Practice Project 4 (Credit Card Fraud).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CREDIT CARD FRAUD DETECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Downloading Dataset (https://github.com/shubh2002/DataSets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fraud detection is one of the top priorities for banks and financial institutions, which can be addressed using machine learning. According to a report published by Nilson, in 2017 the worldwide losses in card fraud related cases reached 22.8 billion dollars. The problem is forecasted to get worse in the following years, by 2021, the card fraud bill is expected to be 32.96 billion dollars.\n",
    "It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have near 40 frauds out of 500 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n",
    "It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.\n"
   ]
  },
  {
   "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",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('credit_card_fraud.csv',names=range(30),header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(662, 30)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "         0         1         2         3         4         5         6   \\\n",
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       "2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
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       "\n",
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       "\n",
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       "2 -0.232794  0.105915  0.253844  0.081080    3.67   0  \n",
       "\n",
       "[5 rows x 30 columns]"
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     "metadata": {},
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   "source": [
    "df.head()"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(662, 30)"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    636\n",
       "1     26\n",
       "Name: 29, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[29].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "df['Amount']=sc.fit_transform(df[28].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop([28],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>29</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.327141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>1.494201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>0.258109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.001114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.425966</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>-0.371407</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208254</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.322393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "2 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "\n",
       "          7         8         9  ...        20        21        22        23  \\\n",
       "0  0.085102 -0.255425 -0.166974  ... -0.225775 -0.638672  0.101288 -0.339846   \n",
       "1  0.247676 -1.514654  0.207643  ...  0.247998  0.771679  0.909412 -0.689281   \n",
       "1  0.377436 -1.387024 -0.054952  ... -0.108300  0.005274 -0.190321 -1.175575   \n",
       "2 -0.270533  0.817739  0.753074  ... -0.009431  0.798278 -0.137458  0.141267   \n",
       "2  0.260314 -0.568671 -0.371407  ... -0.208254 -0.559825 -0.026398 -0.371427   \n",
       "\n",
       "         24        25        26        27  29    Amount  \n",
       "0  0.167170  0.125895 -0.008983  0.014724   0 -0.327141  \n",
       "1 -0.327642 -0.139097 -0.055353 -0.059752   0  1.494201  \n",
       "1  0.647376 -0.221929  0.062723  0.061458   0  0.258109  \n",
       "2 -0.206010  0.502292  0.219422  0.215153   0 -0.001114  \n",
       "2 -0.232794  0.105915  0.253844  0.081080   0 -0.322393  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>-0.327141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>1.494201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0.258109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>-0.001114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.425966</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>-0.371407</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084968</td>\n",
       "      <td>-0.208254</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>-0.322393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "2 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "\n",
       "          7         8         9  ...        19        20        21        22  \\\n",
       "0  0.085102 -0.255425 -0.166974  ... -0.069083 -0.225775 -0.638672  0.101288   \n",
       "1  0.247676 -1.514654  0.207643  ...  0.524980  0.247998  0.771679  0.909412   \n",
       "1  0.377436 -1.387024 -0.054952  ... -0.208038 -0.108300  0.005274 -0.190321   \n",
       "2 -0.270533  0.817739  0.753074  ...  0.408542 -0.009431  0.798278 -0.137458   \n",
       "2  0.260314 -0.568671 -0.371407  ...  0.084968 -0.208254 -0.559825 -0.026398   \n",
       "\n",
       "         23        24        25        26        27    Amount  \n",
       "0 -0.339846  0.167170  0.125895 -0.008983  0.014724 -0.327141  \n",
       "1 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  1.494201  \n",
       "1 -1.175575  0.647376 -0.221929  0.062723  0.061458  0.258109  \n",
       "2  0.141267 -0.206010  0.502292  0.219422  0.215153 -0.001114  \n",
       "2 -0.371427 -0.232794  0.105915  0.253844  0.081080 -0.322393  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,df.columns!=29]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   29\n",
       "0   0\n",
       "1   0\n",
       "1   0\n",
       "2   0\n",
       "2   0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,df.columns == 29]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.30,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9698492462311558"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model1=RandomForestClassifier(n_estimators=100)\n",
    "model1=model1.fit(x_train,y_train)\n",
    "y_pred=model1.predict(x_test)\n",
    "acc1=accuracy_score(y_test,y_pred)\n",
    "acc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9547738693467337"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model2=GradientBoostingClassifier(n_estimators=100,learning_rate=0.1)\n",
    "model2=model2.fit(x_train,y_train)\n",
    "y_pred=model2.predict(x_test)\n",
    "acc2=accuracy_score(y_test,y_pred)\n",
    "acc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9547738693467337"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "model3=AdaBoostClassifier(n_estimators=100,learning_rate=1.0)\n",
    "model3=model3.fit(x_train,y_train)\n",
    "y_pred=model3.predict(x_test)\n",
    "acc3=accuracy_score(y_test,y_pred)\n",
    "acc3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9698492462311558"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "model4=XGBClassifier(learning_rate=0.1,n_estimators=100)\n",
    "model4=model4.fit(x_train,y_train)\n",
    "y_pred=model4.predict(x_test)\n",
    "acc4=accuracy_score(y_test,y_pred)\n",
    "acc4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>MODELS</th>\n",
       "      <th>SCORE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RANDOM FOREST</td>\n",
       "      <td>0.969849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XGBOOST</td>\n",
       "      <td>0.969849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GRADIENT BOOSTING</td>\n",
       "      <td>0.954774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ADA BOOST</td>\n",
       "      <td>0.954774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              MODELS     SCORE\n",
       "0      RANDOM FOREST  0.969849\n",
       "3            XGBOOST  0.969849\n",
       "1  GRADIENT BOOSTING  0.954774\n",
       "2          ADA BOOST  0.954774"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models=pd.DataFrame({'MODELS':['RANDOM FOREST','GRADIENT BOOSTING','ADA BOOST','XGBOOST'],\n",
    "                    'SCORE':[acc1,acc2,acc3,acc4]})\n",
    "models.sort_values(by='SCORE',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Practice_project.sav']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"Serialization\"\"\"\n",
    "\n",
    "import joblib\n",
    "filename='Practice_project.sav'\n",
    "joblib.dump(model1,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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