model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('processed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>password</th>\n",
       "      <th>strength</th>\n",
       "      <th>length</th>\n",
       "      <th>capital</th>\n",
       "      <th>small</th>\n",
       "      <th>special</th>\n",
       "      <th>numeric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kzde5577</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kino3434</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>visi7k1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>megzy123</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lamborghin1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      password  strength  length  capital  small  special  numeric\n",
       "0     kzde5577         1       8        0      4        0        4\n",
       "1     kino3434         1       8        0      4        0        4\n",
       "2    visi7k1yr         1       9        0      7        0        2\n",
       "3     megzy123         1       8        0      5        0        3\n",
       "4  lamborghin1         1      11        0     10        0        1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data['strength'].values\n",
    "x=data[['length','capital','small','special','numeric']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(669639, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(535711, 5) (535711,)\n",
      "(133928, 5) (133928,)\n"
     ]
    }
   ],
   "source": [
    "xtrain,xtest,ytrain,ytest=tts(x,y,test_size=0.2,random_state=42)\n",
    "print(xtrain.shape,ytrain.shape)\n",
    "print(xtest.shape,ytest.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=StandardScaler()\n",
    "xtrain=sc.fit_transform(xtrain)\n",
    "xtest=sc.transform(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(16, 16), max_iter=300)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=MLPClassifier(hidden_layer_sizes=(16,16),max_iter=300)\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model is:  99.99925333014754  % !!!\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy of the model is: \",accuracy_score(ytest,ypred)*100,\" % !!!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     17908\n",
      "           1       1.00      1.00      1.00     99519\n",
      "           2       1.00      1.00      1.00     16501\n",
      "\n",
      "    accuracy                           1.00    133928\n",
      "   macro avg       1.00      1.00      1.00    133928\n",
      "weighted avg       1.00      1.00      1.00    133928\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The confusion matrix is: \n",
      "[[17908     0     0]\n",
      " [    0 99518     1]\n",
      " [    0     0 16501]]\n"
     ]
    }
   ],
   "source": [
    "print(\"The confusion matrix is: \")\n",
    "cf=confusion_matrix(ytest,ypred)\n",
    "print(cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(sc,'scaler.pkl')\n",
    "joblib.dump(model,'model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(cf,annot=True,cmap='Blues')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.savefig('confusion.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}