Iris Species Classification using Decision Trees in Machine Learning with scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e6f8332",
   "metadata": {},
   "source": [
    "## Classification Machine Learning Exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "a5ed3ff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, classification_report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "c794f030",
   "metadata": {},
   "outputs": [],
   "source": [
    " from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "a71b2fbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "e47d5b3c",
   "metadata": {},
   "outputs": [
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       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'iris.csv',\n",
       " 'data_module': 'sklearn.datasets.data'}"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "f2ba65fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data['data'],columns = data['feature_names'])\n",
    "df['target'] = data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "a017585e",
   "metadata": {},
   "outputs": [
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       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)   \n",
       "0                5.1               3.5                1.4               0.2  \\\n",
       "1                4.9               3.0                1.4               0.2   \n",
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       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
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       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)   \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",
       "     target  \n",
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       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  "
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     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "0237ee6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    0\n",
       "sepal width (cm)     0\n",
       "petal length (cm)    0\n",
       "petal width (cm)     0\n",
       "target               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "46501c27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the Data into X,y\n",
    "X= df.drop(\"target\",axis =1)\n",
    "y= df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "d6e2050f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify feature names\n",
    "#feature_names = data['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "10b30d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting into Train Test Set\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "cad4c322",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a model with feature names\n",
    "\n",
    "model = tree.DecisionTreeClassifier()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "7b7e45ba",
   "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        13\n",
      "           1       1.00      1.00      1.00        12\n",
      "           2       1.00      1.00      1.00         5\n",
      "\n",
      "    accuracy                           1.00        30\n",
      "   macro avg       1.00      1.00      1.00        30\n",
      "weighted avg       1.00      1.00      1.00        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the testing data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "report = classification_report(y_test, y_pred)\n",
    "\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(\"Classification Report:\\n\", report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "4d03815d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class 1: [2]\n",
      "Predicted class 2: [0]\n",
      "Predicted class name 1: virginica\n",
      "Predicted class name 2: setosa\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dell\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n",
      "C:\\Users\\Dell\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:439: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#Predicting Iris Species using model\n",
    "\n",
    "new_data1 = np.array([[6,5,3,2]])\n",
    "new_data2 = np.array([[5,3,1,0]])\n",
    "\n",
    "result1 = model.predict(new_data1)\n",
    "result2 = model.predict(new_data2)\n",
    "\n",
    "print(\"Predicted class 1:\", result1)\n",
    "print(\"Predicted class 2:\", result2)\n",
    "print(\"Predicted class name 1:\", data.target_names[result1[0]])\n",
    "print(\"Predicted class name 2:\", data.target_names[result2[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "b7f1bf70",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73c5f9c2",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}