Practice Project  7 (Abalone Dataset).ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Abalone Case Study\n",
    "\n",
    "Predicting the age of abalone from physical measurements. \n",
    "The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age. Further information, such as weather patterns and location (hence food availability) may be required to solve the problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('abalone_data.csv',names=['Sex','Length','Diameter','height','whole weight',\n",
    "                                                  'Shucked weight','Viscera weight','Shell weight','Rings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4177, 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbb7626390>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='Length',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbb79487b8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='height',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbb79b39e8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['height'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbb7f37dd8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['whole weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbbc13f6a0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Diameter'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbbc211ef0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Shell weight'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15,  7,  9, 10,  8, 20, 16, 19, 14, 11, 12, 18, 13,  5,  4,  6, 21,\n",
       "       17, 22,  1,  3, 26, 23, 29,  2, 27, 25, 24], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rings'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.get_dummies(data=df, columns=['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Length            0\n",
       "Diameter          0\n",
       "height            0\n",
       "whole weight      0\n",
       "Shucked weight    0\n",
       "Viscera weight    0\n",
       "Shell weight      0\n",
       "Rings             0\n",
       "Sex_I             0\n",
       "Sex_M             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dbb7aacf98>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr=df.corr()\n",
    "plt.figure(figsize=(12,10))\n",
    "sns.heatmap(corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Sex_I</th>\n",
       "      <th>Sex_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Length  Diameter  height  whole weight  Shucked weight  Viscera weight  \\\n",
       "0   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Sex_I  Sex_M  \n",
       "0         0.150      0      1  \n",
       "1         0.070      0      1  \n",
       "2         0.210      0      0  \n",
       "3         0.155      0      1  \n",
       "4         0.055      1      0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,df.columns!='Rings']\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaling=StandardScaler()\n",
    "x=scaling.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.iloc[:,df.columns=='Rings']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.25,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2679425837320574"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model1=LogisticRegression()\n",
    "model1=model1.fit(x_train,y_train)\n",
    "y_pred=model1.predict(x_test)\n",
    "acc1=accuracy_score(y_test,y_pred)\n",
    "acc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3043062200956938"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model2=SVC()\n",
    "model2=model2.fit(x_train,y_train)\n",
    "y_pred=model2.predict(x_test)\n",
    "acc2=accuracy_score(y_test,y_pred)\n",
    "acc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26190957440155127"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(model1,x,y,cv=100,scoring='accuracy')\n",
    "score.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"\"\"Let's do some feature engineering\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15,  7,  9, 10,  8, 20, 16, 19, 14, 11, 12, 18, 13,  5,  4,  6, 21,\n",
       "       17, 22,  1,  3, 26, 23, 29,  2, 27, 25, 24], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rings'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Because rings reflects the age we are going to change rings into three labels \n",
    "\n",
    "1: Infant\n",
    "2: Young\n",
    "3: Adult\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "Age=[]\n",
    "for i in df['Rings']:\n",
    "    if  i<=10:\n",
    "        Age.append(1)\n",
    "    elif i<=20:\n",
    "        Age.append(2)\n",
    "    else:\n",
    "        Age.append(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age']=Age\n",
    "df=df.drop(['Rings'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Sex_I</th>\n",
       "      <th>Sex_M</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Length  Diameter  height  whole weight  Shucked weight  Viscera weight  \\\n",
       "0   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Sex_I  Sex_M  Age  \n",
       "0         0.150      0      1    2  \n",
       "1         0.070      0      1    1  \n",
       "2         0.210      0      0    1  \n",
       "3         0.155      0      1    1  \n",
       "4         0.055      1      0    1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2730\n",
       "2    1411\n",
       "3      36\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,df.columns!='Age']\n",
    "y=df.iloc[:,df.columns=='Age']\n",
    "df['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.25,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7617224880382775"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model7=LogisticRegression()\n",
    "model7=model7.fit(x_train,y_train)\n",
    "y_pred=model7.predict(x_test)\n",
    "acc7=accuracy_score(y_test,y_pred)\n",
    "acc7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7492822966507177"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model8=SVC()\n",
    "model8=model8.fit(x_train,y_train)\n",
    "y_pred=model8.predict(x_test)\n",
    "acc8=accuracy_score(y_test,y_pred)\n",
    "acc8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7419168172330848"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "score=cross_val_score(model8,x,y,cv=10,scoring='accuracy')\n",
    "score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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