Facial Recognition Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('Skin_NonSkin.txt',sep='\\t',names=['B','G','R','Skin or No-Skin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(245057, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.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>B</th>\n",
       "      <th>G</th>\n",
       "      <th>R</th>\n",
       "      <th>Skin or No-Skin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>85</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>84</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "      <td>81</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>81</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    B   G    R  Skin or No-Skin\n",
       "0  74  85  123                1\n",
       "1  73  84  122                1\n",
       "2  72  83  121                1\n",
       "3  70  81  119                1\n",
       "4  70  81  119                1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 245057 entries, 0 to 245056\n",
      "Data columns (total 4 columns):\n",
      "B                  245057 non-null int64\n",
      "G                  245057 non-null int64\n",
      "R                  245057 non-null int64\n",
      "Skin or No-Skin    245057 non-null int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 7.5 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>B</th>\n",
       "      <th>G</th>\n",
       "      <th>R</th>\n",
       "      <th>Skin or No-Skin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>245057.000000</td>\n",
       "      <td>245057.000000</td>\n",
       "      <td>245057.000000</td>\n",
       "      <td>245057.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>125.065446</td>\n",
       "      <td>132.507327</td>\n",
       "      <td>123.177151</td>\n",
       "      <td>1.792461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>62.255653</td>\n",
       "      <td>59.941197</td>\n",
       "      <td>72.562165</td>\n",
       "      <td>0.405546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B              G              R  Skin or No-Skin\n",
       "count  245057.000000  245057.000000  245057.000000    245057.000000\n",
       "mean      125.065446     132.507327     123.177151         1.792461\n",
       "std        62.255653      59.941197      72.562165         0.405546\n",
       "min         0.000000       0.000000       0.000000         1.000000\n",
       "25%        68.000000      87.000000      70.000000         2.000000\n",
       "50%       139.000000     153.000000     128.000000         2.000000\n",
       "75%       176.000000     177.000000     164.000000         2.000000\n",
       "max       255.000000     255.000000     255.000000         2.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>B</th>\n",
       "      <th>G</th>\n",
       "      <th>R</th>\n",
       "      <th>Skin or No-Skin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>50859</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>158</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50860</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>158</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50861</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>158</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50862</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>158</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50863</td>\n",
       "      <td>198</td>\n",
       "      <td>198</td>\n",
       "      <td>158</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>245052</td>\n",
       "      <td>163</td>\n",
       "      <td>162</td>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>245053</td>\n",
       "      <td>163</td>\n",
       "      <td>162</td>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>245054</td>\n",
       "      <td>163</td>\n",
       "      <td>162</td>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>245055</td>\n",
       "      <td>163</td>\n",
       "      <td>162</td>\n",
       "      <td>112</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>245056</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194198 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          B    G    R  Skin or No-Skin\n",
       "50859   198  198  158                2\n",
       "50860   198  198  158                2\n",
       "50861   198  198  158                2\n",
       "50862   198  198  158                2\n",
       "50863   198  198  158                2\n",
       "...     ...  ...  ...              ...\n",
       "245052  163  162  112                2\n",
       "245053  163  162  112                2\n",
       "245054  163  162  112                2\n",
       "245055  163  162  112                2\n",
       "245056  255  255  255                2\n",
       "\n",
       "[194198 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Skin or No-Skin']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B                  0\n",
       "G                  0\n",
       "R                  0\n",
       "Skin or No-Skin    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    194198\n",
       "1     50859\n",
       "Name: Skin or No-Skin, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Skin or No-Skin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Skin or No-Skin'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245057"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skin=data['Skin or No-Skin'].value_counts()[1]\n",
    "skin\n",
    "no_skin=data['Skin or No-Skin'].value_counts()[2]\n",
    "no_skin\n",
    "sum=skin+no_skin\n",
    "sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skin Percentage: 20.753947040892527\n",
      "No-Skin Percentage: 79.24605295910747\n"
     ]
    }
   ],
   "source": [
    "print('Skin Percentage: {}'.format(100*(skin/sum)))\n",
    "print('No-Skin Percentage: {}'.format(100*(no_skin/sum)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Skin or No-Skin'].replace(to_replace=2,value=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>B</th>\n",
       "      <th>G</th>\n",
       "      <th>R</th>\n",
       "      <th>Skin or No-Skin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>85</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>84</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>72</td>\n",
       "      <td>83</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "      <td>81</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>81</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    B   G    R  Skin or No-Skin\n",
       "0  74  85  123                1\n",
       "1  73  84  122                1\n",
       "2  72  83  121                1\n",
       "3  70  81  119                1\n",
       "4  70  81  119                1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    194198\n",
       "1     50859\n",
       "Name: Skin or No-Skin, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Skin or No-Skin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2161af33608>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Skin or No-Skin',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2161b13d908>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Skin or No-Skin',y='B',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2161b944988>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Skin or No-Skin',y='G',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2161b9a3048>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Skin or No-Skin',y='R',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data[[ 'B','G','R']]\n",
    "y=data['Skin or No-Skin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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=0.2,random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log_reg=LogisticRegression(solver='lbfgs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 91.988%\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy is {0}%'.format(round(accuracy_score(y_test,pred)*100,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.94      0.95     38867\n",
      "           1       0.79      0.83      0.81     10145\n",
      "\n",
      "    accuracy                           0.92     49012\n",
      "   macro avg       0.87      0.89      0.88     49012\n",
      "weighted avg       0.92      0.92      0.92     49012\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[36682  2185]\n",
      " [ 1742  8403]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2161aeee1c8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "sns.heatmap(confusion_matrix(y_test,pred),annot=True,fmt='.2f',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating ROC curve of our test data\n",
    "# To Do Replace 1,2,3,4\n",
    "# 1 = Model name, 2 = train data, 3 = train label, 4 = test data\n",
    "y_score = log_reg.fit(X_train,y_train).decision_function(X_test)\n",
    "\n",
    "# fpr stands for false positive rate, and tpr stands for true positive rate.\n",
    "#To do replace 5 with test label\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC is 0.9516220263254362\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Seaborns Beautiful Styling\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "\n",
    "# printing AUC info\n",
    "print('AUC is {}'.format(auc(fpr, tpr)))\n",
    "\n",
    "# plotting the tpr and fpr\n",
    "plt.figure(figsize=(10,8))\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve of test data')\n",
    "\n",
    "# aesthetics\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.yticks([i/20.0 for i in range(21)])\n",
    "plt.xticks([i/20.0 for i in range(21)])\n",
    "plt.xlabel('False Positive Rate (Specificity)')\n",
    "plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "plt.title('Receiver operating characteristic (ROC) Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled = MinMaxScaler()\n",
    "x_scaled = scaled.fit_transform(data[['B','G','R']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_train,scale_test,result_train,result_test = train_test_split(x_scaled,\n",
    "                                                                  y,\n",
    "                                                                  test_size = 0.2,\n",
    "                                                                  random_state = 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(scale_train,result_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=log_reg.predict(scale_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 92.098%\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy is {0}%'.format(round(accuracy_score(result_test,pred)*100,3)))"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}