IRIS Flower Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0020ce88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d8d0f062",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\khursheed alam\\Downloads\\Data Science Intern\\IRIS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0d539d53",
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0be73d25",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac8ddeaa",
   "metadata": {},
   "source": [
    "***Data Cleaning Process***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a4197b33",
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ed2858e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f92235df",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "787464af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64366ce0",
   "metadata": {},
   "source": [
    "***Exploratory Data Analysis(EDA)***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b1afb505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-versicolor    50\n",
       "Iris-virginica     49\n",
       "Iris-setosa        48\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1704931e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x=\"sepal_length\", y=\"sepal_width\",hue=\"species\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1dc2479c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x=\"petal_length\", y=\"petal_width\",hue=\"species\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "08ff7c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2d2a80ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['species'] = label_encoder.fit_transform(df['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "290a3c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4603a_row0_col0, #T_4603a_row1_col1, #T_4603a_row2_col2, #T_4603a_row3_col3, #T_4603a_row4_col4 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row0_col1 {\n",
       "  background-color: #82a6fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row0_col2 {\n",
       "  background-color: #d44e41;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row0_col3 {\n",
       "  background-color: #e0654f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row0_col4 {\n",
       "  background-color: #e46e56;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row1_col0, #T_4603a_row1_col2, #T_4603a_row1_col3, #T_4603a_row1_col4, #T_4603a_row2_col1, #T_4603a_row4_col1 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row2_col0 {\n",
       "  background-color: #da5a49;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row2_col3, #T_4603a_row3_col4 {\n",
       "  background-color: #be242e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row2_col4, #T_4603a_row4_col2 {\n",
       "  background-color: #c12b30;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row3_col0 {\n",
       "  background-color: #e7745b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row3_col1 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row3_col2 {\n",
       "  background-color: #bd1f2d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row4_col0 {\n",
       "  background-color: #ed8366;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_4603a_row4_col3 {\n",
       "  background-color: #c0282f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4603a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4603a_level0_col0\" class=\"col_heading level0 col0\" >sepal_length</th>\n",
       "      <th id=\"T_4603a_level0_col1\" class=\"col_heading level0 col1\" >sepal_width</th>\n",
       "      <th id=\"T_4603a_level0_col2\" class=\"col_heading level0 col2\" >petal_length</th>\n",
       "      <th id=\"T_4603a_level0_col3\" class=\"col_heading level0 col3\" >petal_width</th>\n",
       "      <th id=\"T_4603a_level0_col4\" class=\"col_heading level0 col4\" >species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4603a_level0_row0\" class=\"row_heading level0 row0\" >sepal_length</th>\n",
       "      <td id=\"T_4603a_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_4603a_row0_col1\" class=\"data row0 col1\" >-0.109321</td>\n",
       "      <td id=\"T_4603a_row0_col2\" class=\"data row0 col2\" >0.871305</td>\n",
       "      <td id=\"T_4603a_row0_col3\" class=\"data row0 col3\" >0.817058</td>\n",
       "      <td id=\"T_4603a_row0_col4\" class=\"data row0 col4\" >0.782904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4603a_level0_row1\" class=\"row_heading level0 row1\" >sepal_width</th>\n",
       "      <td id=\"T_4603a_row1_col0\" class=\"data row1 col0\" >-0.109321</td>\n",
       "      <td id=\"T_4603a_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_4603a_row1_col2\" class=\"data row1 col2\" >-0.421057</td>\n",
       "      <td id=\"T_4603a_row1_col3\" class=\"data row1 col3\" >-0.356376</td>\n",
       "      <td id=\"T_4603a_row1_col4\" class=\"data row1 col4\" >-0.418348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4603a_level0_row2\" class=\"row_heading level0 row2\" >petal_length</th>\n",
       "      <td id=\"T_4603a_row2_col0\" class=\"data row2 col0\" >0.871305</td>\n",
       "      <td id=\"T_4603a_row2_col1\" class=\"data row2 col1\" >-0.421057</td>\n",
       "      <td id=\"T_4603a_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_4603a_row2_col3\" class=\"data row2 col3\" >0.961883</td>\n",
       "      <td id=\"T_4603a_row2_col4\" class=\"data row2 col4\" >0.948339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4603a_level0_row3\" class=\"row_heading level0 row3\" >petal_width</th>\n",
       "      <td id=\"T_4603a_row3_col0\" class=\"data row3 col0\" >0.817058</td>\n",
       "      <td id=\"T_4603a_row3_col1\" class=\"data row3 col1\" >-0.356376</td>\n",
       "      <td id=\"T_4603a_row3_col2\" class=\"data row3 col2\" >0.961883</td>\n",
       "      <td id=\"T_4603a_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_4603a_row3_col4\" class=\"data row3 col4\" >0.955693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4603a_level0_row4\" class=\"row_heading level0 row4\" >species</th>\n",
       "      <td id=\"T_4603a_row4_col0\" class=\"data row4 col0\" >0.782904</td>\n",
       "      <td id=\"T_4603a_row4_col1\" class=\"data row4 col1\" >-0.418348</td>\n",
       "      <td id=\"T_4603a_row4_col2\" class=\"data row4 col2\" >0.948339</td>\n",
       "      <td id=\"T_4603a_row4_col3\" class=\"data row4 col3\" >0.955693</td>\n",
       "      <td id=\"T_4603a_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1543772ed00>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr().style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "af3cca9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('species', axis=1)\n",
    "y = df['species']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57d987a",
   "metadata": {},
   "source": [
    "**Splitting the data into Train and Test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "565dc19e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "17a3c10d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85afa0d6",
   "metadata": {},
   "source": [
    "***Importing Libraries***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5f340597",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a6b7ed97",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b19509b7",
   "metadata": {},
   "source": [
    "**Initializing the models**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1c440dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "models = {\n",
    "    'Logistic Regression': LogisticRegression(),\n",
    "    'K-Nearest Neighbors': KNeighborsClassifier(),\n",
    "    'Support Vector Machine': SVC(),\n",
    "    'Decision Tree': DecisionTreeClassifier(),\n",
    "    'Random Forest': RandomForestClassifier(),\n",
    "    'Gradient Boosting': GradientBoostingClassifier(),\n",
    "    'Naive Bayes': GaussianNB(),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6d60c4f",
   "metadata": {},
   "source": [
    "***Train and evaluate models***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8a21a3d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.9778\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        17\n",
      "           1       1.00      0.92      0.96        12\n",
      "           2       0.94      1.00      0.97        16\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.98      0.97      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n",
      "[[17  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  0 16]]\n",
      "----------------------------------------\n",
      "K-Nearest Neighbors Accuracy: 0.9556\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        17\n",
      "           1       0.92      0.92      0.92        12\n",
      "           2       0.94      0.94      0.94        16\n",
      "\n",
      "    accuracy                           0.96        45\n",
      "   macro avg       0.95      0.95      0.95        45\n",
      "weighted avg       0.96      0.96      0.96        45\n",
      "\n",
      "[[17  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  1 15]]\n",
      "----------------------------------------\n",
      "Support Vector Machine Accuracy: 0.9556\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        17\n",
      "           1       0.92      0.92      0.92        12\n",
      "           2       0.94      0.94      0.94        16\n",
      "\n",
      "    accuracy                           0.96        45\n",
      "   macro avg       0.95      0.95      0.95        45\n",
      "weighted avg       0.96      0.96      0.96        45\n",
      "\n",
      "[[17  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  1 15]]\n",
      "----------------------------------------\n",
      "Decision Tree Accuracy: 0.9778\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        17\n",
      "           1       1.00      0.92      0.96        12\n",
      "           2       0.94      1.00      0.97        16\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.98      0.97      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n",
      "[[17  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  0 16]]\n",
      "----------------------------------------\n",
      "Random Forest Accuracy: 0.9556\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        17\n",
      "           1       0.92      0.92      0.92        12\n",
      "           2       0.94      0.94      0.94        16\n",
      "\n",
      "    accuracy                           0.96        45\n",
      "   macro avg       0.95      0.95      0.95        45\n",
      "weighted avg       0.96      0.96      0.96        45\n",
      "\n",
      "[[17  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  1 15]]\n",
      "----------------------------------------\n",
      "Gradient Boosting Accuracy: 0.9556\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        17\n",
      "           1       0.92      0.92      0.92        12\n",
      "           2       0.94      0.94      0.94        16\n",
      "\n",
      "    accuracy                           0.96        45\n",
      "   macro avg       0.95      0.95      0.95        45\n",
      "weighted avg       0.96      0.96      0.96        45\n",
      "\n",
      "[[17  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  1 15]]\n",
      "----------------------------------------\n",
      "Naive Bayes Accuracy: 0.9778\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        17\n",
      "           1       0.92      1.00      0.96        12\n",
      "           2       1.00      0.94      0.97        16\n",
      "\n",
      "    accuracy                           0.98        45\n",
      "   macro avg       0.97      0.98      0.98        45\n",
      "weighted avg       0.98      0.98      0.98        45\n",
      "\n",
      "[[17  0  0]\n",
      " [ 0 12  0]\n",
      " [ 0  1 15]]\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "results = {}\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    results[name] = accuracy\n",
    "    print(f'{name} Accuracy: {accuracy:.4f}')\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    print(confusion_matrix(y_test, y_pred))\n",
    "    print('-' * 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accd4dca",
   "metadata": {},
   "source": [
    "**Model Comparison**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fc8293e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model_names = list(results.keys())\n",
    "model_scores = list(results.values())\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.barh(model_names, model_scores, color='skyblue')\n",
    "plt.xlabel('Accuracy')\n",
    "plt.title('Model Comparison')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}