iris_flower_Detection_ML-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "76d2fade",
   "metadata": {},
   "source": [
    "                                                     # Iris Flower Detection ML                                                                      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c89a08",
   "metadata": {},
   "source": [
    "by: Lucky Sharma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f36bf0de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90f1d6b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=pd.read_csv('https://raw.githubusercontent.com/itsluckysharma01/Datasets/refs/heads/main/IRIS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cab03872",
   "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",
       "  </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": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cffbbaf",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c170d962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.hist()\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56bed7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8a2e6a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.hist(linewidth=2,edgecolor='black',figsize=(10,10))\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f48d2fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='petal_length', ylabel='petal_width'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.plot(kind=\"scatter\", x=\"sepal_length\", y=\"sepal_width\")\n",
    "iris.plot(kind=\"scatter\", x=\"petal_length\", y=\"petal_width\" )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce75462",
   "metadata": {},
   "source": [
    " # Predicting the species of iris flower based on its features using machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2c1edef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388c2509",
   "metadata": {},
   "source": [
    "# Label Encoding The Non-Numeric Data on Species Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c08414fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "145    2\n",
       "146    2\n",
       "147    2\n",
       "148    2\n",
       "149    2\n",
       "Name: species, Length: 150, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Label Encoding the Non-Numeric Data on Species Column\n",
    "le = LabelEncoder()\n",
    "iris['species'] = le.fit_transform(iris['species'])\n",
    "iris['species']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1519888",
   "metadata": {},
   "source": [
    "Divide the Dataset into test and train data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae844616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (112, 5)\n",
      "Test shape: (38, 5)\n"
     ]
    }
   ],
   "source": [
    "train, test = train_test_split(iris, test_size=0.25)\n",
    "print(\"Train shape:\", train.shape) # Divide into 25 and 75 percent\n",
    "print(\"Test shape:\", test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "17fa3fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>0</td>\n",
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       "      <td>0</td>\n",
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      ],
      "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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "55ff100f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = train.drop(columns=['species'])\n",
    "train_y = train['species']\n",
    "\n",
    "test_x = test.drop(columns=['species'])\n",
    "test_y = test['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ad99bf0",
   "metadata": {},
   "outputs": [
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       "      <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",
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       "      <td>5.4</td>\n",
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       "      <td>0.2</td>\n",
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       "      <th>26</th>\n",
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       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5.6</td>\n",
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       "      <td>1.1</td>\n",
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       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "10            5.4          3.7           1.5          0.2\n",
       "26            5.0          3.4           1.6          0.4\n",
       "69            5.6          2.5           3.9          1.1\n",
       "111           6.4          2.7           5.3          1.9\n",
       "77            6.7          3.0           5.0          1.7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0139547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10     0\n",
       "26     0\n",
       "69     1\n",
       "111    2\n",
       "77     1\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dd735025",
   "metadata": {},
   "outputs": [
    {
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       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
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       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
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       "      <td>2.1</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>6.1</td>\n",
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       "      <td>1.3</td>\n",
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      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "105           7.6          3.0           6.6          2.1\n",
       "71            6.1          2.8           4.0          1.3"
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     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "test_x.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7fd8e9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105    2\n",
       "71     1\n",
       "76     1\n",
       "134    2\n",
       "29     0\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e7570b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((112, 4), (112,), (38, 4), (38,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape, train_y.shape, test_x.shape, test_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b51fbe7",
   "metadata": {},
   "source": [
    "# There are various split mehod to divide the dataset into test and train data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "99cfe47f",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "  </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        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"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3e75c275",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     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]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((150, 4), (150,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1=iris.drop(columns=['species'])\n",
    "y1=iris['species']\n",
    "print(x1)\n",
    "x1.shape, y1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fce62130",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0\n",
      "1      0\n",
      "2      0\n",
      "3      0\n",
      "4      0\n",
      "      ..\n",
      "145    2\n",
      "146    2\n",
      "147    2\n",
      "148    2\n",
      "149    2\n",
      "Name: species, Length: 150, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((150, 3), (150,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=iris.iloc[:,0:3]\n",
    "y=iris.iloc[:,4]\n",
    "print(y)\n",
    "x.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac3b2f6",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cdd946c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  100.0\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(train_x, train_y)\n",
    "\n",
    "predictions = model.predict(test_x)\n",
    "print(\"Accuracy : \", metrics.accuracy_score(predictions, test_y)*100)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d0cde07b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 94.73684210526315\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(train_x,train_y)\n",
    "predictions=model.predict(test_x)\n",
    "print('accuracy:',metrics.accuracy_score(predictions,test_y) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0a68eb1",
   "metadata": {},
   "source": [
    "\n",
    "# 1. Preprocessing\n",
    "\n",
    "# In this case:\n",
    "\n",
    "# No missing values\n",
    "\n",
    "# Features are all numeric\n",
    "\n",
    "# scikit-learn automatically handles string Labels(Target) in classification problems, but not for categorical features.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8875d616",
   "metadata": {},
   "source": [
    "# 2. Feature and Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0d7e7df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x=train_x\n",
    "# y=train_y\n",
    "\n",
    "#Feature set\n",
    "x=iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]\n",
    "\n",
    "#Target variable\n",
    "y=iris['species']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab718a4c",
   "metadata": {},
   "source": [
    "Encode target labels (setosa → 0, versicolor → 1, virginica → 2)\n",
    "#scikit-learn automatically handles string Labels(Target) in classification problems, but not for categorical features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c1e5dc",
   "metadata": {},
   "source": [
    "# Split the dataset into Test train data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64426fb",
   "metadata": {},
   "source": [
    "# 3. Train Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c490a38",
   "metadata": {},
   "source": [
    "Train a model (Logistic Regression model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13770e59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted_species is :  Iris-versicolor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PANDIT JI\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(x,y)\n",
    "\n",
    "pred=model.predict([[5.4,3.0,4.5,1.5]]) # Fill values in the list to predict\n",
    "predicted_species = le.inverse_transform(pred)  # Inverse transform to get original species name\n",
    "print(\"predicted_species is : \", predicted_species[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fe301287",
   "metadata": {},
   "outputs": [
    {
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       "      <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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc8b6b6e",
   "metadata": {},
   "source": [
    "# 5. Test Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7145f84e",
   "metadata": {},
   "source": [
    "# Check for another values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "de54ebdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted_species is :  Iris-virginica\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PANDIT JI\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(x,y)\n",
    "\n",
    "pred=model.predict([[5.9,3.0,5.1,1.8]]) # Fill values in the list to predict\n",
    "predicted_species = le.inverse_transform(pred)  # Inverse transform to get original species name\n",
    "print(\"predicted_species is : \", predicted_species[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa658d1c",
   "metadata": {},
   "source": [
    "# Predict For Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "181afd43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted_species is :  Iris-setosa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PANDIT JI\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(x,y)\n",
    "\n",
    "pred=model.predict([[5.,3.0,1,.5]]) # Fill values in the list to predict\n",
    "predicted_species = le.inverse_transform(pred)  # Inverse transform to get original species name\n",
    "print(\"predicted_species is : \", predicted_species[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77b7b266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted_species is :  Iris-setosa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PANDIT JI\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(x,y)\n",
    "\n",
    "pred=model.predict([[5,3,2,.6]]) # Fill values in the list to predict\n",
    "predicted_species = le.inverse_transform(pred)  # Inverse transform to get original species name\n",
    "print(\"predicted_species is : \", predicted_species[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5861a560",
   "metadata": {},
   "source": [
    "we put nearest neighbors values to predict the species of iris flower. It can be used to predict the species of iris flower based on its features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53520c6e",
   "metadata": {},
   "source": [
    "# 4. Model Evaluation\n",
    "***\n",
    "Accuracy Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "53ede795",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.33333333333334\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(max_iter=200)\n",
    "model.fit(x, y)\n",
    "prediction = model.predict(x)\n",
    "print('Accuracy:',metrics.accuracy_score(prediction,y)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cf68bbb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 100.0\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(x,y)\n",
    "prediction=model.predict(x)\n",
    "print('accuracy:',metrics.accuracy_score(prediction,y)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cc6674dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9260465a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy with k=3: 96.0 %\n"
     ]
    }
   ],
   "source": [
    "# Train model using k=3 and print accuracy\n",
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "model.fit(x, y)\n",
    "prediction = model.predict(x)\n",
    "print(\" Accuracy with k=3:\", metrics.accuracy_score(y, prediction) * 100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e40b19c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "460366db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 96.0 %\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        50\n",
      "           1       0.94      0.94      0.94        50\n",
      "           2       0.94      0.94      0.94        50\n",
      "\n",
      "    accuracy                           0.96       150\n",
      "   macro avg       0.96      0.96      0.96       150\n",
      "weighted avg       0.96      0.96      0.96       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(x)\n",
    "print(\"Accuracy:\", accuracy_score(y, y_pred)*100, \"%\")\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35b1c120",
   "metadata": {},
   "source": [
    "# Saving the Model with joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0d0ca1c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3a13ddc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved as 'iris_model.pkl'\n"
     ]
    }
   ],
   "source": [
    "model.fit(x, y)\n",
    "joblib.dump(model, 'iris_model.pkl') #save the model to a file\n",
    "print(\"Model saved as 'iris_model.pkl'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5e358195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iris_model.pkl']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "iris = load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X, y)\n",
    "joblib.dump(model, 'iris_model.pkl')\n"
   ]
  }
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