4MzoCVQ0axLjE+Mo/dBVs2VHnSV+Y8xZqWr0rWIR7K4egPjYGK6aPZmKhnYOth4P+utFK0v8xpizUtXQTlyMMGFcfEher+icScTHCpv22N68Z8oSvzHmrFQ1tpOZmkjMGe66NVLjEuKY50lne00Lx7u9IXnNaGOJ3xhzVqoaO8gIQTePvwXnTOaEVymvs5U7z4QlfmPMGes84aWm5diorME/EtPSk8hOS2TbvpaQvm60sMRvjDlje5s6UA3NjB5/IsIlMyZS03L85OCyGT5L/MaYM1bVELqpnIHmeXz757xcfjDkrz3WWeI3xpyxqsZ2RGDy+NAn/rTkeGZMGkfJjkMhf+2xLuiJ39mx6F0Reck5dv2m1MZEi6rGdqanJ5MQF5425AXT0th58Cj77U7eEQnFT+s+YJffses3pTYmWlQ1tjM7MyVsr3/+tAkA/GWntfpHIqiJX0Q8wC3Ar/xOu35TamOiQW+vUtXQEdbEP3F8AvlZKZR+GD17F4dCsNfj/zfgASDV79zHNqUWkX43pRaR5cBygOzsbEpLS4McanSxz8sE26GjnRw/4WV21viwxrFwTibPvLWPjq4exifaFiPDEbRPSUQ+CTSo6lYRWTjS+qq6BlgDUFhYqAsXjvgp3KvkZezzGlhJSQn33XcfXq+Xe+6557TrIiLAo0AxcAz4oqpuE5Ec4FlgCtALrFHVR5063wO+DPQ1Pb+rqq8E/c2EUd80ytmZKexxFmoLh4VzsnjijWrermri+vOywxbHWBLMP49XAreKSDGQBKSJyG9wNqV2Wvuu25TahJfX62XFihWsX78ej8dDUVER+H4//d0M5DuPBcBjztce4NvOH4FUYKuIrFfVnU69n6vqT0PzTsKvqiEyEn/hrIkkx8fyRkWjJf5hClofv6r+s6p6VHUWsAx4TVU/j8s3pTbhVVZWRl5eHrm5uSQkJLBs2TKA9IBiS4Bn1WcTkN7XWFHVbQCq2oZv0sL0kL6BCFLV2EFaUhwZKQlhjSMxLpbCWRNt0bYRCMccLFdvSm3Cq66ujpycnJPHHo8HIDBzTQdq/I5rCUjwIjIL3/67m/1Of01EykXkqcGmKYvIchHZIiJbGhvH7qBkZUM7s7NSkBAtzjaYy3Mn82F9G03tthn7cAya+EUkR0TWicgbIvJdEYn3u/Zfw30RVS1V1U863zep6iJVzXe+2p9pEzIDbNkXeLK/THayjIikAM8D96vqUef0Y8BsYD5wEPjXQWJYo6qFqlqYmZk5gugjS7incvq7PHcyAJurLZ0Mx1At/qeAUuDrwFTgf0RksnNtZhDjMiYoPB4PNTWnGvO1tbUAJwKK1QI5/tWAAwBO4+d54Leq+kJfAVWtV1WvqvYCTwCXBeUNRIijnSdoaOuKmMR/kWcC4xJi2bSnKdyhjAlDJf5MVX1cVber6teBfwdeF5HZnN5KMibiFRUVUVFRQXV1Nd3d3axbtw4gcG3fF4E7xedyoNWZjCDAk8AuVf2ZfwVnokKfTwE7gvg2wq5vMHd2ZnincvaJj41x1ui3ZZqHY6jEHy8iJ2c8qOpv8N2J+2d8/wMwJmweffTRYZ3zFxcXx+rVq1m8eDEFBQUsXboUoFNE7hWRe51irwB7gEp8rfevOuevBL4AXCci251HsXPtxyLyvoiUA9cC3zzLtxfRTs7oyYqMFj/AxTPS2XngKJ0nbHOWoQyV+H+FbxrbSar638BnifIWjYl8zzzzzGnnnn766SHrFRcX89FHH1FVVcXDDz8MgPM/28ed71VVV6jqbFW9UFW3OOffVFVR1YtUdb7zeMW59gWn7EWqemvfTYrRqqrRt93ijEnjwh3KSfNz0unpVT440BruUCLeoPP4VfXnA5x/F9+MHGNCbu3atTz33HNUV1dz6623njzf1tbG5MmTB6lpRktVYzszJ48jPjZyFvidP8M3K/fd/Ue4dOakMEcT2YZ1A5eIZOK7K3GWfx1V/YfghGXMwD7xiU8wdepUDh8+zLe//e2T51NTU7nooovCGJl7VDWGd42e/mSlJjE9PZl3rZ9/SMO9c/ePwBvAfwPWgWbCaubMmcycOZO333473KG40glvL/uaOrghAu+SvXhGOu/ut8Q/lOEm/nGq+mBQIzFmhF544QUefPBBGhoaUFVUFRHh6NGjQ1c2Z6z6cAcnvMqc7NShC4fYxTMm8lL5QRqOdpKVFrgSh+kz3MT/kogUR/uiU5Fs3vf/QuvxwOnmA5v10MvDKjchOZ73/veNZxpWWD3wwAP86U9/oqCgINyhuMpH9W0A5GdHVlcP+AZ4Ad6tOcLi86eEOZrINWjiF5E2fPP1BfiuiHThu9lF8E1+SAt+iAag9fgJ9q66ZVhlS0tLh70653D/QESi7OxsS/ph8NGhNmKEiOvjBzh/WhrxscK7+y3xD2aoWT2R938543ovvOC7YbawsJC///u/57bbbiMx8dSer5/+9KfDFZorfFjfxqyM8STFx4Y7lNMkxcdy3tQ03rMB3kENd1bPBlVdNNQ5Y0LhT3/608nvx40bx1/+8peTxyJiiT/IKurbOTcC+/f7nD99Ai+XHzw55mNON1RXTxIwHshwVhvs+xTTgGlBjs2Yfv36178Odwiu1XnCy96mDj45L3L/+Z83NY3nNu/nQGsn09OTwx1ORBqqxf+PwP34kvw2v/NHgV8GKyhjhuMb3/jGaecmTJhAYWEhS5YsCUNE0a+yoZ1eJaJm9Dy3ef/HjmubjwHweGkVBVNPDUPesWBGSOOKZIPedqeqj6rqOcA/qeo5fo95qro6RDEa06/Ozk62b99Ofn4++fn5lJeX09zczJNPPsn9998f7vCiUkWDb0bPuRE4o6dP9oQkBDjYejzcoUSs4U7nrBORwI7TVuB9VbWtE01YVFZW8tprrxEX5/s1/spXvsKNN97I+vXrufDCC8McXXT68FA78bHCrIzIWJWzP4lxsUwan8DB1s5whxKxhpv47wauADY6xwuBTcC5IvL/qur/CUJsxgyqrq6Ojo4OJkyYAEBHRwcHDhwgNjb2Y7N8zOj5qL6N3IyUiFqjpz9T05M5cMRa/AMZbuLvBQpUtR5ARLI5tQH164AlfhNyDzzwAPPnz2fhwoWoKq+//jrf/e536ejo4Prrrw93eFHpo/o2Lp4x4K6SEWPahCR21LXSecIbkdNOw224iX9WX9J3NADnqmqziAz/dlJjRtHdd99NcXExZWVlqCo/+tGPmDbNN9vkJz/5SZijiz7tXT3UthxnWVHO0IXDbMoE33INh1o7I7pbKlyGm/jfEJGXgP90jv8O305c4zl99yJjgmr37t3MnTuXbdt8E836Nk8/dOgQhw4d4pJLLglneFFr90HfGkhzpkT+DftTJ/imcR48aom/P8NN/CvwJfsr8c3lfxZ4Xn07V18bpNiM6dfPfvYz1qxZ87ElmfuICK+99tqg9UtKSrjvvvvwer3cc889/T2HAI8CxcAx4Iuquk1EcvD97k/B1/25RlUfdepMAn6Hb+nyvcBSVW054zcZgd6v821wcpFnQpgjGVpaUhzjEmI5aP38/RpW4ncS/B+chzFhtWbNGgA2btw4RMnTeb1eVqxYwfr16/F4PBQVFQEELuN4M5DvPBZwajyrB/i280cgFdgqIutVdSfwELBBVVeJyEPOcVStaPt+bSuZqYlkj4FVL0WEqROSbGbPAIY1NC8inxaRChFpFZGjItImIrb2rQmrY8eO8cMf/pDly5cDUFFRwUsvvTRonbKyMvLy8sjNzSUhIYFly5YBpAcUWwI862zBuAlIF5GpqnpQVbcBqGobsAuY7lenby/IZ4DbRuEtRpT361q5aHrkt/b7TJ2QTP3RTnpVwx1KxBnunKwfA7eq6gRVTVPVVFuZ04Tbl770JRISEnjrrbcA8Hg8rFy5ctA6dXV1J8cE+uoACQHFpgM1fse1nErwAIjILOBiYLNzKrtvn13na9ZAMYjIchHZIiJbGhsbB403UnR09VDZ2M6FY6Cbp09WaiI9vUpLR3e4Q4k4w0389aq6K6iRGDNCVVVVPPDAA8THxwOQnJyMDtG6G+B64Mn+VvY6WUZEUoDngftVdcT/81XVNapaqKqFmZmZI60eFh8cOIoqXDiGWvx9XVL1R7vCHEnkGe7g7hYR+R3wX8DJT1FVXwhKVMYMQ0JCAsePHz+5AmNVVdWQN255PB5qak415mtra8G3x4S/WsB/zqIHOAAgIvH4kv5vA37/6/u6g0RkKr4pz1Gjb2B3LCX+rFTf70JDWyfnYR0U/obb4k/DN7vhRuBvnccngxWUMcPx/e9/n5tuuomamho+97nPsWjRIn784x8PWqeoqIiKigqqq6vp7u5m3bp1cPqU5BeBO8XncqDVSegCPAnsUtWf9VPnLuf7u/DtUx013q89QnZa4pjazjAxPpYJyfE0tFmLP9BwZ/V8KdiBGDNSzz77LLfccguf+cxnyM3N5dFHHyUjI2PQOnFxcaxevZrFixfj9Xr5h3/4B8rLyztF5F4AVX0ceAXfVM5KfA2evt//K4EvAO+LyHbn3HedLUlXAb8XkbuB/cBnR/v9htP7da1cOD1wDDzyZaclUn/UZvYEGu5GLOfim9KWraoXiMhF+AZ7fxjU6IwZxJe+9CXefPNN1q9fz549e5g/fz7XXHMN991336D1iouLKS4uPnm8cuXKvoQPnJy+vCKwnqq+Sf/9/6hqExCVGxO1d/Ww53AHt86bPnThCJOVmsSexiab2RNguF09TwD/jNMXqqrlwLLBKohIkoiUich7IvKBiHzfOT9JRNY700PXOxu8GDNi1113HQ8//DA/+MEPuOeee9iyZQuPPfZYuMOKOjvqWlEdGzduBcpO883sabaZPR8z3MHdcapaFrCNWc8QdbqA61S13RkQe1NEXgU+TZTf6GJCY9GiRXR0dHDFFVdw9dVX884775CVNeAsSnOGtu7z3YA8L2fsdfVkpfrGJBqsu+djhtviPywis3GmtInIZ4CDg1Vwbn5pdw7jnYfightdTGhcdNFFJCQksGPHDsrLy9mxYwfHj9st+qOtrLqZc7NTmDQ+8HaHyHdqZo8N8PobyVo9a4C5IlIHVAOfG6qSiMQCW4E84JequllEPnaji4j020QTkeXAcoDs7GxKS0uHGWr0Gu5n0N7ePqLPa6x+tkuWLGHJkiUcP36cV199lTvuuIPm5uaPbb5uzk6Pt5et+1q47eLI3WN3MInxsaQnx9sAb4DhzurZA1zvrMYZo6ptInI/8G9D1PMC80UkHfi/InLBcANT1TX4/thQWFioCxcuHG7V6FTyMsP9DEpLS4dddiTPG2lWr17NG2+8wdatW5k5cyZf+cpXuPrqq8fs+4lEuw620d7Vw2XnTA53KGcsKy3RWvwBhtviB0BVO/wOv8UQid+v3hERKQVuIspvdDGhc/z4cb71rW9x6aWXntx+0YyuzdVNAFw2a1KYIzlz2c7MHm+vEhvT76Qs1zmb/dMG/QRFJNNp6SMiycD1wG6i/EYXEzrf+c53WLBggSX9ICqrbmbm5HEnNzYZi7KcmT37mjqGLuwSZ/MvZqiJsVOBZ5x+/hjg96r6koi8TRTf6GJMtOjtVd7Z28z1BdnhDuWs9M3sqWhoJzczJczRRIZBE7+ItNF/ghcgebC6zlz/i/s5H7U3uhgzVjy3ef+QZeqPdtJy7ASXnTN2u3nA1+IHqKhvY/H5U8IcTWQYNPGramqoAjHGRJbqw76ukQVjeGAXIDEulvRx8XxU3z50YZc4mz5+Y0wUq2psZ9qEJHImDfqf+zEhOzWJj+rbwh1GxLDEb4w5jbdXqWps55pzMwm4Y39MykpNZE9jBz3e3nCHEhEs8RtjTlPXcozOE71cnT82NooZSlZaEt3eXvY1Hwt3KBHB5sGNEakFD3HhMw8Nv8IzQxfxPS/ALWcSkoliFQ3tCPCJ2WO7f79P9skB3nZm28weS/xjRduuVexdNbwEPZI7d2c99PJZRGWiVUVDO9MnJjNxDK7P059MZ82eyiWxOMMAABfcSURBVIY2wGb2WFePMeZjjnd7qW05Rl5W9LSME+NimZ6eTEWDzewBS/zGmAB7DrfTq5CfFV2zufOzU2xKp8MSv3GdkpIS5syZQ15eHqtWrTrturPX7i9EpFJEykXkEr9rT4lIg4jsCKjzPRGpE5HtzqP4tCceIyoa2kmIi4mKaZz+zs1OpaqxHW+v7cZlffzGVbxeLytWrGD9+vV4PB6KiooAAheiuRnIdx4L8G07usC59jSwGni2n6f/uar+NCiBh1BlQzu5GeOJi4mudmFeVgrdPb3sbz7GORnjwx1OWEXXT9aYIZSVlZGXl0dubi4JCQksW7YMIHBrqSXAs85mQpuAdGclWVT1daA5tFGHTlN7F80d3eRHUf9+n773VGE3clniN+5SV1dHTk7OyWOPxwMQOHVlOlDjd1zrnBvK15yuoacG20taRJaLyBYR2dLY2Dj84EOgstHXB54XZf37APnZvvdkA7yW+I3LqPbbvxt4sr9bVYfqGH4MmA3Mx7ct6b8OEsMaVS1U1cLMzMi6Qaqivp305HgyUqJjGqe/lMQ4pk1IshY/lviNy3g8HmpqTjXma2trAU4EFKsFcvyrAQcGe15VrVdVr6r2Ak8Al41KwCHk7VX2HG4nLyslKpZp6E9edqq1+LHEb1ymqKiIiooKqqur6e7uZt26dQBHAoq9CNzpzO65HGjt2yd6IH1jAI5PATsGKhup+pZpiKb5+4HOzUqhssFm9ljiN64SFxfH6tWrWbx4MQUFBSxduhSgU0TuFZF7nWKvAHuASnyt96/21ReRtcDbwBwRqXU2FAL4sYi8LyLlwLXAN0P1nkZLRaNvmYa8KF7SID87ha6eXmpb3L1mj03nNK5TXFxMcfGpafYrV65EVR/vO1bfQMCK/uqq6u0DnP/CaMcZapX17UxLT2ZcYvSmhZMDvPXtzJzs3imd1uI3xtB5wktNy7GonMbpr68by+39/Jb4jTHsaeygVyEvO7oTf1pSPFPSbGaPJX5jDBUNbSTExjBj0rhwhxJ0+dkp1uIPdwDGmPCrbGjnnChcpqE/+VmpVDa00+vimT3R/1M2xgyq9fgJmjq6mZ3pjsHOc7NTOH7CS92R4+EOJWws8RvjcnsPdwBwTkZ09+/3yc/uG+B1bz9/9M7bMsYMS3VTB4lxMUxND1ykFJ7bvD8MEQVX3zpEH9W3c93c7DBHEx7W4jfG5aoPdzBz8jhionSZhkATkuOZNiGJ3QePhjuUsLEW/xgyov1xS4ZXdkJy/BlGY6JBe1cPjW1dXDJjwMVEo9LcqWnsPmRdPSbCDXejdfD9gRhJeeNeJ/v3J0f/NE5/c6ek8vpHjXT39JIQ576OD/e9Y2PMSdVNHcTHCtMmRtc2i0OZOzWNnl6lqtGd8/kt8RvjYnsPdzBj0jhXzN/3VzDFN8C7+5A7+/mD9tMWkRwR2Sgiu0TkAxG5zzk/SUTWi0iF89VdnYvGRIjj3V4OtXYyy4X7z56TMZ6EuBh2HXRnP38w/8z3AN9W1QLgcmCFiJwHPARsUNV8YINzbIwJsZqWYygwy4WrVMbFxnBudgq7XDqzJ2iDu87GFQed79tEZBe+fUuXAAudYs8ApcCDwYrDGNO//c3HEMDjkv79wHsSEmJj2b7/yGnn71gwI5RhhUVIZvWIyCzgYmAzkN23m5GqHhSRrAHqLAeWA2RnZ1NaWhqKUKOGfV5mKPubjzFlQhKJcbHhDiUspkxIYtv+Ftq7ekiJ4j0I+hP0dysiKcDzwP2qenS4e3mq6hpgDUBhYaEuXLgwaDFGnZKXsc9rYCUlJdx33314vV7uueee066L75f0UaAYOAZ8UVW3OdeeAj4JNKjqBX51JgG/A2YBe4GlqtoS5LdyxnpVqWk+xjxPerhDCZspab47lQ+1dkb1dpP9CepQvojE40v6v1XVF5zT9X37kzpfG4IZgzH+vF4vK1as4NVXX2Xnzp2sXbsWIHCtgpuBfOexHHjM79rTwE39PPWYGrtqbOuiq6fXFcswD2TKhL7E777F2oI5q0eAJ4Fdqvozv0svAnc5398F/DFYMRgTqKysjLy8PHJzc0lISGDZsmUAgc3eJcCz6rMJSO9rrKjq60BzP0+9BN+YFc7X24LzDkbH/mbfnrNuTvwpiXGkJsZxsLUz3KGEXDBb/FcCXwCuE5HtzqMYWAXcICIVwA3OsTEhUVdXR05Ozsljj8cDkBBQbDpQ43dc65wbzMfGroB+x67AN34lIltEZEtjY+MIoh89+5uPMS4hlskpgW/dXaalJ7tyeeZgzup5ExioQ39RsF7XmMH49lE//XTAcX+/t6O2a0fg+NVoPe9I7G8+Rs7EcQx3zC1aTZ+YzEf1bXT1eF01yO2u2/WM63k8HmpqTjXma2trAU4EFKsFcvyrAQeGeOoxM3bVeuwEjW1d5Li4m6ePZ2IyChw44q7uHkv8xlWKioqoqKigurqa7u5u1q1bB3AkoNiLwJ3icznQ2teNM4gxM3b1bo1vspGb+/f7eCb6PoPalmNhjiS0LPEbV4mLi2P16tUsXryYgoICli5dCtApIveKyL1OsVeAPUAl8ATw1b76IrIWeBuYIyK1InK3c2nMjF29u/8IAuS45MatwaQkxpGeHE9ti7v6+d1114IxQHFxMcXFxSePV65ciao+3nesvoGAFf3VVdXbBzjfxBgZu9q2v4XstCQS493Tpz2Y6RPdN8BrLX5jXKS3V9lec8S6efx40pNp7ujmWFdPuEMJGUv8xrhIZWM7bZ09NrDrZ7rTz++mVr8lfmNcZNs+G9gNND3dN9ZRa4nfGBONtu1vIX1cPBkuv3HLX3JCLBkpCa4a4LXEb4yLvLv/CBfnpLv+xq1AORPHsb/52EA3+EUdS/zGuETr8RNUNLRzyQzb9C7QrIzxdHT10NjWFe5QQsISvzEusb3Gd5/axZb4T5PrbD9Z3dQR5khCwxK/MS6xbV8LIjAvZ0K4Q4k4k8YnkJYUx55GS/zGmCiydV8Lc6ekkZoUH+5QIo6IkJuZQvXhDlf081viN8YFery9vLu/hcKZ1s0zkHMyxtPe1UOVC1r9lviNcYHdh9ro6PZSOMsS/0D6+vk3VzeFOZLgs8RvjAtsdW7cutRa/APq6+fftKe/DdaiiyV+Y1xgy74WpqQlnbxL1ZxORDgnYzyb9jRFfT+/JX5jXGDr3mYunTXRbtwaQl5WCo1tXew62BbuUILKEr8xUe7AkeMcaO2kyLp5hnRudioAG3bVhzmS4LLEb0yU2+L07xfOmhTmSCJfalI883LS2bA7YnfOHBWW+I3rlJSUMGfOHPLy8li16vSNspwtF38hIpUiUi4il/hdu0lEPnSuPeR3/nsiUici251H8WlPHCZb9zYzLiGWuVNSwx3KmLBobhbv1R6h4Wj07sNrid+4itfrZcWKFbz66qvs3LmTtWvXAiQFFLsZyHcey4HHAEQkFvilc/084HYROc+v3s9Vdb7zeCXY72W43t7TxKUzJxIXa//ch+PmC6agCq+8P9Q2y2OX/SYYVykrKyMvL4/c3FwSEhJYtmwZQHpAsSXAs+qzCUgXkanAZUClqu5R1W5gnVM2YjUc7eSj+nauyssIdyhjRn52KnOnpPJSuSV+Y6JCXV0dOTk5J489Hg9A4OL004Eav+Na59xA5/t8zekaekpEBhxJFZHlIrJFRLY0Njae2RsZpreqfDcjXWmJf0T+dt40tuxrobblWLhDCQpL/MZVBpifHXiyvzmPOsh58HUHzQbmAweBfx0khjWqWqiqhZmZmUPGfDberDxM+rh4zpuaFtTXiTZL5k9DBP5zS224QwkKS/zGVTweDzU1pxrttbW1ACcCitUCOf7VgAODnEdV61XVq6q9wBP4uoXCSlV5q/Iwn5g9mZgYm78/Ep6J47gmP5Pfb6nB2xt9N3NZ4jeuUlRUREVFBdXV1XR3d7Nu3TqAIwHFXgTudGb3XA60qupB4B0gX0TOEZEEYJlTFmcMoM+ngB1BfzNDqD7cwYHWTuvmOUO3XzaDg62d/HcUzum3xG9cJS4ujtWrV7N48WIKCgpYunQpQKeI3Csi9zrFXgH2AJX4Wu9fBVDVHuBrwJ+BXcDvVfUDp86PReR9ESkHrgW+Gbp31b+/Vh4G4MrZlvjPxPUFWeRMSuax0qqoW8IhLtwBGBNqxcXFFBefmma/cuVKVPXxvmP1/Stf0V9dZ5rmaVM1VfULQQj1rPy1sonp6cnMnDwu3KGMSXGxMSy/Zjb/z3/t4O2qJj4RRf9zsha/MVGou6eXv1Ye5ur8DFuf5yx89lIPUycksapkN71R1NcftMTvTGlrEJEdfucmich6EalwvtriIcYEwaY9TbR19XB9QXa4QxnTkuJj+c7iOZTXtvL8tuiZ4RPMFv/TwE0B5x4CNqhqPrDBOTbGjLL/3lVPcnwsV+VHT/dEuNw2fzqFMyfyg5d2cqg1OpZxCFriV9XXgcAdDZYAzzjfPwPcFqzXN8atenuV9TvruSo/g6T42HCHM+bFxAg/+ew8ur29fGPtu3T39IY7pLMW6sHdbGdaHKp6UESyBiooIsvxrZNCdnY2paWloYkwStjn5V5b9rVwsLWTh26eG+5QosY5GeP5l7+7iPvWbefB58v56Wfn8bt3aoau6LhjwYwgRjdyETurR1XXAGsACgsLdeHCheENaCwpeRn7vNzrxffqSIqPsf79UbZk/nRqmo/x0798RLe3l6KZk0iIG5vzY0Kd+OtFZKrT2p8KRPei18aEWHdPLy+XH+SG86YwPjFi23Vj1teuyyc+NoZVJbvZsreZ2+ZPZ+bk8eEOa8RC/efqReAu5/u7gD+G+PWNiWolHxyi5dgJ/u6S6UMXNmfkH/9mNk99sYjOE738x+t7eH5rLU3tXeEOa0SC1iQQkbXAQiBDRGqB/w2sAn4vIncD+4HPBuv1jXGj327aR86kZK7JD+7ib2537Zws7r8+n9d2NfDWnia27W/hvGlpXJ2fyYxJkX/DXNASv6rePsClRcF6TWPcbOeBo2yububBm+baomwhkBgXy80XTuWq/Azermpic3UzHxw4ysxJ47g6P4O5U9OIidCb56wTcAwb7I5M+Zf+z0fbmiPmlF9urCQ1MS7iZpBEu9SkeG48fwp/MyeTrfta+GvlYX6zeT+TxyewcE4WF88I3Ocn/MbmkLQBfEm8v8fGjRsHvGai0+5DR3llx0Hu/MRMJiTHhzscV0qMi+UTszP41g1zuP2yGSTFx/L8tlp+saGC9TvrI+rfn7X4jRnjVJXvv7iTtKR47rkqN9zhjHnPbd5/VvVjY4QLp0/ggmlp7DhwlL98cIgvP7uFy2ZN4n/97XlcMH3CKEV65izxGzPGvbCtjrf3NPGDJeczcXzgLpImXER8fwDOm5qGCPx8/Uf87eo3WXppDv+0eA6ZqYlhi826eowZw/Y0tvO//riDy2ZN4o4FM8MdjulHbIzw+ctnsvE7C7nnqnN4flst1/60lP/4nyq6erxhickSvzFjVP3RTu58qozE+Fh+vmw+sTaTJ6KlJcXz8C3n8edvXsNl50zi/3t1N4t//npY+v8t8RszBu2oa+VTv/wrLR3d/PqLRUxPTw53SGaYZmem8NQXi3j6S0XExghffnYLS375V14uP0iPNzQLwFniN65TUlLCnDlzyMvLY9WqVaddd/ba/YWIVIpIuYhc4nftJhH50Ln2kN/5kOw1UdtyjO//6QOW/PKv9Cr87h+vYF5O5E0XNENbOCeLkvuv4UefupCjx0+w4rltXLHqNX7w0k7e2dvMiSD+EbDBXeMqXq+XFStWsH79ejweD0VFRQBJAcVuBvKdxwLgMWCBiMQCvwRuAGqBd0TkRVXdyam9JlY5fxAeAh48kxhPeHtp7ujm6PETHDl+gn1Nx6hsaOeNikY+OHCU2Bjhs5d6+OebC5gwzqZujmXxsTHcsWAGf1+Uw4Zd9Ty/rZZn397Lk29WMy4hlvk56ZybncrszPFkpiaSkZLIxPEJJMbFEB8bQ1yMMC4hjuSEkS2/bYnfuEpZWRl5eXnk5vqmPS5btozy8vLAJvMS4Fln791NIpLuLCo4C6hU1T0AIrLOKbvT+brQqf8MUMoZJv63qpq466myj52LixHm5aTz0M1zueXCqeSMgWUBzPDFxgg3nj+FG8+fQuuxE7y95zB/rWyivPYIv99Sw7HugQeBP3/5DH5424Ujer0xkfi3bt16WET2hTuOMSQDOBzuICLURCDN7/dpEjA1oMx0wH+x9VrnXH/nFzjfn9FeE0C7iHw4nMCrgBc+fiqSfs6RFAtEWDyfC2I8jziPAfQ71WtMJH5VtRWnRkBEtqhqYbjjiEQi8llgsare4xx/AbgssFg/VXWQ8yPiv9fE2Yikn3MkxQIWz1BscNe4TS2Q43fsAQ4Ms8xgdeud7iBsrwkT6SzxG7d5B8gXkXNEJAFYhm+fCH8vAnc6s3suB1qdbpzB6tpeE2bMGBNdPWbEzrobIVqpao+IfA34MxALPKWqH4jIvc71x4FXgGKgEjgGfGmwus5Th2OviUj6OUdSLGDxDEoiacU4Y4wxwWddPcYY4zKW+I0xxmUs8UcIEWkP8vPfLyLj/I77fT0R+ZaI7HSWKtggIrbkY4QZaNmIEL5+johsFJFdIvKBiNznnA/JshUDxBQrIu+KyEsREEu6iPxBRHY7n9EV4YynP5b43eN+YDi3e74LFKrqRcAfgB8HNSozIn7LRtwMnAfcLiLnhTiMHuDbqloAXA6scGLoW7YiH9jgHIfKfcAuv+NwxvIoUKKqc4F5TlzhjOc0lvgjmIjMFpESEdkqIm+IyFzn/NPOImJvicgeEfmMcz5GRP7daYW9JCKviMhnROQbwDRgo4hs9Hv+R0TkPRHZJCLZAKq6UVWPOUU24ZurbiLHZTjLRqhqN9C3bETIqOpBVd3mfN+GL7FNd+J4xin2DHBbKOIREQ9wC/Arv9PhiiUNuAZ4EkBVu1X1SLjiGYgl/si2Bvi6ql4K/BPw737XpgJXAZ/EN5UQ4NP41pO5ELgHuAJAVX+B70aja1X1WqfseGCTqs4DXge+3M/r3w28Oorvx5y9gZaTCAsRmQVcDGwmYNkKYMBlK0bZvwEPAP7LWYYrllygEfi10/X0KxEZH8Z4+mXz+COUiKQAnwD+U+TkSgH+e7X9l6r2Ajv7Wuv4/hD8p3P+kH/rvh/dwEvO91vxrTjp//qfBwqBvzmrN2JG26gsGzEanN/R54H7VfWo3+9pKGP4JNCgqltFZGHIAzhdHHAJvgbbZhF5lDB36/THEn/kigGOqOr8Aa53+X0vAV+H44SeuonDi9/vgohcDzwM/I2qdvVX2YTNcJacCDoRiceX9H+rqn1rx9WLyFRnkbpQLVtxJXCriBTjW147TUR+E6ZYwPfzqVXVzc7xH/Al/nDF0y/r6olQqnoUqHYWFevbHGTeENXeBP7O6evP5tQywQBtQOpQrysiFwP/AdyqqrbeTOQZzpITQSW+pv2TwC5V/ZnfpZAvW6Gq/6yqHlWdhe+zeE1VPx+OWJx4DgE1IjLHObUI37LdEbWkh7X4I8c4Ean1O/4Z8DngMRFZCcTjG8h7b5DneB7fL9oO4CN8/a6tzrU1wKsictCvn78/PwFSONXFtF9Vbz2D92OCYIhlI0LlSuALwPsist05913Cs2zFQMIZy9eB3zp/mPfgW/IjJozxnMaWbIgyIpKiqu0iMhkoA650WiHGGANYiz8avSQi6UAC8ANL+saYQNbiN8YYl7HBXWOMcRlL/MYY4zKW+I0xxmUs8RtjgsZWnY1MlviNMWOZrTp7Bmw6pzEmpERkNr6lpTPx7Wn8ZVXdLSJPA0fxrRE1BXhAVf8gIjHAanzrRlXja7A+hW/F2b5VZw/33ZgoIo/gW7zwOLBEVetV1X/dqk3A54P/TiOXtfiNMaFmq86GmbX4jTEhY6vORgZL/MaYULJVZyOAdfUYY0LGVp2NDJb4jTHBNE5Eav0e38K36uzdIvIe8AFDbx35PL517nfgS979rTo7WPcPfHzV2e0iEtKlrCONrdVjjIl4turs6LI+fmPMWGCrzo4ia/EbY4zLWB+/Mca4jCV+Y4xxGUv8xhjjMpb4jTHGZSzxG2OMy/z/nbHadTLTcC0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"# checking for outliers and distribution\\n\\nnumerical = [\\\"Weight\\\", \\\"Length1\\\", \\\"Length2\\\", \\\"Length3\\\", \\\"Height\\\", \\\"Width\\\"]\\n\\nfor feature in numerical:\\n\\n    plt.subplot(1, 2, 1)\\n    fig = fish_data.boxplot(column=feature)\\n    fig.set_title(\\\" \\\")\\n    fig.set_ylabel(feature)\\n\\n    plt.subplot(1, 2, 2)\\n    fig = sns.distplot(fish_data[feature])\\n    fig.set_ylabel(\\\"weight\\\")\\n    fig.set_xlabel(feature)\\n\\n    plt.show()\";\n",
       "                var nbb_formatted_code = \"# checking for outliers and distribution\\n\\nnumerical = [\\\"Weight\\\", \\\"Length1\\\", \\\"Length2\\\", \\\"Length3\\\", \\\"Height\\\", \\\"Width\\\"]\\n\\nfor feature in numerical:\\n\\n    plt.subplot(1, 2, 1)\\n    fig = fish_data.boxplot(column=feature)\\n    fig.set_title(\\\" \\\")\\n    fig.set_ylabel(feature)\\n\\n    plt.subplot(1, 2, 2)\\n    fig = sns.distplot(fish_data[feature])\\n    fig.set_ylabel(\\\"weight\\\")\\n    fig.set_xlabel(feature)\\n\\n    plt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for outliers and distribution\n",
    "\n",
    "numerical = [\"Weight\", \"Length1\", \"Length2\", \"Length3\", \"Height\", \"Width\"]\n",
    "\n",
    "for feature in numerical:\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    fig = fish_data.boxplot(column=feature)\n",
    "    fig.set_title(\" \")\n",
    "    fig.set_ylabel(feature)\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    fig = sns.distplot(fish_data[feature])\n",
    "    fig.set_ylabel(\"weight\")\n",
    "    fig.set_xlabel(feature)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There outliers present in target variable \"Weight\" and features \"Lenght1\", \"Length2\". Rest of the data looks good.<br>\n",
    "The distributions look skewed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13    1550.0\n",
      "Name: Weight, dtype: float64\n",
      "13    56.0\n",
      "Name: Length1, dtype: float64\n",
      "13    60.0\n",
      "Name: Length2, dtype: float64\n",
      "Series([], Name: Length3, dtype: float64)\n",
      "Series([], Name: Height, dtype: float64)\n",
      "Series([], Name: Width, dtype: float64)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"# finding the outliers\\nfor col in numerical:\\n    df = fish_data[col]\\n    df_Q1 = df.quantile(0.25)\\n    df_Q3 = df.quantile(0.75)\\n    df_IQR = df_Q3 - df_Q1\\n    df_lowerend = df_Q1 - (1.5 * df_IQR)\\n    df_upperend = df_Q3 + (1.5 * df_IQR)\\n\\n    df_outliers = df[(df < df_lowerend) | (df > df_upperend)]\\n    print(df_outliers)\";\n",
       "                var nbb_formatted_code = \"# finding the outliers\\nfor col in numerical:\\n    df = fish_data[col]\\n    df_Q1 = df.quantile(0.25)\\n    df_Q3 = df.quantile(0.75)\\n    df_IQR = df_Q3 - df_Q1\\n    df_lowerend = df_Q1 - (1.5 * df_IQR)\\n    df_upperend = df_Q3 + (1.5 * df_IQR)\\n\\n    df_outliers = df[(df < df_lowerend) | (df > df_upperend)]\\n    print(df_outliers)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# finding the outliers\n",
    "for col in numerical:\n",
    "    df = fish_data[col]\n",
    "    df_Q1 = df.quantile(0.25)\n",
    "    df_Q3 = df.quantile(0.75)\n",
    "    df_IQR = df_Q3 - df_Q1\n",
    "    df_lowerend = df_Q1 - (1.5 * df_IQR)\n",
    "    df_upperend = df_Q3 + (1.5 * df_IQR)\n",
    "\n",
    "    df_outliers = df[(df < df_lowerend) | (df > df_upperend)]\n",
    "    print(df_outliers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Record at row # 13 seems to be the outlier for weight, lenght1 and length2. <br>\n",
    "Dropping this record from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"# dropping the outlier at row # 13\\nfish_data = fish_data.drop([13])\";\n",
       "                var nbb_formatted_code = \"# dropping the outlier at row # 13\\nfish_data = fish_data.drop([13])\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dropping the outlier at row # 13\n",
    "fish_data = fish_data.drop([13])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Temporarily applying log transformations just to show how it will normalize the distributions of features and the target variable to some extent.\n",
    "* Note that I have not transformed the actual features/target yet in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"# rechecking the linear relationships and distributions\\n\\nnumerical = [\\\"Length1\\\", \\\"Length2\\\", \\\"Length3\\\", \\\"Height\\\", \\\"Width\\\"]\\n\\nfor feature in numerical:\\n\\n    plt.subplot(1, 2, 2)\\n    fig = sns.distplot(np.log(fish_data[feature]))\\n    fig.set_ylabel(\\\"weight\\\")\\n    fig.set_xlabel(feature)\\n\\n    plt.subplot(1, 2, 1)\\n    plt.scatter(x=np.log(fish_data[feature]), y=np.log(fish_data[\\\"Weight\\\"]))\\n    plt.xlabel(feature)\\n    plt.ylabel(\\\"weight\\\")\\n    plt.show()\\n\\n    plt.show()\";\n",
       "                var nbb_formatted_code = \"# rechecking the linear relationships and distributions\\n\\nnumerical = [\\\"Length1\\\", \\\"Length2\\\", \\\"Length3\\\", \\\"Height\\\", \\\"Width\\\"]\\n\\nfor feature in numerical:\\n\\n    plt.subplot(1, 2, 2)\\n    fig = sns.distplot(np.log(fish_data[feature]))\\n    fig.set_ylabel(\\\"weight\\\")\\n    fig.set_xlabel(feature)\\n\\n    plt.subplot(1, 2, 1)\\n    plt.scatter(x=np.log(fish_data[feature]), y=np.log(fish_data[\\\"Weight\\\"]))\\n    plt.xlabel(feature)\\n    plt.ylabel(\\\"weight\\\")\\n    plt.show()\\n\\n    plt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rechecking the linear relationships and distributions\n",
    "\n",
    "numerical = [\"Length1\", \"Length2\", \"Length3\", \"Height\", \"Width\"]\n",
    "\n",
    "for feature in numerical:\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    fig = sns.distplot(np.log(fish_data[feature]))\n",
    "    fig.set_ylabel(\"weight\")\n",
    "    fig.set_xlabel(feature)\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.scatter(x=np.log(fish_data[feature]), y=np.log(fish_data[\"Weight\"]))\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel(\"weight\")\n",
    "    plt.show()\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear relationship between the features and the target variable looks better now. <br>\n",
    "The distributions are still a little skewed but for I will move ahead with this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perch        39\n",
      "Bream        24\n",
      "Roach        14\n",
      "Pike         11\n",
      "Smelt        10\n",
      "Parkki        8\n",
      "Whitefish     4\n",
      "Name: Species, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"# checking the categorical feature - Species\\nprint(fish_data[\\\"Species\\\"].value_counts())\\nfish_data[\\\"Species\\\"].value_counts().plot(kind=\\\"bar\\\")\\nplt.ylabel(\\\"count\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# checking the categorical feature - Species\\nprint(fish_data[\\\"Species\\\"].value_counts())\\nfish_data[\\\"Species\\\"].value_counts().plot(kind=\\\"bar\\\")\\nplt.ylabel(\\\"count\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking the categorical feature - Species\n",
    "print(fish_data[\"Species\"].value_counts())\n",
    "fish_data[\"Species\"].value_counts().plot(kind=\"bar\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This catgorical feature will need encoding. I will handle this using OneHotEncoder later in this code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Length1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999424</td>\n",
       "      <td>0.990713</td>\n",
       "      <td>0.891403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length2</th>\n",
       "      <td>0.999424</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993216</td>\n",
       "      <td>0.895063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length3</th>\n",
       "      <td>0.990713</td>\n",
       "      <td>0.993216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.901948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.891403</td>\n",
       "      <td>0.895063</td>\n",
       "      <td>0.901948</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Length1   Length2   Length3    Weight\n",
       "Length1  1.000000  0.999424  0.990713  0.891403\n",
       "Length2  0.999424  1.000000  0.993216  0.895063\n",
       "Length3  0.990713  0.993216  1.000000  0.901948\n",
       "Weight   0.891403  0.895063  0.901948  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"# checking for collinerity between the 3 length variables\\nfish_data[[\\\"Length1\\\", \\\"Length2\\\", \\\"Length3\\\", \\\"Weight\\\"]].corr()\";\n",
       "                var nbb_formatted_code = \"# checking for collinerity between the 3 length variables\\nfish_data[[\\\"Length1\\\", \\\"Length2\\\", \\\"Length3\\\", \\\"Weight\\\"]].corr()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for collinerity between the 3 length variables\n",
    "fish_data[[\"Length1\", \"Length2\", \"Length3\", \"Weight\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 3 lenght variables are highly correlated. Let's check their values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>25.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.3</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30.4</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13.8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.4</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25.4</td>\n",
       "      <td>27.5</td>\n",
       "      <td>28.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.3</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Length1  Length2  Length3\n",
       "0     26.5     29.0     34.0\n",
       "1     20.0     22.0     23.5\n",
       "2     20.5     22.5     25.3\n",
       "3     14.3     15.5     17.4\n",
       "4     30.4     33.0     38.3\n",
       "5     13.8     15.0     16.0\n",
       "6     30.4     33.0     38.5\n",
       "7     25.4     27.5     28.9\n",
       "8      9.3      9.8     10.8\n",
       "9     23.2     25.4     30.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"fish_data[[\\\"Length1\\\", \\\"Length2\\\", \\\"Length3\\\"]].head(10)\";\n",
       "                var nbb_formatted_code = \"fish_data[[\\\"Length1\\\", \\\"Length2\\\", \\\"Length3\\\"]].head(10)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fish_data[[\"Length1\", \"Length2\", \"Length3\"]].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Length3 values are the highest for each row, it is probably the Total Length of the fish. <br>\n",
    "So keeping Length3 and dropping the other two length variables from the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"fish_data = fish_data.drop([\\\"Length1\\\", \\\"Length2\\\"], axis=1)\";\n",
       "                var nbb_formatted_code = \"fish_data = fish_data.drop([\\\"Length1\\\", \\\"Length2\\\"], axis=1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fish_data = fish_data.drop([\"Length1\", \"Length2\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"# dividing the data into features and target variable\\nX = fish_data.drop(\\\"Weight\\\", axis=1)\\ny = fish_data[\\\"Weight\\\"]\";\n",
       "                var nbb_formatted_code = \"# dividing the data into features and target variable\\nX = fish_data.drop(\\\"Weight\\\", axis=1)\\ny = fish_data[\\\"Weight\\\"]\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dividing the data into features and target variable\n",
    "X = fish_data.drop(\"Weight\", axis=1)\n",
    "y = fish_data[\"Weight\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"# for encoding the categorical feature - species and for transforming the numerical features using log transformation\\n\\nnumerical = [\\\"Length3\\\", \\\"Height\\\", \\\"Width\\\"]\\ncategorical = [\\\"Species\\\"]\\n\\npreprocesser = ColumnTransformer(\\n    transformers=[\\n        (\\\"one_hot_encoder\\\", OneHotEncoder(), categorical),\\n        (\\\"log_transformer\\\", FunctionTransformer(np.log, validate=False), numerical,),\\n    ],\\n    remainder=\\\"passthrough\\\",\\n)\";\n",
       "                var nbb_formatted_code = \"# for encoding the categorical feature - species and for transforming the numerical features using log transformation\\n\\nnumerical = [\\\"Length3\\\", \\\"Height\\\", \\\"Width\\\"]\\ncategorical = [\\\"Species\\\"]\\n\\npreprocesser = ColumnTransformer(\\n    transformers=[\\n        (\\\"one_hot_encoder\\\", OneHotEncoder(), categorical),\\n        (\\\"log_transformer\\\", FunctionTransformer(np.log, validate=False), numerical,),\\n    ],\\n    remainder=\\\"passthrough\\\",\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for encoding the categorical feature - species and for transforming the numerical features using log transformation\n",
    "\n",
    "numerical = [\"Length3\", \"Height\", \"Width\"]\n",
    "categorical = [\"Species\"]\n",
    "\n",
    "preprocesser = ColumnTransformer(\n",
    "    transformers=[\n",
    "        (\"one_hot_encoder\", OneHotEncoder(), categorical),\n",
    "        (\"log_transformer\", FunctionTransformer(np.log, validate=False), numerical,),\n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"# splitting the data into train test\\nX_train, X_test, y_train, y_test = train_test_split(\\n    X, y, test_size=0.25, random_state=0\\n)\";\n",
       "                var nbb_formatted_code = \"# splitting the data into train test\\nX_train, X_test, y_train, y_test = train_test_split(\\n    X, y, test_size=0.25, random_state=0\\n)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# splitting the data into train test\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.25, random_state=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"# building the pipeline\\nlr_pipe = make_pipeline(\\n    preprocesser,\\n    TransformedTargetRegressor(\\n        regressor=LinearRegression(), func=np.log, inverse_func=np.exp\\n    ),\\n)\\n\\n# fitting the data\\nlr_pipe.fit(X_train, y_train)\\n\\n# predicting\\ny_predict = lr_pipe.predict(X_test)\";\n",
       "                var nbb_formatted_code = \"# building the pipeline\\nlr_pipe = make_pipeline(\\n    preprocesser,\\n    TransformedTargetRegressor(\\n        regressor=LinearRegression(), func=np.log, inverse_func=np.exp\\n    ),\\n)\\n\\n# fitting the data\\nlr_pipe.fit(X_train, y_train)\\n\\n# predicting\\ny_predict = lr_pipe.predict(X_test)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# building the pipeline\n",
    "lr_pipe = make_pipeline(\n",
    "    preprocesser,\n",
    "    TransformedTargetRegressor(\n",
    "        regressor=LinearRegression(), func=np.log, inverse_func=np.exp\n",
    "    ),\n",
    ")\n",
    "\n",
    "# fitting the data\n",
    "lr_pipe.fit(X_train, y_train)\n",
    "\n",
    "# predicting\n",
    "y_predict = lr_pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Note that I have to reapply the log transformation to get the plot for transformed target variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"# Residual Plot\\nplt.scatter(np.log(y_predict), np.log(y_test) - np.log(y_predict))\\nplt.xlabel(\\\"Predicted Values\\\")\\nplt.ylabel(\\\"Residuals\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# Residual Plot\\nplt.scatter(np.log(y_predict), np.log(y_test) - np.log(y_predict))\\nplt.xlabel(\\\"Predicted Values\\\")\\nplt.ylabel(\\\"Residuals\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residual Plot\n",
    "plt.scatter(np.log(y_predict), np.log(y_test) - np.log(y_predict))\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"# Normal Q-Q plot\\nsns.set(style=\\\"whitegrid\\\")\\nplt.figure(figsize=(8, 4))\\nstats.probplot(np.log(y_test) - np.log(y_predict), dist=\\\"norm\\\", plot=plt)\\nplt.title(\\\"Normal Q-Q plot\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# Normal Q-Q plot\\nsns.set(style=\\\"whitegrid\\\")\\nplt.figure(figsize=(8, 4))\\nstats.probplot(np.log(y_test) - np.log(y_predict), dist=\\\"norm\\\", plot=plt)\\nplt.title(\\\"Normal Q-Q plot\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normal Q-Q plot\n",
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(8, 4))\n",
    "stats.probplot(np.log(y_test) - np.log(y_predict), dist=\"norm\", plot=plt)\n",
    "plt.title(\"Normal Q-Q plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a534ed4ac8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"# distplot\\nsns.distplot(np.log(y_test) - np.log(y_predict))\";\n",
       "                var nbb_formatted_code = \"# distplot\\nsns.distplot(np.log(y_test) - np.log(y_predict))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distplot\n",
    "sns.distplot(np.log(y_test) - np.log(y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear Regression Test MSE: 1023.7636893532946\n",
      "Linear Regression Test Accuracy (R-Squared): 0.9820780111246779\n",
      "\n",
      "Linear Regression Cross Validation Score: 2217.9015265675725\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"# Test MSE and R2\\nprint(f\\\"\\\\nLinear Regression Test MSE: {mean_squared_error(y_test, y_predict)}\\\")\\nprint(f\\\"Linear Regression Test Accuracy (R-Squared): {r2_score(y_test, y_predict)}\\\")\\n\\n\\n# cross validation score\\ncv_score = np.mean(\\n    (cross_val_score(lr_pipe, X, y, cv=5, scoring=\\\"neg_mean_squared_error\\\")) * -1\\n)\\nprint(f\\\"\\\\nLinear Regression Cross Validation Score: {cv_score}\\\")\";\n",
       "                var nbb_formatted_code = \"# Test MSE and R2\\nprint(f\\\"\\\\nLinear Regression Test MSE: {mean_squared_error(y_test, y_predict)}\\\")\\nprint(f\\\"Linear Regression Test Accuracy (R-Squared): {r2_score(y_test, y_predict)}\\\")\\n\\n\\n# cross validation score\\ncv_score = np.mean(\\n    (cross_val_score(lr_pipe, X, y, cv=5, scoring=\\\"neg_mean_squared_error\\\")) * -1\\n)\\nprint(f\\\"\\\\nLinear Regression Cross Validation Score: {cv_score}\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test MSE and R2\n",
    "print(f\"\\nLinear Regression Test MSE: {mean_squared_error(y_test, y_predict)}\")\n",
    "print(f\"Linear Regression Test Accuracy (R-Squared): {r2_score(y_test, y_predict)}\")\n",
    "\n",
    "\n",
    "# cross validation score\n",
    "cv_score = np.mean(\n",
    "    (cross_val_score(lr_pipe, X, y, cv=5, scoring=\"neg_mean_squared_error\")) * -1\n",
    ")\n",
    "print(f\"\\nLinear Regression Cross Validation Score: {cv_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TransformedTargetRegressor(func=<ufunc 'log'>, inverse_func=<ufunc 'exp'>,\n",
       "                           regressor=GridSearchCV(cv=5,\n",
       "                                                  estimator=Pipeline(steps=[('columntransformer',\n",
       "                                                                             ColumnTransformer(remainder='passthrough',\n",
       "                                                                                               transformers=[('one_hot_encoder',\n",
       "                                                                                                              OneHotEncoder(),\n",
       "                                                                                                              ['Species']),\n",
       "                                                                                                             ('log_transformer',\n",
       "                                                                                                              FunctionTransformer(func=<ufunc 'log'>),\n",
       "                                                                                                              ['Length3',\n",
       "                                                                                                               'Height',\n",
       "                                                                                                               'Width'])])),\n",
       "                                                                            ('standardscaler',\n",
       "                                                                             StandardScaler()),\n",
       "                                                                            ('lasso',\n",
       "                                                                             Lasso())]),\n",
       "                                                  param_grid={'lasso__alpha': [1e-15,\n",
       "                                                                               1e-10,\n",
       "                                                                               1e-08,\n",
       "                                                                               0.001,\n",
       "                                                                               0.01,\n",
       "                                                                               1,\n",
       "                                                                               5,\n",
       "                                                                               10,\n",
       "                                                                               20,\n",
       "                                                                               30,\n",
       "                                                                               35,\n",
       "                                                                               40]},\n",
       "                                                  scoring='neg_mean_squared_error'))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 25;\n",
       "                var nbb_unformatted_code = \"import warnings\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\\nwarnings.simplefilter(\\\"ignore\\\")\\n\\n# building lasso pipeline\\nlasso_pipe = make_pipeline(preprocesser, StandardScaler(), Lasso())\\n\\nparameters = {\\n    \\\"lasso__alpha\\\": [1e-15, 1e-10, 1e-8, 1e-3, 1e-2, 1, 5, 10, 20, 30, 35, 40]\\n}\\n\\nlasso_regressor = TransformedTargetRegressor(\\n    GridSearchCV(lasso_pipe, parameters, scoring=\\\"neg_mean_squared_error\\\", cv=5),\\n    func=np.log,\\n    inverse_func=np.exp,\\n)\\n\\nlasso_regressor.fit(X_train, y_train)\";\n",
       "                var nbb_formatted_code = \"import warnings\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\\nwarnings.simplefilter(\\\"ignore\\\")\\n\\n# building lasso pipeline\\nlasso_pipe = make_pipeline(preprocesser, StandardScaler(), Lasso())\\n\\nparameters = {\\n    \\\"lasso__alpha\\\": [1e-15, 1e-10, 1e-8, 1e-3, 1e-2, 1, 5, 10, 20, 30, 35, 40]\\n}\\n\\nlasso_regressor = TransformedTargetRegressor(\\n    GridSearchCV(lasso_pipe, parameters, scoring=\\\"neg_mean_squared_error\\\", cv=5),\\n    func=np.log,\\n    inverse_func=np.exp,\\n)\\n\\nlasso_regressor.fit(X_train, y_train)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "warnings.simplefilter(\"ignore\")\n",
    "\n",
    "# building lasso pipeline\n",
    "lasso_pipe = make_pipeline(preprocesser, StandardScaler(), Lasso())\n",
    "\n",
    "parameters = {\n",
    "    \"lasso__alpha\": [1e-15, 1e-10, 1e-8, 1e-3, 1e-2, 1, 5, 10, 20, 30, 35, 40]\n",
    "}\n",
    "\n",
    "lasso_regressor = TransformedTargetRegressor(\n",
    "    GridSearchCV(lasso_pipe, parameters, scoring=\"neg_mean_squared_error\", cv=5),\n",
    "    func=np.log,\n",
    "    inverse_func=np.exp,\n",
    ")\n",
    "\n",
    "lasso_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'lasso__alpha': 0.001}\n",
      "-0.010462517081477527\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 26;\n",
       "                var nbb_unformatted_code = \"print(lasso_regressor.regressor_.best_params_)\\nprint(lasso_regressor.regressor_.best_score_)\\n\\nprediction_lasso = lasso_regressor.predict(X_test)\";\n",
       "                var nbb_formatted_code = \"print(lasso_regressor.regressor_.best_params_)\\nprint(lasso_regressor.regressor_.best_score_)\\n\\nprediction_lasso = lasso_regressor.predict(X_test)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(lasso_regressor.regressor_.best_params_)\n",
    "print(lasso_regressor.regressor_.best_score_)\n",
    "\n",
    "prediction_lasso = lasso_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Note that I have to reapply the log transformation to get the plot for transformed target variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 27;\n",
       "                var nbb_unformatted_code = \"# Lasso Residual Plot\\nplt.scatter(np.log(prediction_lasso), np.log(y_test) - np.log(prediction_lasso))\\nplt.xlabel(\\\"Predicted Values\\\")\\nplt.ylabel(\\\"Residuals\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# Lasso Residual Plot\\nplt.scatter(np.log(prediction_lasso), np.log(y_test) - np.log(prediction_lasso))\\nplt.xlabel(\\\"Predicted Values\\\")\\nplt.ylabel(\\\"Residuals\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lasso Residual Plot\n",
    "plt.scatter(np.log(prediction_lasso), np.log(y_test) - np.log(prediction_lasso))\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 28;\n",
       "                var nbb_unformatted_code = \"# Lasso Normal Q-Q plot\\nsns.set(style=\\\"whitegrid\\\")\\n\\nplt.figure(figsize=(8, 4))\\nstats.probplot(np.log(y_test) - np.log(prediction_lasso), dist=\\\"norm\\\", plot=plt)\\nplt.title(\\\"Normal Q-Q plot\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# Lasso Normal Q-Q plot\\nsns.set(style=\\\"whitegrid\\\")\\n\\nplt.figure(figsize=(8, 4))\\nstats.probplot(np.log(y_test) - np.log(prediction_lasso), dist=\\\"norm\\\", plot=plt)\\nplt.title(\\\"Normal Q-Q plot\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lasso Normal Q-Q plot\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "stats.probplot(np.log(y_test) - np.log(prediction_lasso), dist=\"norm\", plot=plt)\n",
    "plt.title(\"Normal Q-Q plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a53512fe08>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 29;\n",
       "                var nbb_unformatted_code = \"# distplot\\nsns.distplot(np.log(y_test) - np.log(prediction_lasso))\";\n",
       "                var nbb_formatted_code = \"# distplot\\nsns.distplot(np.log(y_test) - np.log(prediction_lasso))\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distplot\n",
    "sns.distplot(np.log(y_test) - np.log(prediction_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso Test MSE: 1053.3297395919487\n",
      "\n",
      "Lasso Test Accuracy (R-Squared): 0.9815604283768475\n",
      "\n",
      "Lasso Cross Validation Score: 2204.0598271684985\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 30;\n",
       "                var nbb_unformatted_code = \"# # MSE for test data\\nprint(\\\"Lasso Test MSE:\\\", mean_squared_error(y_test, prediction_lasso))\\n\\n# R2 for test data\\nprint(\\n    \\\"\\\\nLasso Test Accuracy (R-Squared):\\\", r2_score(y_test, prediction_lasso),\\n)\\n\\n# cross validation score\\ncv_score = np.mean(\\n    (cross_val_score(lasso_regressor, X, y, cv=5, scoring=\\\"neg_mean_squared_error\\\"))\\n    * -1\\n)\\nprint(\\\"\\\\nLasso Cross Validation Score:\\\", cv_score)\";\n",
       "                var nbb_formatted_code = \"# # MSE for test data\\nprint(\\\"Lasso Test MSE:\\\", mean_squared_error(y_test, prediction_lasso))\\n\\n# R2 for test data\\nprint(\\n    \\\"\\\\nLasso Test Accuracy (R-Squared):\\\", r2_score(y_test, prediction_lasso),\\n)\\n\\n# cross validation score\\ncv_score = np.mean(\\n    (cross_val_score(lasso_regressor, X, y, cv=5, scoring=\\\"neg_mean_squared_error\\\"))\\n    * -1\\n)\\nprint(\\\"\\\\nLasso Cross Validation Score:\\\", cv_score)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # MSE for test data\n",
    "print(\"Lasso Test MSE:\", mean_squared_error(y_test, prediction_lasso))\n",
    "\n",
    "# R2 for test data\n",
    "print(\n",
    "    \"\\nLasso Test Accuracy (R-Squared):\", r2_score(y_test, prediction_lasso),\n",
    ")\n",
    "\n",
    "# cross validation score\n",
    "cv_score = np.mean(\n",
    "    (cross_val_score(lasso_regressor, X, y, cv=5, scoring=\"neg_mean_squared_error\"))\n",
    "    * -1\n",
    ")\n",
    "print(\"\\nLasso Cross Validation Score:\", cv_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 31;\n",
       "                var nbb_unformatted_code = \"# building random forest pipeline\\nrf_pipe = make_pipeline(\\n    preprocesser,\\n    TransformedTargetRegressor(\\n        regressor=RandomForestRegressor(n_estimators=10),\\n        func=np.log,\\n        inverse_func=np.exp,\\n    ),\\n)\\n\\n# fitting the data\\nrf_pipe.fit(X_train, y_train)\\n\\n# predicting\\nrf_predictions = rf_pipe.predict(X_test)\";\n",
       "                var nbb_formatted_code = \"# building random forest pipeline\\nrf_pipe = make_pipeline(\\n    preprocesser,\\n    TransformedTargetRegressor(\\n        regressor=RandomForestRegressor(n_estimators=10),\\n        func=np.log,\\n        inverse_func=np.exp,\\n    ),\\n)\\n\\n# fitting the data\\nrf_pipe.fit(X_train, y_train)\\n\\n# predicting\\nrf_predictions = rf_pipe.predict(X_test)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# building random forest pipeline\n",
    "rf_pipe = make_pipeline(\n",
    "    preprocesser,\n",
    "    TransformedTargetRegressor(\n",
    "        regressor=RandomForestRegressor(n_estimators=10),\n",
    "        func=np.log,\n",
    "        inverse_func=np.exp,\n",
    "    ),\n",
    ")\n",
    "\n",
    "# fitting the data\n",
    "rf_pipe.fit(X_train, y_train)\n",
    "\n",
    "# predicting\n",
    "rf_predictions = rf_pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Note that I have to reapply the log transformation to get the plot for transformed target variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 32;\n",
       "                var nbb_unformatted_code = \"# Random Forest Residual Plot\\nplt.scatter(np.log(rf_predictions), np.log(y_test) - np.log(rf_predictions))\\nplt.xlabel(\\\"Predicted Values\\\")\\nplt.ylabel(\\\"Residuals\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# Random Forest Residual Plot\\nplt.scatter(np.log(rf_predictions), np.log(y_test) - np.log(rf_predictions))\\nplt.xlabel(\\\"Predicted Values\\\")\\nplt.ylabel(\\\"Residuals\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest Residual Plot\n",
    "plt.scatter(np.log(rf_predictions), np.log(y_test) - np.log(rf_predictions))\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 33;\n",
       "                var nbb_unformatted_code = \"# Random Forest Normal Q-Q plot\\nsns.set(style=\\\"whitegrid\\\")\\n\\nplt.figure(figsize=(8, 4))\\nstats.probplot(np.log(y_test) - np.log(rf_predictions), dist=\\\"norm\\\", plot=plt)\\nplt.title(\\\"Normal Q-Q plot\\\")\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# Random Forest Normal Q-Q plot\\nsns.set(style=\\\"whitegrid\\\")\\n\\nplt.figure(figsize=(8, 4))\\nstats.probplot(np.log(y_test) - np.log(rf_predictions), dist=\\\"norm\\\", plot=plt)\\nplt.title(\\\"Normal Q-Q plot\\\")\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest Normal Q-Q plot\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "stats.probplot(np.log(y_test) - np.log(rf_predictions), dist=\"norm\", plot=plt)\n",
    "plt.title(\"Normal Q-Q plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Test MSE: 2889.863426547324\n",
      "Random Forest Test Accuracy: 0.949410102428522\n",
      "Random Forest Cross Validation Score: 4740.943231397679\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 34;\n",
       "                var nbb_unformatted_code = \"# test MSE\\nprint(f\\\"Random Forest Test MSE: {mean_squared_error(y_test, rf_predictions)}\\\")\\n\\n# test R2\\nprint(f\\\"Random Forest Test Accuracy: {r2_score(y_test, rf_predictions)}\\\")\\n\\n# cross validation score\\ncv_score = np.mean(\\n    (cross_val_score(rf_pipe, X, y, cv=5, scoring=\\\"neg_mean_squared_error\\\")) * -1\\n)\\nprint(f\\\"Random Forest Cross Validation Score: {cv_score}\\\")\";\n",
       "                var nbb_formatted_code = \"# test MSE\\nprint(f\\\"Random Forest Test MSE: {mean_squared_error(y_test, rf_predictions)}\\\")\\n\\n# test R2\\nprint(f\\\"Random Forest Test Accuracy: {r2_score(y_test, rf_predictions)}\\\")\\n\\n# cross validation score\\ncv_score = np.mean(\\n    (cross_val_score(rf_pipe, X, y, cv=5, scoring=\\\"neg_mean_squared_error\\\")) * -1\\n)\\nprint(f\\\"Random Forest Cross Validation Score: {cv_score}\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test MSE\n",
    "print(f\"Random Forest Test MSE: {mean_squared_error(y_test, rf_predictions)}\")\n",
    "\n",
    "# test R2\n",
    "print(f\"Random Forest Test Accuracy: {r2_score(y_test, rf_predictions)}\")\n",
    "\n",
    "# cross validation score\n",
    "cv_score = np.mean(\n",
    "    (cross_val_score(rf_pipe, X, y, cv=5, scoring=\"neg_mean_squared_error\")) * -1\n",
    ")\n",
    "print(f\"Random Forest Cross Validation Score: {cv_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest didn't do very well for this dataset as compared to the Linear and Lasso Models. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting Linear Regression as the final model for this dataset. Lasso model has the smallest CV MSE but the difference with Linear Regression MSE was insignificant. So I decided to go with the simpler LR Model for this dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('columntransformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('one_hot_encoder',\n",
       "                                                  OneHotEncoder(),\n",
       "                                                  ['Species']),\n",
       "                                                 ('log_transformer',\n",
       "                                                  FunctionTransformer(func=<ufunc 'log'>),\n",
       "                                                  ['Length3', 'Height',\n",
       "                                                   'Width'])])),\n",
       "                ('transformedtargetregressor',\n",
       "                 TransformedTargetRegressor(func=<ufunc 'log'>,\n",
       "                                            inverse_func=<ufunc 'exp'>,\n",
       "                                            regressor=LinearRegression()))])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 35;\n",
       "                var nbb_unformatted_code = \"# retraining the LR model on entire dataset\\nlr_pipe.fit(X, y)\";\n",
       "                var nbb_formatted_code = \"# retraining the LR model on entire dataset\\nlr_pipe.fit(X, y)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# retraining the LR model on entire dataset\n",
    "lr_pipe.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reg_notebook_model.joblib']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 36;\n",
       "                var nbb_unformatted_code = \"dump(lr_pipe, \\\"reg_notebook_model.joblib\\\")\";\n",
       "                var nbb_formatted_code = \"dump(lr_pipe, \\\"reg_notebook_model.joblib\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dump(lr_pipe, \"reg_notebook_model.joblib\")"
   ]
  }
 ],
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