Hack_Night_1.0 (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from keras import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import svm, tree\n",
    "import xgboost\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasett=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('trainset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=dataset.iloc[:,7:57]\n",
    "y=dataset['treatment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_testt=datasett.iloc[:,7:57]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\santhosh kumar\\Anaconda3\\envs\\HC#1958_stage1\\lib\\site-packages\\sklearn\\preprocessing\\data.py:617: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\santhosh kumar\\Anaconda3\\envs\\HC#1958_stage1\\lib\\site-packages\\sklearn\\base.py:462: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "# standardizing the input feature\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x = sc.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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=0.3, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = Sequential()\n",
    "#First Hidden Layer\n",
    "classifier.add(Dense(32, activation='relu', kernel_initializer='random_normal', input_dim=50))\n",
    "#Second  Hidden Layer\n",
    "classifier.add(Dense(32, activation='relu', kernel_initializer='random_normal'))\n",
    "\n",
    "classifier.add(Dense(32, activation='relu', kernel_initializer='random_normal'))\n",
    "#Output Layer\n",
    "classifier.add(Dense(1, activation='sigmoid', kernel_initializer='random_normal'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compiling the neural network\n",
    "classifier.compile(optimizer ='adadelta',loss='binary_crossentropy', metrics =['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Epoch 1/100\n",
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      "Epoch 3/100\n",
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      "Epoch 5/100\n",
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      "Epoch 6/100\n",
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      "Epoch 7/100\n",
      "689/689 [==============================] - 0s 12us/step - loss: 0.6928 - acc: 0.5109\n",
      "Epoch 8/100\n",
      "689/689 [==============================] - 0s 10us/step - loss: 0.6928 - acc: 0.5109\n",
      "Epoch 9/100\n",
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      "Epoch 10/100\n",
      "689/689 [==============================] - 0s 12us/step - loss: 0.6927 - acc: 0.5109\n",
      "Epoch 11/100\n",
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      "Epoch 12/100\n",
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      "Epoch 13/100\n",
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      "Epoch 14/100\n",
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      "Epoch 88/100\n",
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      "Epoch 89/100\n",
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      "Epoch 90/100\n",
      "689/689 [==============================] - 0s 10us/step - loss: 0.0518 - acc: 0.9913\n",
      "Epoch 91/100\n",
      "689/689 [==============================] - ETA: 0s - loss: 0.0469 - acc: 0.992 - 0s 10us/step - loss: 0.0486 - acc: 0.9898\n",
      "Epoch 92/100\n",
      "689/689 [==============================] - 0s 10us/step - loss: 0.0436 - acc: 0.9913\n",
      "Epoch 93/100\n",
      "689/689 [==============================] - 0s 10us/step - loss: 0.0407 - acc: 0.9913\n",
      "Epoch 94/100\n",
      "689/689 [==============================] - 0s 12us/step - loss: 0.0466 - acc: 0.9898\n",
      "Epoch 95/100\n",
      "689/689 [==============================] - 0s 13us/step - loss: 0.0350 - acc: 0.9942\n",
      "Epoch 96/100\n",
      "689/689 [==============================] - 0s 10us/step - loss: 0.0333 - acc: 0.9942\n",
      "Epoch 97/100\n",
      "689/689 [==============================] - 0s 10us/step - loss: 0.0288 - acc: 0.9971\n",
      "Epoch 98/100\n",
      "689/689 [==============================] - 0s 13us/step - loss: 0.0282 - acc: 0.9971\n",
      "Epoch 99/100\n",
      "689/689 [==============================] - 0s 14us/step - loss: 0.0307 - acc: 0.9913\n",
      "Epoch 100/100\n",
      "689/689 [==============================] - 0s 12us/step - loss: 0.0239 - acc: 0.9971\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x250b1b37160>"
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     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting the data to the training dataset\n",
    "classifier.fit(x_train,y_train, batch_size=128, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "689/689 [==============================] - 0s 136us/step\n"
     ]
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     "data": {
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     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "eval_model=classifier.evaluate(x_train, y_train)\n",
    "eval_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= classifier.predict(x_testt)\n",
    "y_pred=(y_pred>0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(y_pred)\n",
    "df.to_csv('sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>256 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0\n",
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "7    False\n",
       "8    False\n",
       "9    False\n",
       "10   False\n",
       "11   False\n",
       "12    True\n",
       "13   False\n",
       "14    True\n",
       "15   False\n",
       "16   False\n",
       "17   False\n",
       "18   False\n",
       "19   False\n",
       "20   False\n",
       "21   False\n",
       "22    True\n",
       "23   False\n",
       "24    True\n",
       "25   False\n",
       "26   False\n",
       "27   False\n",
       "28   False\n",
       "29   False\n",
       "..     ...\n",
       "226  False\n",
       "227  False\n",
       "228  False\n",
       "229   True\n",
       "230   True\n",
       "231  False\n",
       "232  False\n",
       "233   True\n",
       "234  False\n",
       "235   True\n",
       "236  False\n",
       "237  False\n",
       "238  False\n",
       "239  False\n",
       "240  False\n",
       "241  False\n",
       "242  False\n",
       "243  False\n",
       "244  False\n",
       "245  False\n",
       "246  False\n",
       "247  False\n",
       "248   True\n",
       "249  False\n",
       "250   True\n",
       "251  False\n",
       "252  False\n",
       "253  False\n",
       "254  False\n",
       "255  False\n",
       "\n",
       "[256 rows x 1 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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