Logistic_Regression_Penguins_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species  bill_length_mm  bill_depth_mm  flipper_length_mm\n",
       "0  Adelie            39.1           18.7                181\n",
       "1  Adelie            39.5           17.4                186\n",
       "2  Adelie            40.3           18.0                195\n",
       "3  Adelie            36.7           19.3                193\n",
       "4  Adelie            39.3           20.6                190"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('Penguins_clean_dataset.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of     species  bill_length_mm  bill_depth_mm  flipper_length_mm\n",
       "0    Adelie            39.1           18.7                181\n",
       "1    Adelie            39.5           17.4                186\n",
       "2    Adelie            40.3           18.0                195\n",
       "3    Adelie            36.7           19.3                193\n",
       "4    Adelie            39.3           20.6                190\n",
       "..      ...             ...            ...                ...\n",
       "337  Gentoo            47.2           13.7                214\n",
       "338  Gentoo            46.8           14.3                215\n",
       "339  Gentoo            50.4           15.7                222\n",
       "340  Gentoo            45.2           14.8                212\n",
       "341  Gentoo            49.9           16.1                213\n",
       "\n",
       "[342 rows x 4 columns]>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of     species  bill_length_mm  bill_depth_mm  flipper_length_mm\n",
       "0    Adelie            39.1           18.7                181\n",
       "1    Adelie            39.5           17.4                186\n",
       "2    Adelie            40.3           18.0                195\n",
       "3    Adelie            36.7           19.3                193\n",
       "4    Adelie            39.3           20.6                190\n",
       "..      ...             ...            ...                ...\n",
       "337  Gentoo            47.2           13.7                214\n",
       "338  Gentoo            46.8           14.3                215\n",
       "339  Gentoo            50.4           15.7                222\n",
       "340  Gentoo            45.2           14.8                212\n",
       "341  Gentoo            49.9           16.1                213\n",
       "\n",
       "[342 rows x 4 columns]>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame._add_numeric_operations.<locals>.sum of species                3\n",
       "bill_length_mm       164\n",
       "bill_depth_mm         80\n",
       "flipper_length_mm     55\n",
       "dtype: int64>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique().sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.nunique of     species  bill_length_mm  bill_depth_mm  flipper_length_mm\n",
       "0    Adelie            39.1           18.7                181\n",
       "1    Adelie            39.5           17.4                186\n",
       "2    Adelie            40.3           18.0                195\n",
       "3    Adelie            36.7           19.3                193\n",
       "4    Adelie            39.3           20.6                190\n",
       "..      ...             ...            ...                ...\n",
       "337  Gentoo            47.2           13.7                214\n",
       "338  Gentoo            46.8           14.3                215\n",
       "339  Gentoo            50.4           15.7                222\n",
       "340  Gentoo            45.2           14.8                212\n",
       "341  Gentoo            49.9           16.1                213\n",
       "\n",
       "[342 rows x 4 columns]>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(342, 4)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1368"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gulmu\\AppData\\Local\\Temp\\ipykernel_9756\\385664179.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.dropna will be keyword-only.\n",
      "  data.dropna(0)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>39.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>47.2</td>\n",
       "      <td>13.7</td>\n",
       "      <td>214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>46.8</td>\n",
       "      <td>14.3</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>50.4</td>\n",
       "      <td>15.7</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>45.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Gentoo</td>\n",
       "      <td>49.9</td>\n",
       "      <td>16.1</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    species  bill_length_mm  bill_depth_mm  flipper_length_mm\n",
       "0    Adelie            39.1           18.7                181\n",
       "1    Adelie            39.5           17.4                186\n",
       "2    Adelie            40.3           18.0                195\n",
       "3    Adelie            36.7           19.3                193\n",
       "4    Adelie            39.3           20.6                190\n",
       "..      ...             ...            ...                ...\n",
       "337  Gentoo            47.2           13.7                214\n",
       "338  Gentoo            46.8           14.3                215\n",
       "339  Gentoo            50.4           15.7                222\n",
       "340  Gentoo            45.2           14.8                212\n",
       "341  Gentoo            49.9           16.1                213\n",
       "\n",
       "[342 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input\n",
    "x = data.iloc[:, [1, 2, 3]].values\n",
    " \n",
    "# output\n",
    "y = data.iloc[:, 0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.27745956 -1.51734952  1.16214812]\n",
      " [-1.18413176 -0.57414885 -1.23657271]\n",
      " [ 1.27745956  1.10265235 -0.5512239 ]\n",
      " [ 1.02406045 -0.52174881  0.81947371]\n",
      " [-1.36513113  0.8930522  -0.96243319]\n",
      " [-0.85833291  0.15945168 -1.03096807]\n",
      " [-0.33343476  0.10705164 -1.37364247]\n",
      " [-0.40583451  1.62665272 -0.75682855]\n",
      " [ 0.35436281 -1.36014941  0.54533419]\n",
      " [ 0.26386313 -0.05014847 -0.41415414]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_x = StandardScaler()\n",
    "xtrain = sc_x.fit_transform(X_train)\n",
    "xtest = sc_x.transform(X_test)\n",
    "\n",
    "print (xtrain[0:10, :])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LogisticRegression(random_state=0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(xtrain, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Gentoo'], dtype=object)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([[39.1,18.7,181]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Gentoo'], dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([[50.4,48.9,199]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Gentoo'], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([[46.8,14.3,215]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Gentoo'], dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict([[36.7,19.3,193]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Adelie', 'Gentoo', 'Adelie', 'Gentoo', 'Adelie', 'Gentoo',\n",
       "       'Gentoo', 'Adelie', 'Gentoo', 'Adelie', 'Adelie', 'Chinstrap',\n",
       "       'Chinstrap', 'Adelie', 'Adelie', 'Chinstrap', 'Chinstrap',\n",
       "       'Chinstrap', 'Gentoo', 'Adelie', 'Gentoo', 'Adelie', 'Gentoo',\n",
       "       'Gentoo', 'Adelie', 'Adelie', 'Adelie', 'Gentoo', 'Gentoo',\n",
       "       'Gentoo', 'Adelie', 'Adelie', 'Adelie', 'Chinstrap', 'Gentoo',\n",
       "       'Adelie', 'Gentoo', 'Chinstrap', 'Gentoo', 'Adelie', 'Adelie',\n",
       "       'Adelie', 'Adelie', 'Gentoo', 'Adelie', 'Adelie', 'Gentoo',\n",
       "       'Chinstrap', 'Chinstrap', 'Gentoo', 'Adelie', 'Adelie', 'Adelie',\n",
       "       'Adelie', 'Gentoo', 'Adelie', 'Gentoo', 'Adelie', 'Adelie',\n",
       "       'Gentoo', 'Adelie', 'Adelie', 'Adelie', 'Chinstrap', 'Adelie',\n",
       "       'Adelie', 'Adelie', 'Chinstrap', 'Adelie', 'Gentoo', 'Adelie',\n",
       "       'Gentoo', 'Adelie', 'Adelie', 'Gentoo', 'Adelie', 'Gentoo',\n",
       "       'Adelie', 'Adelie', 'Gentoo', 'Adelie', 'Gentoo', 'Gentoo',\n",
       "       'Adelie', 'Gentoo', 'Chinstrap'], dtype=object)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(xtest)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix : \n",
      " [[44  0  0]\n",
      " [ 1 12  0]\n",
      " [ 0  0 29]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print (\"Confusion Matrix : \\n\", cm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.9883720930232558\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print (\"Accuracy : \", accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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