Human Activity Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Your smartphone knows your current activity using sensors data\n",
    "    *In this example with this real world mobile sensors datasets we'll compare some machine learning algorithms to get to  know that which one could perform better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data"
   ]
  },
  {
   "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>tBodyAcc-mean()-X</th>\n",
       "      <th>tBodyAcc-mean()-Y</th>\n",
       "      <th>tBodyAcc-mean()-Z</th>\n",
       "      <th>tBodyAcc-std()-X</th>\n",
       "      <th>tBodyAcc-std()-Y</th>\n",
       "      <th>tBodyAcc-std()-Z</th>\n",
       "      <th>tBodyAcc-mad()-X</th>\n",
       "      <th>tBodyAcc-mad()-Y</th>\n",
       "      <th>tBodyAcc-mad()-Z</th>\n",
       "      <th>tBodyAcc-max()-X</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMag-kurtosis()</th>\n",
       "      <th>angle(tBodyAccMean,gravity)</th>\n",
       "      <th>angle(tBodyAccJerkMean),gravityMean)</th>\n",
       "      <th>angle(tBodyGyroMean,gravityMean)</th>\n",
       "      <th>angle(tBodyGyroJerkMean,gravityMean)</th>\n",
       "      <th>angle(X,gravityMean)</th>\n",
       "      <th>angle(Y,gravityMean)</th>\n",
       "      <th>angle(Z,gravityMean)</th>\n",
       "      <th>subject</th>\n",
       "      <th>Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.288585</td>\n",
       "      <td>-0.020294</td>\n",
       "      <td>-0.132905</td>\n",
       "      <td>-0.995279</td>\n",
       "      <td>-0.983111</td>\n",
       "      <td>-0.913526</td>\n",
       "      <td>-0.995112</td>\n",
       "      <td>-0.983185</td>\n",
       "      <td>-0.923527</td>\n",
       "      <td>-0.934724</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.710304</td>\n",
       "      <td>-0.112754</td>\n",
       "      <td>0.030400</td>\n",
       "      <td>-0.464761</td>\n",
       "      <td>-0.018446</td>\n",
       "      <td>-0.841247</td>\n",
       "      <td>0.179941</td>\n",
       "      <td>-0.058627</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.278419</td>\n",
       "      <td>-0.016411</td>\n",
       "      <td>-0.123520</td>\n",
       "      <td>-0.998245</td>\n",
       "      <td>-0.975300</td>\n",
       "      <td>-0.960322</td>\n",
       "      <td>-0.998807</td>\n",
       "      <td>-0.974914</td>\n",
       "      <td>-0.957686</td>\n",
       "      <td>-0.943068</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.861499</td>\n",
       "      <td>0.053477</td>\n",
       "      <td>-0.007435</td>\n",
       "      <td>-0.732626</td>\n",
       "      <td>0.703511</td>\n",
       "      <td>-0.844788</td>\n",
       "      <td>0.180289</td>\n",
       "      <td>-0.054317</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.279653</td>\n",
       "      <td>-0.019467</td>\n",
       "      <td>-0.113462</td>\n",
       "      <td>-0.995380</td>\n",
       "      <td>-0.967187</td>\n",
       "      <td>-0.978944</td>\n",
       "      <td>-0.996520</td>\n",
       "      <td>-0.963668</td>\n",
       "      <td>-0.977469</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.760104</td>\n",
       "      <td>-0.118559</td>\n",
       "      <td>0.177899</td>\n",
       "      <td>0.100699</td>\n",
       "      <td>0.808529</td>\n",
       "      <td>-0.848933</td>\n",
       "      <td>0.180637</td>\n",
       "      <td>-0.049118</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.279174</td>\n",
       "      <td>-0.026201</td>\n",
       "      <td>-0.123283</td>\n",
       "      <td>-0.996091</td>\n",
       "      <td>-0.983403</td>\n",
       "      <td>-0.990675</td>\n",
       "      <td>-0.997099</td>\n",
       "      <td>-0.982750</td>\n",
       "      <td>-0.989302</td>\n",
       "      <td>-0.938692</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.482845</td>\n",
       "      <td>-0.036788</td>\n",
       "      <td>-0.012892</td>\n",
       "      <td>0.640011</td>\n",
       "      <td>-0.485366</td>\n",
       "      <td>-0.848649</td>\n",
       "      <td>0.181935</td>\n",
       "      <td>-0.047663</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.276629</td>\n",
       "      <td>-0.016570</td>\n",
       "      <td>-0.115362</td>\n",
       "      <td>-0.998139</td>\n",
       "      <td>-0.980817</td>\n",
       "      <td>-0.990482</td>\n",
       "      <td>-0.998321</td>\n",
       "      <td>-0.979672</td>\n",
       "      <td>-0.990441</td>\n",
       "      <td>-0.942469</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.699205</td>\n",
       "      <td>0.123320</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.693578</td>\n",
       "      <td>-0.615971</td>\n",
       "      <td>-0.847865</td>\n",
       "      <td>0.185151</td>\n",
       "      <td>-0.043892</td>\n",
       "      <td>1</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 563 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  tBodyAcc-std()-X  \\\n",
       "0           0.288585          -0.020294          -0.132905         -0.995279   \n",
       "1           0.278419          -0.016411          -0.123520         -0.998245   \n",
       "2           0.279653          -0.019467          -0.113462         -0.995380   \n",
       "3           0.279174          -0.026201          -0.123283         -0.996091   \n",
       "4           0.276629          -0.016570          -0.115362         -0.998139   \n",
       "\n",
       "   tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  tBodyAcc-mad()-Y  \\\n",
       "0         -0.983111         -0.913526         -0.995112         -0.983185   \n",
       "1         -0.975300         -0.960322         -0.998807         -0.974914   \n",
       "2         -0.967187         -0.978944         -0.996520         -0.963668   \n",
       "3         -0.983403         -0.990675         -0.997099         -0.982750   \n",
       "4         -0.980817         -0.990482         -0.998321         -0.979672   \n",
       "\n",
       "   tBodyAcc-mad()-Z  tBodyAcc-max()-X    ...     \\\n",
       "0         -0.923527         -0.934724    ...      \n",
       "1         -0.957686         -0.943068    ...      \n",
       "2         -0.977469         -0.938692    ...      \n",
       "3         -0.989302         -0.938692    ...      \n",
       "4         -0.990441         -0.942469    ...      \n",
       "\n",
       "   fBodyBodyGyroJerkMag-kurtosis()  angle(tBodyAccMean,gravity)  \\\n",
       "0                        -0.710304                    -0.112754   \n",
       "1                        -0.861499                     0.053477   \n",
       "2                        -0.760104                    -0.118559   \n",
       "3                        -0.482845                    -0.036788   \n",
       "4                        -0.699205                     0.123320   \n",
       "\n",
       "   angle(tBodyAccJerkMean),gravityMean)  angle(tBodyGyroMean,gravityMean)  \\\n",
       "0                              0.030400                         -0.464761   \n",
       "1                             -0.007435                         -0.732626   \n",
       "2                              0.177899                          0.100699   \n",
       "3                             -0.012892                          0.640011   \n",
       "4                              0.122542                          0.693578   \n",
       "\n",
       "   angle(tBodyGyroJerkMean,gravityMean)  angle(X,gravityMean)  \\\n",
       "0                             -0.018446             -0.841247   \n",
       "1                              0.703511             -0.844788   \n",
       "2                              0.808529             -0.848933   \n",
       "3                             -0.485366             -0.848649   \n",
       "4                             -0.615971             -0.847865   \n",
       "\n",
       "   angle(Y,gravityMean)  angle(Z,gravityMean)  subject  Activity  \n",
       "0              0.179941             -0.058627        1  STANDING  \n",
       "1              0.180289             -0.054317        1  STANDING  \n",
       "2              0.180637             -0.049118        1  STANDING  \n",
       "3              0.181935             -0.047663        1  STANDING  \n",
       "4              0.185151             -0.043892        1  STANDING  \n",
       "\n",
       "[5 rows x 563 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data=pd.read_csv('train.csv')\n",
    "test_data=pd.read_csv('test.csv')\n",
    "# Know your data\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7352, 563)\n"
     ]
    }
   ],
   "source": [
    "print(train_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize your data (first 10 features representation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(2,5,figsize=(16,6))\n",
    "count=0 #count as iterator\n",
    "for ax in axes:\n",
    "    for a in ax:\n",
    "        a.scatter(train_data[train_data.columns[-1]],train_data[train_data.columns[count]])\n",
    "        count+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare data for training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove labels from training dataset feature\n",
    "X_train=train_data.drop(columns=train_data.columns[-1])\n",
    "y_train=train_data[train_data.columns[-1]]\n",
    "\n",
    "# Do the same with test data\n",
    "X_test=test_data.drop(columns=train_data.columns[-1])\n",
    "y_test=test_data[test_data.columns[-1]]\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "enc=LabelEncoder().fit(y_train)\n",
    "y_train=enc.fit_transform(y_train)\n",
    "y_test=enc.fit_transform(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now the dataset is ready to fit into a model. Let's start with KNearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.98\n",
      "Testing accuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "model=KNeighborsClassifier(n_neighbors=10)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "train_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print(\"Training accuracy: {:.2f}\".format(train_score))\n",
    "print(\"Testing accuracy: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.97\n",
      "Testing accuracy: 0.95\n"
     ]
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "model.fit(X_train,y_train)\n",
    "model.predict(X_test)\n",
    "\n",
    "train_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print(\"Training accuracy: {:.2f}\".format(train_score))\n",
    "print(\"Testing accuracy: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.99\n",
      "Testing accuracy: 0.96\n"
     ]
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "train_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print(\"Training accuracy: {:.2f}\".format(train_score))\n",
    "print(\"Testing accuracy: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 1.00\n",
      "Testing accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "model=DecisionTreeClassifier().fit(X_train,y_train)\n",
    "\n",
    "train_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print(\"Training accuracy: {:.2f}\".format(train_score))\n",
    "print(\"Testing accuracy: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 1.00\n",
      "Testing accuracy: 0.90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    1.0s finished\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "model=RandomForestClassifier(n_estimators=10,verbose=1)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "train_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print(\"Training accuracy: {:.2f}\".format(train_score))\n",
    "print(\"Testing accuracy: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.86\n",
      "Testing accuracy: 0.85\n"
     ]
    }
   ],
   "source": [
    "model=BernoulliNB(alpha=0.5).fit(X_train,y_train)\n",
    "\n",
    "train_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print(\"Training accuracy: {:.2f}\".format(train_score))\n",
    "print(\"Testing accuracy: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM]Training accuracy: 0.99\n",
      "Testing accuracy: 0.95\n"
     ]
    }
   ],
   "source": [
    "model=SVC(C=10,verbose=True)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "train_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print(\"Training accuracy: {:.2f}\".format(train_score))\n",
    "print(\"Testing accuracy: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Shallow Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 1.18254162\n",
      "Iteration 2, loss = 0.61803673\n",
      "Iteration 3, loss = 0.43061490\n",
      "Iteration 4, loss = 0.32752916\n",
      "Iteration 5, loss = 0.26761825\n",
      "Iteration 6, loss = 0.23157542\n",
      "Iteration 7, loss = 0.20000472\n",
      "Iteration 8, loss = 0.17721180\n",
      "Iteration 9, loss = 0.16113467\n",
      "Iteration 10, loss = 0.14666258\n",
      "Iteration 11, loss = 0.13487858\n",
      "Iteration 12, loss = 0.12681642\n",
      "Iteration 13, loss = 0.11811306\n",
      "Iteration 14, loss = 0.11007129\n",
      "Iteration 15, loss = 0.10706951\n",
      "Iteration 16, loss = 0.10073250\n",
      "Iteration 17, loss = 0.09459407\n",
      "Iteration 18, loss = 0.08915419\n",
      "Iteration 19, loss = 0.08900941\n",
      "Iteration 20, loss = 0.08230705\n",
      "Iteration 21, loss = 0.07834855\n",
      "Iteration 22, loss = 0.07490411\n",
      "Iteration 23, loss = 0.07329199\n",
      "Iteration 24, loss = 0.07239551\n",
      "Iteration 25, loss = 0.07258925\n",
      "Iteration 26, loss = 0.06718467\n",
      "Iteration 27, loss = 0.06397433\n",
      "Iteration 28, loss = 0.07002200\n",
      "Iteration 29, loss = 0.06253323\n",
      "Iteration 30, loss = 0.05878763\n",
      "Iteration 31, loss = 0.05876800\n",
      "Iteration 32, loss = 0.05616805\n",
      "Iteration 33, loss = 0.05641676\n",
      "Iteration 34, loss = 0.05796868\n",
      "Iteration 35, loss = 0.05747333\n",
      "Training loss did not improve more than tol=0.000100 for two consecutive epochs. Stopping.\n",
      "Training accuracy: 0.98\n",
      "Testing accuracy: 0.95\n"
     ]
    }
   ],
   "source": [
    "model=MLPClassifier(activation='relu',hidden_layer_sizes=(30),solver='adam',verbose=True)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "train_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print(\"Training accuracy: {:.2f}\".format(train_score))\n",
    "print(\"Testing accuracy: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Deep Neural Netwok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 1.12478418\n",
      "Iteration 2, loss = 0.41514919\n",
      "Iteration 3, loss = 0.27080776\n",
      "Iteration 4, loss = 0.19466710\n",
      "Iteration 5, loss = 0.16727822\n",
      "Iteration 6, loss = 0.13890241\n",
      "Iteration 7, loss = 0.10808448\n",
      "Iteration 8, loss = 0.08961608\n",
      "Iteration 9, loss = 0.07715987\n",
      "Iteration 10, loss = 0.08831252\n",
      "Iteration 11, loss = 0.07598972\n",
      "Iteration 12, loss = 0.07404374\n",
      "Iteration 13, loss = 0.05578302\n",
      "Iteration 14, loss = 0.06831314\n",
      "Iteration 15, loss = 0.05584709\n",
      "Iteration 16, loss = 0.05677673\n",
      "Training loss did not improve more than tol=0.000100 for two consecutive epochs. Stopping.\n",
      "Training accuracy: 0.98\n",
      "Testing accuracy: 0.95\n"
     ]
    }
   ],
   "source": [
    "model=MLPClassifier(hidden_layer_sizes=(64,64,32),activation='relu',solver='adam',verbose=True)\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "train_score=model.score(X_train,y_train)\n",
    "test_score=model.score(X_test,y_test)\n",
    "print(\"Training accuracy: {:.2f}\".format(train_score))\n",
    "print(\"Testing accuracy: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bonus: Let's try with keras.\n",
    "if you don't have cuda installed than delete this and below code cells as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=to_categorical(y_train,6)\n",
    "y_test=to_categorical(y_test,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_15 (Dense)             (None, 512)               288256    \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 6)                 774       \n",
      "=================================================================\n",
      "Total params: 453,254\n",
      "Trainable params: 453,254\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Let's build a deep neural netowork\n",
    "model=Sequential()\n",
    "model.add(Dense(512,activation='relu',input_shape=(X_train.shape[1],)))\n",
    "model.add(Dense(256,activation='relu'))\n",
    "model.add(Dense(128,activation='relu'))\n",
    "model.add(Dense(6,activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5881 samples, validate on 1471 samples\n",
      "Epoch 1/10\n",
      "5881/5881 [==============================] - 1s 157us/step - loss: 0.0383 - acc: 0.9850 - val_loss: 0.0512 - val_acc: 0.9782\n",
      "Epoch 2/10\n",
      "5881/5881 [==============================] - 1s 109us/step - loss: 0.0435 - acc: 0.9840 - val_loss: 0.0355 - val_acc: 0.9857\n",
      "Epoch 3/10\n",
      "5881/5881 [==============================] - 1s 105us/step - loss: 0.0518 - acc: 0.9779 - val_loss: 0.0651 - val_acc: 0.9728\n",
      "Epoch 4/10\n",
      "5881/5881 [==============================] - 1s 105us/step - loss: 0.0351 - acc: 0.9871 - val_loss: 0.0427 - val_acc: 0.9830\n",
      "Epoch 5/10\n",
      "5881/5881 [==============================] - 1s 107us/step - loss: 0.0319 - acc: 0.9878 - val_loss: 0.0641 - val_acc: 0.9755\n",
      "Epoch 6/10\n",
      "5881/5881 [==============================] - 1s 104us/step - loss: 0.0268 - acc: 0.9896 - val_loss: 0.0496 - val_acc: 0.9789\n",
      "Epoch 7/10\n",
      "5881/5881 [==============================] - 1s 105us/step - loss: 0.0313 - acc: 0.9881 - val_loss: 0.0673 - val_acc: 0.9762\n",
      "Epoch 8/10\n",
      "5881/5881 [==============================] - 1s 107us/step - loss: 0.0264 - acc: 0.9908 - val_loss: 0.0707 - val_acc: 0.9769\n",
      "Epoch 9/10\n",
      "5881/5881 [==============================] - 1s 105us/step - loss: 0.0174 - acc: 0.9942 - val_loss: 0.1666 - val_acc: 0.9531\n",
      "Epoch 10/10\n",
      "5881/5881 [==============================] - 1s 104us/step - loss: 0.0221 - acc: 0.9922 - val_loss: 0.1238 - val_acc: 0.9592\n"
     ]
    }
   ],
   "source": [
    "# fit the data\n",
    "history=model.fit(X_train,y_train,verbose=1,epochs=10,batch_size=128,validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2947/2947 [==============================] - 1s 219us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.20169052958203568, 0.9355276552426196]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 5, 5, 5], dtype=int64)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training and Validation loss')"
      ]
     },
     "execution_count": 135,
     "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": [
    "# Visualize output\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.plot(history.history['loss'])\n",
    "plt.legend([\"validation loss\",\"loss\"])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title(\"Training and Validation loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training and Validation Accuracy')"
      ]
     },
     "execution_count": 136,
     "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": [
    "plt.plot(history.history['val_acc'])\n",
    "plt.plot(history.history['acc'])\n",
    "plt.legend([\"validation accuracy\",\"Accuracy\"])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Training and Validation Accuracy\")"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}