classification-logistic-regression-sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2485525",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e749726",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width         species\n",
       "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
       "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    './data/iris.data',\n",
    "    names=[\n",
    "        \"sepal_length\",\n",
    "        \"sepal_width\",\n",
    "        \"petal_length\",\n",
    "        \"petal_width\",\n",
    "        \"species\"\n",
    "    ]\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc63b19d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b145a23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f3a1558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "015a25a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['sepal_length'].hist()\n",
    "df['sepal_width'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d331789e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['petal_length'].hist()\n",
    "df['petal_width'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f0a093b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yt/7qgsg9zd6kxc4ng4dfmd74c80000gn/T/ipykernel_46090/1134722465.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.109369      0.871754     0.817954\n",
       "sepal_width      -0.109369     1.000000     -0.420516    -0.356544\n",
       "petal_length      0.871754    -0.420516      1.000000     0.962757\n",
       "petal_width       0.817954    -0.356544      0.962757     1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6e8a6f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width  species\n",
       "0             5.1          3.5           1.4          0.2        0\n",
       "1             4.9          3.0           1.4          0.2        0\n",
       "2             4.7          3.2           1.3          0.2        0\n",
       "3             4.6          3.1           1.5          0.2        0\n",
       "4             5.0          3.6           1.4          0.2        0\n",
       "..            ...          ...           ...          ...      ...\n",
       "145           6.7          3.0           5.2          2.3        2\n",
       "146           6.3          2.5           5.0          1.9        2\n",
       "147           6.5          3.0           5.2          2.0        2\n",
       "148           6.2          3.4           5.4          2.3        2\n",
       "149           5.9          3.0           5.1          1.8        2\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df['species'] = le.fit_transform(df['species'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "51f3acd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2cffa029",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns = ['species'])\n",
    "Y = df['species']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3198f566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5692e6eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  94.73684210526315\n"
     ]
    }
   ],
   "source": [
    "print(\"Testing Accuracy: \", model.score(X_test, Y_test) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99a6f23e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  99.10714285714286\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Accuracy: \", model.score(X_train, Y_train) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "de01c6b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([X.iloc[149]])"
   ]
  }
 ],
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