predict whether a bank currency note is authentic or not based upon four attributes of the note.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bankdata = pd.read_csv(\"../../datasets/bill_authentication.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1372, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Curtosis</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.62160</td>\n",
       "      <td>8.6661</td>\n",
       "      <td>-2.8073</td>\n",
       "      <td>-0.44699</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.54590</td>\n",
       "      <td>8.1674</td>\n",
       "      <td>-2.4586</td>\n",
       "      <td>-1.46210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.86600</td>\n",
       "      <td>-2.6383</td>\n",
       "      <td>1.9242</td>\n",
       "      <td>0.10645</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.45660</td>\n",
       "      <td>9.5228</td>\n",
       "      <td>-4.0112</td>\n",
       "      <td>-3.59440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.32924</td>\n",
       "      <td>-4.4552</td>\n",
       "      <td>4.5718</td>\n",
       "      <td>-0.98880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bankdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = bankdata.drop('Class', axis=1)\n",
    "y = bankdata['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svclassifier = SVC(kernel='linear')\n",
    "svclassifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svclassifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[151   1]\n",
      " [  1 122]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       152\n",
      "           1       0.99      0.99      0.99       123\n",
      "\n",
      "    accuracy                           0.99       275\n",
      "   macro avg       0.99      0.99      0.99       275\n",
      "weighted avg       0.99      0.99      0.99       275\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
    "\n",
    "# Assign colum names to the dataset\n",
    "colnames = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']\n",
    "\n",
    "# Read dataset to pandas dataframe\n",
    "irisdata = pd.read_csv(url, names=colnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepal-length  sepal-width  petal-length  petal-width           Class\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\n",
      "..            ...          ...           ...          ...             ...\n",
      "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
      "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
      "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
      "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
      "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
      "\n",
      "[150 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "print(irisdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = irisdata.drop('Class', axis=1)\n",
    "y = irisdata['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(degree=8, kernel='poly')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svclassifier = SVC(kernel='poly', degree=8)\n",
    "svclassifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = svclassifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11  0  0]\n",
      " [ 0 10  1]\n",
      " [ 0  0  8]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        11\n",
      "Iris-versicolor       1.00      0.91      0.95        11\n",
      " Iris-virginica       0.89      1.00      0.94         8\n",
      "\n",
      "       accuracy                           0.97        30\n",
      "      macro avg       0.96      0.97      0.96        30\n",
      "   weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11  0  0]\n",
      " [ 0 10  1]\n",
      " [ 0  0  8]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        11\n",
      "Iris-versicolor       1.00      0.91      0.95        11\n",
      " Iris-virginica       0.89      1.00      0.94         8\n",
      "\n",
      "       accuracy                           0.97        30\n",
      "      macro avg       0.96      0.97      0.96        30\n",
      "   weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svclassifier = SVC(kernel='rbf')\n",
    "svclassifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  0 11]\n",
      " [ 0  0 11]\n",
      " [ 0  0  8]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       0.00      0.00      0.00        11\n",
      "Iris-versicolor       0.00      0.00      0.00        11\n",
      " Iris-virginica       0.27      1.00      0.42         8\n",
      "\n",
      "       accuracy                           0.27        30\n",
      "      macro avg       0.09      0.33      0.14        30\n",
      "   weighted avg       0.07      0.27      0.11        30\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files (x86)\\python38-32\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svclassifier = SVC(kernel='sigmoid')\n",
    "svclassifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = svclassifier.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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