SVM - Bank Notes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3b7397af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from math import sqrt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.svm import LinearSVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a20d171",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank=pd.read_csv(\"C:/Users/user/Desktop/Sneha's Program/ML Datasets/BankNote_Authentication_MLP.csv\")\n",
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "479f8c00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1372, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b32ac9b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['variance', 'skewness', 'curtosis', 'entropy', 'class'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f9d5abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "13ba2594",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test =train_test_split(bank, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c5495a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of       variance  skewness  curtosis  entropy  class\n",
       "0      3.62160   8.66610   -2.8073 -0.44699      0\n",
       "1      4.54590   8.16740   -2.4586 -1.46210      0\n",
       "2      3.86600  -2.63830    1.9242  0.10645      0\n",
       "3      3.45660   9.52280   -4.0112 -3.59440      0\n",
       "4      0.32924  -4.45520    4.5718 -0.98880      0\n",
       "...        ...       ...       ...      ...    ...\n",
       "1367   0.40614   1.34920   -1.4501 -0.55949      1\n",
       "1368  -1.38870  -4.87730    6.4774  0.34179      1\n",
       "1369  -3.75030 -13.45860   17.5932 -2.77710      1\n",
       "1370  -3.56370  -8.38270   12.3930 -1.28230      1\n",
       "1371  -2.54190  -0.65804    2.6842  1.19520      1\n",
       "\n",
       "[1372 rows x 5 columns]>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d8525db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train.drop('class',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "687cf6c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "46e36cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test= test.drop('class',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dee6767e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = test['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0eeffe53",
   "metadata": {},
   "outputs": [],
   "source": [
    "svm = LinearSVC(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c5a0714d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\svm\\_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVC(random_state=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3451d4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9927083333333333"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0a5a244a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=svm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "464339c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11016316230980794"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = sqrt(mean_squared_error (y_test, y_pred))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "45cc0e1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[223,   1],\n",
       "       [  4, 184]], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ec134eb",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}