Iris Flowers Classification.ipynb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "799f13fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a4315bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Iris dataset\n",
    "iris = load_iris()\n",
    "iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "iris_df['target'] = iris.target\n",
    "\n",
    "# Select features (petal length and sepal length) and target\n",
    "X = iris_df[['petal length (cm)', 'sepal length (cm)']]\n",
    "y = iris_df['target']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b9e4c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data\n",
    "plt.scatter(X[y == 0]['petal length (cm)'], X[y == 0]['sepal length (cm)'], label='Iris-setosa')\n",
    "plt.scatter(X[y == 1]['petal length (cm)'], X[y == 1]['sepal length (cm)'], label='Iris-versicolor')\n",
    "plt.scatter(X[y == 2]['petal length (cm)'], X[y == 2]['sepal length (cm)'], label='Iris-virginica')\n",
    "\n",
    "plt.xlabel('Petal Length (cm)')\n",
    "plt.ylabel('Sepal Length (cm)')\n",
    "plt.title('Iris Flowers Classification')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b77c855",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a40a4266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize and train the Logistic Regression model\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bedc24a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        19\n",
      "           1       1.00      1.00      1.00        13\n",
      "           2       1.00      1.00      1.00        13\n",
      "\n",
      "    accuracy                           1.00        45\n",
      "   macro avg       1.00      1.00      1.00        45\n",
      "weighted avg       1.00      1.00      1.00        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predict using the trained model\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('Accuracy:', accuracy)\n",
    "print('Classification Report:\\n', classification_report(y_test, y_pred))\n"
   ]
  }
 ],
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
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 },
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