Rings of Abalone - (Practice Project - 4).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "40aa70d5",
   "metadata": {},
   "source": [
    "####  Practice Project - 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34403d1a",
   "metadata": {},
   "source": [
    "# Rings of Abalone"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abcabcdf",
   "metadata": {},
   "source": [
    "* You have to predict the rings of each abalone which will lead us to the age of that abalone. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7a962d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import important libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, roc_auc_score\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9a59d06",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings  \n",
       "0         0.150     15  \n",
       "1         0.070      7  \n",
       "2         0.210      9  \n",
       "3         0.155     10  \n",
       "4         0.055      7  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/dsrscientist/dataset1/master/abalone.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f2d856c",
   "metadata": {},
   "source": [
    "* Data Description\n",
    "1. Sex: Sex (M: Male, F: Female, I: Infant)\n",
    "2. Length: Longest Shell measurement (millimetres - mm)\n",
    "3. Diameter: Diameter - perpendicular to length (mm)\n",
    "4. Height: Height - with meat in shell (mm)\n",
    "5. Whole weight: Weight of whole abalone (grams)\n",
    "6. Shucked weight: Weight of meat (grams)\n",
    "7. Viscera weight: Gut weight after bleeding (grams)\n",
    "8. Shell weight: Shell weight - after being dried (grams)\n",
    "9. Rings: Rings - value + 1.5 gives age in years (eg. 4 = 5.5 years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c08edc51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4177 entries, 0 to 4176\n",
      "Data columns (total 9 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Sex             4177 non-null   object \n",
      " 1   Length          4177 non-null   float64\n",
      " 2   Diameter        4177 non-null   float64\n",
      " 3   Height          4177 non-null   float64\n",
      " 4   Whole weight    4177 non-null   float64\n",
      " 5   Shucked weight  4177 non-null   float64\n",
      " 6   Viscera weight  4177 non-null   float64\n",
      " 7   Shell weight    4177 non-null   float64\n",
      " 8   Rings           4177 non-null   int64  \n",
      "dtypes: float64(7), int64(1), object(1)\n",
      "memory usage: 293.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking infromation of df\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbdc6b9b",
   "metadata": {},
   "source": [
    "* there is no null value. 'sex' column contains object type data. 'rings' are integer. and all other columns are float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48059f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4177, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking shape of df\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78fd9412",
   "metadata": {},
   "source": [
    "#### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "135f3359",
   "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>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.523992</td>\n",
       "      <td>0.407881</td>\n",
       "      <td>0.139516</td>\n",
       "      <td>0.828742</td>\n",
       "      <td>0.359367</td>\n",
       "      <td>0.180594</td>\n",
       "      <td>0.238831</td>\n",
       "      <td>9.933684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.120093</td>\n",
       "      <td>0.099240</td>\n",
       "      <td>0.041827</td>\n",
       "      <td>0.490389</td>\n",
       "      <td>0.221963</td>\n",
       "      <td>0.109614</td>\n",
       "      <td>0.139203</td>\n",
       "      <td>3.224169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.055000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.441500</td>\n",
       "      <td>0.186000</td>\n",
       "      <td>0.093500</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.545000</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.799500</td>\n",
       "      <td>0.336000</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.234000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.615000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>1.153000</td>\n",
       "      <td>0.502000</td>\n",
       "      <td>0.253000</td>\n",
       "      <td>0.329000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.815000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>2.825500</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Length     Diameter       Height  Whole weight  Shucked weight  \\\n",
       "count  4177.000000  4177.000000  4177.000000   4177.000000     4177.000000   \n",
       "mean      0.523992     0.407881     0.139516      0.828742        0.359367   \n",
       "std       0.120093     0.099240     0.041827      0.490389        0.221963   \n",
       "min       0.075000     0.055000     0.000000      0.002000        0.001000   \n",
       "25%       0.450000     0.350000     0.115000      0.441500        0.186000   \n",
       "50%       0.545000     0.425000     0.140000      0.799500        0.336000   \n",
       "75%       0.615000     0.480000     0.165000      1.153000        0.502000   \n",
       "max       0.815000     0.650000     1.130000      2.825500        1.488000   \n",
       "\n",
       "       Viscera weight  Shell weight        Rings  \n",
       "count     4177.000000   4177.000000  4177.000000  \n",
       "mean         0.180594      0.238831     9.933684  \n",
       "std          0.109614      0.139203     3.224169  \n",
       "min          0.000500      0.001500     1.000000  \n",
       "25%          0.093500      0.130000     8.000000  \n",
       "50%          0.171000      0.234000     9.000000  \n",
       "75%          0.253000      0.329000    11.000000  \n",
       "max          0.760000      1.005000    29.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking stats of df\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3739a2",
   "metadata": {},
   "source": [
    "The analysis shows that the Ring attribute values ranges from 2 to 29 rings on an abalone specimen. However, the most frequent values of Rings are highly concentrated around the median of the distribution, so that, the 2nd and 3rd quartiles are defined in a range of less than 1 std deviation. I observe that its possible to approximate the distribution of this attribute to a normal curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88e8885d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chceking duplicate rows\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "78d7af55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking outliers\n",
    "df1 = df.drop(columns=['Sex'])\n",
    "plt.figure(figsize=(20,15))\n",
    "point = 1\n",
    "for column in df1:\n",
    "    if point<=8:\n",
    "        plt.subplot(2,5,point)\n",
    "        ax = sns.boxplot(data = df1[column])\n",
    "        plt.xlabel(column,fontsize=10)\n",
    "    point+=1\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e9f3aa6",
   "metadata": {},
   "source": [
    "* i observe that all columns contain much outliers. i will remove them in data cleaning phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5122218e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVMAAAFACAYAAAABCZ9BAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUf0lEQVR4nO3df6zdd33f8ecLG0IoS0nmm9T1tWe3Mmx2Rgu580KjTikZi7cyHFVN52gsHmTzFnlAq3VtPKRl6mYpWjNWEpFIFoQ4HYtrMdp4VVNIvRVEG3BvgJI4qYfXsORiE9802wiMmtl974/zJZzeHDs3159zzr328yEdne/3/f18z3nnKHr58/1x7klVIUk6O68YdwOSdC4wTCWpAcNUkhowTCWpAcNUkhowTCWpgaGFaZJ7khxP8tic+nuSHE5yKMm/66vvTHKk23ZtX/2KJI922+5IkmH1LEkLNcyZ6b3A5v5Ckp8AtgBvrKqNwO1dfQOwFdjY7XNXkmXdbncD24H13ePPvaYkLQZDC9Oq+gzw3JzyzcBtVXWiG3O8q28B9lbViap6EjgCbEqyErioqh6u3rcL7gOuG1bPkrRQy0f8fq8HfjzJLuBPgZ+vqj8AVgGf6xs309X+X7c8t/6SVqxYUWvXrm3RsyS94JFHHnm2qibm1kcdpsuBi4Ergb8G7EvyQ8Cg86B1hvpASbbTOyXAmjVrmJ6ePuuGJalfkv85qD7qq/kzwCeq5yDwZ8CKrr66b9wkcLSrTw6oD1RVu6tqqqqmJiZe9A+HJA3NqMP0N4C3AiR5PfAq4FlgP7A1yQVJ1tG70HSwqo4Bzye5sruKfyPwwIh7lqSXNLTD/CT3A1cDK5LMALcC9wD3dLdLfQfY1l1YOpRkH/A4cBLYUVWnupe6md6dARcCD3YPSVpUcq7+Cb6pqanynKmk1pI8UlVTc+t+A0qSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGhj1d/MXlSv+xX3jbmFJeOSXbxx3C9Ki58xUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhoYWpgmuSfJ8SSPDdj280kqyYq+2s4kR5IcTnJtX/2KJI922+5IkmH1LEkLNcyZ6b3A5rnFJKuBtwFP9dU2AFuBjd0+dyVZ1m2+G9gOrO8eL3pNSRq3oYVpVX0GeG7Apv8A/AJQfbUtwN6qOlFVTwJHgE1JVgIXVdXDVVXAfcB1w+pZkhZqpOdMk7wD+FpV/eGcTauAp/vWZ7raqm55bl2SFpWR/TppktcA7wf+1qDNA2p1hvrp3mM7vVMCrFmzZgFdStLCjHJm+sPAOuAPk3wVmAS+kOQH6M04V/eNnQSOdvXJAfWBqmp3VU1V1dTExETj9iXp9EYWplX1aFVdWlVrq2otvaB8c1V9HdgPbE1yQZJ19C40HayqY8DzSa7sruLfCDwwqp4lab6GeWvU/cDDwBuSzCS56XRjq+oQsA94HPhtYEdVneo23wx8mN5Fqf8BPDisniVpoYZ2zrSqbniJ7WvnrO8Cdg0YNw1c3rQ5SWrMb0BJUgOGqSQ1YJhKUgOGqSQ1MLKb9iWNx1V3XjXuFpaE33vP753V/s5MJakBw1SSGjBMJakBw1SSGjBMJakBr+ZrpJ76pb867haWhDX/6tFxt6CXyZmpJDVgmEpSA4apJDVgmEpSA4apJDVgmEpSA4apJDVgmEpSA4apJDVgmEpSA4apJDVgmEpSA4apJDVgmEpSA0ML0yT3JDme5LG+2i8n+aMkX07y60le17dtZ5IjSQ4nubavfkWSR7ttdyTJsHqWpIUa5sz0XmDznNpDwOVV9UbgvwM7AZJsALYCG7t97kqyrNvnbmA7sL57zH1NSRq7oYVpVX0GeG5O7VNVdbJb/Rww2S1vAfZW1YmqehI4AmxKshK4qKoerqoC7gOuG1bPkrRQ4zxn+m7gwW55FfB037aZrraqW55bl6RFZSxhmuT9wEngY98tDRhWZ6if7nW3J5lOMj07O3v2jUrSPI08TJNsA94O/P3u0B16M87VfcMmgaNdfXJAfaCq2l1VU1U1NTEx0bZxSTqDkYZpks3ALwLvqKr/27dpP7A1yQVJ1tG70HSwqo4Bzye5sruKfyPwwCh7lqT5GNqvkya5H7gaWJFkBriV3tX7C4CHujucPldV/7SqDiXZBzxO7/B/R1Wd6l7qZnp3BlxI7xzrg0jSIjO0MK2qGwaUP3KG8buAXQPq08DlDVuTpOb8BpQkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNTC0ME1yT5LjSR7rq12S5KEkX+meL+7btjPJkSSHk1zbV78iyaPdtjuSZFg9S9JCDXNmei+weU7tFuBAVa0HDnTrJNkAbAU2dvvclWRZt8/dwHZgffeY+5qSNHZDC9Oq+gzw3JzyFmBPt7wHuK6vvreqTlTVk8ARYFOSlcBFVfVwVRVwX98+krRojPqc6WVVdQyge760q68Cnu4bN9PVVnXLc+sDJdmeZDrJ9OzsbNPGJelMFssFqEHnQesM9YGqandVTVXV1MTERLPmJOmljDpMn+kO3emej3f1GWB137hJ4GhXnxxQl6RFZdRhuh/Y1i1vAx7oq29NckGSdfQuNB3sTgU8n+TK7ir+jX37SNKisXxYL5zkfuBqYEWSGeBW4DZgX5KbgKeA6wGq6lCSfcDjwElgR1Wd6l7qZnp3BlwIPNg9JGlRGVqYVtUNp9l0zWnG7wJ2DahPA5c3bE2SmlssF6AkaUkzTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpgXmFaZID86lJ0vnqjH9pP8mrgdfQ++mRi/ner4VeBPzgkHuTpCXjpX625J8AP0svOB/he2H6DeBDw2tLkpaWM4ZpVX0Q+GCS91TVnSPqSZKWnHn9oF5V3Znkx4C1/ftU1X1D6kuSlpR5hWmSXwV+GPgS8N2fYC7AMJUk5v9Tz1PAhqqqYTYjSUvVfO8zfQz4gWE2IklL2XxnpiuAx5McBE58t1hV7xhKV5K0xMw3TP91yzdN8nPAP6J33vVR4F307mf9NXoXub4K/ExV/a9u/E7gJnrna99bVZ9s2Y8kna35Xs3/dKs3TLIKeC+9c7DfTrIP2ApsAA5U1W1JbgFuAX4xyYZu+0Z697v+TpLXV9Wp07yFJI3cfL9O+nySb3SPP01yKsk3zuJ9lwMXJllOb0Z6FNgC7Om27wGu65a3AHur6kRVPQkcATadxXtLUnPznZn+hf71JNexwECrqq8luR14Cvg28Kmq+lSSy6rqWDfmWJJLu11WAZ/re4mZrvYiSbYD2wHWrFmzkPYkaUEW9Fejquo3gLcuZN/uO/5bgHX0Dtu/L8k7z7TLoBZO09fuqpqqqqmJiYmFtCdJCzLfm/Z/qm/1FfTuO13oPad/E3iyqma71/4E8GPAM0lWdrPSlcDxbvwMsLpv/0l6pwUkadGY79X8v9u3fJLe1fYtC3zPp4Ark7yG3mH+NcA08C1gG3Bb9/xAN34/8J+SfIDeTHY9cHCB7y1JQzHfc6bvavWGVfX5JB8HvkAvmL8I7AZeC+xLchO9wL2+G3+ou+L/eDd+h1fyJS028z3MnwTuBK6id3j/WeB9VTWzkDetqluBW+eUT9CbpQ4avwvYtZD3kqRRmO8FqI/SO9z+QXpX0v9LV5MkMf8wnaiqj1bVye5xL+DlcknqzDdMn03yziTLusc7gT8ZZmOStJTMN0zfDfwM8HXgGPDT9L5PL0li/rdG/RtgW98fHrkEuJ1eyErSeW++M9M3fjdIAarqOeBNw2lJkpae+YbpK7qvgQIvzEznO6uVpHPefAPx3wO/391sX/TOn3rfpyR15vsNqPuSTNP74yYBfqqqHh9qZ5K0hMz7UL0LTwNUkgZY0J/gkyT9eYapJDVgmEpSA4apJDVgmEpSA4apJDVgmEpSA4apJDVgmEpSA4apJDVgmEpSA4apJDVgmEpSA4apJDVgmEpSA2MJ0ySvS/LxJH+U5Ikkb0lySZKHknyle+7/mZSdSY4kOZzk2nH0LElnMq6Z6QeB366qvwz8CPAEcAtwoKrWAwe6dZJsALYCG4HNwF1Jlo2la0k6jZGHaZKLgL8BfASgqr5TVf8b2ALs6YbtAa7rlrcAe6vqRFU9CRwBNo2yZ0l6KeOYmf4QMAt8NMkXk3w4yfcBl1XVMYDu+dJu/Crg6b79Z7raiyTZnmQ6yfTs7Ozw/gskaY5xhOly4M3A3VX1JuBbdIf0p5EBtRo0sKp2V9VUVU1NTEycfaeSNE/jCNMZYKaqPt+tf5xeuD6TZCVA93y8b/zqvv0ngaMj6lWS5mXkYVpVXweeTvKGrnQNvV893Q9s62rbgAe65f3A1iQXJFkHrAcOjrBlSXpJ8/6p58beA3wsyauAPwbeRS/Y9yW5CXgKuB6gqg4l2UcvcE8CO6rq1HjalqTBxhKmVfUlYGrApmtOM34XsGuYPUnS2fAbUJLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ0YppLUgGEqSQ2MLUyTLEvyxSS/2a1fkuShJF/pni/uG7szyZEkh5NcO66eJel0xjkzfR/wRN/6LcCBqloPHOjWSbIB2ApsBDYDdyVZNuJeJemMxhKmSSaBnwQ+3FfeAuzplvcA1/XV91bViap6EjgCbBpRq5I0L+Oamf4K8AvAn/XVLquqYwDd86VdfRXwdN+4ma4mSYvGyMM0yduB41X1yHx3GVCr07z29iTTSaZnZ2cX3KMkvVzjmJleBbwjyVeBvcBbk/xH4JkkKwG65+Pd+Blgdd/+k8DRQS9cVburaqqqpiYmJobVvyS9yMjDtKp2VtVkVa2ld2Hpv1bVO4H9wLZu2DbggW55P7A1yQVJ1gHrgYMjbluSzmj5uBvocxuwL8lNwFPA9QBVdSjJPuBx4CSwo6pOja9NSXqxsYZpVf0u8Lvd8p8A15xm3C5g18gak6SXyW9ASVIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDhqkkNWCYSlIDIw/TJKuT/LckTyQ5lOR9Xf2SJA8l+Ur3fHHfPjuTHElyOMm1o+5Zkl7KOGamJ4F/XlV/BbgS2JFkA3ALcKCq1gMHunW6bVuBjcBm4K4ky8bQtySd1sjDtKqOVdUXuuXngSeAVcAWYE83bA9wXbe8BdhbVSeq6kngCLBppE1L0ksY6znTJGuBNwGfBy6rqmPQC1zg0m7YKuDpvt1mupokLRpjC9MkrwX+M/CzVfWNMw0dUKvTvOb2JNNJpmdnZ1u0KUnzMpYwTfJKekH6sar6RFd+JsnKbvtK4HhXnwFW9+0+CRwd9LpVtbuqpqpqamJiYjjNS9IA47iaH+AjwBNV9YG+TfuBbd3yNuCBvvrWJBckWQesBw6Oql9Jmo/lY3jPq4B/ADya5Etd7V8CtwH7ktwEPAVcD1BVh5LsAx6ndyfAjqo6NfKuJekMRh6mVfVZBp8HBbjmNPvsAnYNrSlJOkt+A0qSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGjBMJakBw1SSGlgyYZpkc5LDSY4kuWXc/UhSvyURpkmWAR8C/jawAbghyYbxdiVJ37MkwhTYBBypqj+uqu8Ae4EtY+5Jkl6wVMJ0FfB03/pMV5OkRWH5uBuYpwyo1YsGJduB7d3qN5McHmpXw7ECeHbcTfTL7dvG3cKwLbrPnFsH/S9/Tll0n3neO+/P/C8NKi6VMJ0BVvetTwJH5w6qqt3A7lE1NQxJpqtqatx9nE/8zEfvXPzMl8ph/h8A65OsS/IqYCuwf8w9SdILlsTMtKpOJvlnwCeBZcA9VXVozG1J0guWRJgCVNVvAb817j5GYEmfplii/MxH75z7zFP1ous4kqSXaamcM5WkRc0wXQSSVJJf7VtfnmQ2yW+Os69zXZJTSb7U91g77p7OF0m+Oe4eWlsy50zPcd8CLk9yYVV9G3gb8LUx93Q++HZV/ei4m9C5wZnp4vEg8JPd8g3A/WPsRdLLZJguHnuBrUleDbwR+PyY+zkfXNh3iP/r425GS5uH+YtEVX25O2d3A+fHLWCLgYf5asYwXVz2A7cDVwN/cbytSHo5DNPF5R7g/1TVo0muHnMvkl4Gw3QRqaoZ4IPj7kPSy+c3oCSpAa/mS1IDhqkkNWCYSlIDhqkkNWCYSlIDhqnOC0nen+RQki93Xx/96+PuSecW7zPVOS/JW4C3A2+uqhNJVgCvGnNbOsc4M9X5YCXwbFWdAKiqZ6vqaJIrknw6ySNJPplkZZLvT3I4yRsAktyf5B+PtXstCd60r3NektcCnwVeA/wO8GvA7wOfBrZU1WySvwdcW1XvTvI24JfofRvtH1bV5jG1riXEw3yd86rqm0muAH4c+Al6YfpvgcuBh5JA71dvj3XjH0pyPfAh4EfG0rSWHGemOu8k+WlgB/DqqnrLgO2voDdrXQf8nar68ohb1BLkOVOd85K8Icn6vtKPAk8AE93FKZK8MsnGbvvPddtvAO5J8spR9qulyZmpznndIf6dwOuAk8ARYDswCdwBfD+9U16/Qm9G+gCwqaqeT/IB4PmqunX0nWspMUwlqQEP8yWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhowTCWpAcNUkhr4/ya0SHFW9VyNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# analysing sex column\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.countplot(df.Sex);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc2213dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see data distribution for every row\n",
    "plt.figure(figsize=(20,15))\n",
    "plotnumber = 1\n",
    "for column in df1:\n",
    "    if plotnumber<=8:\n",
    "        plt.subplot(2,4,plotnumber)\n",
    "        sns.distplot(df1[column])\n",
    "        plt.xlabel(column,fontsize = 15)\n",
    "    plotnumber+=1\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "26297fda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Length           -0.639873\n",
       "Diameter         -0.609198\n",
       "Height            3.128817\n",
       "Whole weight      0.530959\n",
       "Shucked weight    0.719098\n",
       "Viscera weight    0.591852\n",
       "Shell weight      0.620927\n",
       "Rings             1.114102\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a089de34",
   "metadata": {},
   "source": [
    "* I can observe that most of the data in columns are skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db8d8979",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1584x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checcking correlation \n",
    "plt.figure(figsize=(22,7))\n",
    "sns.heatmap(df.corr(),annot=True,linewidths=0.1,linecolor=\"black\",fmt=\"0.2f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3aced8d",
   "metadata": {},
   "source": [
    "* i observe that all columns are correlated to rings and other columns as well but rings number depends on all of these factors so, i will not drop any column as all are important and less columns will affect the accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0597078",
   "metadata": {},
   "source": [
    "###  Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0c9cd109",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
       "0    2   0.455     0.365   0.095        0.5140          0.2245   \n",
       "1    2   0.350     0.265   0.090        0.2255          0.0995   \n",
       "2    0   0.530     0.420   0.135        0.6770          0.2565   \n",
       "3    2   0.440     0.365   0.125        0.5160          0.2155   \n",
       "4    1   0.330     0.255   0.080        0.2050          0.0895   \n",
       "\n",
       "   Viscera weight  Shell weight  Rings  \n",
       "0          0.1010         0.150     15  \n",
       "1          0.0485         0.070      7  \n",
       "2          0.1415         0.210      9  \n",
       "3          0.1140         0.155     10  \n",
       "4          0.0395         0.055      7  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert categorical data to numerical data for machine learning model using label encoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df['Sex']= le.fit_transform(df['Sex'])\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1de918bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Sex    Length  Diameter    Height  Whole weight  Shucked weight  \\\n",
      "0     1.151980  0.574558  0.432149  1.064424      0.641898        0.607685   \n",
      "1     1.151980  1.448986  1.439929  1.183978      1.230277        1.170910   \n",
      "2     1.280690  0.050033  0.122130  0.107991      0.309469        0.463500   \n",
      "3     1.151980  0.699476  0.432149  0.347099      0.637819        0.648238   \n",
      "4     0.064355  1.615544  1.540707  1.423087      1.272086        1.215968   \n",
      "...        ...       ...       ...       ...           ...             ...   \n",
      "4172  1.280690  0.341509  0.424464  0.609334      0.118813        0.047908   \n",
      "4173  1.151980  0.549706  0.323686  0.107991      0.279929        0.358808   \n",
      "4174  1.151980  0.632985  0.676409  1.565767      0.708212        0.748559   \n",
      "4175  1.280690  0.841182  0.777187  0.250672      0.541998        0.773341   \n",
      "4176  1.151980  1.549052  1.482634  1.326659      2.283681        2.640993   \n",
      "\n",
      "      Viscera weight  Shell weight     Rings  \n",
      "0           0.726212      0.638217  1.571544  \n",
      "1           1.205221      1.212987  0.910013  \n",
      "2           0.356690      0.207139  0.289624  \n",
      "3           0.607600      0.602294  0.020571  \n",
      "4           1.287337      1.320757  0.910013  \n",
      "...              ...           ...       ...  \n",
      "4172        0.532900      0.073062  0.330765  \n",
      "4173        0.309362      0.155685  0.020571  \n",
      "4174        0.975413      0.496955  0.289624  \n",
      "4175        0.733627      0.410739  0.020571  \n",
      "4176        1.787449      1.840481  0.640960  \n",
      "\n",
      "[4177 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "# Outlier detection and removal using z sccore\n",
    "from scipy import stats\n",
    "df_outlier= df.copy()\n",
    "z = np.abs(stats.zscore(df_outlier))\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e12a3ea",
   "metadata": {},
   "source": [
    "Looking the code and the output above, it is difficult to say which data point is an outlier. Let’s try and define a threshold to identify an outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2af34996",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([   6,   72,   81,   83,  129,  163,  164,  165,  165,  165,  166,\n",
      "        166,  167,  168,  170,  181,  232,  236,  236,  236,  237,  237,\n",
      "        238,  238,  239,  270,  275,  277,  278,  294,  310,  313,  314,\n",
      "        334,  351,  355,  358,  358,  362,  375,  416,  417,  428,  431,\n",
      "        433,  469,  478,  480,  501,  526,  526,  530,  572,  628,  673,\n",
      "        674,  675,  678,  694,  696,  696,  719,  719,  720,  720,  744,\n",
      "        754,  756,  811,  891,  891,  891, 1048, 1051, 1051, 1052, 1193,\n",
      "       1197, 1199, 1202, 1204, 1206, 1206, 1207, 1207, 1207, 1207, 1209,\n",
      "       1209, 1257, 1417, 1417, 1418, 1422, 1426, 1427, 1427, 1427, 1428,\n",
      "       1429, 1429, 1528, 1754, 1756, 1757, 1759, 1761, 1762, 1762, 1762,\n",
      "       1763, 1763, 1763, 1821, 1982, 1986, 1987, 1987, 2051, 2090, 2108,\n",
      "       2108, 2114, 2114, 2157, 2161, 2176, 2180, 2199, 2201, 2208, 2209,\n",
      "       2274, 2275, 2305, 2334, 2334, 2334, 2335, 2344, 2358, 2368, 2381,\n",
      "       2436, 2459, 2471, 2544, 2623, 2624, 2624, 2624, 2625, 2709, 2710,\n",
      "       2710, 2810, 2810, 2811, 2811, 2811, 2862, 2863, 2863, 2863, 3007,\n",
      "       3008, 3140, 3148, 3149, 3149, 3151, 3151, 3188, 3193, 3280, 3359,\n",
      "       3427, 3427, 3427, 3599, 3599, 3628, 3713, 3715, 3715, 3715, 3715,\n",
      "       3800, 3837, 3899, 3899, 3902, 3924, 3930, 3944, 3961, 3962, 3996,\n",
      "       4145, 4148], dtype=int64), array([8, 8, 7, 8, 7, 7, 7, 4, 5, 7, 7, 8, 8, 7, 6, 8, 8, 1, 2, 3, 1, 2,\n",
      "       1, 2, 1, 8, 8, 7, 8, 8, 8, 8, 8, 7, 8, 8, 4, 7, 8, 8, 8, 8, 8, 8,\n",
      "       8, 8, 8, 8, 8, 1, 2, 8, 8, 8, 8, 8, 8, 8, 2, 1, 2, 1, 2, 1, 2, 8,\n",
      "       8, 8, 8, 4, 5, 7, 6, 4, 5, 4, 5, 5, 5, 5, 6, 5, 6, 4, 5, 6, 7, 4,\n",
      "       5, 3, 3, 5, 5, 6, 5, 4, 5, 6, 7, 1, 2, 5, 5, 5, 6, 6, 5, 4, 5, 6,\n",
      "       4, 5, 6, 5, 5, 1, 1, 2, 3, 7, 7, 8, 1, 2, 7, 7, 8, 8, 8, 8, 7, 8,\n",
      "       7, 8, 8, 4, 6, 8, 8, 8, 8, 7, 1, 8, 8, 8, 5, 4, 4, 5, 6, 5, 6, 5,\n",
      "       6, 5, 6, 4, 5, 6, 5, 4, 5, 6, 5, 7, 8, 7, 7, 8, 7, 8, 7, 8, 8, 8,\n",
      "       4, 5, 6, 4, 5, 6, 5, 4, 5, 6, 7, 4, 2, 1, 2, 1, 8, 8, 8, 5, 5, 3,\n",
      "       7, 6], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "threshold = 3\n",
    "print(np.where(z > 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5dc082",
   "metadata": {},
   "source": [
    "The above data shows outlier where z score more than 3. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b40d9efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4177, 9)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the outliers and get the clean data.\n",
    "df_outlier= df_outlier[(z < 3).all(axis=1)]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "702df9e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4027, 9)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_outlier.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1a76b5",
   "metadata": {},
   "source": [
    "So, above code removed around 150 rows from the dataset i.e. outliers have been removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a73af22d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex              -0.094886\n",
       "Length           -0.623076\n",
       "Diameter         -0.598650\n",
       "Height           -0.244251\n",
       "Whole weight      0.334204\n",
       "Shucked weight    0.458295\n",
       "Viscera weight    0.437116\n",
       "Shell weight      0.349471\n",
       "Rings             0.725464\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_outlier.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "25633b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets remove skewness using yeo-johnson\n",
    "from sklearn.preprocessing import power_transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8deeb574",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into features and columns\n",
    "x = df_outlier.drop(columns=['Rings'])\n",
    "y = df_outlier.Rings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a06cfef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=power_transform(x,method='yeo-johnson')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b2df2d",
   "metadata": {},
   "source": [
    "* scaling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7f336b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "x_scaled = scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ba3bb37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "# split the data\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_scaled,y,test_size=0.3, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "023daf79",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5699fddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logistic regression\n",
    "log_reg = LogisticRegression()\n",
    "log_reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "66f362af",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = log_reg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4b9a0da6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2696443341604632"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking model accuracy\n",
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "78679167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  2, 10,  3,  1,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  1, 12,  8,  2,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0, 10, 15, 35,  8,  2,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0,  1, 14, 53, 22,  9,  3,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0,  1,  6, 28, 60, 69, 12,  1,  0,  0,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  3, 15, 33, 90, 47,  9,  1,  4,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  3, 12, 14, 76, 55, 23,  0,  8,  0,  0,  1,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  2,  5,  7, 39, 60, 27,  0,  4,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  0,  2,  1, 28, 26, 21,  0, 12,  0,  0,  1,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  0,  1,  2, 10, 19, 14,  0, 12,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  0,  0,  5,  5, 15,  6,  0,  8,  0,  0,  0,  1,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  0,  0,  1, 11,  8,  4,  0,  7,  0,  0,  0,  1,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  0,  0,  1,  3,  4,  2,  0, 10,  0,  0,  0,  1,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  2,  3,  5,  0,  4,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  2,  2,  0,  3,  0,  0,  0,  0,  0,\n",
       "         0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  1,  3,  6,  0,  7,  0,  0,  0,  0,  0,\n",
       "         0]], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con_mat = confusion_matrix(y_test,y_pred)\n",
    "con_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "26a3a507",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         3\n",
      "           4       0.40      0.12      0.19        16\n",
      "           5       0.34      0.52      0.41        23\n",
      "           6       0.28      0.21      0.24        70\n",
      "           7       0.34      0.52      0.41       102\n",
      "           8       0.39      0.34      0.36       177\n",
      "           9       0.26      0.45      0.33       202\n",
      "          10       0.21      0.29      0.24       192\n",
      "          11       0.23      0.19      0.20       144\n",
      "          12       0.00      0.00      0.00        91\n",
      "          13       0.15      0.21      0.18        58\n",
      "          14       0.00      0.00      0.00        40\n",
      "          15       0.00      0.00      0.00        32\n",
      "          16       0.00      0.00      0.00        21\n",
      "          17       0.00      0.00      0.00        14\n",
      "          18       0.00      0.00      0.00         7\n",
      "          19       0.00      0.00      0.00        17\n",
      "\n",
      "    accuracy                           0.27      1209\n",
      "   macro avg       0.15      0.17      0.15      1209\n",
      "weighted avg       0.23      0.27      0.24      1209\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180cac33",
   "metadata": {},
   "source": [
    "* randomforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "35d1c1a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'mse', 'max_features': 'log2'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "parameters = {'criterion' :['mse','mae'],\n",
    "             'max_features':[\"auto\",\"sqrt\",\"log2\"]}\n",
    "\n",
    "rf=RandomForestRegressor()\n",
    "clf = GridSearchCV(rf,parameters)\n",
    "clf.fit(x_train,y_train)\n",
    "\n",
    "print(clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e12b4075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.14, 10.56, 12.76, ..., 10.04,  6.16, 12.79])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(criterion=\"mse\",max_features=\"log2\")\n",
    "rf.fit(x_train,y_train)\n",
    "rf.score(x_train,y_train)\n",
    "pred_decision = rf.predict(x_test)\n",
    "\n",
    "pred_decision\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579f0f1e",
   "metadata": {},
   "source": [
    "### decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6ff43086",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier()\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e7c3c219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9c47fde7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18982630272952852"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# move to prediction part\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "# check the accuracy\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "eb8bce0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are tuning hyper parameter now by passing different values for both parameters\n",
    "drid_param = {\n",
    "    'criterion':['gini','entropy'],\n",
    "    'max_depth': range(10,15),\n",
    "    'min_samples_leaf': range(2,10),\n",
    "    'min_samples_split': range(3,10),\n",
    "    'max_leaf_nodes': range(5,10)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "72972c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(estimator=clf,param_grid=drid_param,cv=5,\n",
    "                           n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a8d0820c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': range(10, 15),\n",
       "                         'max_leaf_nodes': range(5, 10),\n",
       "                         'min_samples_leaf': range(2, 10),\n",
       "                         'min_samples_split': range(3, 10)})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b1c33759",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'entropy', 'max_depth': 10, 'max_leaf_nodes': 9, 'min_samples_leaf': 2, 'min_samples_split': 3}\n"
     ]
    }
   ],
   "source": [
    "best_parameters = grid_search.best_params_\n",
    "print(best_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cab936f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  one function and call as many as times to check accuracy score of different model\n",
    "def metric_score(clf, x_train,x_test,y_train,y_test, train=True):\n",
    "    if train:\n",
    "        y_pred = clf.predict(x_train)\n",
    "        \n",
    "        print(\"\\n==================== Train Result =======================\")\n",
    "        \n",
    "        print(f\"Accuracy score :{accuracy_score(y_train,y_pred)*100:.2f}%\")\n",
    "        \n",
    "    elif train==False:\n",
    "        pred = clf.predict(x_test)\n",
    "        \n",
    "        print(\"\\n===================== Test Result =======================\")\n",
    "        \n",
    "        print(f\"Accuracy score :{accuracy_score(y_test, pred)*100:.2f}%\")\n",
    "        \n",
    "        print('\\n \\n Test Classification Report \\n',classification_report(y_test, pred,digits=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cc3fd23b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=10, max_leaf_nodes=9,\n",
       "                       min_samples_leaf=2, min_samples_split=3)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initaiting DecisionTreeClassifier with new parameters and train\n",
    "clf = DecisionTreeClassifier(criterion = 'entropy',min_samples_split=3,max_depth=10,min_samples_leaf=2,max_leaf_nodes=9)\n",
    "\n",
    "# train the model\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0834e697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================== Train Result =======================\n",
      "Accuracy score :28.03%\n",
      "\n",
      "===================== Test Result =======================\n",
      "Accuracy score :25.68%\n",
      "\n",
      " \n",
      " Test Classification Report \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           3       0.00      0.00      0.00         2\n",
      "           4       0.00      0.00      0.00         4\n",
      "           5       0.38      0.78      0.51        23\n",
      "           6       0.00      0.00      0.00        51\n",
      "           7       0.30      0.68      0.41        80\n",
      "           8       0.22      0.22      0.22       100\n",
      "           9       0.24      0.40      0.30       126\n",
      "          10       0.21      0.21      0.21       121\n",
      "          11       0.30      0.31      0.30       100\n",
      "          12       0.00      0.00      0.00        54\n",
      "          13       0.12      0.12      0.12        41\n",
      "          14       0.00      0.00      0.00        28\n",
      "          15       0.00      0.00      0.00        19\n",
      "          16       0.00      0.00      0.00        17\n",
      "          17       0.00      0.00      0.00        18\n",
      "          18       0.00      0.00      0.00        12\n",
      "          19       0.00      0.00      0.00        10\n",
      "\n",
      "    accuracy                           0.26       806\n",
      "   macro avg       0.10      0.16      0.12       806\n",
      "weighted avg       0.18      0.26      0.21       806\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# call the function and pass dataset to check the train and test score\n",
    "metric_score(clf,x_train,x_test,y_train,y_test, train=True) # for tarining score\n",
    "\n",
    "metric_score(clf,x_train,x_test,y_train,y_test, train=False) # for testing score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ef4ba684",
   "metadata": {},
   "outputs": [],
   "source": [
    "# logistic regression has max accuracy so i will go with it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b5ed7847",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "34f0d383",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename = 'rings'\n",
    "pickle.dump(log_reg, open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23a093d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}