model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Train data:  (7352, 564)\n",
      "Shape of Test data:  (2947, 564)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of Train data: \",train.shape)\n",
    "print(\"Shape of Test data: \",test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done....\n"
     ]
    }
   ],
   "source": [
    "## dividing the data into dependent and independent data\n",
    "xtrain = train.drop(['subject', 'Activity', 'ActivityName'], axis=1)\n",
    "ytrain = train['ActivityName']\n",
    "\n",
    "xtest = test.drop(['subject', 'Activity', 'ActivityName'], axis=1)\n",
    "ytest = test['ActivityName']\n",
    "print(\"Done....\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xtrain and ytrain : ((7352, 561),(7352,))\n",
      "xtest  and ytest  : ((2947, 561),(2947,))\n"
     ]
    }
   ],
   "source": [
    "print('xtrain and ytrain : ({},{})'.format(xtrain.shape, ytrain.shape))\n",
    "print('xtest  and ytest  : ({},{})'.format(xtest.shape, ytest.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "xtrain  = sc.fit_transform(xtrain)\n",
    "xtest = sc.transform(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=['LAYING', 'SITTING','STANDING','WALKING','WALKING_DOWNSTAIRS','WALKING_UPSTAIRS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 12 candidates, totalling 36 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:  2.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=LogisticRegression(max_iter=1000), n_jobs=-1,\n",
       "             param_grid={'C': [0.01, 0.1, 1, 10, 20, 30],\n",
       "                         'penalty': ['l2', 'l1']},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {'C':[0.01, 0.1, 1, 10, 20, 30], 'penalty':['l2','l1']}\n",
    "log_reg = LogisticRegression(max_iter=1000)\n",
    "log_reg_grid = GridSearchCV(log_reg, param_grid=parameters, cv=3, verbose=1, n_jobs=-1)\n",
    "log_reg_grid.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "print(log_reg_grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score is:  95.48693586698337\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "ypred = log_reg_grid.predict(xtest)\n",
    "print(\"Accuracy score is: \",metrics.accuracy_score(ytest,ypred)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion metrics: \n",
      " [[533   0   4   0   0   0]\n",
      " [  1 428  60   0   0   2]\n",
      " [  0  14 518   0   0   0]\n",
      " [  0   0   0 493   3   0]\n",
      " [  0   0   1   7 396  16]\n",
      " [  0   0   0  24   1 446]]\n"
     ]
    }
   ],
   "source": [
    "cf_log = metrics.confusion_matrix(ytest,ypred)\n",
    "print(\"confusion metrics: \\n\",cf_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(cf_log,annot=True,cmap=plt.cm.Greens)\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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}