Logistic Regression - predicting iris flower on four features (multiclass classification).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f2d9ff5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba084405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Desktop/IRIS.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f947a227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0             5.1          3.5           1.4          0.2\n",
       "1             4.9          3.0           1.4          0.2\n",
       "2             4.7          3.2           1.3          0.2\n",
       "3             4.6          3.1           1.5          0.2\n",
       "4             5.0          3.6           1.4          0.2\n",
       "..            ...          ...           ...          ...\n",
       "145           6.7          3.0           5.2          2.3\n",
       "146           6.3          2.5           5.0          1.9\n",
       "147           6.5          3.0           5.2          2.0\n",
       "148           6.2          3.4           5.4          2.3\n",
       "149           5.9          3.0           5.1          1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(['species'], axis='columns')\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "49fd81c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Iris-setosa\n",
       "1         Iris-setosa\n",
       "2         Iris-setosa\n",
       "3         Iris-setosa\n",
       "4         Iris-setosa\n",
       "            ...      \n",
       "145    Iris-virginica\n",
       "146    Iris-virginica\n",
       "147    Iris-virginica\n",
       "148    Iris-virginica\n",
       "149    Iris-virginica\n",
       "Name: species, Length: 150, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.species\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ecfa407",
   "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, train_size=0.90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d2c88ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d14b549b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "946b90b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0ec366cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0b000139",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fde1c3b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(33.0, 0.5, 'truth')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure\n",
    "sn.heatmap(cm, annot = True)\n",
    "plt.xlabel('predicted')\n",
    "plt.ylabel('truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "524b31d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[4.7,3.2,1.3,0.2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc978b0e",
   "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
}