Binary_and_Multi-class_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binary Classification and Multi-class Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data for Binary Classification:\n",
    "* URL: https://www.kaggle.com/lodetomasi1995/income-classification\n",
    "* Column “income” is the target label to classify.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from scipy.stats import norm, probplot\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On Kaggle Notebook, after adding the data, you can import the data as Pandas DataFrame as follows\n",
    "df = pd.read_csv(\"income_evaluation.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying columns and Length of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', ' workclass', ' fnlwgt', ' education', ' education-num',\n",
      "       ' marital-status', ' occupation', ' relationship', ' race', ' sex',\n",
      "       ' capital-gain', ' capital-loss', ' hours-per-week', ' native-country',\n",
      "       ' income'],\n",
      "      dtype='object')\n",
      " \n",
      "Lenght of columns:  15\n",
      " \n",
      "Total numbe of data fields: 32561\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n",
    "print(\" \")\n",
    "print(\"Lenght of columns: \",len(df.columns))\n",
    "print(\" \")\n",
    "print(\"Total numbe of data fields:\",len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>As we can see some spaces in the column names we have processed the data to remove the spaces in the coulumns.</blue>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## we could see some spaces in the columns names so we are removing it manually \n",
    "\n",
    "df.columns = ['age', 'workclass', 'fnlwgt', 'education', 'education-num','marital-status', 'occupation', 'relationship', 'race', \n",
    "              'sex','capital-gain', 'capital-loss', 'hours-per-week', 'native-country','income']\n",
    "\n",
    "df.columns = df.columns.str.replace('-','_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education_num</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education_num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital_status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital_gain  capital_loss  hours_per_week  native_country  income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 1:\n",
    "Examine the features of the data. Identify which feature is continuous and which feature is categorical. Make some analyses and use your analyses to discuss whether each feature could be useful for classify the \"income.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      "age               32561 non-null int64\n",
      "workclass         32561 non-null object\n",
      "fnlwgt            32561 non-null int64\n",
      "education         32561 non-null object\n",
      "education_num     32561 non-null int64\n",
      "marital_status    32561 non-null object\n",
      "occupation        32561 non-null object\n",
      "relationship      32561 non-null object\n",
      "race              32561 non-null object\n",
      "sex               32561 non-null object\n",
      "capital_gain      32561 non-null int64\n",
      "capital_loss      32561 non-null int64\n",
      "hours_per_week    32561 non-null int64\n",
      "native_country    32561 non-null object\n",
      "income            32561 non-null object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>32561.0</td>\n",
       "      <td>38.581647</td>\n",
       "      <td>13.640433</td>\n",
       "      <td>17.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fnlwgt</td>\n",
       "      <td>32561.0</td>\n",
       "      <td>189778.366512</td>\n",
       "      <td>105549.977697</td>\n",
       "      <td>12285.0</td>\n",
       "      <td>117827.0</td>\n",
       "      <td>178356.0</td>\n",
       "      <td>237051.0</td>\n",
       "      <td>1484705.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>education_num</td>\n",
       "      <td>32561.0</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>capital_gain</td>\n",
       "      <td>32561.0</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>capital_loss</td>\n",
       "      <td>32561.0</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hours_per_week</td>\n",
       "      <td>32561.0</td>\n",
       "      <td>40.437456</td>\n",
       "      <td>12.347429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count           mean            std      min       25%  \\\n",
       "age             32561.0      38.581647      13.640433     17.0      28.0   \n",
       "fnlwgt          32561.0  189778.366512  105549.977697  12285.0  117827.0   \n",
       "education_num   32561.0      10.080679       2.572720      1.0       9.0   \n",
       "capital_gain    32561.0    1077.648844    7385.292085      0.0       0.0   \n",
       "capital_loss    32561.0      87.303830     402.960219      0.0       0.0   \n",
       "hours_per_week  32561.0      40.437456      12.347429      1.0      40.0   \n",
       "\n",
       "                     50%       75%        max  \n",
       "age                 37.0      48.0       90.0  \n",
       "fnlwgt          178356.0  237051.0  1484705.0  \n",
       "education_num       10.0      12.0       16.0  \n",
       "capital_gain         0.0       0.0    99999.0  \n",
       "capital_loss         0.0       0.0     4356.0  \n",
       "hours_per_week      40.0      45.0       99.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have total of 15 columns in the datset. As per the initial analysis on the dataset we can say that we have 6 continuous features and 9 categorical fetures. \n",
    "\n",
    "## Categorical Features:\n",
    "\n",
    "1. workclass\n",
    "2. education\n",
    "3. marital-status\n",
    "4. occupation\n",
    "5. relationship\n",
    "6. race\n",
    "7. sex\n",
    "8. native-country\n",
    "9. income\n",
    "\n",
    "## Continuous Features:\n",
    "\n",
    "1. age\n",
    "2. fnlwgt\n",
    "3. education-num\n",
    "4. capital-gain\n",
    "5. capital-loss\n",
    "6. hours-per-week\n",
    "\n",
    "From the above feature analysis we could see that the task is classification task and the target variable is \"income\". By observing the dataset we have continuous and categorical variables for doing the classification analysis on the target variable (income).\n",
    "\n",
    "Here, both continuous and categorical features plays important role in classifying the target variable. further analysis on each feature is done in the below steps on how important the feature is useful in classification. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education_num</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.076646</td>\n",
       "      <td>0.036527</td>\n",
       "      <td>0.077674</td>\n",
       "      <td>0.057775</td>\n",
       "      <td>0.068756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fnlwgt</td>\n",
       "      <td>-0.076646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043195</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>-0.010252</td>\n",
       "      <td>-0.018768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>education_num</td>\n",
       "      <td>0.036527</td>\n",
       "      <td>-0.043195</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122630</td>\n",
       "      <td>0.079923</td>\n",
       "      <td>0.148123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>capital_gain</td>\n",
       "      <td>0.077674</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.122630</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031615</td>\n",
       "      <td>0.078409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>capital_loss</td>\n",
       "      <td>0.057775</td>\n",
       "      <td>-0.010252</td>\n",
       "      <td>0.079923</td>\n",
       "      <td>-0.031615</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hours_per_week</td>\n",
       "      <td>0.068756</td>\n",
       "      <td>-0.018768</td>\n",
       "      <td>0.148123</td>\n",
       "      <td>0.078409</td>\n",
       "      <td>0.054256</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age    fnlwgt  education_num  capital_gain  capital_loss  \\\n",
       "age             1.000000 -0.076646       0.036527      0.077674      0.057775   \n",
       "fnlwgt         -0.076646  1.000000      -0.043195      0.000432     -0.010252   \n",
       "education_num   0.036527 -0.043195       1.000000      0.122630      0.079923   \n",
       "capital_gain    0.077674  0.000432       0.122630      1.000000     -0.031615   \n",
       "capital_loss    0.057775 -0.010252       0.079923     -0.031615      1.000000   \n",
       "hours_per_week  0.068756 -0.018768       0.148123      0.078409      0.054256   \n",
       "\n",
       "                hours_per_week  \n",
       "age                   0.068756  \n",
       "fnlwgt               -0.018768  \n",
       "education_num         0.148123  \n",
       "capital_gain          0.078409  \n",
       "capital_loss          0.054256  \n",
       "hours_per_week        1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation = df.corr()\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x186b2fa0f08>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 10))\n",
    "sns.heatmap(correlation, annot=True, fmt='.2f',mask=np.zeros_like(correlation, dtype=np.bool), \n",
    "            cmap=sns.diverging_palette(220, 10, as_cmap=True),square=True, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 2:\n",
    "Create plots to visualize the distribution of the label \"income\" and the distribution of  each continuous feature. Do you notice anything that might require special treatment? Discuss your findings and suggestions on wrangling the data for the classification task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = [\"age\", \"fnlwgt\", \"education_num\",\n",
    "              \"capital_gain\", \"capital_loss\", \"hours_per_week\"]\n",
    "scatter_matrix(df[attributes], figsize=(12, 12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x186b2ebb1c8>"
      ]
     },
     "execution_count": 11,
     "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": [
    "sns.distplot(df[\"fnlwgt\"], fit = norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x186b34b5d08>"
      ]
     },
     "execution_count": 12,
     "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": [
    "sns.distplot(df[\"education_num\"], fit = norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x186b5232c08>"
      ]
     },
     "execution_count": 13,
     "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": [
    "sns.distplot(df[\"capital_gain\"], fit = norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of each continuous feature\n",
    "\n",
    "<font color='blue'>age: From the distribution plot of age variable the data is slightly positively skewed. The variable seems fine with the data.</blue>\n",
    "\n",
    "<font color='blue'>fnlwgt: From the distribution of the data(fnlwgt) the majority of the data lies with in the normal curve. looks like the data is normal.</blue>\n",
    "\n",
    "<font color='blue'>education_num and hours_per_wek: From the data distribution plot of the eduation and hours_per_week variable the data is more concentated on some categories. </blue>\n",
    "\n",
    "<font color='blue'>Catpital_gain and capital_loss: from the data distribution plot of the gain and loss looks same like the data is peaked at one place. </blue>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of some categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x186b39d4088>"
      ]
     },
     "execution_count": 15,
     "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": [
    "df.workclass.value_counts().plot(kind=\"bar\", color=\"orange\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x186b38820c8>"
      ]
     },
     "execution_count": 16,
     "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": [
    "df.education.value_counts().plot(kind=\"bar\", color=\"orange\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x186b353d348>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uPbV0aXk20RipqjVdr0Gah+X0IrC+b+appUvMVwaSJE8tlTRakvx8kt1JHmy3z03yp12v60RnDCSNmpuADwE/BmifS3RFpysaA8ZA0qh5VVUd/imlz3WykjFiDCSNmseTnE074y3J5fQ+Zl1LyAPIkkZKkjcBW4FfpvfGyG8D7/dNZ0vLGEgaCUnWVdU9fbdfDZxUVU91uKyx4W4iSaPihtkrSe6uqv8zBMNjDCSNivRdf2VnqxhTvgNZ0qg4KckKev9Inb3+k0D42VlLy2MGkkZC++ysF/jZVwiz/OysJWYMJEkeM5AkGQNJEsZAkoQxkCRhDCRJGANJEsZAkoQxkCRhDCRJwP8DpyL7aKGLdZAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.sex.value_counts().plot(kind=\"bar\", color=\"orange\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x186b39a6308>"
      ]
     },
     "execution_count": 18,
     "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": [
    "df.occupation.value_counts().plot(kind=\"bar\", color=\"orange\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Identification of missing values \n",
    "<font color='blue'>We could see some missing varibales in the data with special charecter which needs to be taken care of. Listing out the features which have special charecters. </blue>\n",
    "\n",
    "\n",
    "<font color='blue'>Examining each feature for the special charecters: From the below obervations we could see that the numerical features are good and does not have any missing values. </blue>\n",
    "\n",
    "\n",
    "<font color='blue'>For the categorical features we have some variables with missing values. workclass, occupation and native_country have the missing values which can be replaced with Nan values and later it is imputed with most frequent values.</blue>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'education_num',\n",
       "       'marital_status', 'occupation', 'relationship', 'race', 'sex',\n",
       "       'capital_gain', 'capital_loss', 'hours_per_week', 'native_country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " United-States                 29170\n",
       " Mexico                          643\n",
       " ?                               583\n",
       " Philippines                     198\n",
       " Germany                         137\n",
       " Canada                          121\n",
       " Puerto-Rico                     114\n",
       " El-Salvador                     106\n",
       " India                           100\n",
       " Cuba                             95\n",
       " England                          90\n",
       " Jamaica                          81\n",
       " South                            80\n",
       " China                            75\n",
       " Italy                            73\n",
       " Dominican-Republic               70\n",
       " Vietnam                          67\n",
       " Guatemala                        64\n",
       " Japan                            62\n",
       " Poland                           60\n",
       " Columbia                         59\n",
       " Taiwan                           51\n",
       " Haiti                            44\n",
       " Iran                             43\n",
       " Portugal                         37\n",
       " Nicaragua                        34\n",
       " Peru                             31\n",
       " Greece                           29\n",
       " France                           29\n",
       " Ecuador                          28\n",
       " Ireland                          24\n",
       " Hong                             20\n",
       " Trinadad&Tobago                  19\n",
       " Cambodia                         19\n",
       " Thailand                         18\n",
       " Laos                             18\n",
       " Yugoslavia                       16\n",
       " Outlying-US(Guam-USVI-etc)       14\n",
       " Honduras                         13\n",
       " Hungary                          13\n",
       " Scotland                         12\n",
       " Holand-Netherlands                1\n",
       "Name: native_country, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['native_country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Prof-specialty       4140\n",
       " Craft-repair         4099\n",
       " Exec-managerial      4066\n",
       " Adm-clerical         3770\n",
       " Sales                3650\n",
       " Other-service        3295\n",
       " Machine-op-inspct    2002\n",
       " ?                    1843\n",
       " Transport-moving     1597\n",
       " Handlers-cleaners    1370\n",
       " Farming-fishing       994\n",
       " Tech-support          928\n",
       " Protective-serv       649\n",
       " Priv-house-serv       149\n",
       " Armed-Forces            9\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Private             22696\n",
       " Self-emp-not-inc     2541\n",
       " Local-gov            2093\n",
       " ?                    1836\n",
       " State-gov            1298\n",
       " Self-emp-inc         1116\n",
       " Federal-gov           960\n",
       " Without-pay            14\n",
       " Never-worked            7\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the special charecters with Nan values \n",
    "## Now the Dataset filled with null values, which is easy in modeling.\n",
    "df.replace(' ?', np.nan, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "workclass         1836\n",
       "fnlwgt               0\n",
       "education            0\n",
       "education_num        0\n",
       "marital_status       0\n",
       "occupation        1843\n",
       "relationship         0\n",
       "race                 0\n",
       "sex                  0\n",
       "capital_gain         0\n",
       "capital_loss         0\n",
       "hours_per_week       0\n",
       "native_country     583\n",
       "income               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelEncoder = LabelEncoder()\n",
    "df['income'] = labelEncoder.fit_transform(df['income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the Dataset filled with null values, which is easy in modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 3: \n",
    "Split the data into train and test data, What parameters do you use for splitting the data? Why? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(['income'],axis=1)\n",
    "y=df['income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, stratify = y,random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>We have used stratified sampling for the Target variable. In Stratified random sample we split the data in proportion to the category size instead of whole data.</blue>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "workclass         1473\n",
       "fnlwgt               0\n",
       "education            0\n",
       "education_num        0\n",
       "marital_status       0\n",
       "occupation        1479\n",
       "relationship         0\n",
       "race                 0\n",
       "sex                  0\n",
       "capital_gain         0\n",
       "capital_loss         0\n",
       "hours_per_week       0\n",
       "native_country     458\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Checking for the missing values in the Train and test sets \n",
    "x_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 0\n",
       "workclass         363\n",
       "fnlwgt              0\n",
       "education           0\n",
       "education_num       0\n",
       "marital_status      0\n",
       "occupation        364\n",
       "relationship        0\n",
       "race                0\n",
       "sex                 0\n",
       "capital_gain        0\n",
       "capital_loss        0\n",
       "hours_per_week      0\n",
       "native_country    125\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecting numerical dataframe in train set\n",
    "x_train_num = x_train.select_dtypes(include=np.number)\n",
    "\n",
    "#Selecting categorical dataframe in train set\n",
    "x_train_cat = x_train.select_dtypes(exclude=['number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_copy = x_train_cat.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in [x_train_copy]:\n",
    "    df['workclass'].fillna(x_train_copy['workclass'].mode()[0], inplace=True)\n",
    "    df['occupation'].fillna(x_train_copy['occupation'].mode()[0], inplace=True)\n",
    "    df['native_country'].fillna(x_train_copy['native_country'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 4: \n",
    "Use Pipeline, ColumnTransformer and OneHotEncoder to fit and evaluate Logistic Regression, Support Vector Machines, Naïve Bayes and K-Nearest Neighbors, with cross-validation to classify the \"price\". Compare the performance of different algorithms on Test set. Discuss your results and findings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>we are Using OneHotEncoder for converting the categorical variables in to OneHot vectors. Usually OneHotEncoder returns a sparse array, we are using toarray() method to conevrt it into dense array. we can also acheive this by setting the sparse parameter as False in OneHotEncoder(sparse=False).</font>\n",
    "\n",
    "<font color='blue'> The below is tested using the copy of the categorical variables for onehot encoding. </blue>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OneHotEncoding for the categorical variables \n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "cat_encoder = OneHotEncoder()\n",
    "x_train_cat_hot = cat_encoder.fit_transform(x_train_copy)\n",
    "x_train_cat_hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([' Federal-gov', ' Local-gov', ' Never-worked', ' Private',\n",
       "        ' Self-emp-inc', ' Self-emp-not-inc', ' State-gov', ' Without-pay'],\n",
       "       dtype=object),\n",
       " array([' 10th', ' 11th', ' 12th', ' 1st-4th', ' 5th-6th', ' 7th-8th',\n",
       "        ' 9th', ' Assoc-acdm', ' Assoc-voc', ' Bachelors', ' Doctorate',\n",
       "        ' HS-grad', ' Masters', ' Preschool', ' Prof-school',\n",
       "        ' Some-college'], dtype=object),\n",
       " array([' Divorced', ' Married-AF-spouse', ' Married-civ-spouse',\n",
       "        ' Married-spouse-absent', ' Never-married', ' Separated',\n",
       "        ' Widowed'], dtype=object),\n",
       " array([' Adm-clerical', ' Armed-Forces', ' Craft-repair',\n",
       "        ' Exec-managerial', ' Farming-fishing', ' Handlers-cleaners',\n",
       "        ' Machine-op-inspct', ' Other-service', ' Priv-house-serv',\n",
       "        ' Prof-specialty', ' Protective-serv', ' Sales', ' Tech-support',\n",
       "        ' Transport-moving'], dtype=object),\n",
       " array([' Husband', ' Not-in-family', ' Other-relative', ' Own-child',\n",
       "        ' Unmarried', ' Wife'], dtype=object),\n",
       " array([' Amer-Indian-Eskimo', ' Asian-Pac-Islander', ' Black', ' Other',\n",
       "        ' White'], dtype=object),\n",
       " array([' Female', ' Male'], dtype=object),\n",
       " array([' Cambodia', ' Canada', ' China', ' Columbia', ' Cuba',\n",
       "        ' Dominican-Republic', ' Ecuador', ' El-Salvador', ' England',\n",
       "        ' France', ' Germany', ' Greece', ' Guatemala', ' Haiti',\n",
       "        ' Holand-Netherlands', ' Honduras', ' Hong', ' Hungary', ' India',\n",
       "        ' Iran', ' Ireland', ' Italy', ' Jamaica', ' Japan', ' Laos',\n",
       "        ' Mexico', ' Nicaragua', ' Outlying-US(Guam-USVI-etc)', ' Peru',\n",
       "        ' Philippines', ' Poland', ' Portugal', ' Puerto-Rico',\n",
       "        ' Scotland', ' South', ' Taiwan', ' Thailand', ' Trinadad&Tobago',\n",
       "        ' United-States', ' Vietnam', ' Yugoslavia'], dtype=object)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_encoder.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='constant', fill_value=0)),('Robust_scaler',RobustScaler()),])\n",
    "cat_pipeline = Pipeline([('imputer', SimpleImputer(strategy='most_frequent', fill_value=0)),('onehot', OneHotEncoder(handle_unknown='ignore')),])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "num_attribs = list(x_train_num.columns)\n",
    "cat_attribs = list(x_train_cat.columns)\n",
    "\n",
    "#full pipeline of numeric and categorical attributes\n",
    "full_pipeline = ColumnTransformer([(\"num\",num_pipeline,num_attribs),(\"cat\",cat_pipeline,cat_attribs)])\n",
    "\n",
    "#Using full_pipeline perform fit and transform on training data\n",
    "x_train_prepared = full_pipeline.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using full_pipeline perform transform on training data\n",
    "x_test_prepared = full_pipeline.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26048, 105)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_prepared.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6513, 105)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_prepared.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method ColumnTransformer.get_feature_names of ColumnTransformer(transformers=[('num',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(fill_value=0,\n",
       "                                                                strategy='constant')),\n",
       "                                                 ('Robust_scaler',\n",
       "                                                  RobustScaler())]),\n",
       "                                 ['age', 'fnlwgt', 'education_num',\n",
       "                                  'capital_gain', 'capital_loss',\n",
       "                                  'hours_per_week']),\n",
       "                                ('cat',\n",
       "                                 Pipeline(steps=[('imputer',\n",
       "                                                  SimpleImputer(fill_value=0,\n",
       "                                                                strategy='most_frequent')),\n",
       "                                                 ('onehot',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
       "                                 ['workclass', 'education', 'marital_status',\n",
       "                                  'occupation', 'relationship', 'race', 'sex',\n",
       "                                  'native_country'])])>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_pipeline.get_feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>Pipeline: we have imputed the missing values in the pipeline with simple imupter. Missing values in the categorical features is filled with the most frequent variable. Later we have column transformed the pipeline and attributes. the full pipeline is made fit_transformed with the train data and applied the transformation for the test data. </font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Prediction with Train and Test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data : 0.8381833538083538\n",
      "Accuracy on Test data : 0.8424689083371718\n",
      "Cross validation accuracy: 0.8434042415182903\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "lgr_reg = LogisticRegression(random_state=42)\n",
    "lgr_mod = lgr_reg.fit(x_train_prepared, y_train)\n",
    "logreg_train_predictions = lgr_mod.predict(x_train_prepared)\n",
    "logreg_test_predictions = lgr_mod.predict(x_test_prepared)\n",
    "\n",
    "print(\"Accuracy on Train data :\", metrics.accuracy_score(y_train,logreg_train_predictions)) \n",
    "print(\"Accuracy on Test data :\", metrics.accuracy_score(y_test,logreg_test_predictions)) \n",
    "print(\"Cross validation accuracy:\", cross_val_score(lgr_reg, x_train_prepared, y_train, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data : 0.8015586609336609\n",
      "Accuracy on Test data : 0.8054659910947336\n",
      "Cross validation accuracy: 0.8015587417556336\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "svm_clf = svm.SVC()\n",
    "svm_mod = svm_clf.fit(x_train_prepared, y_train)\n",
    "svm_train_predictions = svm_mod.predict(x_train_prepared)\n",
    "svm_test_predictions = svm_mod.predict(x_test_prepared)\n",
    "print(\"Accuracy on Train data :\", metrics.accuracy_score(y_train,svm_train_predictions)) \n",
    "print(\"Accuracy on Test data :\", metrics.accuracy_score(y_test,svm_test_predictions)) \n",
    "print(\"Cross validation accuracy:\", cross_val_score(svm_clf, x_train_prepared, y_train, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naïve Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data : 0.7990248771498771\n",
      "Accuracy on Test data : 0.8034699831107017\n",
      "Cross validation accuracy: 0.7987942955028584\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb_mod=gnb.fit(x_train_prepared.toarray(), y_train)\n",
    "gnb_train_predictions = gnb_mod.predict(x_train_prepared.toarray())\n",
    "gnb_test_predictions = gnb_mod.predict(x_test_prepared.toarray())\n",
    "\n",
    "print(\"Accuracy on Train data :\", metrics.accuracy_score(y_train,gnb_train_predictions)) \n",
    "print(\"Accuracy on Test data :\", metrics.accuracy_score(y_test,gnb_test_predictions)) \n",
    "print(\"Cross validation accuracy:\", cross_val_score(gnb, x_train_prepared.toarray(), y_train, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data : 0.8976504914004914\n",
      "Accuracy on Test data : 0.8587440503608168\n",
      "Cross validation accuracy: 0.8539621164545318\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()\n",
    "knn_mod=knn.fit(x_train_prepared, y_train)\n",
    "knn_train_predictions = knn_mod.predict(x_train_prepared)\n",
    "knn_test_predictions = knn_mod.predict(x_test_prepared)\n",
    "\n",
    "print(\"Accuracy on Train data :\", metrics.accuracy_score(y_train,knn_train_predictions)) \n",
    "print(\"Accuracy on Test data :\", metrics.accuracy_score(y_test,knn_test_predictions)) \n",
    "print(\"Cross validation accuracy:\", cross_val_score(knn, x_train_prepared, y_train, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results and model comparisions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|  Score/ Metric                          |  LogisticReression   | Support Vector Machines | Naïve Bayes   | K-Nearest Neighbors | \n",
    "|----------------------------------------|----------------------|-------------------------|---------------|---------------------|\n",
    "|Accuracy on Train data                  |    0.83818           |  0.80155                |    0.79902    | 0.89765             |\n",
    "|Accuray on Test data                    |    0.84246           |  0.80546                |    0.80346    | 0.85874             |\n",
    "|Cross validation Acuuracy on Train data |    0.84340           |  0.80155                |    0.79879    | 0.85396             |\n",
    "\n",
    "<font color='blue'>Logistic Regression: From the performence metrics of logistic regression on Train with tarin data is 0.838 and Test data is 0.842. we have done the Cross validation on the Train data, the accuracy of the of the cross validation is 0.8434 which is slighlty greater than normal Train accuracy.From the cross validation results,It is clear that the classification task accuracy is correctly defined with the Logistic Regreesion. </font>\n",
    "\n",
    "<font color='blue'>Support Vector Machines: From the performence metrics of Support Vector Machines on Train with tarin data is 0.801 and Test data is 0.8054. we have done the Cross validation on the Train data, the accuracy of the of the cross validation is 0.801 which is equal to Train accuracy.from the Cross validation results It is clear that the classification task accuracy is correctly defined with the Support Vector Machines.</font>\n",
    "\n",
    "<font color='blue'>Naïve Bayes: From the performence metrics of Naïve Bayes on Train with tarin data is 0.799 and Test data is 0.8034. we have done the Cross validation on the Train data, the accuracy of the of the cross validation is 0.798 which is equal to Train accuracy.So form the results it is clear that the correct classification derived through the method of cross validation. </font>\n",
    " \n",
    "<font color='blue'>K-Nearest Neighbors: From the performence metrics of K-Nearest Neighbors on Train with tarin data is 0.897 and Test data is 0.858. we have done the Cross validation on the Train data, the accuracy of the of the cross validation is 0.853 which is less than normal Train accuracy. Here, the Corss validation does not give equal accuracy with the Train vaidation.There is a differnce in the accuracy so it is slightly deviating.</font>\n",
    "\n",
    "<font color= 'blue'>K-Nearest Neighbors performed very good when compared with the other models with an accuracy of 0.897 which is highest of all models.</font> \n",
    "\n",
    "<font color= 'blue'>K-Nearest Neighbors performed very good when compared with the other models with an accuracy of test data which is 0.858 and it is highest of all models</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quesetion 5:\n",
    "Tune the parameters of each algorithms, and evaluate the performance of the best estimators on the Test dataset, does the results improved? Discuss your results and findings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Hyper Parameter with Accuracy Metrics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={'C': [0.01, 0.1, 1, 10, 20, 30],\n",
       "                         'penalty': ['l2', 'l1']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {'C':[0.01, 0.1, 1, 10, 20, 30], 'penalty':['l2','l1']}\n",
    "logreg = LogisticRegression()\n",
    "print(\"Tuning Hyper Parameter with Accuracy Metrics\")\n",
    "clf = GridSearchCV(logreg, parameters, cv=10, scoring= 'accuracy', n_jobs = -1)\n",
    "clf.fit(x_train_prepared,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report:\n",
      "0.8489175495163519\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.93      0.90      4945\n",
      "           1       0.73      0.59      0.65      1568\n",
      "\n",
      "    accuracy                           0.85      6513\n",
      "   macro avg       0.80      0.76      0.78      6513\n",
      "weighted avg       0.84      0.85      0.84      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(\"Report:\")\n",
    "logreg_pred = final_model.predict(x_test_prepared)\n",
    "print(metrics.accuracy_score(y_test,logreg_pred))\n",
    "print(classification_report(y_test, logreg_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Hyper Parameter with Accuracy Metrics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LinearSVC(), n_jobs=-1,\n",
       "                   param_distributions={'C': array([1, 3, 5, 7, 9])},\n",
       "                   scoring='accuracy')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "parameters = {'C':np.arange(1,10,2)}\n",
    "svm_model = LinearSVC()\n",
    "print(\"Tuning Hyper Parameter with Accuracy Metrics\")\n",
    "clf = RandomizedSearchCV(svm_model, parameters, cv=5, scoring= 'accuracy', n_jobs = -1)\n",
    "clf.fit(x_train_prepared,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report:\n",
      "0.852141870105942\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.92      0.90      4945\n",
      "           1       0.72      0.64      0.68      1568\n",
      "\n",
      "    accuracy                           0.85      6513\n",
      "   macro avg       0.80      0.78      0.79      6513\n",
      "weighted avg       0.85      0.85      0.85      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Report:\")\n",
    "svm_pred = final_model.predict(x_test_prepared)\n",
    "print(metrics.accuracy_score(y_test,svm_pred))\n",
    "print(classification_report(y_test, svm_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Hyper Parameter with Accuracy Metrics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=GaussianNB(), n_jobs=-1,\n",
       "             param_grid={'var_smoothing': array([1.00000000e+00, 8.11130831e-01, 6.57933225e-01, 5.33669923e-01,\n",
       "       4.32876128e-01, 3.51119173e-01, 2.84803587e-01, 2.31012970e-01,\n",
       "       1.87381742e-01, 1.51991108e-01, 1.23284674e-01, 1.00000000e-01,\n",
       "       8.11130831e-02, 6.57933225e-02, 5.33669923e-02, 4.32876128e-02,\n",
       "       3.51119173e-02, 2.848035...\n",
       "       1.23284674e-07, 1.00000000e-07, 8.11130831e-08, 6.57933225e-08,\n",
       "       5.33669923e-08, 4.32876128e-08, 3.51119173e-08, 2.84803587e-08,\n",
       "       2.31012970e-08, 1.87381742e-08, 1.51991108e-08, 1.23284674e-08,\n",
       "       1.00000000e-08, 8.11130831e-09, 6.57933225e-09, 5.33669923e-09,\n",
       "       4.32876128e-09, 3.51119173e-09, 2.84803587e-09, 2.31012970e-09,\n",
       "       1.87381742e-09, 1.51991108e-09, 1.23284674e-09, 1.00000000e-09])},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}\n",
    "gnb = GaussianNB()\n",
    "\n",
    "print(\"Tuning Hyper Parameter with Accuracy Metrics\")\n",
    "gnb_clf = GridSearchCV(gnb, params_NB, cv=10, scoring= 'accuracy', n_jobs = -1, return_train_score=True)\n",
    "gnb_clf.fit(x_train_prepared.toarray(),y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = gnb_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report:\n",
      "0.8341777982496545\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.91      0.89      4945\n",
      "           1       0.67      0.60      0.64      1568\n",
      "\n",
      "    accuracy                           0.83      6513\n",
      "   macro avg       0.78      0.76      0.76      6513\n",
      "weighted avg       0.83      0.83      0.83      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Report:\")\n",
    "gnb_pred = final_model.predict(x_test_prepared.toarray())\n",
    "print(metrics.accuracy_score(y_test,gnb_pred))\n",
    "print(classification_report(y_test, gnb_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Hyper Parameter with Accuracy Metrics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(), n_jobs=-1,\n",
       "             param_grid={'n_neighbors': [1, 3, 5], 'p': [1, 2]},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_KNN = {'n_neighbors': [1, 3, 5], \n",
    "              'p': [1, 2]}\n",
    "knn = KNeighborsClassifier()\n",
    "print(\"Tuning Hyper Parameter with Accuracy Metrics\")\n",
    "knn_clf = GridSearchCV(knn, params_KNN, cv=10, scoring= 'accuracy', n_jobs = -1, return_train_score=True)\n",
    "knn_clf.fit(x_train_prepared,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = knn_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report:\n",
      "0.857976354982343\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.92      0.91      4945\n",
      "           1       0.72      0.67      0.70      1568\n",
      "\n",
      "    accuracy                           0.86      6513\n",
      "   macro avg       0.81      0.80      0.80      6513\n",
      "weighted avg       0.86      0.86      0.86      6513\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Report:\")\n",
    "knn_pred = final_model.predict(x_test_prepared)\n",
    "print(metrics.accuracy_score(y_test,knn_pred))\n",
    "print(classification_report(y_test, knn_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  performance of the best estimators on the Test dataset with parameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|  Score/ Metric                          |  LogisticReression   | Support Vector Machines | Naïve Bayes   | K-Nearest Neighbors | \n",
    "|----------------------------------------|----------------------|-------------------------|---------------|--------------------\n",
    "|Accuray on Test data without parameter tuning |    0.842          |  0.805                |    0.803     | 0.858             |\n",
    "|Accuray on Test data with parameter tuning    |    0.848          |  0.852                |    0.834     | 0.857             |\n",
    "\n",
    "<font color='blue'> We performed paarmeter tuning on the models with different tuning parameters and observed increase in the performence metrics of Support Vector Machines and Naïve Bayes classification algorithms.\n",
    "    \n",
    "<font color='blue'>Where as there is not much change in the performence of LogisticReression and K-Nearest Neighbors. Even after tuning the parameters in the algorithm K-Nearest Neighbors is the best classification model next to Support Vector Machines.</font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data for Multiple-class Classification:\n",
    "* URL: https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones\n",
    "* Column “Activity” is the target label to classify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Import libraries\n",
    "import pandas as pd\n",
    "# To do linear algebra\n",
    "import numpy as np\n",
    "from numpy import pi\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# To create plots\n",
    "from matplotlib.colors import rgb2hex\n",
    "from matplotlib.cm import get_cmap\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# To create nicer plots\n",
    "import seaborn as sns\n",
    "\n",
    "# To create interactive plots\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "import plotly.graph_objs as go\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On Kaggle Notebook, after adding the data, you can import the data as Pandas DataFrame as follows\n",
    "import pandas as pd\n",
    "df_train = pd.read_csv(\"train.csv\")\n",
    "df_test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7352, 563)\n",
      "(2947, 563)\n"
     ]
    }
   ],
   "source": [
    "print(df_train.shape)\n",
    "print(df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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-skewness()</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "      <td>7352.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.274488</td>\n",
       "      <td>-0.017695</td>\n",
       "      <td>-0.109141</td>\n",
       "      <td>-0.605438</td>\n",
       "      <td>-0.510938</td>\n",
       "      <td>-0.604754</td>\n",
       "      <td>-0.630512</td>\n",
       "      <td>-0.526907</td>\n",
       "      <td>-0.606150</td>\n",
       "      <td>-0.468604</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.307009</td>\n",
       "      <td>-0.625294</td>\n",
       "      <td>0.008684</td>\n",
       "      <td>0.002186</td>\n",
       "      <td>0.008726</td>\n",
       "      <td>-0.005981</td>\n",
       "      <td>-0.489547</td>\n",
       "      <td>0.058593</td>\n",
       "      <td>-0.056515</td>\n",
       "      <td>17.413085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.070261</td>\n",
       "      <td>0.040811</td>\n",
       "      <td>0.056635</td>\n",
       "      <td>0.448734</td>\n",
       "      <td>0.502645</td>\n",
       "      <td>0.418687</td>\n",
       "      <td>0.424073</td>\n",
       "      <td>0.485942</td>\n",
       "      <td>0.414122</td>\n",
       "      <td>0.544547</td>\n",
       "      <td>...</td>\n",
       "      <td>0.321011</td>\n",
       "      <td>0.307584</td>\n",
       "      <td>0.336787</td>\n",
       "      <td>0.448306</td>\n",
       "      <td>0.608303</td>\n",
       "      <td>0.477975</td>\n",
       "      <td>0.511807</td>\n",
       "      <td>0.297480</td>\n",
       "      <td>0.279122</td>\n",
       "      <td>8.975143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.999873</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.995357</td>\n",
       "      <td>-0.999765</td>\n",
       "      <td>-0.976580</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.262975</td>\n",
       "      <td>-0.024863</td>\n",
       "      <td>-0.120993</td>\n",
       "      <td>-0.992754</td>\n",
       "      <td>-0.978129</td>\n",
       "      <td>-0.980233</td>\n",
       "      <td>-0.993591</td>\n",
       "      <td>-0.978162</td>\n",
       "      <td>-0.980251</td>\n",
       "      <td>-0.936219</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.542602</td>\n",
       "      <td>-0.845573</td>\n",
       "      <td>-0.121527</td>\n",
       "      <td>-0.289549</td>\n",
       "      <td>-0.482273</td>\n",
       "      <td>-0.376341</td>\n",
       "      <td>-0.812065</td>\n",
       "      <td>-0.017885</td>\n",
       "      <td>-0.143414</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.277193</td>\n",
       "      <td>-0.017219</td>\n",
       "      <td>-0.108676</td>\n",
       "      <td>-0.946196</td>\n",
       "      <td>-0.851897</td>\n",
       "      <td>-0.859365</td>\n",
       "      <td>-0.950709</td>\n",
       "      <td>-0.857328</td>\n",
       "      <td>-0.857143</td>\n",
       "      <td>-0.881637</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.343685</td>\n",
       "      <td>-0.711692</td>\n",
       "      <td>0.009509</td>\n",
       "      <td>0.008943</td>\n",
       "      <td>0.008735</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>-0.709417</td>\n",
       "      <td>0.182071</td>\n",
       "      <td>0.003181</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>0.288461</td>\n",
       "      <td>-0.010783</td>\n",
       "      <td>-0.097794</td>\n",
       "      <td>-0.242813</td>\n",
       "      <td>-0.034231</td>\n",
       "      <td>-0.262415</td>\n",
       "      <td>-0.292680</td>\n",
       "      <td>-0.066701</td>\n",
       "      <td>-0.265671</td>\n",
       "      <td>-0.017129</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.126979</td>\n",
       "      <td>-0.503878</td>\n",
       "      <td>0.150865</td>\n",
       "      <td>0.292861</td>\n",
       "      <td>0.506187</td>\n",
       "      <td>0.359368</td>\n",
       "      <td>-0.509079</td>\n",
       "      <td>0.248353</td>\n",
       "      <td>0.107659</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.916238</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.989538</td>\n",
       "      <td>0.956845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998702</td>\n",
       "      <td>0.996078</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 562 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tBodyAcc-mean()-X  tBodyAcc-mean()-Y  tBodyAcc-mean()-Z  \\\n",
       "count        7352.000000        7352.000000        7352.000000   \n",
       "mean            0.274488          -0.017695          -0.109141   \n",
       "std             0.070261           0.040811           0.056635   \n",
       "min            -1.000000          -1.000000          -1.000000   \n",
       "25%             0.262975          -0.024863          -0.120993   \n",
       "50%             0.277193          -0.017219          -0.108676   \n",
       "75%             0.288461          -0.010783          -0.097794   \n",
       "max             1.000000           1.000000           1.000000   \n",
       "\n",
       "       tBodyAcc-std()-X  tBodyAcc-std()-Y  tBodyAcc-std()-Z  tBodyAcc-mad()-X  \\\n",
       "count       7352.000000       7352.000000       7352.000000       7352.000000   \n",
       "mean          -0.605438         -0.510938         -0.604754         -0.630512   \n",
       "std            0.448734          0.502645          0.418687          0.424073   \n",
       "min           -1.000000         -0.999873         -1.000000         -1.000000   \n",
       "25%           -0.992754         -0.978129         -0.980233         -0.993591   \n",
       "50%           -0.946196         -0.851897         -0.859365         -0.950709   \n",
       "75%           -0.242813         -0.034231         -0.262415         -0.292680   \n",
       "max            1.000000          0.916238          1.000000          1.000000   \n",
       "\n",
       "       tBodyAcc-mad()-Y  tBodyAcc-mad()-Z  tBodyAcc-max()-X  ...  \\\n",
       "count       7352.000000       7352.000000       7352.000000  ...   \n",
       "mean          -0.526907         -0.606150         -0.468604  ...   \n",
       "std            0.485942          0.414122          0.544547  ...   \n",
       "min           -1.000000         -1.000000         -1.000000  ...   \n",
       "25%           -0.978162         -0.980251         -0.936219  ...   \n",
       "50%           -0.857328         -0.857143         -0.881637  ...   \n",
       "75%           -0.066701         -0.265671         -0.017129  ...   \n",
       "max            0.967664          1.000000          1.000000  ...   \n",
       "\n",
       "       fBodyBodyGyroJerkMag-skewness()  fBodyBodyGyroJerkMag-kurtosis()  \\\n",
       "count                      7352.000000                      7352.000000   \n",
       "mean                         -0.307009                        -0.625294   \n",
       "std                           0.321011                         0.307584   \n",
       "min                          -0.995357                        -0.999765   \n",
       "25%                          -0.542602                        -0.845573   \n",
       "50%                          -0.343685                        -0.711692   \n",
       "75%                          -0.126979                        -0.503878   \n",
       "max                           0.989538                         0.956845   \n",
       "\n",
       "       angle(tBodyAccMean,gravity)  angle(tBodyAccJerkMean),gravityMean)  \\\n",
       "count                  7352.000000                           7352.000000   \n",
       "mean                      0.008684                              0.002186   \n",
       "std                       0.336787                              0.448306   \n",
       "min                      -0.976580                             -1.000000   \n",
       "25%                      -0.121527                             -0.289549   \n",
       "50%                       0.009509                              0.008943   \n",
       "75%                       0.150865                              0.292861   \n",
       "max                       1.000000                              1.000000   \n",
       "\n",
       "       angle(tBodyGyroMean,gravityMean)  angle(tBodyGyroJerkMean,gravityMean)  \\\n",
       "count                       7352.000000                           7352.000000   \n",
       "mean                           0.008726                             -0.005981   \n",
       "std                            0.608303                              0.477975   \n",
       "min                           -1.000000                             -1.000000   \n",
       "25%                           -0.482273                             -0.376341   \n",
       "50%                            0.008735                             -0.000368   \n",
       "75%                            0.506187                              0.359368   \n",
       "max                            0.998702                              0.996078   \n",
       "\n",
       "       angle(X,gravityMean)  angle(Y,gravityMean)  angle(Z,gravityMean)  \\\n",
       "count           7352.000000           7352.000000           7352.000000   \n",
       "mean              -0.489547              0.058593             -0.056515   \n",
       "std                0.511807              0.297480              0.279122   \n",
       "min               -1.000000             -1.000000             -1.000000   \n",
       "25%               -0.812065             -0.017885             -0.143414   \n",
       "50%               -0.709417              0.182071              0.003181   \n",
       "75%               -0.509079              0.248353              0.107659   \n",
       "max                1.000000              0.478157              1.000000   \n",
       "\n",
       "           subject  \n",
       "count  7352.000000  \n",
       "mean     17.413085  \n",
       "std       8.975143  \n",
       "min       1.000000  \n",
       "25%       8.000000  \n",
       "50%      19.000000  \n",
       "75%      26.000000  \n",
       "max      30.000000  \n",
       "\n",
       "[8 rows x 562 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tBodyAccmeanX', 'tBodyAccmeanY', 'tBodyAccmeanZ', 'tBodyAccstdX',\n",
       "       'tBodyAccstdY', 'tBodyAccstdZ', 'tBodyAccmadX', 'tBodyAccmadY',\n",
       "       'tBodyAccmadZ', 'tBodyAccmaxX',\n",
       "       ...\n",
       "       'fBodyBodyGyroJerkMagkurtosis', 'angletBodyAccMeangravity',\n",
       "       'angletBodyAccJerkMeangravityMean', 'angletBodyGyroMeangravityMean',\n",
       "       'angletBodyGyroJerkMeangravityMean', 'angleXgravityMean',\n",
       "       'angleYgravityMean', 'angleZgravityMean', 'subject', 'Activity'],\n",
       "      dtype='object', length=563)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df_train.columns\n",
    "\n",
    "# Removing '()' from column names\n",
    "columns = columns.str.replace('[()]','')\n",
    "columns = columns.str.replace('[-]', '')\n",
    "columns = columns.str.replace('[,]','')\n",
    "\n",
    "df_train.columns = columns\n",
    "df_test.columns = columns\n",
    "\n",
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels: ['STANDING' 'SITTING' 'LAYING' 'WALKING' 'WALKING_DOWNSTAIRS'\n",
      " 'WALKING_UPSTAIRS']\n"
     ]
    }
   ],
   "source": [
    "print('Labels:', df_train['Activity'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels: ['STANDING' 'SITTING' 'LAYING' 'WALKING' 'WALKING_DOWNSTAIRS'\n",
      " 'WALKING_UPSTAIRS']\n"
     ]
    }
   ],
   "source": [
    "print('Labels:', df_test['Activity'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7352 entries, 0 to 7351\n",
      "Columns: 563 entries, tBodyAccmeanX to Activity\n",
      "dtypes: float64(561), int64(1), object(1)\n",
      "memory usage: 31.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2947 entries, 0 to 2946\n",
      "Columns: 563 entries, tBodyAccmeanX to Activity\n",
      "dtypes: float64(561), int64(1), object(1)\n",
      "memory usage: 12.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null values in train data: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of null values in train data:\" ,df_train.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null values in test data: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of null values in test data:\" ,df_test.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of duplicates in train data: 0\n"
     ]
    }
   ],
   "source": [
    "print('No of duplicates in train data: {}'.format(sum(df_train.duplicated())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of duplicates in test data: 0\n"
     ]
    }
   ],
   "source": [
    "print('No of duplicates in test data: {}'.format(sum(df_test.duplicated())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 1:\n",
    "Create plots to visualize the distribution of the label \"Activity\". If the distribution of each class is balanced?\n",
    "If imbalanced, what strategies would you adopt and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>Imbalanced data is one of the major issues in machine learning as there won't be any guaranty that data collected will always be balanced. Here we can say that distribution of data is balanced as data seems equally distributed with small fluctuations.\n",
    "    \n",
    "We can assume and divide the activities into two categories, Laying, Standing, Sitting activities into one category and Walking, Walking upstairs, Walking downstairs as another category. We can observe some variance between Walking upstairs and Walking downstairs activities, but we believe that we can ignore this small variation as this might be because of different walking styles of individuals having a constant sampling rate in smartphones.\n",
    "\n",
    "If data is imbalanced, we can adopt strategies like Downsampling, Upsampling, Synthetic Minority Oversampling Technique (SMOTE). We prefer to perform SMOTE if the data distribution is imbalanced as this technique tries to add a new instance that is similar to the original data.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LAYING                1407\n",
       "STANDING              1374\n",
       "SITTING               1286\n",
       "WALKING               1226\n",
       "WALKING_UPSTAIRS      1073\n",
       "WALKING_DOWNSTAIRS     986\n",
       "Name: Activity, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Activity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LAYING                537\n",
       "STANDING              532\n",
       "WALKING               496\n",
       "SITTING               491\n",
       "WALKING_UPSTAIRS      471\n",
       "WALKING_DOWNSTAIRS    420\n",
       "Name: Activity, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.Activity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Activity</th>\n",
       "      <th>LAYING</th>\n",
       "      <th>SITTING</th>\n",
       "      <th>STANDING</th>\n",
       "      <th>WALKING</th>\n",
       "      <th>WALKING_DOWNSTAIRS</th>\n",
       "      <th>WALKING_UPSTAIRS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subject</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>95</td>\n",
       "      <td>49</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>52</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>49</td>\n",
       "      <td>59</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>57</td>\n",
       "      <td>55</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "      <td>48</td>\n",
       "      <td>53</td>\n",
       "      <td>57</td>\n",
       "      <td>47</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>54</td>\n",
       "      <td>46</td>\n",
       "      <td>54</td>\n",
       "      <td>48</td>\n",
       "      <td>38</td>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>57</td>\n",
       "      <td>53</td>\n",
       "      <td>47</td>\n",
       "      <td>59</td>\n",
       "      <td>46</td>\n",
       "      <td>54</td>\n",
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       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>51</td>\n",
       "      <td>54</td>\n",
       "      <td>60</td>\n",
       "      <td>59</td>\n",
       "      <td>45</td>\n",
       "      <td>54</td>\n",
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       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>72</td>\n",
       "      <td>59</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>42</td>\n",
       "      <td>48</td>\n",
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       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "      <td>78</td>\n",
       "      <td>51</td>\n",
       "      <td>47</td>\n",
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       "      <td>17</td>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>78</td>\n",
       "      <td>61</td>\n",
       "      <td>46</td>\n",
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       "      <td>19</td>\n",
       "      <td>83</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>52</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
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       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>89</td>\n",
       "      <td>52</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
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       "      <td>22</td>\n",
       "      <td>72</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>46</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
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       "      <td>23</td>\n",
       "      <td>72</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>59</td>\n",
       "      <td>54</td>\n",
       "      <td>51</td>\n",
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       "      <td>25</td>\n",
       "      <td>73</td>\n",
       "      <td>65</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>58</td>\n",
       "      <td>65</td>\n",
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       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>74</td>\n",
       "      <td>59</td>\n",
       "      <td>50</td>\n",
       "      <td>55</td>\n",
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       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>74</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "      <td>51</td>\n",
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       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>80</td>\n",
       "      <td>72</td>\n",
       "      <td>79</td>\n",
       "      <td>54</td>\n",
       "      <td>46</td>\n",
       "      <td>51</td>\n",
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       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>69</td>\n",
       "      <td>60</td>\n",
       "      <td>65</td>\n",
       "      <td>53</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
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       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>70</td>\n",
       "      <td>62</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>62</td>\n",
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       "Activity  LAYING  SITTING  STANDING  WALKING  WALKING_DOWNSTAIRS  \\\n",
       "subject                                                            \n",
       "1             50       47        53       95                  49   \n",
       "3             62       52        61       58                  49   \n",
       "5             52       44        56       56                  47   \n",
       "6             57       55        57       57                  48   \n",
       "7             52       48        53       57                  47   \n",
       "8             54       46        54       48                  38   \n",
       "11            57       53        47       59                  46   \n",
       "14            51       54        60       59                  45   \n",
       "15            72       59        53       54                  42   \n",
       "16            70       69        78       51                  47   \n",
       "17            71       64        78       61                  46   \n",
       "19            83       73        73       52                  39   \n",
       "21            90       85        89       52                  45   \n",
       "22            72       62        63       46                  36   \n",
       "23            72       68        68       59                  54   \n",
       "25            73       65        74       74                  58   \n",
       "26            76       78        74       59                  50   \n",
       "27            74       70        80       57                  44   \n",
       "28            80       72        79       54                  46   \n",
       "29            69       60        65       53                  48   \n",
       "30            70       62        59       65                  62   \n",
       "\n",
       "Activity  WALKING_UPSTAIRS  \n",
       "subject                     \n",
       "1                       53  \n",
       "3                       59  \n",
       "5                       47  \n",
       "6                       51  \n",
       "7                       51  \n",
       "8                       41  \n",
       "11                      54  \n",
       "14                      54  \n",
       "15                      48  \n",
       "16                      51  \n",
       "17                      48  \n",
       "19                      40  \n",
       "21                      47  \n",
       "22                      42  \n",
       "23                      51  \n",
       "25                      65  \n",
       "26                      55  \n",
       "27                      51  \n",
       "28                      51  \n",
       "29                      49  \n",
       "30                      65  "
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       "Activity  LAYING  SITTING  STANDING  WALKING  WALKING_DOWNSTAIRS  \\\n",
       "subject                                                            \n",
       "2             48       46        54       59                  47   \n",
       "4             54       50        56       60                  45   \n",
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       "10            58       54        44       53                  38   \n",
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       "20            68       66        73       51                  45   \n",
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       "2                       48  \n",
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       "12                      52  \n",
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       "24                      59  "
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   "source": [
    "# Plotting data\n",
    "label_counts = df_train.Activity.value_counts()\n",
    "\n",
    "\n",
    "# Get colors\n",
    "n = label_counts.shape[0]\n",
    "colormap = get_cmap('viridis')\n",
    "colors = [rgb2hex(colormap(col)) for col in np.arange(0, 1.01, 1/(n-1))]\n",
    "\n",
    "# Create plot\n",
    "activity_data = go.Bar(x = label_counts.index,\n",
    "              y = label_counts,\n",
    "              marker = dict(color = colors))\n",
    "\n",
    "fig_layout = go.Layout(title = 'Smartphone Activity Label Distribution for train data',\n",
    "                   xaxis = dict(title = 'Activity'),\n",
    "                   yaxis = dict(title = 'Count'))\n",
    "\n",
    "fig = go.Figure(data=[activity_data], layout=fig_layout)\n",
    "iplot(fig)"
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  },
  {
   "cell_type": "code",
   "execution_count": 70,
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\"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Smartphone Activity Label Distribution for test data\"}, \"xaxis\": {\"title\": {\"text\": \"Activity\"}}, \"yaxis\": {\"title\": {\"text\": \"Count\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('d065a3e6-ba3f-4305-9733-947b4b287d8b');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting data\n",
    "label_counts1 = df_test.Activity.value_counts()\n",
    "\n",
    "\n",
    "# Get colors\n",
    "n = label_counts1.shape[0]\n",
    "colormap = get_cmap('viridis')\n",
    "colors = [rgb2hex(colormap(col)) for col in np.arange(0, 1.01, 1/(n-1))]\n",
    "\n",
    "# Create plot\n",
    "activity_data = go.Bar(x = label_counts1.index,\n",
    "              y = label_counts1,\n",
    "              marker = dict(color = colors))\n",
    "\n",
    "fig_layout = go.Layout(title = 'Smartphone Activity Label Distribution for test data',\n",
    "                   xaxis = dict(title = 'Activity'),\n",
    "                   yaxis = dict(title = 'Count'))\n",
    "\n",
    "fig = go.Figure(data=[activity_data], layout=fig_layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "plt.rcParams['font.family'] = 'Dejavu Sans'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.title('Data provided by each user', fontsize=20)\n",
    "sns.countplot(x='subject',hue='Activity', data = df_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.title('Data provided by each user', fontsize=20)\n",
    "sns.countplot(x='subject',hue='Activity', data = df_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 2:\n",
    "Evaluate the performance of Logistic Regression, Support Vector Machines, Naïve Bayes and K-Nearest Neighbors, with cross-validation. Which classifiers is not capable of handling multiple classes natively? Then what strategy/strategies can be applied to perform multiclass classification for those algorithms. Compare the performance of different algorithms on Test set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>Native classifiers like Logistic Regression, Perceptron, Support Vector Machines are designed for binary classification problems. We cannot use these types of algorithms directly on multiclass classification problems. To handle this problem we can use heuristic methods that can be used to split a multi-class classification problem into multiple binary classification datasets and train a binary classification model on each dataset. The heuristic methods are One-vs-Rest (OvR) and One-vs-One (OvO). For algorithms like Support Vector Machine classifiers, which perform poorly with the size of the training set, we can prefer OvO as it is faster to train many classifiers on small training datasets than training few classifiers on large training datasets. For binary classification algorithms like Logistic regression, OvR methods are preferred.</font>\n",
    "\n",
    "<font color='blue'>While utilizing these algorithms from Scikit-Learn, we no need to handle these methods explicitly, as Scikit-Learn automatically detects when we try to use a binary classification algorithm for a multiclass classification problem. OvR method will be the default for binary classification algorithms except for SVM classifiers, which utilizes the OvO method.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "Activity_prepared = df_train.drop(['subject', 'Activity'], axis=1) # drop labels for training set\n",
    "Activity_labels = df_train[\"Activity\"].copy()\n",
    "Activity_test = df_test.drop(['subject', 'Activity'], axis=1)\n",
    "Activity_test_labels = df_test[\"Activity\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "encode = LabelEncoder()\n",
    "Activity_labels = encode.fit_transform(Activity_labels)\n",
    "Activity_test_labels = encode.fit_transform(Activity_test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>tBodyAccmeanX</th>\n",
       "      <th>tBodyAccmeanY</th>\n",
       "      <th>tBodyAccmeanZ</th>\n",
       "      <th>tBodyAccstdX</th>\n",
       "      <th>tBodyAccstdY</th>\n",
       "      <th>tBodyAccstdZ</th>\n",
       "      <th>tBodyAccmadX</th>\n",
       "      <th>tBodyAccmadY</th>\n",
       "      <th>tBodyAccmadZ</th>\n",
       "      <th>tBodyAccmaxX</th>\n",
       "      <th>...</th>\n",
       "      <th>fBodyBodyGyroJerkMagmeanFreq</th>\n",
       "      <th>fBodyBodyGyroJerkMagskewness</th>\n",
       "      <th>fBodyBodyGyroJerkMagkurtosis</th>\n",
       "      <th>angletBodyAccMeangravity</th>\n",
       "      <th>angletBodyAccJerkMeangravityMean</th>\n",
       "      <th>angletBodyGyroMeangravityMean</th>\n",
       "      <th>angletBodyGyroJerkMeangravityMean</th>\n",
       "      <th>angleXgravityMean</th>\n",
       "      <th>angleYgravityMean</th>\n",
       "      <th>angleZgravityMean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>-0.074323</td>\n",
       "      <td>-0.298676</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>0.158075</td>\n",
       "      <td>-0.595051</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>0.414503</td>\n",
       "      <td>-0.390748</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>0.404573</td>\n",
       "      <td>-0.117290</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>0.087753</td>\n",
       "      <td>-0.351471</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7347</td>\n",
       "      <td>0.299665</td>\n",
       "      <td>-0.057193</td>\n",
       "      <td>-0.181233</td>\n",
       "      <td>-0.195387</td>\n",
       "      <td>0.039905</td>\n",
       "      <td>0.077078</td>\n",
       "      <td>-0.282301</td>\n",
       "      <td>0.043616</td>\n",
       "      <td>0.060410</td>\n",
       "      <td>0.210795</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.070157</td>\n",
       "      <td>-0.588433</td>\n",
       "      <td>-0.880324</td>\n",
       "      <td>-0.190437</td>\n",
       "      <td>0.829718</td>\n",
       "      <td>0.206972</td>\n",
       "      <td>-0.425619</td>\n",
       "      <td>-0.791883</td>\n",
       "      <td>0.238604</td>\n",
       "      <td>0.049819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7348</td>\n",
       "      <td>0.273853</td>\n",
       "      <td>-0.007749</td>\n",
       "      <td>-0.147468</td>\n",
       "      <td>-0.235309</td>\n",
       "      <td>0.004816</td>\n",
       "      <td>0.059280</td>\n",
       "      <td>-0.322552</td>\n",
       "      <td>-0.029456</td>\n",
       "      <td>0.080585</td>\n",
       "      <td>0.117440</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165259</td>\n",
       "      <td>-0.390738</td>\n",
       "      <td>-0.680744</td>\n",
       "      <td>0.064907</td>\n",
       "      <td>0.875679</td>\n",
       "      <td>-0.879033</td>\n",
       "      <td>0.400219</td>\n",
       "      <td>-0.771840</td>\n",
       "      <td>0.252676</td>\n",
       "      <td>0.050053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7349</td>\n",
       "      <td>0.273387</td>\n",
       "      <td>-0.017011</td>\n",
       "      <td>-0.045022</td>\n",
       "      <td>-0.218218</td>\n",
       "      <td>-0.103822</td>\n",
       "      <td>0.274533</td>\n",
       "      <td>-0.304515</td>\n",
       "      <td>-0.098913</td>\n",
       "      <td>0.332584</td>\n",
       "      <td>0.043999</td>\n",
       "      <td>...</td>\n",
       "      <td>0.195034</td>\n",
       "      <td>0.025145</td>\n",
       "      <td>-0.304029</td>\n",
       "      <td>0.052806</td>\n",
       "      <td>-0.266724</td>\n",
       "      <td>0.864404</td>\n",
       "      <td>0.701169</td>\n",
       "      <td>-0.779133</td>\n",
       "      <td>0.249145</td>\n",
       "      <td>0.040811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7350</td>\n",
       "      <td>0.289654</td>\n",
       "      <td>-0.018843</td>\n",
       "      <td>-0.158281</td>\n",
       "      <td>-0.219139</td>\n",
       "      <td>-0.111412</td>\n",
       "      <td>0.268893</td>\n",
       "      <td>-0.310487</td>\n",
       "      <td>-0.068200</td>\n",
       "      <td>0.319473</td>\n",
       "      <td>0.101702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013865</td>\n",
       "      <td>0.063907</td>\n",
       "      <td>-0.344314</td>\n",
       "      <td>-0.101360</td>\n",
       "      <td>0.700740</td>\n",
       "      <td>0.936674</td>\n",
       "      <td>-0.589479</td>\n",
       "      <td>-0.785181</td>\n",
       "      <td>0.246432</td>\n",
       "      <td>0.025339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7351</td>\n",
       "      <td>0.351503</td>\n",
       "      <td>-0.012423</td>\n",
       "      <td>-0.203867</td>\n",
       "      <td>-0.269270</td>\n",
       "      <td>-0.087212</td>\n",
       "      <td>0.177404</td>\n",
       "      <td>-0.377404</td>\n",
       "      <td>-0.038678</td>\n",
       "      <td>0.229430</td>\n",
       "      <td>0.269013</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058402</td>\n",
       "      <td>-0.387052</td>\n",
       "      <td>-0.740738</td>\n",
       "      <td>-0.280088</td>\n",
       "      <td>-0.007739</td>\n",
       "      <td>-0.056088</td>\n",
       "      <td>-0.616956</td>\n",
       "      <td>-0.783267</td>\n",
       "      <td>0.246809</td>\n",
       "      <td>0.036695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7352 rows × 561 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      tBodyAccmeanX  tBodyAccmeanY  tBodyAccmeanZ  tBodyAccstdX  tBodyAccstdY  \\\n",
       "0          0.288585      -0.020294      -0.132905     -0.995279     -0.983111   \n",
       "1          0.278419      -0.016411      -0.123520     -0.998245     -0.975300   \n",
       "2          0.279653      -0.019467      -0.113462     -0.995380     -0.967187   \n",
       "3          0.279174      -0.026201      -0.123283     -0.996091     -0.983403   \n",
       "4          0.276629      -0.016570      -0.115362     -0.998139     -0.980817   \n",
       "...             ...            ...            ...           ...           ...   \n",
       "7347       0.299665      -0.057193      -0.181233     -0.195387      0.039905   \n",
       "7348       0.273853      -0.007749      -0.147468     -0.235309      0.004816   \n",
       "7349       0.273387      -0.017011      -0.045022     -0.218218     -0.103822   \n",
       "7350       0.289654      -0.018843      -0.158281     -0.219139     -0.111412   \n",
       "7351       0.351503      -0.012423      -0.203867     -0.269270     -0.087212   \n",
       "\n",
       "      tBodyAccstdZ  tBodyAccmadX  tBodyAccmadY  tBodyAccmadZ  tBodyAccmaxX  \\\n",
       "0        -0.913526     -0.995112     -0.983185     -0.923527     -0.934724   \n",
       "1        -0.960322     -0.998807     -0.974914     -0.957686     -0.943068   \n",
       "2        -0.978944     -0.996520     -0.963668     -0.977469     -0.938692   \n",
       "3        -0.990675     -0.997099     -0.982750     -0.989302     -0.938692   \n",
       "4        -0.990482     -0.998321     -0.979672     -0.990441     -0.942469   \n",
       "...            ...           ...           ...           ...           ...   \n",
       "7347      0.077078     -0.282301      0.043616      0.060410      0.210795   \n",
       "7348      0.059280     -0.322552     -0.029456      0.080585      0.117440   \n",
       "7349      0.274533     -0.304515     -0.098913      0.332584      0.043999   \n",
       "7350      0.268893     -0.310487     -0.068200      0.319473      0.101702   \n",
       "7351      0.177404     -0.377404     -0.038678      0.229430      0.269013   \n",
       "\n",
       "      ...  fBodyBodyGyroJerkMagmeanFreq  fBodyBodyGyroJerkMagskewness  \\\n",
       "0     ...                     -0.074323                     -0.298676   \n",
       "1     ...                      0.158075                     -0.595051   \n",
       "2     ...                      0.414503                     -0.390748   \n",
       "3     ...                      0.404573                     -0.117290   \n",
       "4     ...                      0.087753                     -0.351471   \n",
       "...   ...                           ...                           ...   \n",
       "7347  ...                     -0.070157                     -0.588433   \n",
       "7348  ...                      0.165259                     -0.390738   \n",
       "7349  ...                      0.195034                      0.025145   \n",
       "7350  ...                      0.013865                      0.063907   \n",
       "7351  ...                     -0.058402                     -0.387052   \n",
       "\n",
       "      fBodyBodyGyroJerkMagkurtosis  angletBodyAccMeangravity  \\\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",
       "7347                     -0.880324                 -0.190437   \n",
       "7348                     -0.680744                  0.064907   \n",
       "7349                     -0.304029                  0.052806   \n",
       "7350                     -0.344314                 -0.101360   \n",
       "7351                     -0.740738                 -0.280088   \n",
       "\n",
       "      angletBodyAccJerkMeangravityMean  angletBodyGyroMeangravityMean  \\\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",
       "7347                          0.829718                       0.206972   \n",
       "7348                          0.875679                      -0.879033   \n",
       "7349                         -0.266724                       0.864404   \n",
       "7350                          0.700740                       0.936674   \n",
       "7351                         -0.007739                      -0.056088   \n",
       "\n",
       "      angletBodyGyroJerkMeangravityMean  angleXgravityMean  angleYgravityMean  \\\n",
       "0                             -0.018446          -0.841247           0.179941   \n",
       "1                              0.703511          -0.844788           0.180289   \n",
       "2                              0.808529          -0.848933           0.180637   \n",
       "3                             -0.485366          -0.848649           0.181935   \n",
       "4                             -0.615971          -0.847865           0.185151   \n",
       "...                                 ...                ...                ...   \n",
       "7347                          -0.425619          -0.791883           0.238604   \n",
       "7348                           0.400219          -0.771840           0.252676   \n",
       "7349                           0.701169          -0.779133           0.249145   \n",
       "7350                          -0.589479          -0.785181           0.246432   \n",
       "7351                          -0.616956          -0.783267           0.246809   \n",
       "\n",
       "      angleZgravityMean  \n",
       "0             -0.058627  \n",
       "1             -0.054317  \n",
       "2             -0.049118  \n",
       "3             -0.047663  \n",
       "4             -0.043892  \n",
       "...                 ...  \n",
       "7347           0.049819  \n",
       "7348           0.050053  \n",
       "7349           0.040811  \n",
       "7350           0.025339  \n",
       "7351           0.036695  \n",
       "\n",
       "[7352 rows x 561 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Activity_prepared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9907508161044614\n",
      "Accuracy on Test data: 0.9586019681031558\n",
      "Cross validation accuracy: 0.9451909568175096\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(Activity_prepared, Activity_labels)\n",
    "logreg_train_predictions = logreg.predict(Activity_prepared)\n",
    "logreg_test_predictions = logreg.predict(Activity_test)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,logreg_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,logreg_test_predictions))\n",
    "\n",
    "print(\"Cross validation accuracy:\",cross_val_score(logreg, Activity_prepared, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OneVsRestClassifier with LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9910228509249184\n",
      "Accuracy on Test data: 0.9647098744485918\n",
      "Cross validation accuracy: 0.9484540446613428\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "logreg = OneVsRestClassifier(LogisticRegression())\n",
    "logreg.fit(Activity_prepared, Activity_labels)\n",
    "logreg_train_predictions = logreg.predict(Activity_prepared)\n",
    "logreg_test_predictions = logreg.predict(Activity_test)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,logreg_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,logreg_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(logreg, Activity_prepared, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9790533188248096\n",
      "Accuracy on Test data: 0.9504580929759077\n",
      "Cross validation accuracy: 0.9397478556640048\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc_model = SVC()\n",
    "\n",
    "svc_model.fit(Activity_prepared, Activity_labels)\n",
    "svc_model_train_predictions = svc_model.predict(Activity_prepared)\n",
    "svc_model_test_predictions = svc_model.predict(Activity_test)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,svc_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,svc_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(svc_model, Activity_prepared, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OneVsOneClassifier with Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9793253536452666\n",
      "Accuracy on Test data: 0.9491007804546997\n",
      "Cross validation accuracy: 0.9412442694469092\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "svc_model = OneVsOneClassifier(SVC())\n",
    "\n",
    "svc_model.fit(Activity_prepared, Activity_labels)\n",
    "svc_model_train_predictions = svc_model.predict(Activity_prepared)\n",
    "svc_model_test_predictions = svc_model.predict(Activity_test)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,svc_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,svc_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(svc_model, Activity_prepared, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.7463275299238302\n",
      "Accuracy on Test data: 0.7702748557855447\n",
      "Cross validation accuracy: 0.6979094572611654\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "\n",
    "gnb.fit(Activity_prepared, Activity_labels)\n",
    "gnb_model_train_predictions = gnb.predict(Activity_prepared)\n",
    "gnb_model_test_predictions = gnb.predict(Activity_test)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,gnb_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,gnb_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(gnb, Activity_prepared, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.984221980413493\n",
      "Accuracy on Test data: 0.9002375296912114\n",
      "Cross validation accuracy: 0.9065640712806861\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(Activity_prepared, Activity_labels)\n",
    "knn_model_train_predictions = knn.predict(Activity_prepared)\n",
    "knn_model_test_predictions = knn.predict(Activity_test)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,knn_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,knn_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(knn, Activity_prepared, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results and Model comparisions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|  Score/ Metric                          |  LogisticReression   | Support Vector Machines | Naïve Bayes   | K-Nearest Neighbors | \n",
    "|----------------------------------------|----------------------|-------------------------|---------------|---------------------|\n",
    "|Accuracy on Train data                  |    0.991           |  0.979                |    0.746    | 0.984             |\n",
    "|Accuray on Test data                    |    0.964           |  0.949                |    0.770    | 0.900             |\n",
    "|Cross validation Acuuracy on Train data |    0.948           |  0.941                |    0.697    | 0.906             |\n",
    "\n",
    "<font color='blue'>Logistic Regression: From the performence metrics of logistic regression on Train with tarin data is 0.991 and Test data is 0.964. we have done the Cross validation on the Train data, the accuracy of the of the cross validation is 0.948 which is less than normal Train accuracy.From the cross validation results,It is clear that the classification task accuracy is not correctly defined with the Logistic Regreesion. which is deviating from the Train accuracy.</font>\n",
    "\n",
    "<font color='blue'>Support Vector Machines: From the performence metrics of Support Vector Machines on Train with tarin data is 0.979 and Test data is 0.949. we have done the Cross validation on the Train data, the accuracy of the of the cross validation is 0.941 which is less than Train accuracy.from the Cross validation results It is clear that the classification task accuracy is not correctly defined with the Support Vector Machines.and it is is deviating from the Train accuracy but it is equal to test data accuracy.</font>\n",
    "\n",
    "<font color='blue'>Naïve Bayes: From the performence metrics of Naïve Bayes on Train with tarin data is 0.746 and Test data is 0.770. we have done the Cross validation on the Train data, the accuracy of the of the cross validation is 0.697 which is less than Train accuracy.So form the results it is clear that the classification accuracy is not correctly defined with the train data accuracy.</font>\n",
    " \n",
    "<font color='blue'>K-Nearest Neighbors: From the performence metrics of K-Nearest Neighbors on Train with tarin data is 0.984 and Test data is 0.900. we have done the Cross validation on the Train data, the accuracy of the of the cross validation is 0.906 which is less than normal Train accuracy. Here, the Corss validation does not give equal accuracy with the Train vaidation.There is a differnce in the accuracy so it is highly deviating.But it is euqal with the test data accuracy.</font>\n",
    "\n",
    "<font color= 'blue'>Logistic Regression performed very good when compared with the other models with an accuracy of 0.991 which is highest of all models.</font>\n",
    "\n",
    "<font color= 'blue'>Logistic Regression performed very good when compared with the other models with an accuracy of test data which is 0.964 and it is highest of all models</font>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 3:\n",
    "Would scaling the features affect the classification results? Discuss your results and findings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "stdScaler = StandardScaler()\n",
    "Activity_prepared_scaled = stdScaler.fit_transform(Activity_prepared.values)\n",
    "Activity_test_scaled = stdScaler.transform(Activity_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9964635473340587\n",
      "Accuracy on Test data: 0.9545300305395318\n",
      "Cross validation accuracy: 0.9458712289263531\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(Activity_prepared_scaled, Activity_labels)\n",
    "logreg_train_predictions = logreg.predict(Activity_prepared_scaled)\n",
    "logreg_test_predictions = logreg.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,logreg_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,logreg_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(logreg, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OneVsRestClassifier with LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9949673558215452\n",
      "Accuracy on Test data: 0.9579233118425518\n",
      "Cross validation accuracy: 0.9469580005915409\n"
     ]
    }
   ],
   "source": [
    "logreg = OneVsRestClassifier(LogisticRegression())\n",
    "logreg.fit(Activity_prepared_scaled, Activity_labels)\n",
    "logreg_train_predictions = logreg.predict(Activity_prepared_scaled)\n",
    "logreg_test_predictions = logreg.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,logreg_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,logreg_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(logreg, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9866702937976061\n",
      "Accuracy on Test data: 0.9518154054971157\n",
      "Cross validation accuracy: 0.9377087030464359\n"
     ]
    }
   ],
   "source": [
    "svc_model = SVC()\n",
    "\n",
    "svc_model.fit(Activity_prepared_scaled, Activity_labels)\n",
    "svc_model_train_predictions = svc_model.predict(Activity_prepared_scaled)\n",
    "svc_model_test_predictions = svc_model.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,svc_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,svc_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(svc_model, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OneVsOneClassifier with Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9934711643090316\n",
      "Accuracy on Test data: 0.9548693586698337\n",
      "Cross validation accuracy: 0.9373014640638864\n"
     ]
    }
   ],
   "source": [
    "svc_model = OneVsOneClassifier(SVC())\n",
    "\n",
    "svc_model.fit(Activity_prepared_scaled, Activity_labels)\n",
    "svc_model_train_predictions = svc_model.predict(Activity_prepared_scaled)\n",
    "svc_model_test_predictions = svc_model.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,svc_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,svc_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(svc_model, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.7461915125136017\n",
      "Accuracy on Test data: 0.7702748557855447\n",
      "Cross validation accuracy: 0.697773587695948\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "\n",
    "gnb.fit(Activity_prepared_scaled, Activity_labels)\n",
    "gnb_model_train_predictions = gnb.predict(Activity_prepared_scaled)\n",
    "gnb_model_test_predictions = gnb.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,gnb_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,gnb_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(gnb, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9828618063112078\n",
      "Accuracy on Test data: 0.8836104513064132\n",
      "Cross validation accuracy: 0.8826240387459332\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(Activity_prepared_scaled, Activity_labels)\n",
    "knn_model_train_predictions = knn.predict(Activity_prepared_scaled)\n",
    "knn_model_test_predictions = knn.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,knn_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,knn_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(knn, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Robust scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "rbtScaler = RobustScaler()\n",
    "Activity_prepared_scaled = rbtScaler.fit_transform(Activity_prepared.values)\n",
    "Activity_test_scaled = rbtScaler.transform(Activity_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9946953210010882\n",
      "Accuracy on Test data: 0.9528333898880217\n",
      "Cross validation accuracy: 0.9402942916296952\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(Activity_prepared_scaled, Activity_labels)\n",
    "logreg_train_predictions = logreg.predict(Activity_prepared_scaled)\n",
    "logreg_test_predictions = logreg.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,logreg_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,logreg_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(logreg, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OneVsRestClassifier with LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9929270946681176\n",
      "Accuracy on Test data: 0.9596199524940617\n",
      "Cross validation accuracy: 0.9396138346643005\n"
     ]
    }
   ],
   "source": [
    "logreg = OneVsRestClassifier(LogisticRegression())\n",
    "logreg.fit(Activity_prepared_scaled, Activity_labels)\n",
    "logreg_train_predictions = logreg.predict(Activity_prepared_scaled)\n",
    "logreg_test_predictions = logreg.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,logreg_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,logreg_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(logreg, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9775571273122959\n",
      "Accuracy on Test data: 0.9355276552426196\n",
      "Cross validation accuracy: 0.9099556344276843\n"
     ]
    }
   ],
   "source": [
    "svc_model = SVC()\n",
    "\n",
    "svc_model.fit(Activity_prepared_scaled, Activity_labels)\n",
    "svc_model_train_predictions = svc_model.predict(Activity_prepared_scaled)\n",
    "svc_model_test_predictions = svc_model.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,svc_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,svc_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(svc_model, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OneVsOneClassifier with Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9915669205658324\n",
      "Accuracy on Test data: 0.9453681710213777\n",
      "Cross validation accuracy: 0.9196160529429163\n"
     ]
    }
   ],
   "source": [
    "svc_model = OneVsOneClassifier(SVC())\n",
    "\n",
    "svc_model.fit(Activity_prepared_scaled, Activity_labels)\n",
    "svc_model_train_predictions = svc_model.predict(Activity_prepared_scaled)\n",
    "svc_model_test_predictions = svc_model.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,svc_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,svc_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(svc_model, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.7463275299238302\n",
      "Accuracy on Test data: 0.7702748557855447\n",
      "Cross validation accuracy: 0.6979094572611654\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "\n",
    "gnb.fit(Activity_prepared_scaled, Activity_labels)\n",
    "gnb_model_train_predictions = gnb.predict(Activity_prepared_scaled)\n",
    "gnb_model_test_predictions = gnb.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,gnb_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,gnb_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(gnb, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Train data: 0.9453210010881393\n",
      "Accuracy on Test data: 0.829317950458093\n",
      "Cross validation accuracy: 0.8450728334812186\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(Activity_prepared_scaled, Activity_labels)\n",
    "knn_model_train_predictions = knn.predict(Activity_prepared_scaled)\n",
    "knn_model_test_predictions = knn.predict(Activity_test_scaled)\n",
    "\n",
    "print(\"Accuracy on Train data:\",metrics.accuracy_score(Activity_labels,knn_model_train_predictions)) \n",
    "print(\"Accuracy on Test data:\",metrics.accuracy_score(Activity_test_labels,knn_model_test_predictions))\n",
    "print(\"Cross validation accuracy:\",cross_val_score(knn, Activity_prepared_scaled, Activity_labels, cv=10, scoring='accuracy').mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results and Comparisions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Standard Scalar\n",
    "\n",
    "|  Score/ Metric                          |  LogisticReression   | Support Vector Machines | Naïve Bayes   | K-Nearest Neighbors | \n",
    "|----------------------------------------|----------------------|-------------------------|---------------|---------------------|\n",
    "|Accuracy on Train data                  |    0.994           |  0.993                |    0.746    | 0.982             |\n",
    "|Accuray on Test data                    |    0.957           |  0.954               |    0.770    | 0.883             |\n",
    "|Cross validation Acuuracy on Train data |    0.946           |  0.937                |    0.697    | 0.982             |\n",
    "\n",
    "\n",
    "### Robust Scalar\n",
    "\n",
    "|  Score/ Metric                          |  LogisticReression   | Support Vector Machines | Naïve Bayes   | K-Nearest Neighbors | \n",
    "|----------------------------------------|----------------------|-------------------------|---------------|---------------------|\n",
    "|Accuracy on Train data                  |    0.992           |  0.991                |    0.746    | 0.945             |\n",
    "|Accuray on Test data                    |    0.959           |  0.945                |    0.770    | 0.829             |\n",
    "|Cross validation Acuuracy on Train data |    0.939           |  0.919                |    0.697    | 0.845             |\n",
    "\n",
    "\n",
    "<font color = 'blue'>We perfomed scaling on the models usind standard scalar and Robust scalar methods, Both methods did not show improvement in the performence of Logistic Regression and Naive Bayes classification algorithms. \n",
    "\n",
    "We have observed an increase in the performence of Support Vector Machines on both scaling methods. For KNN algorithm performence reduced in the Robust scalar method.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 4:\n",
    "Tune the parameters of each algorithms, then evaluate the performance of the best estimators on the Test dataset, does the results improved? Discuss your results and findings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Hyper Parameter with Accuracy Metrics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],\n",
       "                          'kernel': ['rbf']},\n",
       "                         {'C': [1, 10, 100, 1000], 'kernel': ['linear']}],\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import svm\n",
    "model = svm.SVC()\n",
    "tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n",
    "                     'C': [1, 10, 100, 1000]},\n",
    "                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n",
    "\n",
    "#score = 'accuracy'\n",
    "print(\"Tuning Hyper Parameter with Accuracy Metrics\")\n",
    "svc_clf = GridSearchCV(model, tuned_parameters, cv=10, scoring= 'accuracy')\n",
    "svc_clf.fit(Activity_prepared_scaled,Activity_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf.fit(Activity_prepared_scaled,Activity_labels)\n",
    "final_model = svc_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report:\n",
      "0.9552086868001357\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       537\n",
      "           1       0.96      0.90      0.93       491\n",
      "           2       0.92      0.97      0.94       532\n",
      "           3       0.94      0.99      0.96       496\n",
      "           4       0.98      0.92      0.95       420\n",
      "           5       0.94      0.95      0.94       471\n",
      "\n",
      "    accuracy                           0.96      2947\n",
      "   macro avg       0.96      0.95      0.95      2947\n",
      "weighted avg       0.96      0.96      0.96      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Report:\")\n",
    "svc_pred = final_model.predict(Activity_test_scaled)\n",
    "print(metrics.accuracy_score(Activity_test_labels,svc_pred)) \n",
    "print(classification_report(Activity_test_labels, svc_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Hyper Parameter with Accuracy Metrics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(), n_jobs=-1,\n",
       "             param_grid={'C': [0.01, 0.1, 1, 10, 20, 30],\n",
       "                         'penalty': ['l2', 'l1']},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'C':[0.01, 0.1, 1, 10, 20, 30], 'penalty':['l2','l1']}\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "print(\"Tuning Hyper Parameter with Accuracy Metrics\")\n",
    "logreg_clf = GridSearchCV(logreg, parameters, cv=10, scoring= 'accuracy', n_jobs = -1,  return_train_score=True)\n",
    "logreg_clf.fit(Activity_prepared_scaled,Activity_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = logreg_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report:\n",
      "0.9528333898880217\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99       537\n",
      "           1       0.96      0.87      0.91       491\n",
      "           2       0.91      0.96      0.94       532\n",
      "           3       0.94      0.99      0.96       496\n",
      "           4       0.99      0.94      0.96       420\n",
      "           5       0.96      0.94      0.95       471\n",
      "\n",
      "    accuracy                           0.95      2947\n",
      "   macro avg       0.95      0.95      0.95      2947\n",
      "weighted avg       0.95      0.95      0.95      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Report:\")\n",
    "logreg_pred = final_model.predict(Activity_test_scaled)\n",
    "print(metrics.accuracy_score(Activity_test_labels,logreg_pred)) \n",
    "print(classification_report(Activity_test_labels, logreg_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Hyper Parameter with Accuracy Metrics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=GaussianNB(), n_jobs=-1,\n",
       "             param_grid={'var_smoothing': array([1.00000000e+00, 8.11130831e-01, 6.57933225e-01, 5.33669923e-01,\n",
       "       4.32876128e-01, 3.51119173e-01, 2.84803587e-01, 2.31012970e-01,\n",
       "       1.87381742e-01, 1.51991108e-01, 1.23284674e-01, 1.00000000e-01,\n",
       "       8.11130831e-02, 6.57933225e-02, 5.33669923e-02, 4.32876128e-02,\n",
       "       3.51119173e-02, 2.848035...\n",
       "       1.23284674e-07, 1.00000000e-07, 8.11130831e-08, 6.57933225e-08,\n",
       "       5.33669923e-08, 4.32876128e-08, 3.51119173e-08, 2.84803587e-08,\n",
       "       2.31012970e-08, 1.87381742e-08, 1.51991108e-08, 1.23284674e-08,\n",
       "       1.00000000e-08, 8.11130831e-09, 6.57933225e-09, 5.33669923e-09,\n",
       "       4.32876128e-09, 3.51119173e-09, 2.84803587e-09, 2.31012970e-09,\n",
       "       1.87381742e-09, 1.51991108e-09, 1.23284674e-09, 1.00000000e-09])},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}\n",
    "gnb = GaussianNB()\n",
    "\n",
    "print(\"Tuning Hyper Parameter with Accuracy Metrics\")\n",
    "gnb_clf = GridSearchCV(gnb, params_NB, cv=10, scoring= 'accuracy', n_jobs = -1, return_train_score=True)\n",
    "gnb_clf.fit(Activity_prepared_scaled,Activity_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = gnb_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report:\n",
      "0.8177807940278249\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       537\n",
      "           1       0.95      0.50      0.66       491\n",
      "           2       0.68      0.95      0.79       532\n",
      "           3       0.83      0.84      0.83       496\n",
      "           4       0.83      0.61      0.70       420\n",
      "           5       0.76      0.96      0.85       471\n",
      "\n",
      "    accuracy                           0.82      2947\n",
      "   macro avg       0.84      0.81      0.80      2947\n",
      "weighted avg       0.84      0.82      0.81      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Report:\")\n",
    "gnb_pred = final_model.predict(Activity_test_scaled)\n",
    "print(metrics.accuracy_score(Activity_test_labels,gnb_pred)) \n",
    "print(classification_report(Activity_test_labels, gnb_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning Hyper Parameter with Accuracy Metrics\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(), n_jobs=-1,\n",
       "             param_grid={'n_neighbors': [1, 3, 5], 'p': [1, 2]},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_KNN = {'n_neighbors': [1, 3, 5], \n",
    "              'p': [1, 2]}\n",
    "knn = KNeighborsClassifier()\n",
    "print(\"Tuning Hyper Parameter with Accuracy Metrics\")\n",
    "knn_clf = GridSearchCV(knn, params_KNN, cv=10, scoring= 'accuracy', n_jobs = -1, return_train_score=True)\n",
    "knn_clf.fit(Activity_prepared_scaled,Activity_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = knn_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report:\n",
      "0.9015948422124194\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       537\n",
      "           1       0.94      0.83      0.88       491\n",
      "           2       0.86      0.95      0.90       532\n",
      "           3       0.81      0.98      0.89       496\n",
      "           4       0.97      0.71      0.82       420\n",
      "           5       0.87      0.90      0.88       471\n",
      "\n",
      "    accuracy                           0.90      2947\n",
      "   macro avg       0.91      0.89      0.90      2947\n",
      "weighted avg       0.91      0.90      0.90      2947\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Report:\")\n",
    "knn_pred = final_model.predict(Activity_test_scaled)\n",
    "print(metrics.accuracy_score(Activity_test_labels,knn_pred)) \n",
    "print(classification_report(Activity_test_labels, knn_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## performance of the best estimators on the Test dataset with parameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|  Score/ Metric                          |  LogisticReression   | Support Vector Machines | Naïve Bayes   | K-Nearest Neighbors | \n",
    "|----------------------------------------|----------------------|-------------------------|---------------|--------------------\n",
    "|Accuray on Test data without parameter tuning |    0.959          |  0.945                |    0.770     | 0.829            |\n",
    "|Accuray on Test data with parameter tuning    |    0.952          |  0.955                |    0.817     | 0.901             |\n",
    "\n",
    "<font color='blue'> We performed paarmeter tuning on the models with different tuning parameters and observed increase in the performence metrics of Support Vector Machines, Naïve Bayes classification and K-Nearest Neighbors algorithms.\n",
    "    \n",
    "<font color='blue'>Where as there is not much change in the performence of LogisticReression. After tuning the parameters in the algorithm Support Vector Machines is the best classification model next to LogisticReression.</font>\n",
    "\n"
   ]
  }
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