Decision Tree.ipynb

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    "![title](logo.jpg)"
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    "![title](B03905_05_01-compressor.png)"
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    "\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
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   "id": "9cf8f5b1",
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   "source": [
    "dataset=pd.read_csv('bill_authentication.csv')"
   ]
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1372 entries, 0 to 1371\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Variance  1372 non-null   float64\n",
      " 1   Skewness  1372 non-null   float64\n",
      " 2   Curtosis  1372 non-null   float64\n",
      " 3   Entropy   1372 non-null   float64\n",
      " 4   Class     1372 non-null   int64  \n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 53.7 KB\n"
     ]
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   "source": [
    "dataset.info()"
   ]
  },
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       "      <th></th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Curtosis</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Class</th>\n",
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       "   Variance  Skewness  Curtosis  Entropy  Class\n",
       "0   3.62160    8.6661   -2.8073 -0.44699      0\n",
       "1   4.54590    8.1674   -2.4586 -1.46210      0\n",
       "2   3.86600   -2.6383    1.9242  0.10645      0\n",
       "3   3.45660    9.5228   -4.0112 -3.59440      0\n",
       "4   0.32924   -4.4552    4.5718 -0.98880      0"
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    "dataset.head()"
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   "source": [
    "X=dataset.drop('Class',axis=1)\n",
    "y=dataset['Class']"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Curtosis</th>\n",
       "      <th>Entropy</th>\n",
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       "      <td>-1.46210</td>\n",
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       "   Variance  Skewness  Curtosis  Entropy\n",
       "0   3.62160    8.6661   -2.8073 -0.44699\n",
       "1   4.54590    8.1674   -2.4586 -1.46210\n",
       "2   3.86600   -2.6383    1.9242  0.10645\n",
       "3   3.45660    9.5228   -4.0112 -3.59440\n",
       "4   0.32924   -4.4552    4.5718 -0.98880"
      ]
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   "source": [
    "X.head()"
   ]
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   "execution_count": 7,
   "id": "e23bfd63",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Class, dtype: int64"
      ]
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "y.head()"
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31fe0688",
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    {
     "data": {
      "text/plain": [
       "0    762\n",
       "1    610\n",
       "Name: Class, dtype: int64"
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     "execution_count": 11,
     "metadata": {},
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   "source": [
    "y.value_counts()"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3f954272",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b1bf9f5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier=DecisionTreeClassifier()\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6909369",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb775a87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[158   4]\n",
      " [  1 112]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.98       162\n",
      "           1       0.97      0.99      0.98       113\n",
      "\n",
      "    accuracy                           0.98       275\n",
      "   macro avg       0.98      0.98      0.98       275\n",
      "weighted avg       0.98      0.98      0.98       275\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6bf4be",
   "metadata": {},
   "source": [
    "#### Machine Learning Fundamentals: The Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "011bf85d",
   "metadata": {},
   "source": [
    "https://www.youtube.com/watch?v=Kdsp6soqA7o&t=184s"
   ]
  }
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