Fish Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\harika\\anaconda3\\lib\\site-packages (1.2.4)\n",
      "Requirement already satisfied: sklearn in c:\\users\\harika\\anaconda3\\lib\\site-packages (0.0)\n",
      "Requirement already satisfied: numpy>=1.16.5 in c:\\users\\harika\\anaconda3\\lib\\site-packages (from pandas) (1.20.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\harika\\anaconda3\\lib\\site-packages (from pandas) (2019.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\harika\\anaconda3\\lib\\site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\harika\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\harika\\anaconda3\\lib\\site-packages (from sklearn) (0.24.2)\n",
      "Requirement already satisfied: scipy>=0.19.1 in c:\\users\\harika\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (1.4.1)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\harika\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (0.14.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\harika\\anaconda3\\lib\\site-packages (from scikit-learn->sklearn) (2.1.0)\n"
     ]
    }
   ],
   "source": [
    "#install necessary libraries\n",
    "!pip install pandas sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary libraries\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bream</td>\n",
       "      <td>242.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.5200</td>\n",
       "      <td>4.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bream</td>\n",
       "      <td>290.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>31.2</td>\n",
       "      <td>12.4800</td>\n",
       "      <td>4.3056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bream</td>\n",
       "      <td>340.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>26.5</td>\n",
       "      <td>31.1</td>\n",
       "      <td>12.3778</td>\n",
       "      <td>4.6961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bream</td>\n",
       "      <td>363.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>12.7300</td>\n",
       "      <td>4.4555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bream</td>\n",
       "      <td>430.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>12.4440</td>\n",
       "      <td>5.1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>12.2</td>\n",
       "      <td>11.5</td>\n",
       "      <td>12.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>2.0904</td>\n",
       "      <td>1.3936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>13.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>12.4</td>\n",
       "      <td>13.5</td>\n",
       "      <td>2.4300</td>\n",
       "      <td>1.2690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>12.2</td>\n",
       "      <td>12.1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>2.2770</td>\n",
       "      <td>1.2558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>19.7</td>\n",
       "      <td>13.2</td>\n",
       "      <td>14.3</td>\n",
       "      <td>15.2</td>\n",
       "      <td>2.8728</td>\n",
       "      <td>2.0672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Smelt</td>\n",
       "      <td>19.9</td>\n",
       "      <td>13.8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2.9322</td>\n",
       "      <td>1.8792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>159 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Species  Weight  Length1  Length2  Length3   Height   Width\n",
       "0     Bream   242.0     23.2     25.4     30.0  11.5200  4.0200\n",
       "1     Bream   290.0     24.0     26.3     31.2  12.4800  4.3056\n",
       "2     Bream   340.0     23.9     26.5     31.1  12.3778  4.6961\n",
       "3     Bream   363.0     26.3     29.0     33.5  12.7300  4.4555\n",
       "4     Bream   430.0     26.5     29.0     34.0  12.4440  5.1340\n",
       "..      ...     ...      ...      ...      ...      ...     ...\n",
       "154   Smelt    12.2     11.5     12.2     13.4   2.0904  1.3936\n",
       "155   Smelt    13.4     11.7     12.4     13.5   2.4300  1.2690\n",
       "156   Smelt    12.2     12.1     13.0     13.8   2.2770  1.2558\n",
       "157   Smelt    19.7     13.2     14.3     15.2   2.8728  2.0672\n",
       "158   Smelt    19.9     13.8     15.0     16.2   2.9322  1.8792\n",
       "\n",
       "[159 rows x 7 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe from the Fish dataset \n",
    "dataset_url = \"https://raw.githubusercontent.com/harika-bonthu/SupportVectorClassifier/main/datasets_229906_491820_Fish.csv\"\n",
    "fish = pd.read_csv(dataset_url)\n",
    "fish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the features and target variables\n",
    "# X -> features, y -> target\n",
    "X = fish.drop(['Species'], axis = 'columns')\n",
    "y = fish.Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training/testing sets using train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2)\n",
    "# X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate linear SVC object\n",
    "model = SVC(kernel = 'linear', C = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, kernel='linear')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training a linear SVC classifier\n",
    "model.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96875"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model accuracy for X_test. Score method takes X_test and predicts the output and calculates the accuracy by comparing the predicted outputs with y_test.\n",
    "accuracy = model.score(X_test, y_test)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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