scaling-features-and-classification-modeling.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Mobile Price Classification\n",
    "\n",
    "\n",
    "### Meet Bob\n",
    "Bob has started his own mobile company. He wants to give tough fight to big companies like Apple,Samsung etc.\n",
    "\n",
    "He does not know how to estimate price of mobiles his company creates. In this competitive mobile phone market you cannot simply assume things. To solve this problem he collects sales data of mobile phones of various companies.\n",
    "\n",
    "Bob wants to find out some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price. But he is not so good at Machine Learning. So he needs your help to solve this problem.\n",
    "\n",
    "> **Notice**: In this problem you do not have to predict actual price but a price range indicating how high the price is.\n",
    "\n",
    "---\n",
    "\n",
    "## Target\n",
    "In this project,on the basis of the mobile specification like Battery power, 3G enabled , wifi ,Bluetooth, Ram, etc. we are predicting Price range of the mobile.\n",
    "\n",
    "## About the Dataset\n",
    "- **battery_power**: Total energy a battery can store in one time measured in mAh\n",
    "- **blue**: Has bluetooth or not\n",
    "- **clock_speed**: Speed at which microprocessor executes instructions\n",
    "- **dual_sim**: Has dual sim support or not\n",
    "- **fc**: Front Camera mega pixels\n",
    "- **four_g**: Has 4G or not\n",
    "- **int_memory**: Internal Memory in Gigabytes\n",
    "- **m_dep**: Mobile Depth in cm\n",
    "- **mobile_wt**: Weight of mobile phone\n",
    "- **n_cores**: Number of cores of processor\n",
    "- **pc**: Primary Camera mega pixels\n",
    "- **px_height**: Pixel Resolution Height\n",
    "- **px_width**: Pixel Resolution Width\n",
    "- **ram**: Random Access Memory in Megabytes\n",
    "- **sc_h**: Screen Height of mobile in cm\n",
    "- **sc_w**: Screen Width of mobile in cm\n",
    "- **talk_time**: Longest time that a single battery charge will last when you are\n",
    "- **three_g**: Has 3G or not\n",
    "- **touch_screen**: Has touch screen or not\n",
    "- **wifi**: Has wifi or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./input/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0            842     0          2.2         0   1       0           7    0.6   \n",
       "1           1021     1          0.5         1   0       1          53    0.7   \n",
       "2            563     1          0.5         1   2       1          41    0.9   \n",
       "3            615     1          2.5         0   0       0          10    0.8   \n",
       "4           1821     1          1.2         0  13       1          44    0.6   \n",
       "\n",
       "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2  ...         20       756  2549     9     7         19   \n",
       "1        136        3  ...        905      1988  2631    17     3          7   \n",
       "2        145        5  ...       1263      1716  2603    11     2          9   \n",
       "3        131        6  ...       1216      1786  2769    16     8         11   \n",
       "4        141        2  ...       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  price_range  \n",
       "0        0             0     1            1  \n",
       "1        1             1     0            2  \n",
       "2        1             1     0            2  \n",
       "3        1             0     0            2  \n",
       "4        1             1     0            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 21 columns):\n",
      "battery_power    2000 non-null int64\n",
      "blue             2000 non-null int64\n",
      "clock_speed      2000 non-null float64\n",
      "dual_sim         2000 non-null int64\n",
      "fc               2000 non-null int64\n",
      "four_g           2000 non-null int64\n",
      "int_memory       2000 non-null int64\n",
      "m_dep            2000 non-null float64\n",
      "mobile_wt        2000 non-null int64\n",
      "n_cores          2000 non-null int64\n",
      "pc               2000 non-null int64\n",
      "px_height        2000 non-null int64\n",
      "px_width         2000 non-null int64\n",
      "ram              2000 non-null int64\n",
      "sc_h             2000 non-null int64\n",
      "sc_w             2000 non-null int64\n",
      "talk_time        2000 non-null int64\n",
      "three_g          2000 non-null int64\n",
      "touch_screen     2000 non-null int64\n",
      "wifi             2000 non-null int64\n",
      "price_range      2000 non-null int64\n",
      "dtypes: float64(2), int64(19)\n",
      "memory usage: 328.2 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if this data contains missing values\n",
    "data.isnull().sum().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    500\n",
       "2    500\n",
       "1    500\n",
       "0    500\n",
       "Name: price_range, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['price_range'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To sum up**: The dataset is already well-balanced and does not contain any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <th>battery_power</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>1238.51850</td>\n",
       "      <td>439.418206</td>\n",
       "      <td>501.0</td>\n",
       "      <td>851.75</td>\n",
       "      <td>1226.0</td>\n",
       "      <td>1615.25</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.49500</td>\n",
       "      <td>0.500100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock_speed</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>1.52225</td>\n",
       "      <td>0.816004</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dual_sim</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.50950</td>\n",
       "      <td>0.500035</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fc</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>4.30950</td>\n",
       "      <td>4.341444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four_g</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.52150</td>\n",
       "      <td>0.499662</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_memory</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>32.04650</td>\n",
       "      <td>18.145715</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>32.0</td>\n",
       "      <td>48.00</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m_dep</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.50175</td>\n",
       "      <td>0.288416</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mobile_wt</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>140.24900</td>\n",
       "      <td>35.399655</td>\n",
       "      <td>80.0</td>\n",
       "      <td>109.00</td>\n",
       "      <td>141.0</td>\n",
       "      <td>170.00</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_cores</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>4.52050</td>\n",
       "      <td>2.287837</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pc</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>9.91650</td>\n",
       "      <td>6.064315</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>px_height</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>645.10800</td>\n",
       "      <td>443.780811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>282.75</td>\n",
       "      <td>564.0</td>\n",
       "      <td>947.25</td>\n",
       "      <td>1960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>px_width</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>1251.51550</td>\n",
       "      <td>432.199447</td>\n",
       "      <td>500.0</td>\n",
       "      <td>874.75</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>1633.00</td>\n",
       "      <td>1998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>2124.21300</td>\n",
       "      <td>1084.732044</td>\n",
       "      <td>256.0</td>\n",
       "      <td>1207.50</td>\n",
       "      <td>2146.5</td>\n",
       "      <td>3064.50</td>\n",
       "      <td>3998.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sc_h</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>12.30650</td>\n",
       "      <td>4.213245</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sc_w</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>5.76700</td>\n",
       "      <td>4.356398</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>talk_time</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>11.01100</td>\n",
       "      <td>5.463955</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three_g</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.76150</td>\n",
       "      <td>0.426273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>touch_screen</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.50300</td>\n",
       "      <td>0.500116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wifi</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.50700</td>\n",
       "      <td>0.500076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price_range</th>\n",
       "      <td>2000.0</td>\n",
       "      <td>1.50000</td>\n",
       "      <td>1.118314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count        mean          std    min      25%     50%  \\\n",
       "battery_power  2000.0  1238.51850   439.418206  501.0   851.75  1226.0   \n",
       "blue           2000.0     0.49500     0.500100    0.0     0.00     0.0   \n",
       "clock_speed    2000.0     1.52225     0.816004    0.5     0.70     1.5   \n",
       "dual_sim       2000.0     0.50950     0.500035    0.0     0.00     1.0   \n",
       "fc             2000.0     4.30950     4.341444    0.0     1.00     3.0   \n",
       "four_g         2000.0     0.52150     0.499662    0.0     0.00     1.0   \n",
       "int_memory     2000.0    32.04650    18.145715    2.0    16.00    32.0   \n",
       "m_dep          2000.0     0.50175     0.288416    0.1     0.20     0.5   \n",
       "mobile_wt      2000.0   140.24900    35.399655   80.0   109.00   141.0   \n",
       "n_cores        2000.0     4.52050     2.287837    1.0     3.00     4.0   \n",
       "pc             2000.0     9.91650     6.064315    0.0     5.00    10.0   \n",
       "px_height      2000.0   645.10800   443.780811    0.0   282.75   564.0   \n",
       "px_width       2000.0  1251.51550   432.199447  500.0   874.75  1247.0   \n",
       "ram            2000.0  2124.21300  1084.732044  256.0  1207.50  2146.5   \n",
       "sc_h           2000.0    12.30650     4.213245    5.0     9.00    12.0   \n",
       "sc_w           2000.0     5.76700     4.356398    0.0     2.00     5.0   \n",
       "talk_time      2000.0    11.01100     5.463955    2.0     6.00    11.0   \n",
       "three_g        2000.0     0.76150     0.426273    0.0     1.00     1.0   \n",
       "touch_screen   2000.0     0.50300     0.500116    0.0     0.00     1.0   \n",
       "wifi           2000.0     0.50700     0.500076    0.0     0.00     1.0   \n",
       "price_range    2000.0     1.50000     1.118314    0.0     0.75     1.5   \n",
       "\n",
       "                   75%     max  \n",
       "battery_power  1615.25  1998.0  \n",
       "blue              1.00     1.0  \n",
       "clock_speed       2.20     3.0  \n",
       "dual_sim          1.00     1.0  \n",
       "fc                7.00    19.0  \n",
       "four_g            1.00     1.0  \n",
       "int_memory       48.00    64.0  \n",
       "m_dep             0.80     1.0  \n",
       "mobile_wt       170.00   200.0  \n",
       "n_cores           7.00     8.0  \n",
       "pc               15.00    20.0  \n",
       "px_height       947.25  1960.0  \n",
       "px_width       1633.00  1998.0  \n",
       "ram            3064.50  3998.0  \n",
       "sc_h             16.00    19.0  \n",
       "sc_w              9.00    18.0  \n",
       "talk_time        16.00    20.0  \n",
       "three_g           1.00     1.0  \n",
       "touch_screen      1.00     1.0  \n",
       "wifi              1.00     1.0  \n",
       "price_range       2.25     3.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20463f5bb08>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data.corr()\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(corr, square=True, annot=True, annot_kws={'size':8})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Low positive correlation: \n",
    "- `pc` and `fc`\n",
    "- `three_g` and `four_g`\n",
    "- `px_width` and `px_height`\n",
    "- `sc_w` and `sc_height`\n",
    "\n",
    "#### High positive correlation:\n",
    "- `ram` and `price_range`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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.boxplot(data['price_range'], data['ram'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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.boxplot(data['price_range'], data['battery_power'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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.boxplot(data['price_range'], data['px_height'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing Data for Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(columns='price_range')\n",
    "y = data['price_range']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection\n",
    "- Eliminate non-numerical\n",
    "- Eliminate non-ordinal\n",
    "- Eliminate features that have missing values\n",
    "- Eliminate low variance features (since they do not carry much information)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "battery_power    1.930884e+05\n",
       "blue             2.501001e-01\n",
       "clock_speed      6.658629e-01\n",
       "dual_sim         2.500348e-01\n",
       "fc               1.884813e+01\n",
       "four_g           2.496626e-01\n",
       "int_memory       3.292670e+02\n",
       "m_dep            8.318353e-02\n",
       "mobile_wt        1.253136e+03\n",
       "n_cores          5.234197e+00\n",
       "pc               3.677592e+01\n",
       "px_height        1.969414e+05\n",
       "px_width         1.867964e+05\n",
       "ram              1.176644e+06\n",
       "sc_h             1.775143e+01\n",
       "sc_w             1.897820e+01\n",
       "talk_time        2.985481e+01\n",
       "three_g          1.817086e-01\n",
       "touch_screen     2.501161e-01\n",
       "wifi             2.500760e-01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2046a38a608>"
      ]
     },
     "execution_count": 38,
     "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(X['m_dep'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 12 columns):\n",
      "battery_power    2000 non-null int64\n",
      "fc               2000 non-null int64\n",
      "int_memory       2000 non-null int64\n",
      "mobile_wt        2000 non-null int64\n",
      "n_cores          2000 non-null int64\n",
      "pc               2000 non-null int64\n",
      "px_height        2000 non-null int64\n",
      "px_width         2000 non-null int64\n",
      "ram              2000 non-null int64\n",
      "sc_h             2000 non-null int64\n",
      "sc_w             2000 non-null int64\n",
      "talk_time        2000 non-null int64\n",
      "dtypes: int64(12)\n",
      "memory usage: 187.6 KB\n"
     ]
    }
   ],
   "source": [
    "# Remove non-ordinal\n",
    "X = X.drop(columns=['blue', 'dual_sim', 'four_g', 'three_g', 'touch_screen', 'wifi'])\n",
    "# Remove low variance\n",
    "X = X.drop(columns=['m_dep', 'clock_speed'])\n",
    "\n",
    "\n",
    "# X = X[['ram']] # Using ram features only\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling the Features\n",
    "Most of the times, your dataset will contain features highly varying in magnitudes, units and range. But since, most of the machine learning algorithms use Eucledian distance between two data points in their computations, this is a problem.\n",
    "\n",
    "If left alone, these algorithms only take in the magnitude of features neglecting the units. The results would vary greatly between different units, 5kg and 5000gms. The features with high magnitudes will weigh in a lot more in the distance calculations than features with low magnitudes.\n",
    "\n",
    "To supress this effect, we need to bring all features to the same level of magnitudes. This can be acheived by scaling.\n",
    "\n",
    "Since both of KNN and SVM utitlize distance calculation behind the scene, we need to scale our features so that the large-valued features do not dominate the other features. More can be referenced [here](https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e) and [here](http://sebastianraschka.com/Articles/2014_about_feature_scaling.html#about-standardization).\n",
    "\n",
    "**Notice**: It is worth trying not scaling the features. So that we can see that for SVM,\n",
    "- The training time is very long (computationally expensive)\n",
    "- The model overfits\n",
    "\n",
    "### Multiple ways to scale\n",
    "- **Standard Scaler (Standardization)**: Scale the feature by shifting the mean back to 0 and variance to 1. By this way, we only shift the mean value to 0 and keep the distribution the same. Furthermore, this way can presereve the outliers in case they can contribute additional information to the problem.\n",
    "- **Min-max Scaler (Normalization)**: This one scales the range of values to between 0 and 1 and also eliminates the outliers.\n",
    "- Read more:\n",
    "    - Python machine Learning - SebastianRaschka\n",
    "    - [Medium - Why, How and When to scale your features ?](https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e)\n",
    "    - [Quora](https://www.quora.com/When-should-you-perform-feature-scaling-and-mean-normalization-on-the-given-data-What-are-the-advantages-of-these-techniques)\n",
    "    - [Kaggle - Very good notebook](https://www.kaggle.com/rtatman/data-cleaning-challenge-scale-and-normalize-data?scriptVersionId=2945378)\n",
    "    - [statsStackExchange - Scaling ruins the result](https://stats.stackexchange.com/questions/172795/scaling-for-svm-destroys-my-results)\n",
    "    - [GeeksforGeeks - How and when to apply Scaling](https://www.geeksforgeeks.org/python-how-and-where-to-apply-feature-scaling/)\n",
    "\n",
    "**Question here is:** \n",
    "- Which one keeps the original distribution of the data ?\n",
    "    - Ans: Both\n",
    "    \n",
    "### More about Standardization and Normalization\n",
    "- [StatisticsHowTo - Standardized Data](https://www.statisticshowto.datasciencecentral.com/standardized-values-examples/)\n",
    "- [StatisticsHowTo - Normalized Data](https://www.statisticshowto.datasciencecentral.com/normalized/)\n",
    "\n",
    "### Multiple ways to process the scaling\n",
    "- Fit the scaler on the whole dataset, scale the whole dataset and then train/test split\n",
    "- Train/test split and Fit the scaler (to obtain `mean` and `std`) to training set and then scale both training + testing set\n",
    "- Train/test split and Fit 2 different scalers on the training and testing set and scale them.\n",
    "- **The correct way is:** the 2nd answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "# Normalize Training Data \n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train_std = scaler.transform(X_train)\n",
    "X_test_std = scaler.transform(X_test)\n",
    "\n",
    "#Converting numpy array to dataframe\n",
    "X_train_std_df = pd.DataFrame(X_train_std, index=X_train.index, columns=X_train.columns)\n",
    "X_test_std_df = pd.DataFrame(X_test_std, index=X_test.index, columns=X_test.columns) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "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>battery_power</th>\n",
       "      <th>fc</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>pc</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>0.984823</td>\n",
       "      <td>-0.066906</td>\n",
       "      <td>-1.156036</td>\n",
       "      <td>-0.451426</td>\n",
       "      <td>0.646912</td>\n",
       "      <td>-0.312840</td>\n",
       "      <td>-0.841894</td>\n",
       "      <td>0.512558</td>\n",
       "      <td>1.507969</td>\n",
       "      <td>0.645076</td>\n",
       "      <td>0.043393</td>\n",
       "      <td>0.176984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>-0.673224</td>\n",
       "      <td>-0.766578</td>\n",
       "      <td>1.044235</td>\n",
       "      <td>0.114337</td>\n",
       "      <td>-1.102237</td>\n",
       "      <td>0.849672</td>\n",
       "      <td>-1.018037</td>\n",
       "      <td>-0.852864</td>\n",
       "      <td>0.724930</td>\n",
       "      <td>1.120599</td>\n",
       "      <td>-1.094793</td>\n",
       "      <td>-0.937146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>-0.400668</td>\n",
       "      <td>-0.999802</td>\n",
       "      <td>1.429282</td>\n",
       "      <td>-0.140256</td>\n",
       "      <td>1.084199</td>\n",
       "      <td>-1.641426</td>\n",
       "      <td>-1.275477</td>\n",
       "      <td>-1.329604</td>\n",
       "      <td>-1.295786</td>\n",
       "      <td>-0.068208</td>\n",
       "      <td>0.726305</td>\n",
       "      <td>0.919738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1388</th>\n",
       "      <td>1.545834</td>\n",
       "      <td>-0.300130</td>\n",
       "      <td>1.704316</td>\n",
       "      <td>-0.819173</td>\n",
       "      <td>-1.102237</td>\n",
       "      <td>0.019306</td>\n",
       "      <td>-1.178372</td>\n",
       "      <td>-1.318033</td>\n",
       "      <td>1.189631</td>\n",
       "      <td>0.169554</td>\n",
       "      <td>-0.639519</td>\n",
       "      <td>1.476803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>-1.359156</td>\n",
       "      <td>-0.766578</td>\n",
       "      <td>0.494167</td>\n",
       "      <td>1.613611</td>\n",
       "      <td>1.084199</td>\n",
       "      <td>0.019306</td>\n",
       "      <td>-0.715433</td>\n",
       "      <td>-1.563346</td>\n",
       "      <td>0.767924</td>\n",
       "      <td>-0.068208</td>\n",
       "      <td>0.726305</td>\n",
       "      <td>-1.308523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      battery_power        fc  int_memory  mobile_wt   n_cores        pc  \\\n",
       "668        0.984823 -0.066906   -1.156036  -0.451426  0.646912 -0.312840   \n",
       "1345      -0.673224 -0.766578    1.044235   0.114337 -1.102237  0.849672   \n",
       "373       -0.400668 -0.999802    1.429282  -0.140256  1.084199 -1.641426   \n",
       "1388       1.545834 -0.300130    1.704316  -0.819173 -1.102237  0.019306   \n",
       "132       -1.359156 -0.766578    0.494167   1.613611  1.084199  0.019306   \n",
       "\n",
       "      px_height  px_width       ram      sc_h      sc_w  talk_time  \n",
       "668   -0.841894  0.512558  1.507969  0.645076  0.043393   0.176984  \n",
       "1345  -1.018037 -0.852864  0.724930  1.120599 -1.094793  -0.937146  \n",
       "373   -1.275477 -1.329604 -1.295786 -0.068208  0.726305   0.919738  \n",
       "1388  -1.178372 -1.318033  1.189631  0.169554 -0.639519   1.476803  \n",
       "132   -0.715433 -1.563346  0.767924 -0.068208  0.726305  -1.308523  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_std_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fc               1.000625\n",
       "sc_h             1.000625\n",
       "sc_w             1.000625\n",
       "int_memory       1.000625\n",
       "px_height        1.000625\n",
       "battery_power    1.000625\n",
       "pc               1.000625\n",
       "ram              1.000625\n",
       "n_cores          1.000625\n",
       "px_width         1.000625\n",
       "talk_time        1.000625\n",
       "mobile_wt        1.000625\n",
       "price_range      1.278285\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_std_data = pd.concat([X_train_std_df, y_train], axis=1)\n",
    "train_std_data.var().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning using K-fold Cross Validation\n",
    "\n",
    "We can tune using only Train/Validation set, but there are still problems:\n",
    "- We use less training data\n",
    "- The model can potentially overfit to validation data, because:\n",
    "    - It is optimized based on validation data.\n",
    "    - Validation data can be only a small subset -> Cannot represent the population distribution.\n",
    "    \n",
    "**Solution**: A smart way is using `K-Fold CV`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_validation(param_grid, clf, X_train, y_train):\n",
    "    val_error_rate = []\n",
    "\n",
    "    for key in param_grid.keys():\n",
    "        param_range = param_grid[key]\n",
    "        for param in param_range:\n",
    "            # https://stackoverflow.com/questions/337688/dynamic-keyword-arguments-in-python\n",
    "            val_error = cross_val_score(clf.set_params(**{key: param}), X_train, y_train, cv=5).mean()\n",
    "            val_error_rate.append(val_error)\n",
    "\n",
    "        plt.figure(figsize=(15,7))\n",
    "        plt.plot(param_range, val_error_rate, color='orange', linestyle='dashed', marker='o',\n",
    "                 markerfacecolor='black', markersize=5, label='Validation Error')\n",
    "\n",
    "        plt.xticks(np.arange(param_range.start, param_range.stop, param_range.step), rotation=60)\n",
    "        plt.grid()\n",
    "        plt.legend()\n",
    "        plt.title('Validation Accuracy vs. {}'.format(key))\n",
    "        plt.xlabel(key)\n",
    "        plt.ylabel('Validation Accuracy')\n",
    "        plt.show()\n",
    "    \n",
    "\n",
    "neighbors_range = range(1,500,10)\n",
    "param_grid = {'n_neighbors': neighbors_range}\n",
    "plot_validation(param_grid, knn, X_train_std_df, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6975"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_k = 131\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=best_k)\n",
    "knn.fit(X_train_std_df, y_train)\n",
    "knn.score(X_test_std_df, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8925"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)\n",
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation for KNN\n",
    "The model seemed to be overfitting a little. It is worth noting that for KNN, if we do not scale the features, the model performed much better. This reason for this is because the `ram` feature may contributes a lot towards the target. Also, the scale of `ram` is much bigger comparing to other features (based on median, min, max values and std)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(kernel='linear')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Recall**: The **C** parameter tells the SVM optimization how much you want to avoid misclassifying each training example. \n",
    "- For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly. \n",
    "- Conversely, a very small value of C will cause the optimizer to look for a larger-margin separating hyperplane, even if that hyperplane misclassifies more points.\n",
    "\n",
    "Thus for a very large value of C probably leads to overfitting of the model and for a very small value of C probably leads to underfitting. Thus the value of C must be chosen in such a way that it generalises the unseen data well.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c_range =  range(1,200,20)\n",
    "param_grid = {'C': c_range}\n",
    "plot_validation(param_grid, svm, X_train_std_df, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9625"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_c = 21\n",
    "svm = SVC(kernel='linear',C=best_c)\n",
    "svm.fit(X_train_std_df, y_train)\n",
    "svm.score(X_test_std_df, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation for SVM\n",
    "\n",
    "If using non-scaled features, SVM with linear kernel takes a lot of time training. It is worth trying using non-scaled features to see how long it would take. On the other hand, scaled feature helps reduce the training time drastically, with a decent amount of accuracy (much more than KNN accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try Non-linear SVM\n",
    "\n",
    "Technically, the **gamma** parameter is the inverse of the standard deviation of the RBF kernel (Gaussian function), which is used as similarity measure between two points.  Intuitive explaination can be found [here](https://chrisalbon.com/machine_learning/support_vector_machines/svc_parameters_using_rbf_kernel/).\n",
    "\n",
    "With one hyperparameter, we can plot validation curve as above, but with more than one hyperparameter, we cannot. Therefore, we use GridSearchCV as a more proper and convinient way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridSearchCV\n",
    "Try using GridSearchCV implemented by sklearn to tune the best C and gamma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
       "                           class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': range(1, 200, 20),\n",
       "                         'gamma': [0.1, 0.5, 0.1, 0.25, 0.5, 1]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using GridSearchCV to tune hyperparameters\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {'C': c_range,\n",
    "              'gamma': [.1, .5, .10, .25, .50, 1]}\n",
    "gs = GridSearchCV(SVC(kernel='rbf'), param_grid, cv=5)\n",
    "gs.fit(X_train_std_df,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best hyperparameters {'C': 1, 'gamma': 0.1}.\n",
      "The Mean CV score of the best_estimator is 0.89.\n"
     ]
    }
   ],
   "source": [
    "print(\"The best hyperparameters {}.\".format(gs.best_params_))\n",
    "print(\"The Mean CV score of the best_estimator is {:.2f}.\".format(gs.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8925"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = SVC(**gs.best_params_)\n",
    "svm.fit(X_train_std_df, y_train)\n",
    "svm.score(X_test_std_df, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspecting Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7075\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.94      0.83        94\n",
      "           1       0.61      0.57      0.59       102\n",
      "           2       0.71      0.55      0.62       120\n",
      "           3       0.76      0.85      0.80        84\n",
      "\n",
      "    accuracy                           0.71       400\n",
      "   macro avg       0.71      0.73      0.71       400\n",
      "weighted avg       0.70      0.71      0.70       400\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cc67f9f7c8>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=best_k)\n",
    "knn.fit(X_train_std_df, y_train)\n",
    "pred = knn.predict(X_test_std_df)\n",
    "\n",
    "print(knn.score(X_test_std_df,y_test))\n",
    "print(classification_report(y_test,pred))\n",
    "\n",
    "matrix=confusion_matrix(y_test,pred)\n",
    "plt.figure(figsize = (10,7))\n",
    "sns.heatmap(matrix,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9625\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.98        94\n",
      "           1       0.93      0.98      0.96       102\n",
      "           2       0.98      0.92      0.95       120\n",
      "           3       0.94      0.99      0.97        84\n",
      "\n",
      "    accuracy                           0.96       400\n",
      "   macro avg       0.96      0.97      0.96       400\n",
      "weighted avg       0.96      0.96      0.96       400\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cc663d7ec8>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm = SVC(kernel='linear',C=best_c)\n",
    "svm.fit(X_train_std_df, y_train)\n",
    "pred = svm.predict(X_test_std_df)\n",
    "\n",
    "print(svm.score(X_test_std_df,y_test))\n",
    "print(classification_report(y_test,pred))\n",
    "\n",
    "matrix=confusion_matrix(y_test,pred)\n",
    "plt.figure(figsize = (10,7))\n",
    "sns.heatmap(matrix,annot=True,fmt=\".2f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "- https://www.kaggle.com/azzion/svm-for-beginners-tutorial\n",
    "- https://www.kaggle.com/vikramb/mobile-price-prediction/notebook\n",
    "- https://www.kaggle.com/nirajvermafcb/support-vector-machine-detail-analysis"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}