HumanActivityRecognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HumanActivityRecognition\n",
    "This project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train and test data were saperated\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "70% of the Subject's Reading were taken as trianing data and remaining 30% subjects'recordings were taken for test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Labels\n",
    "WALKING            -> 1<br>\n",
    "WALKING UPSTAIRS   -> 2<br>\n",
    "WALKING DOWNSTAIRS -> 3<br>\n",
    "SITTING            -> 4<br>\n",
    "STANDING           -> 5<br>\n",
    "LYING              -> 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 30 volunteers referred to as subjects  performed the experiment for data collection wearing smartphones sensors on their waist.\n",
    "- The two smartphone sensors captured the 3 axial linear acceleration as well as the 3 axial angular velocity of the subject.\n",
    "- The sensor signals were sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window).\n",
    "- The data were recorded at the constant  frequency of 50Hz  (50 data points  were recorded each second ) \n",
    "- 561 feature vector were engineered from each time window of 2.56 second with both time and frequency domain variables\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We need to predict the output as one of the 6 labels the user is performing using either handcoded engineered 561 features or raw features of 128 readings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of Data\n",
    "Date points given by 30 subjects(volunteers) is randomly split to 70%(21) train and 30%(9) test data.\n",
    "Each data-point corresponds one of the 6 Activities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing all the libraries needed for the LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np       #numpy\n",
    "import pandas as pd      #pandas\n",
    "import matplotlib.pyplot as plt   #matplotlib\n",
    "import seaborn as sns             #seaborn\n",
    "import tensorflow as tf           #tensorflow\n",
    "from keras.models import Sequential    #keras\n",
    "from keras.layers import LSTM          #keras layers\n",
    "from keras.layers.core import Dense, Dropout  #keras layers core\n",
    "from keras.layers.normalization import BatchNormalization  #keras layers normalisation\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "ACTIVITIES = {\n",
    "    0: 'WALKING',\n",
    "    1: 'WALKING_UPSTAIRS',\n",
    "    2: 'WALKING_DOWNSTAIRS',\n",
    "    3: 'SITTING',\n",
    "    4: 'STANDING',\n",
    "    5: 'LAYING',\n",
    "}\n",
    "# Data directory\n",
    "DATADIR = 'UCI_HAR_Dataset'\n",
    "\n",
    "labels=['LAYING', 'SITTING','STANDING','WALKING','WALKING_DOWNSTAIRS','WALKING_UPSTAIRS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtaining the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "      <td>STANDING</td>\n",
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       "\n",
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       "4 -0.847865  0.185151 -0.043892        1         5      STANDING  \n",
       "\n",
       "[5 rows x 564 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = pd.read_csv('UCI_HAR_Dataset/train/X_train.txt', delim_whitespace=True, header=None)\n",
    "\n",
    "\n",
    "X_train['subject'] = pd.read_csv('UCI_HAR_Dataset/train/subject_train.txt', header=None, squeeze=True)\n",
    "\n",
    "y_train = pd.read_csv('UCI_HAR_Dataset/train/y_train.txt', names=['Activity'], squeeze=True)\n",
    "y_train_labels = y_train.map({1: 'WALKING', 2:'WALKING_UPSTAIRS',3:'WALKING_DOWNSTAIRS',4:'SITTING', 5:'STANDING',6:'LAYING'})\n",
    "\n",
    "train = X_train\n",
    "train['Activity'] = y_train\n",
    "train['ActivityName'] = y_train_labels\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7352, 564) 4146528\n"
     ]
    }
   ],
   "source": [
    "print(train.shape,train.size)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtaining the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "      <td>STANDING</td>\n",
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       "\n",
       "          7         8         9  ...       554       555       556       557  \\\n",
       "0 -0.925249 -0.674302 -0.894088  ...  0.006462  0.162920 -0.825886  0.271151   \n",
       "1 -0.968401 -0.945823 -0.894088  ... -0.083495  0.017500 -0.434375  0.920593   \n",
       "2 -0.970735 -0.963483 -0.939260  ... -0.034956  0.202302  0.064103  0.145068   \n",
       "3 -0.974471 -0.968897 -0.938610  ... -0.017067  0.154438  0.340134  0.296407   \n",
       "4 -0.965953 -0.977346 -0.938610  ... -0.002223 -0.040046  0.736715 -0.118545   \n",
       "\n",
       "        558       559       560  subject  Activity  ActivityName  \n",
       "0 -0.720009  0.276801 -0.057978        2         5      STANDING  \n",
       "1 -0.698091  0.281343 -0.083898        2         5      STANDING  \n",
       "2 -0.702771  0.280083 -0.079346        2         5      STANDING  \n",
       "3 -0.698954  0.284114 -0.077108        2         5      STANDING  \n",
       "4 -0.692245  0.290722 -0.073857        2         5      STANDING  \n",
       "\n",
       "[5 rows x 564 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_test = pd.read_csv('UCI_HAR_Dataset/test/X_test.txt', delim_whitespace=True, header=None)\n",
    "\n",
    "\n",
    "X_test['subject'] = pd.read_csv('UCI_HAR_Dataset/test/subject_test.txt', header=None, squeeze=True)\n",
    "\n",
    "\n",
    "y_test = pd.read_csv('UCI_HAR_Dataset/test/y_test.txt', names=['Activity'], squeeze=True)\n",
    "y_test_labels = y_test.map({1: 'WALKING', 2:'WALKING_UPSTAIRS',3:'WALKING_DOWNSTAIRS', 4:'SITTING', 5:'STANDING',6:'LAYING'})\n",
    "\n",
    "\n",
    "test = X_test\n",
    "test['Activity'] = y_test\n",
    "test['ActivityName'] = y_test_labels\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2947, 564) 1662108\n"
     ]
    }
   ],
   "source": [
    "print(test.shape,test.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('UCI_HAR_Dataset/csv_files/train.csv', index=False)\n",
    "test.to_csv('UCI_HAR_Dataset/csv_files/test.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7352, 564) (2947, 564)\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('UCI_HAR_Dataset/csv_files/train.csv')\n",
    "test = pd.read_csv('UCI_HAR_Dataset/csv_files/test.csv')\n",
    "print(train.shape, test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>554</th>\n",
       "      <th>555</th>\n",
       "      <th>556</th>\n",
       "      <th>557</th>\n",
       "      <th>558</th>\n",
       "      <th>559</th>\n",
       "      <th>560</th>\n",
       "      <th>subject</th>\n",
       "      <th>Activity</th>\n",
       "      <th>ActivityName</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>-0.112754</td>\n",
       "      <td>0.0304</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>5</td>\n",
       "      <td>STANDING</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 564 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.288585 -0.020294 -0.132905 -0.995279 -0.983111 -0.913526 -0.995112   \n",
       "\n",
       "          7         8         9  ...       554     555       556       557  \\\n",
       "0 -0.983185 -0.923527 -0.934724  ... -0.112754  0.0304 -0.464761 -0.018446   \n",
       "\n",
       "        558       559       560  subject  Activity  ActivityName  \n",
       "0 -0.841247  0.179941 -0.058627        1         5      STANDING  \n",
       "\n",
       "[1 rows x 564 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Visualising the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('dark')\n",
    "plt.rcParams['lines.linewidth'] = 1\n",
    "plt.rcParams['font.family'] = 'DejaVu Sans'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "plt.title(\"Total Activities performed by each volunteer in Training Data\",fontsize=20)\n",
    "sns.countplot(x=train.subject)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "plt.title(\"Total Activities performed by each volunteer in Test Data\",fontsize=20)\n",
    "sns.countplot(x=test.subject)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train_labels = {1: 'WALKING', 2:'WALKING_UPSTAIRS',3:'WALKING_DOWNSTAIRS',4:'SITTING', 5:'STANDING',6:'LAYING'}\n",
    "lists=sorted(y_train_labels.items())\n",
    "x, y = zip(*lists)\n",
    "plt.scatter(x, y,linewidths = 2,marker =\"s\",edgecolor =\"green\", s = 50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "plt.title('Count of each activity performed by each individual user in Training Data', fontsize=20)\n",
    "sns.countplot(x='subject',hue='ActivityName', data = train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "plt.title('Count of each activity performed by each individual user in Testing Data', fontsize=20)\n",
    "sns.countplot(x='subject',hue='ActivityName', data = test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('No of Datapoints per Activity')\n",
    "sns.countplot(x=train.Activity)\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM model for HAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuring a session\n",
    "session_conf = tf.compat.v1.ConfigProto(\n",
    "    intra_op_parallelism_threads=1,\n",
    "    inter_op_parallelism_threads=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Keras\n",
    "from tensorflow.compat.v1.keras import backend as K\n",
    "sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf)\n",
    "K.set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers.core import Dense, Dropout\n",
    "from keras.layers.normalization import BatchNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = 'UCI_HAR_Dataset'\n",
    "\n",
    "ACTIVITIES = {\n",
    "    0: 'WALKING',\n",
    "    1: 'WALKING_UPSTAIRS',\n",
    "    2: 'WALKING_DOWNSTAIRS',\n",
    "    3: 'SITTING',\n",
    "    4: 'STANDING',\n",
    "    5: 'LAYING',\n",
    "}\n",
    "\n",
    "def confusion_matrix(Y_true, Y_pred):\n",
    "    Y_true = pd.Series([ACTIVITIES[y] for y in np.argmax(Y_true, axis=1)])\n",
    "    Y_pred = pd.Series([ACTIVITIES[y] for y in np.argmax(Y_pred, axis=1)])\n",
    "\n",
    "    return pd.crosstab(Y_true, Y_pred, rownames=['True'], colnames=['Pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "SIGNALS = [\n",
    "    \"body_acc_x\",\n",
    "    \"body_acc_y\",\n",
    "    \"body_acc_z\",\n",
    "    \"body_gyro_x\",\n",
    "    \"body_gyro_y\",\n",
    "    \"body_gyro_z\",\n",
    "    \"total_acc_x\",\n",
    "    \"total_acc_y\",\n",
    "    \"total_acc_z\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _read_csv(filename):\n",
    "    return pd.read_csv(filename, delim_whitespace=True, header=None)\n",
    "\n",
    "def generate_x_data(subset):\n",
    "    signals_data = []\n",
    "\n",
    "    for signal in SIGNALS:\n",
    "        filename = f'UCI_HAR_Dataset/{subset}/Inertial Signals/{signal}_{subset}.txt'\n",
    "        signals_data.append(_read_csv(filename).values) \n",
    "    return np.transpose(signals_data, (1, 2, 0))\n",
    "\n",
    "\n",
    "def _count_classes(y):\n",
    "    return len(set([tuple(category) for category in y]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_y_data(subset):\n",
    "    \n",
    "    filename = f'UCI_HAR_Dataset/{subset}/y_{subset}.txt'\n",
    "    y = _read_csv(filename)[0]\n",
    "\n",
    "    return pd.get_dummies(y).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    \n",
    "    X_train, X_test = generate_x_data('train'), generate_x_data('test')\n",
    "    y_train, y_test = generate_y_data('train'), generate_y_data('test')\n",
    "\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7352\n",
      "128\n",
      "9\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "timesteps = len(X_train[0])\n",
    "input_dim = len(X_train[0][0])\n",
    "n_classes = _count_classes(Y_train)\n",
    "\n",
    "print(len(X_train))\n",
    "print(timesteps)\n",
    "print(input_dim)\n",
    "print(n_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 30\n",
    "batch_size = 32\n",
    "n_hidden = 128\n",
    "pv = 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 128)               70656     \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 6)                 774       \n",
      "=================================================================\n",
      "Total params: 71,942\n",
      "Trainable params: 71,686\n",
      "Non-trainable params: 256\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(n_hidden, input_shape=(timesteps, input_dim)))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Dropout(pv))\n",
    "          \n",
    "model.add(Dense(n_classes, activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='rmsprop',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "230/230 [==============================] - 17s 76ms/step - loss: 0.9775 - accuracy: 0.5635 - val_loss: 0.9544 - val_accuracy: 0.5836\n",
      "Epoch 2/30\n",
      "230/230 [==============================] - 16s 70ms/step - loss: 0.7032 - accuracy: 0.6602 - val_loss: 0.7071 - val_accuracy: 0.6566\n",
      "Epoch 3/30\n",
      "230/230 [==============================] - 17s 74ms/step - loss: 0.6357 - accuracy: 0.7076 - val_loss: 0.7523 - val_accuracy: 0.6203\n",
      "Epoch 4/30\n",
      "230/230 [==============================] - 17s 74ms/step - loss: 0.4320 - accuracy: 0.8298 - val_loss: 0.3244 - val_accuracy: 0.8894\n",
      "Epoch 5/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.2150 - accuracy: 0.9214 - val_loss: 0.3481 - val_accuracy: 0.8823\n",
      "Epoch 6/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1855 - accuracy: 0.9320 - val_loss: 0.3101 - val_accuracy: 0.9006\n",
      "Epoch 7/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1877 - accuracy: 0.9328 - val_loss: 0.3312 - val_accuracy: 0.8887\n",
      "Epoch 8/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1598 - accuracy: 0.9377 - val_loss: 0.3471 - val_accuracy: 0.9019\n",
      "Epoch 9/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1444 - accuracy: 0.9377 - val_loss: 0.5204 - val_accuracy: 0.9087\n",
      "Epoch 10/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1532 - accuracy: 0.9421 - val_loss: 0.3719 - val_accuracy: 0.8911\n",
      "Epoch 11/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1623 - accuracy: 0.9429 - val_loss: 0.2438 - val_accuracy: 0.9240\n",
      "Epoch 12/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1344 - accuracy: 0.9436 - val_loss: 0.2704 - val_accuracy: 0.9125\n",
      "Epoch 13/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1401 - accuracy: 0.9437 - val_loss: 0.3632 - val_accuracy: 0.9036\n",
      "Epoch 14/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1305 - accuracy: 0.9437 - val_loss: 0.2415 - val_accuracy: 0.9138\n",
      "Epoch 15/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1403 - accuracy: 0.9438 - val_loss: 0.3694 - val_accuracy: 0.9067\n",
      "Epoch 16/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1271 - accuracy: 0.9475 - val_loss: 0.3459 - val_accuracy: 0.9128\n",
      "Epoch 17/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1247 - accuracy: 0.9464 - val_loss: 0.3513 - val_accuracy: 0.9179\n",
      "Epoch 18/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1455 - accuracy: 0.9479 - val_loss: 0.2809 - val_accuracy: 0.9118\n",
      "Epoch 19/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1230 - accuracy: 0.9467 - val_loss: 0.3377 - val_accuracy: 0.9237\n",
      "Epoch 20/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1298 - accuracy: 0.9464 - val_loss: 0.4772 - val_accuracy: 0.9050\n",
      "Epoch 21/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1353 - accuracy: 0.9498 - val_loss: 0.2506 - val_accuracy: 0.9203\n",
      "Epoch 22/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1250 - accuracy: 0.9493 - val_loss: 0.3128 - val_accuracy: 0.9094\n",
      "Epoch 23/30\n",
      "230/230 [==============================] - 17s 76ms/step - loss: 0.1241 - accuracy: 0.9480 - val_loss: 0.3552 - val_accuracy: 0.9104\n",
      "Epoch 24/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1196 - accuracy: 0.9525 - val_loss: 0.3073 - val_accuracy: 0.9131\n",
      "Epoch 25/30\n",
      "230/230 [==============================] - 17s 74ms/step - loss: 0.1267 - accuracy: 0.9521 - val_loss: 0.2856 - val_accuracy: 0.9135\n",
      "Epoch 26/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1226 - accuracy: 0.9490 - val_loss: 0.2449 - val_accuracy: 0.9389\n",
      "Epoch 27/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1144 - accuracy: 0.9535 - val_loss: 0.2992 - val_accuracy: 0.9264\n",
      "Epoch 28/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1220 - accuracy: 0.9527 - val_loss: 1.5769 - val_accuracy: 0.7872\n",
      "Epoch 29/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1249 - accuracy: 0.9533 - val_loss: 0.3281 - val_accuracy: 0.9155\n",
      "Epoch 30/30\n",
      "230/230 [==============================] - 17s 75ms/step - loss: 0.1147 - accuracy: 0.9543 - val_loss: 0.3387 - val_accuracy: 0.9270\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f976f067250>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train,batch_size=batch_size,validation_data=(X_test, Y_test),epochs=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pred', ylabel='True'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result=confusion_matrix(Y_test, model.predict(X_test))\n",
    "sn.heatmap(result, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93/93 [==============================] - 2s 18ms/step - loss: 0.3387 - accuracy: 0.9270\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.33865147829055786, 0.9270444512367249]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- With a simple semi tuned stacked LSTM architecture we got 92.7% accuracy and a loss of 0.33"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM Model2\n",
    "<br>\n",
    "stacking 2 LSTM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs1 = 30\n",
    "batch_size1= 32\n",
    "n_hidden1 = 128\n",
    "n_hidden2 =64\n",
    "pv1 = 0.2\n",
    "pv2 = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 128, 128)          70656     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128, 128)          0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 64)                49408     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 6)                 390       \n",
      "=================================================================\n",
      "Total params: 120,454\n",
      "Trainable params: 120,454\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model1 = Sequential()\n",
    "\n",
    "model1.add(LSTM(n_hidden1, return_sequences=True, input_shape=(timesteps, input_dim)))\n",
    "\n",
    "model1.add(Dropout(pv1))\n",
    "\n",
    "model1.add(LSTM(n_hidden2))\n",
    "\n",
    "model1.add(Dropout(pv2))\n",
    "\n",
    "model1.add(Dense(n_classes, activation='sigmoid'))\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1.compile(loss='categorical_crossentropy',optimizer='rmsprop',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "230/230 [==============================] - 30s 129ms/step - loss: 1.1419 - accuracy: 0.5018 - val_loss: 0.9634 - val_accuracy: 0.5304\n",
      "Epoch 2/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.9308 - accuracy: 0.5928 - val_loss: 0.8050 - val_accuracy: 0.6220\n",
      "Epoch 3/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.7290 - accuracy: 0.6783 - val_loss: 0.9499 - val_accuracy: 0.6356\n",
      "Epoch 4/30\n",
      "230/230 [==============================] - 29s 127ms/step - loss: 0.7000 - accuracy: 0.7114 - val_loss: 0.5584 - val_accuracy: 0.7659\n",
      "Epoch 5/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.4265 - accuracy: 0.8513 - val_loss: 0.4920 - val_accuracy: 0.8354\n",
      "Epoch 6/30\n",
      "230/230 [==============================] - 29s 125ms/step - loss: 0.2448 - accuracy: 0.9191 - val_loss: 0.3404 - val_accuracy: 0.8914\n",
      "Epoch 7/30\n",
      "230/230 [==============================] - 29s 127ms/step - loss: 0.1919 - accuracy: 0.9354 - val_loss: 1.1314 - val_accuracy: 0.7862\n",
      "Epoch 8/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1676 - accuracy: 0.9410 - val_loss: 0.3229 - val_accuracy: 0.9067\n",
      "Epoch 9/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1585 - accuracy: 0.9437 - val_loss: 0.4327 - val_accuracy: 0.8938\n",
      "Epoch 10/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1697 - accuracy: 0.9455 - val_loss: 0.3099 - val_accuracy: 0.9036\n",
      "Epoch 11/30\n",
      "230/230 [==============================] - 29s 127ms/step - loss: 0.1355 - accuracy: 0.9495 - val_loss: 0.4072 - val_accuracy: 0.8955\n",
      "Epoch 12/30\n",
      "230/230 [==============================] - 29s 127ms/step - loss: 0.1358 - accuracy: 0.9484 - val_loss: 0.4100 - val_accuracy: 0.9023\n",
      "Epoch 13/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1504 - accuracy: 0.9483 - val_loss: 0.4309 - val_accuracy: 0.9026\n",
      "Epoch 14/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1349 - accuracy: 0.9484 - val_loss: 0.4948 - val_accuracy: 0.9002\n",
      "Epoch 15/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1277 - accuracy: 0.9539 - val_loss: 0.4652 - val_accuracy: 0.9013\n",
      "Epoch 16/30\n",
      "230/230 [==============================] - 29s 127ms/step - loss: 0.1405 - accuracy: 0.9504 - val_loss: 0.3800 - val_accuracy: 0.9135\n",
      "Epoch 17/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1289 - accuracy: 0.9512 - val_loss: 0.4618 - val_accuracy: 0.9006\n",
      "Epoch 18/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1358 - accuracy: 0.9512 - val_loss: 0.4190 - val_accuracy: 0.8975\n",
      "Epoch 19/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1271 - accuracy: 0.9554 - val_loss: 0.4046 - val_accuracy: 0.9019\n",
      "Epoch 20/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1185 - accuracy: 0.9538 - val_loss: 0.4062 - val_accuracy: 0.9026\n",
      "Epoch 21/30\n",
      "230/230 [==============================] - 29s 127ms/step - loss: 0.1183 - accuracy: 0.9532 - val_loss: 0.4987 - val_accuracy: 0.8935\n",
      "Epoch 22/30\n",
      "230/230 [==============================] - 29s 127ms/step - loss: 0.1248 - accuracy: 0.9555 - val_loss: 0.4545 - val_accuracy: 0.9080\n",
      "Epoch 23/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1135 - accuracy: 0.9542 - val_loss: 0.3692 - val_accuracy: 0.9267\n",
      "Epoch 24/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1144 - accuracy: 0.9527 - val_loss: 0.4695 - val_accuracy: 0.9152\n",
      "Epoch 25/30\n",
      "230/230 [==============================] - 29s 127ms/step - loss: 0.1193 - accuracy: 0.9547 - val_loss: 0.5512 - val_accuracy: 0.9019\n",
      "Epoch 26/30\n",
      "230/230 [==============================] - 29s 127ms/step - loss: 0.1119 - accuracy: 0.9558 - val_loss: 0.4392 - val_accuracy: 0.9216\n",
      "Epoch 27/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1163 - accuracy: 0.9566 - val_loss: 0.5549 - val_accuracy: 0.9101\n",
      "Epoch 28/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1242 - accuracy: 0.9544 - val_loss: 0.4873 - val_accuracy: 0.9148\n",
      "Epoch 29/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1130 - accuracy: 0.9551 - val_loss: 0.3664 - val_accuracy: 0.9240\n",
      "Epoch 30/30\n",
      "230/230 [==============================] - 29s 126ms/step - loss: 0.1061 - accuracy: 0.9553 - val_loss: 0.4102 - val_accuracy: 0.9182\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f976401fd30>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.fit(X_train,Y_train,batch_size=batch_size,validation_data=(X_test, Y_test),epochs=epochs1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pred', ylabel='True'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result=confusion_matrix(Y_test, model1.predict(X_test))\n",
    "sn.heatmap(result, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93/93 [==============================] - 3s 28ms/step - loss: 0.4102 - accuracy: 0.9182\n"
     ]
    }
   ],
   "source": [
    "score1 = model1.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.41017231345176697, 0.9182218909263611]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Final Comments</b>\n",
    "* By Simple two layered LSTM, we got a good accuracy of 91.82%. \n",
    "* In short, Deep Learning help us to built models even when we don't have domain expert engineered features. \n",
    "* LSTM model can be further improved by running it for more epochs and more evaluations while tuning hyper-parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine learning model(Logistic Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train and y_train : ((7352, 128, 9),(7352,))\n",
      "X_test  and y_test  : ((2947, 128, 9),(2947,))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0       5\n",
       "1       5\n",
       "2       5\n",
       "3       5\n",
       "4       5\n",
       "       ..\n",
       "2942    2\n",
       "2943    2\n",
       "2944    2\n",
       "2945    2\n",
       "2946    2\n",
       "Name: Activity, Length: 2947, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('X_train and y_train : ({},{})'.format(X_train.shape, y_train.shape))\n",
    "print('X_test  and y_test  : ({},{})'.format(X_test.shape, y_test.shape))\n",
    "y_test.sample()\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train and y_train : ((7352, 561),(7352,))\n",
      "X_test  and y_test  : ((2947, 561),(2947,))\n",
      "0    5\n",
      "1    5\n",
      "2    5\n",
      "3    5\n",
      "4    5\n",
      "Name: Activity, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# get X_train and y_train from csv files\n",
    "X_train = train.drop(['subject', 'Activity', 'ActivityName'], axis=1)\n",
    "y_train = train.Activity\n",
    "X_test = test.drop(['subject', 'Activity', 'ActivityName'], axis=1)\n",
    "y_test = test.Activity\n",
    "print('X_train and y_train : ({},{})'.format(X_train.shape, y_train.shape))\n",
    "print('X_test  and y_test  : ({},{})'.format(X_test.shape, y_test.shape))\n",
    "print(y_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             0         1         2         3         4         5         6  \\\n",
      "0     0.288585 -0.020294 -0.132905 -0.995279 -0.983111 -0.913526 -0.995112   \n",
      "1     0.278419 -0.016411 -0.123520 -0.998245 -0.975300 -0.960322 -0.998807   \n",
      "2     0.279653 -0.019467 -0.113462 -0.995380 -0.967187 -0.978944 -0.996520   \n",
      "3     0.279174 -0.026201 -0.123283 -0.996091 -0.983403 -0.990675 -0.997099   \n",
      "4     0.276629 -0.016570 -0.115362 -0.998139 -0.980817 -0.990482 -0.998321   \n",
      "...        ...       ...       ...       ...       ...       ...       ...   \n",
      "7347  0.299665 -0.057193 -0.181233 -0.195387  0.039905  0.077078 -0.282301   \n",
      "7348  0.273853 -0.007749 -0.147468 -0.235309  0.004816  0.059280 -0.322552   \n",
      "7349  0.273387 -0.017011 -0.045022 -0.218218 -0.103822  0.274533 -0.304515   \n",
      "7350  0.289654 -0.018843 -0.158281 -0.219139 -0.111412  0.268893 -0.310487   \n",
      "7351  0.351503 -0.012423 -0.203867 -0.269270 -0.087212  0.177404 -0.377404   \n",
      "\n",
      "             7         8         9  ...       551       552       553  \\\n",
      "0    -0.983185 -0.923527 -0.934724  ... -0.074323 -0.298676 -0.710304   \n",
      "1    -0.974914 -0.957686 -0.943068  ...  0.158075 -0.595051 -0.861499   \n",
      "2    -0.963668 -0.977469 -0.938692  ...  0.414503 -0.390748 -0.760104   \n",
      "3    -0.982750 -0.989302 -0.938692  ...  0.404573 -0.117290 -0.482845   \n",
      "4    -0.979672 -0.990441 -0.942469  ...  0.087753 -0.351471 -0.699205   \n",
      "...        ...       ...       ...  ...       ...       ...       ...   \n",
      "7347  0.043616  0.060410  0.210795  ... -0.070157 -0.588433 -0.880324   \n",
      "7348 -0.029456  0.080585  0.117440  ...  0.165259 -0.390738 -0.680744   \n",
      "7349 -0.098913  0.332584  0.043999  ...  0.195034  0.025145 -0.304029   \n",
      "7350 -0.068200  0.319473  0.101702  ...  0.013865  0.063907 -0.344314   \n",
      "7351 -0.038678  0.229430  0.269013  ... -0.058402 -0.387052 -0.740738   \n",
      "\n",
      "           554       555       556       557       558       559       560  \n",
      "0    -0.112754  0.030400 -0.464761 -0.018446 -0.841247  0.179941 -0.058627  \n",
      "1     0.053477 -0.007435 -0.732626  0.703511 -0.844788  0.180289 -0.054317  \n",
      "2    -0.118559  0.177899  0.100699  0.808529 -0.848933  0.180637 -0.049118  \n",
      "3    -0.036788 -0.012892  0.640011 -0.485366 -0.848649  0.181935 -0.047663  \n",
      "4     0.123320  0.122542  0.693578 -0.615971 -0.847865  0.185151 -0.043892  \n",
      "...        ...       ...       ...       ...       ...       ...       ...  \n",
      "7347 -0.190437  0.829718  0.206972 -0.425619 -0.791883  0.238604  0.049819  \n",
      "7348  0.064907  0.875679 -0.879033  0.400219 -0.771840  0.252676  0.050053  \n",
      "7349  0.052806 -0.266724  0.864404  0.701169 -0.779133  0.249145  0.040811  \n",
      "7350 -0.101360  0.700740  0.936674 -0.589479 -0.785181  0.246432  0.025339  \n",
      "7351 -0.280088 -0.007739 -0.056088 -0.616956 -0.783267  0.246809  0.036695  \n",
      "\n",
      "[7352 rows x 561 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             0         1         2         3         4         5         6  \\\n",
      "4180  0.282279 -0.009914 -0.107457 -0.933034 -0.787782 -0.835695 -0.945854   \n",
      "\n",
      "             7         8         9  ...       551       552       553  \\\n",
      "4180 -0.802908 -0.832723 -0.865321  ... -0.092777  0.428734  0.231433   \n",
      "\n",
      "           554       555       556       557       558       559      560  \n",
      "4180  0.071163  0.606159 -0.615788  0.762844 -0.606616  0.329528  0.16091  \n",
      "\n",
      "[1 rows x 561 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X_train.sample())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(             0         1         2         3         4         5         6  \\\n",
       " 0     0.288585 -0.020294 -0.132905 -0.995279 -0.983111 -0.913526 -0.995112   \n",
       " 1     0.278419 -0.016411 -0.123520 -0.998245 -0.975300 -0.960322 -0.998807   \n",
       " 2     0.279653 -0.019467 -0.113462 -0.995380 -0.967187 -0.978944 -0.996520   \n",
       " 3     0.279174 -0.026201 -0.123283 -0.996091 -0.983403 -0.990675 -0.997099   \n",
       " 4     0.276629 -0.016570 -0.115362 -0.998139 -0.980817 -0.990482 -0.998321   \n",
       " ...        ...       ...       ...       ...       ...       ...       ...   \n",
       " 7347  0.299665 -0.057193 -0.181233 -0.195387  0.039905  0.077078 -0.282301   \n",
       " 7348  0.273853 -0.007749 -0.147468 -0.235309  0.004816  0.059280 -0.322552   \n",
       " 7349  0.273387 -0.017011 -0.045022 -0.218218 -0.103822  0.274533 -0.304515   \n",
       " 7350  0.289654 -0.018843 -0.158281 -0.219139 -0.111412  0.268893 -0.310487   \n",
       " 7351  0.351503 -0.012423 -0.203867 -0.269270 -0.087212  0.177404 -0.377404   \n",
       " \n",
       "              7         8         9  ...       551       552       553  \\\n",
       " 0    -0.983185 -0.923527 -0.934724  ... -0.074323 -0.298676 -0.710304   \n",
       " 1    -0.974914 -0.957686 -0.943068  ...  0.158075 -0.595051 -0.861499   \n",
       " 2    -0.963668 -0.977469 -0.938692  ...  0.414503 -0.390748 -0.760104   \n",
       " 3    -0.982750 -0.989302 -0.938692  ...  0.404573 -0.117290 -0.482845   \n",
       " 4    -0.979672 -0.990441 -0.942469  ...  0.087753 -0.351471 -0.699205   \n",
       " ...        ...       ...       ...  ...       ...       ...       ...   \n",
       " 7347  0.043616  0.060410  0.210795  ... -0.070157 -0.588433 -0.880324   \n",
       " 7348 -0.029456  0.080585  0.117440  ...  0.165259 -0.390738 -0.680744   \n",
       " 7349 -0.098913  0.332584  0.043999  ...  0.195034  0.025145 -0.304029   \n",
       " 7350 -0.068200  0.319473  0.101702  ...  0.013865  0.063907 -0.344314   \n",
       " 7351 -0.038678  0.229430  0.269013  ... -0.058402 -0.387052 -0.740738   \n",
       " \n",
       "            554       555       556       557       558       559       560  \n",
       " 0    -0.112754  0.030400 -0.464761 -0.018446 -0.841247  0.179941 -0.058627  \n",
       " 1     0.053477 -0.007435 -0.732626  0.703511 -0.844788  0.180289 -0.054317  \n",
       " 2    -0.118559  0.177899  0.100699  0.808529 -0.848933  0.180637 -0.049118  \n",
       " 3    -0.036788 -0.012892  0.640011 -0.485366 -0.848649  0.181935 -0.047663  \n",
       " 4     0.123320  0.122542  0.693578 -0.615971 -0.847865  0.185151 -0.043892  \n",
       " ...        ...       ...       ...       ...       ...       ...       ...  \n",
       " 7347 -0.190437  0.829718  0.206972 -0.425619 -0.791883  0.238604  0.049819  \n",
       " 7348  0.064907  0.875679 -0.879033  0.400219 -0.771840  0.252676  0.050053  \n",
       " 7349  0.052806 -0.266724  0.864404  0.701169 -0.779133  0.249145  0.040811  \n",
       " 7350 -0.101360  0.700740  0.936674 -0.589479 -0.785181  0.246432  0.025339  \n",
       " 7351 -0.280088 -0.007739 -0.056088 -0.616956 -0.783267  0.246809  0.036695  \n",
       " \n",
       " [7352 rows x 561 columns],\n",
       " 0       5\n",
       " 1       5\n",
       " 2       5\n",
       " 3       5\n",
       " 4       5\n",
       "        ..\n",
       " 7347    2\n",
       " 7348    2\n",
       " 7349    2\n",
       " 7350    2\n",
       " 7351    2\n",
       " Name: Activity, Length: 7352, dtype: int64,\n",
       "              0         1         2         3         4         5         6  \\\n",
       " 0     0.257178 -0.023285 -0.014654 -0.938404 -0.920091 -0.667683 -0.952501   \n",
       " 1     0.286027 -0.013163 -0.119083 -0.975415 -0.967458 -0.944958 -0.986799   \n",
       " 2     0.275485 -0.026050 -0.118152 -0.993819 -0.969926 -0.962748 -0.994403   \n",
       " 3     0.270298 -0.032614 -0.117520 -0.994743 -0.973268 -0.967091 -0.995274   \n",
       " 4     0.274833 -0.027848 -0.129527 -0.993852 -0.967445 -0.978295 -0.994111   \n",
       " ...        ...       ...       ...       ...       ...       ...       ...   \n",
       " 2942  0.310155 -0.053391 -0.099109 -0.287866 -0.140589 -0.215088 -0.356083   \n",
       " 2943  0.363385 -0.039214 -0.105915 -0.305388  0.028148 -0.196373 -0.373540   \n",
       " 2944  0.349966  0.030077 -0.115788 -0.329638 -0.042143 -0.250181 -0.388017   \n",
       " 2945  0.237594  0.018467 -0.096499 -0.323114 -0.229775 -0.207574 -0.392380   \n",
       " 2946  0.153627 -0.018437 -0.137018 -0.330046 -0.195253 -0.164339 -0.430974   \n",
       " \n",
       "              7         8         9  ...       551       552       553  \\\n",
       " 0    -0.925249 -0.674302 -0.894088  ...  0.071645 -0.330370 -0.705974   \n",
       " 1    -0.968401 -0.945823 -0.894088  ... -0.401189 -0.121845 -0.594944   \n",
       " 2    -0.970735 -0.963483 -0.939260  ...  0.062891 -0.190422 -0.640736   \n",
       " 3    -0.974471 -0.968897 -0.938610  ...  0.116695 -0.344418 -0.736124   \n",
       " 4    -0.965953 -0.977346 -0.938610  ... -0.121711 -0.534685 -0.846595   \n",
       " ...        ...       ...       ...  ...       ...       ...       ...   \n",
       " 2942 -0.148775 -0.232057  0.185361  ...  0.074472 -0.376278 -0.750809   \n",
       " 2943 -0.030036 -0.270237  0.185361  ...  0.101859 -0.320418 -0.700274   \n",
       " 2944 -0.133257 -0.347029  0.007471  ... -0.066249 -0.118854 -0.467179   \n",
       " 2945 -0.279610 -0.289477  0.007471  ... -0.046467 -0.205445 -0.617737   \n",
       " 2946 -0.218295 -0.229933 -0.111527  ... -0.010386 -0.072237 -0.436940   \n",
       " \n",
       "            554       555       556       557       558       559       560  \n",
       " 0     0.006462  0.162920 -0.825886  0.271151 -0.720009  0.276801 -0.057978  \n",
       " 1    -0.083495  0.017500 -0.434375  0.920593 -0.698091  0.281343 -0.083898  \n",
       " 2    -0.034956  0.202302  0.064103  0.145068 -0.702771  0.280083 -0.079346  \n",
       " 3    -0.017067  0.154438  0.340134  0.296407 -0.698954  0.284114 -0.077108  \n",
       " 4    -0.002223 -0.040046  0.736715 -0.118545 -0.692245  0.290722 -0.073857  \n",
       " ...        ...       ...       ...       ...       ...       ...       ...  \n",
       " 2942 -0.337422  0.346295  0.884904 -0.698885 -0.651732  0.274627  0.184784  \n",
       " 2943 -0.736701 -0.372889 -0.657421  0.322549 -0.655181  0.273578  0.182412  \n",
       " 2944 -0.181560  0.088574  0.696663  0.363139 -0.655357  0.274479  0.181184  \n",
       " 2945  0.444558 -0.819188  0.929294 -0.008398 -0.659719  0.264782  0.187563  \n",
       " 2946  0.598808 -0.287951  0.876030 -0.024965 -0.660080  0.263936  0.188103  \n",
       " \n",
       " [2947 rows x 561 columns],\n",
       " 0       5\n",
       " 1       5\n",
       " 2       5\n",
       " 3       5\n",
       " 4       5\n",
       "        ..\n",
       " 2942    2\n",
       " 2943    2\n",
       " 2944    2\n",
       " 2945    2\n",
       " 2946    2\n",
       " Name: Activity, Length: 2947, dtype: int64)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,y_train,X_test,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_regression= LogisticRegression(solver='lbfgs',max_iter=100000)\n",
    "result=logistic_regression.fit(X_train,y_train)\n",
    "y_pred=logistic_regression.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Predicted', ylabel='Actual'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.grid(b=False)\n",
    "confusion_matrix = pd.crosstab(y_test, y_pred, rownames=['Actual'], colnames=['Predicted'])\n",
    "sn.heatmap(confusion_matrix, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9613165931455717\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy: ',metrics.accuracy_score(y_test, y_pred))\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}