WISDM-with-without.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Unnamed: 0</th>\n",
       "      <th>\"ACTIVITY\"</th>\n",
       "      <th>\"X0\"</th>\n",
       "      <th>\"X1\"</th>\n",
       "      <th>\"X2\"</th>\n",
       "      <th>\"X3\"</th>\n",
       "      <th>\"X4\"</th>\n",
       "      <th>\"X5\"</th>\n",
       "      <th>\"X6\"</th>\n",
       "      <th>\"X7\"</th>\n",
       "      <th>...</th>\n",
       "      <th>\"ZMFCC12\"</th>\n",
       "      <th>\"XYCOS\"</th>\n",
       "      <th>\"XZCOS\"</th>\n",
       "      <th>\"YZCOS\"</th>\n",
       "      <th>\"XYCOR\"</th>\n",
       "      <th>\"XZCOR\"</th>\n",
       "      <th>\"YZCOR\"</th>\n",
       "      <th>\"RESULTANT\"</th>\n",
       "      <th>\"class\"</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10807</td>\n",
       "      <td>b'P'</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.475261</td>\n",
       "      <td>-0.868185</td>\n",
       "      <td>0.128353</td>\n",
       "      <td>-0.192599</td>\n",
       "      <td>0.473250</td>\n",
       "      <td>0.101977</td>\n",
       "      <td>-0.268787</td>\n",
       "      <td>10.3729</td>\n",
       "      <td>b'1638'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8496</td>\n",
       "      <td>b'C'</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.230</td>\n",
       "      <td>...</td>\n",
       "      <td>0.454954</td>\n",
       "      <td>-0.430003</td>\n",
       "      <td>-0.683816</td>\n",
       "      <td>0.159712</td>\n",
       "      <td>0.026523</td>\n",
       "      <td>-0.324429</td>\n",
       "      <td>-0.191616</td>\n",
       "      <td>10.9713</td>\n",
       "      <td>b'1625'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>13875</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.687652</td>\n",
       "      <td>-0.650537</td>\n",
       "      <td>-0.360839</td>\n",
       "      <td>0.069997</td>\n",
       "      <td>-0.497085</td>\n",
       "      <td>0.442408</td>\n",
       "      <td>-0.522440</td>\n",
       "      <td>10.3849</td>\n",
       "      <td>b'1604'</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2553</td>\n",
       "      <td>b'P'</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.035</td>\n",
       "      <td>...</td>\n",
       "      <td>0.708139</td>\n",
       "      <td>-0.573851</td>\n",
       "      <td>0.411031</td>\n",
       "      <td>-0.663696</td>\n",
       "      <td>-0.424415</td>\n",
       "      <td>0.219443</td>\n",
       "      <td>-0.552526</td>\n",
       "      <td>11.9201</td>\n",
       "      <td>b'1622'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>14263</td>\n",
       "      <td>b'M'</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.462296</td>\n",
       "      <td>-0.729113</td>\n",
       "      <td>0.168830</td>\n",
       "      <td>-0.151120</td>\n",
       "      <td>0.468516</td>\n",
       "      <td>-0.202634</td>\n",
       "      <td>0.115061</td>\n",
       "      <td>10.4240</td>\n",
       "      <td>b'1612'</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 \"ACTIVITY\"   \"X0\"   \"X1\"   \"X2\"   \"X3\"   \"X4\"   \"X5\"   \"X6\"  \\\n",
       "0       10807       b'P'  0.000  0.005  0.015  0.045  0.475  0.390  0.070   \n",
       "1        8496       b'C'  0.000  0.000  0.000  0.020  0.175  0.265  0.270   \n",
       "2       13875       b'R'  0.845  0.120  0.015  0.005  0.000  0.000  0.000   \n",
       "3        2553       b'P'  0.125  0.185  0.190  0.180  0.080  0.050  0.045   \n",
       "4       14263       b'M'  0.000  0.035  0.010  0.065  0.150  0.540  0.180   \n",
       "\n",
       "    \"X7\"  ...  \"ZMFCC12\"   \"XYCOS\"   \"XZCOS\"   \"YZCOS\"   \"XYCOR\"   \"XZCOR\"  \\\n",
       "0  0.000  ...   0.475261 -0.868185  0.128353 -0.192599  0.473250  0.101977   \n",
       "1  0.230  ...   0.454954 -0.430003 -0.683816  0.159712  0.026523 -0.324429   \n",
       "2  0.000  ...   0.687652 -0.650537 -0.360839  0.069997 -0.497085  0.442408   \n",
       "3  0.035  ...   0.708139 -0.573851  0.411031 -0.663696 -0.424415  0.219443   \n",
       "4  0.020  ...   0.462296 -0.729113  0.168830 -0.151120  0.468516 -0.202634   \n",
       "\n",
       "    \"YZCOR\"  \"RESULTANT\"  \"class\"  location  \n",
       "0 -0.268787      10.3729  b'1638'         1  \n",
       "1 -0.191616      10.9713  b'1625'         2  \n",
       "2 -0.522440      10.3849  b'1604'         0  \n",
       "3 -0.552526      11.9201  b'1622'         1  \n",
       "4  0.115061      10.4240  b'1612'         2  \n",
       "\n",
       "[5 rows x 95 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"wisdm_watch_accel.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18211, 95)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(columns=['\"class\"'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[:, ~df.columns.str.contains('^Unnamed')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>\"ACTIVITY\"</th>\n",
       "      <th>\"X0\"</th>\n",
       "      <th>\"X1\"</th>\n",
       "      <th>\"X2\"</th>\n",
       "      <th>\"X3\"</th>\n",
       "      <th>\"X4\"</th>\n",
       "      <th>\"X5\"</th>\n",
       "      <th>\"X6\"</th>\n",
       "      <th>\"X7\"</th>\n",
       "      <th>\"X8\"</th>\n",
       "      <th>...</th>\n",
       "      <th>\"ZMFCC11\"</th>\n",
       "      <th>\"ZMFCC12\"</th>\n",
       "      <th>\"XYCOS\"</th>\n",
       "      <th>\"XZCOS\"</th>\n",
       "      <th>\"YZCOS\"</th>\n",
       "      <th>\"XYCOR\"</th>\n",
       "      <th>\"XZCOR\"</th>\n",
       "      <th>\"YZCOR\"</th>\n",
       "      <th>\"RESULTANT\"</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>b'P'</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.390</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.481447</td>\n",
       "      <td>0.475261</td>\n",
       "      <td>-0.868185</td>\n",
       "      <td>0.128353</td>\n",
       "      <td>-0.192599</td>\n",
       "      <td>0.473250</td>\n",
       "      <td>0.101977</td>\n",
       "      <td>-0.268787</td>\n",
       "      <td>10.3729</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>b'C'</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.460876</td>\n",
       "      <td>0.454954</td>\n",
       "      <td>-0.430003</td>\n",
       "      <td>-0.683816</td>\n",
       "      <td>0.159712</td>\n",
       "      <td>0.026523</td>\n",
       "      <td>-0.324429</td>\n",
       "      <td>-0.191616</td>\n",
       "      <td>10.9713</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>b'R'</td>\n",
       "      <td>0.845</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.696604</td>\n",
       "      <td>0.687652</td>\n",
       "      <td>-0.650537</td>\n",
       "      <td>-0.360839</td>\n",
       "      <td>0.069997</td>\n",
       "      <td>-0.497085</td>\n",
       "      <td>0.442408</td>\n",
       "      <td>-0.522440</td>\n",
       "      <td>10.3849</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>b'P'</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.025</td>\n",
       "      <td>...</td>\n",
       "      <td>0.717357</td>\n",
       "      <td>0.708139</td>\n",
       "      <td>-0.573851</td>\n",
       "      <td>0.411031</td>\n",
       "      <td>-0.663696</td>\n",
       "      <td>-0.424415</td>\n",
       "      <td>0.219443</td>\n",
       "      <td>-0.552526</td>\n",
       "      <td>11.9201</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>b'M'</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.540</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.468313</td>\n",
       "      <td>0.462296</td>\n",
       "      <td>-0.729113</td>\n",
       "      <td>0.168830</td>\n",
       "      <td>-0.151120</td>\n",
       "      <td>0.468516</td>\n",
       "      <td>-0.202634</td>\n",
       "      <td>0.115061</td>\n",
       "      <td>10.4240</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 93 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  \"ACTIVITY\"   \"X0\"   \"X1\"   \"X2\"   \"X3\"   \"X4\"   \"X5\"   \"X6\"   \"X7\"   \"X8\"  \\\n",
       "0       b'P'  0.000  0.005  0.015  0.045  0.475  0.390  0.070  0.000  0.000   \n",
       "1       b'C'  0.000  0.000  0.000  0.020  0.175  0.265  0.270  0.230  0.020   \n",
       "2       b'R'  0.845  0.120  0.015  0.005  0.000  0.000  0.000  0.000  0.000   \n",
       "3       b'P'  0.125  0.185  0.190  0.180  0.080  0.050  0.045  0.035  0.025   \n",
       "4       b'M'  0.000  0.035  0.010  0.065  0.150  0.540  0.180  0.020  0.000   \n",
       "\n",
       "   ...  \"ZMFCC11\"  \"ZMFCC12\"   \"XYCOS\"   \"XZCOS\"   \"YZCOS\"   \"XYCOR\"  \\\n",
       "0  ...   0.481447   0.475261 -0.868185  0.128353 -0.192599  0.473250   \n",
       "1  ...   0.460876   0.454954 -0.430003 -0.683816  0.159712  0.026523   \n",
       "2  ...   0.696604   0.687652 -0.650537 -0.360839  0.069997 -0.497085   \n",
       "3  ...   0.717357   0.708139 -0.573851  0.411031 -0.663696 -0.424415   \n",
       "4  ...   0.468313   0.462296 -0.729113  0.168830 -0.151120  0.468516   \n",
       "\n",
       "    \"XZCOR\"   \"YZCOR\"  \"RESULTANT\"  location  \n",
       "0  0.101977 -0.268787      10.3729         1  \n",
       "1 -0.324429 -0.191616      10.9713         2  \n",
       "2  0.442408 -0.522440      10.3849         0  \n",
       "3  0.219443 -0.552526      11.9201         1  \n",
       "4 -0.202634  0.115061      10.4240         2  \n",
       "\n",
       "[5 rows x 93 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df.iloc[:, 0].values\n",
    "y=y.astype('str')\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_y = LabelEncoder()\n",
    "labels= labelencoder_y.fit_transform(y)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(10,8)})\n",
    "ax = sns.countplot(x=labels)\n",
    "for i in ax.patches:\n",
    "  ax.text(x=i.get_x(),y=i.get_height(), s=str(i.get_height()),fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy=[]\n",
    "accuracyl=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WITHOUT LOCATION MODEL\n",
    "y = df.iloc[:, 0].values\n",
    "y=y.astype('str')\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder_y = LabelEncoder()\n",
    "y = labelencoder_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18211, 91)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.iloc[:, 1:92].values\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DECISION TREE\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "clf = DecisionTreeClassifier()\n",
    "clf = clf.fit(x_train,y_train)\n",
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7388535031847133"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "Accuracy_d1=accuracy_score(y_test,y_pred)\n",
    "Accuracy_d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy.append(Accuracy_d1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.84 (+/- 0.01)\n"
     ]
    }
   ],
   "source": [
    "#RANDOM FOREST\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators = 400, criterion = 'gini', random_state = 0)\n",
    "classifier.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "y_pred = classifier.predict(x_test)\n",
    "Accuracy_r1=accuracy_score(y_test,y_pred)\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(classifier, x, y, cv=5)\n",
    "scores\n",
    "\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Accuracy_r1=(scores.mean(), scores.std() * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "del accuracy[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy.insert(1,Accuracy_r1[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ravi\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "Train on 12201 samples, validate on 6010 samples\n",
      "Epoch 1/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 1.8345 - accuracy: 0.4156 - val_loss: 1.4011 - val_accuracy: 0.5471\n",
      "Epoch 2/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 1.2793 - accuracy: 0.5721 - val_loss: 1.2506 - val_accuracy: 0.5814\n",
      "Epoch 3/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 1.0846 - accuracy: 0.6383 - val_loss: 1.0665 - val_accuracy: 0.6389\n",
      "Epoch 4/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.9279 - accuracy: 0.6844 - val_loss: 0.9353 - val_accuracy: 0.6775\n",
      "Epoch 5/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.8520 - accuracy: 0.7099 - val_loss: 0.9225 - val_accuracy: 0.6905\n",
      "Epoch 6/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.7892 - accuracy: 0.7302 - val_loss: 0.8795 - val_accuracy: 0.7025\n",
      "Epoch 7/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.7384 - accuracy: 0.7489 - val_loss: 0.9204 - val_accuracy: 0.7012\n",
      "Epoch 8/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.6852 - accuracy: 0.7672 - val_loss: 0.8190 - val_accuracy: 0.7391\n",
      "Epoch 9/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.6393 - accuracy: 0.7801 - val_loss: 0.8007 - val_accuracy: 0.7419\n",
      "Epoch 10/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.5991 - accuracy: 0.7974 - val_loss: 0.7538 - val_accuracy: 0.7564\n",
      "Epoch 11/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.5735 - accuracy: 0.8049 - val_loss: 0.7706 - val_accuracy: 0.7567\n",
      "Epoch 12/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.5342 - accuracy: 0.8147 - val_loss: 0.8173 - val_accuracy: 0.7526\n",
      "Epoch 13/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.5156 - accuracy: 0.8213 - val_loss: 0.7486 - val_accuracy: 0.7649\n",
      "Epoch 14/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.4813 - accuracy: 0.8317 - val_loss: 0.7280 - val_accuracy: 0.7777\n",
      "Epoch 15/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.4595 - accuracy: 0.8344 - val_loss: 0.7636 - val_accuracy: 0.7752\n",
      "Epoch 16/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.4280 - accuracy: 0.8485 - val_loss: 0.8449 - val_accuracy: 0.7622\n",
      "Epoch 17/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.4203 - accuracy: 0.8480 - val_loss: 0.8054 - val_accuracy: 0.7707\n",
      "Epoch 18/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.3773 - accuracy: 0.8639 - val_loss: 0.7841 - val_accuracy: 0.7739\n",
      "Epoch 19/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.3437 - accuracy: 0.8774 - val_loss: 0.8059 - val_accuracy: 0.7777\n",
      "Epoch 20/20\n",
      "12201/12201 [==============================] - 24s 2ms/step - loss: 0.3465 - accuracy: 0.8760 - val_loss: 0.8321 - val_accuracy: 0.7674\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras import layers\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(64,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(Dense(1024,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(Dense(1024,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(Dense(1024,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(Dense(18,activation = \"softmax\"))\n",
    "optimizer = keras.optimizers.Adam(lr=0.01)\n",
    "model.compile(optimizer = \"adam\", loss = \"sparse_categorical_crossentropy\", metrics = ['accuracy'])\n",
    "history =  model.fit(x,y,validation_split=0.33,epochs = 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_accuracy', 'loss', 'accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 12201 samples, validate on 6010 samples\n",
      "Epoch 1/20\n",
      "12201/12201 [==============================] - 39s 3ms/step - loss: 1.8991 - accuracy: 0.3853 - val_loss: 1.5041 - val_accuracy: 0.4755\n",
      "Epoch 2/20\n",
      "12201/12201 [==============================] - 29s 2ms/step - loss: 1.3250 - accuracy: 0.5594 - val_loss: 1.1873 - val_accuracy: 0.6020\n",
      "Epoch 3/20\n",
      "12201/12201 [==============================] - 38s 3ms/step - loss: 1.1278 - accuracy: 0.6200 - val_loss: 1.0587 - val_accuracy: 0.6471\n",
      "Epoch 4/20\n",
      "12201/12201 [==============================] - 31s 3ms/step - loss: 1.0027 - accuracy: 0.6604 - val_loss: 0.9754 - val_accuracy: 0.6800\n",
      "Epoch 5/20\n",
      "12201/12201 [==============================] - 32s 3ms/step - loss: 0.9125 - accuracy: 0.6885 - val_loss: 0.8926 - val_accuracy: 0.7047\n",
      "Epoch 6/20\n",
      "12201/12201 [==============================] - 28s 2ms/step - loss: 0.8406 - accuracy: 0.7163 - val_loss: 0.9196 - val_accuracy: 0.6975\n",
      "Epoch 7/20\n",
      "12201/12201 [==============================] - 36s 3ms/step - loss: 0.7741 - accuracy: 0.7376 - val_loss: 0.8578 - val_accuracy: 0.7206\n",
      "Epoch 8/20\n",
      "12201/12201 [==============================] - 31s 3ms/step - loss: 0.7331 - accuracy: 0.7481 - val_loss: 0.7924 - val_accuracy: 0.7351\n",
      "Epoch 9/20\n",
      "12201/12201 [==============================] - 31s 3ms/step - loss: 0.7065 - accuracy: 0.7576 - val_loss: 0.7687 - val_accuracy: 0.7499\n",
      "Epoch 10/20\n",
      "12201/12201 [==============================] - 28s 2ms/step - loss: 0.6440 - accuracy: 0.7775 - val_loss: 0.7714 - val_accuracy: 0.7456\n",
      "Epoch 11/20\n",
      "12201/12201 [==============================] - 28s 2ms/step - loss: 0.6264 - accuracy: 0.7858 - val_loss: 0.7739 - val_accuracy: 0.7502\n",
      "Epoch 12/20\n",
      "12201/12201 [==============================] - 28s 2ms/step - loss: 0.6056 - accuracy: 0.7946 - val_loss: 0.7852 - val_accuracy: 0.7471\n",
      "Epoch 13/20\n",
      "12201/12201 [==============================] - 28s 2ms/step - loss: 0.5686 - accuracy: 0.8036 - val_loss: 0.7365 - val_accuracy: 0.7715\n",
      "Epoch 14/20\n",
      "12201/12201 [==============================] - 28s 2ms/step - loss: 0.5389 - accuracy: 0.8137 - val_loss: 0.7679 - val_accuracy: 0.7609\n",
      "Epoch 15/20\n",
      "12201/12201 [==============================] - 28s 2ms/step - loss: 0.5196 - accuracy: 0.8183 - val_loss: 0.7168 - val_accuracy: 0.7774\n",
      "Epoch 16/20\n",
      "12201/12201 [==============================] - 30s 2ms/step - loss: 0.4965 - accuracy: 0.8240 - val_loss: 0.6937 - val_accuracy: 0.7799\n",
      "Epoch 17/20\n",
      "12201/12201 [==============================] - 29s 2ms/step - loss: 0.4781 - accuracy: 0.8317 - val_loss: 0.7282 - val_accuracy: 0.7862\n",
      "Epoch 18/20\n",
      "12201/12201 [==============================] - 28s 2ms/step - loss: 0.4562 - accuracy: 0.8385 - val_loss: 0.7496 - val_accuracy: 0.7794\n",
      "Epoch 19/20\n",
      "12201/12201 [==============================] - 28s 2ms/step - loss: 0.4445 - accuracy: 0.8430 - val_loss: 0.7070 - val_accuracy: 0.7918\n",
      "Epoch 20/20\n",
      "12201/12201 [==============================] - 31s 3ms/step - loss: 0.4042 - accuracy: 0.8585 - val_loss: 0.7233 - val_accuracy: 0.7912\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras import layers\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(64,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(Dense(1024,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(Dense(1024,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(Dense(1024,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(layers.Dropout(0.25))\n",
    "model.add(Dense(18,activation = \"softmax\"))\n",
    "optimizer = keras.optimizers.Adam(lr=0.01)\n",
    "model.compile(optimizer = \"adam\", loss = \"sparse_categorical_crossentropy\", metrics = ['accuracy'])\n",
    "history =  model.fit(x,y,validation_split=0.33,epochs = 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_accuracy', 'loss', 'accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4553/4553 [==============================] - 1s 296us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8346145153045654"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_a1=model.evaluate(x_test,y_test)[1]\n",
    "accuracy_a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy.append(accuracy_a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18211, 92)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#WITH LOCATION\n",
    "x1 = df.iloc[:, 1:].values\n",
    "x1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x1_train,x1_test,y1_train,y1_test=train_test_split(x1,y,test_size=0.25,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7878321985504063"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DECISION TREE WTITH LOCATION\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "clf = DecisionTreeClassifier()\n",
    "clf = clf.fit(x1_train,y1_train)\n",
    "y1_pred = clf.predict(x1_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "Accuracy_d2=accuracy_score(y1_test,y1_pred)\n",
    "Accuracy_d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracyl.append(Accuracy_d2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.88 (+/- 0.01)\n"
     ]
    }
   ],
   "source": [
    "#RANDOM FOREST WITH LOCATION\n",
    "\n",
    "classifier = RandomForestClassifier(n_estimators = 400, criterion = 'gini', random_state = 0)\n",
    "classifier.fit(x1_train, y1_train)\n",
    "\n",
    "\n",
    "y1_pred = classifier.predict(x1_test)\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(classifier, x1, y, cv=5)\n",
    "scores\n",
    "\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
    "Accuracy_r2 = (scores.mean(), scores.std() * 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "del accuracyl[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracyl.insert(1,Accuracy_r2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "13658/13658 [==============================] - 27s 2ms/step - loss: 0.9799 - accuracy: 0.6598\n",
      "Epoch 2/20\n",
      "13658/13658 [==============================] - 27s 2ms/step - loss: 0.5859 - accuracy: 0.7970\n",
      "Epoch 3/20\n",
      "13658/13658 [==============================] - 27s 2ms/step - loss: 0.4882 - accuracy: 0.8260\n",
      "Epoch 4/20\n",
      "13658/13658 [==============================] - 28s 2ms/step - loss: 0.4258 - accuracy: 0.8522\n",
      "Epoch 5/20\n",
      "13658/13658 [==============================] - 25s 2ms/step - loss: 0.3777 - accuracy: 0.8678\n",
      "Epoch 6/20\n",
      "13658/13658 [==============================] - 25s 2ms/step - loss: 0.3437 - accuracy: 0.8809\n",
      "Epoch 7/20\n",
      "13658/13658 [==============================] - 26s 2ms/step - loss: 0.3194 - accuracy: 0.8861 0s - loss: 0.3193 - accuracy: 0.\n",
      "Epoch 8/20\n",
      "13658/13658 [==============================] - 27s 2ms/step - loss: 0.2856 - accuracy: 0.8984\n",
      "Epoch 9/20\n",
      "13658/13658 [==============================] - 29s 2ms/step - loss: 0.2669 - accuracy: 0.9066\n",
      "Epoch 10/20\n",
      "13658/13658 [==============================] - 26s 2ms/step - loss: 0.2401 - accuracy: 0.9170\n",
      "Epoch 11/20\n",
      "13658/13658 [==============================] - 28s 2ms/step - loss: 0.2289 - accuracy: 0.9196\n",
      "Epoch 12/20\n",
      "13658/13658 [==============================] - 27s 2ms/step - loss: 0.2051 - accuracy: 0.9299\n",
      "Epoch 13/20\n",
      "13658/13658 [==============================] - 28s 2ms/step - loss: 0.1770 - accuracy: 0.9367\n",
      "Epoch 14/20\n",
      "13658/13658 [==============================] - 25s 2ms/step - loss: 0.1888 - accuracy: 0.9365\n",
      "Epoch 15/20\n",
      "13658/13658 [==============================] - 25s 2ms/step - loss: 0.1557 - accuracy: 0.9445 1s - loss: 0.1538 - accuracy:  - ETA: 0s -\n",
      "Epoch 16/20\n",
      "13658/13658 [==============================] - 28s 2ms/step - loss: 0.1508 - accuracy: 0.9457\n",
      "Epoch 17/20\n",
      "13658/13658 [==============================] - 27s 2ms/step - loss: 0.1692 - accuracy: 0.9457 1s - loss: 0.1 - ETA: 1s - l\n",
      "Epoch 18/20\n",
      "13658/13658 [==============================] - 26s 2ms/step - loss: 0.1414 - accuracy: 0.9523\n",
      "Epoch 19/20\n",
      "13658/13658 [==============================] - 28s 2ms/step - loss: 0.1211 - accuracy: 0.9564\n",
      "Epoch 20/20\n",
      "13658/13658 [==============================] - 27s 2ms/step - loss: 0.1387 - accuracy: 0.9568\n"
     ]
    }
   ],
   "source": [
    "#ANN WITH LOCATION\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x1_train = sc.fit_transform(x1_train)\n",
    "x1_test = sc.transform(x1_test)\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(64,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(Dense(1024,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(Dense(1024,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(Dense(1024,kernel_initializer='glorot_normal',activation = \"relu\"))\n",
    "model.add(layers.Dropout(0.25))\n",
    "model.add(Dense(18,activation = \"softmax\"))\n",
    "optimizer = keras.optimizers.Adam(lr=0.01)\n",
    "model.compile(optimizer = \"adam\", loss = \"sparse_categorical_crossentropy\", metrics = ['accuracy'])\n",
    "history =  model.fit(x1_train,y1_train,epochs = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4553/4553 [==============================] - 1s 242us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8824950456619263"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_a2=model.evaluate(x1_test,y1_test)\n",
    "accuracy_a2[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracyl.append(accuracy_a2[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracies = [85.94,87.61,87.87, 88.24,86.84, 86.03, 85.74, 84.86]\n",
    "layers = [1,2,3,4,5,6,7,8]\n",
    "df = pd.DataFrame(list(zip(layers, accuracies)), \n",
    "               columns =['layers', 'accuracies']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "layers1=[1,2,3,4,5,6,7,8]\n",
    "accuracies_without = [78.80, 83.08, 83.21, 83.46, 82.84, 81.55, 81.03, 80.98]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(list(zip(layers1, accuracies_without)), \n",
    "               columns =['layers', 'accuracies_without']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(layers, accuracies, label = \"with location\") \n",
    "plt.plot(layers1, accuracies_without, label = \"withoutlocation\")\n",
    "plt.xlabel('layers')\n",
    "plt.ylabel('Accuracy')\n",
    "# Set a title of the current axes.\n",
    "plt.title('change in accuracy w.r.t layers ')\n",
    "# show a legend on the plot\n",
    "plt.legend()\n",
    "# Display a figure.\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7388535031847133, 0.8409703800493078, 0.8346145153045654]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.7878321985504063, 0.8772678426336974, 0.8824950456619263]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracyl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = [ '%.2f' % elem for elem in accuracy ]\n",
    "accuracyl = [ '%.2f' % elem for elem in accuracyl]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = [ '%.2f' % elem for elem in accuracy ]\n",
    "accuracyl = [ '%.2f' % elem for elem in accuracyl]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy=[0.72,0.74,0.84,0.83]\n",
    "accuracyl=[0.75,0.79,0.88,0.88]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['SVM', 'Decision_Tree','Random_forest','ANN']\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.25\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "rects1 = ax.bar(x -0.5*width, accuracy, width, label='withoutlocation')\n",
    "rects2 = ax.bar(x +0.5*width, accuracyl,width, label='withlocation')\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_title('Accuracy with and without location')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "def autolabel(rects):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 4),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 4
}