ML micro Projects/USING_KNN_Market Positioning Of Mobile.ipynb

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   "source": [
    "<table align=\"center\" width=100%>\n",
    "    <tr>\n",
    "        <td width=\"15%\">\n",
    "            <img src=\"mobile.jpg\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"center\">\n",
    "                <font color=\"#21618C\" size=24px>\n",
    "                    <b>Market Positioning Of Mobiles\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "\n",
    "There is a new mobile company and the CEO wants to give tough fight to big companies like Apple,Samsung etc.\n",
    "\n",
    "He has no idea of how to estimate price of mobiles his company creates. A simple assumption of the prices will not be profitable in this competitive world. To solve this problem he collects sales data of mobile phones of various companies.\n",
    "\n",
    "Thus, the CEO has hired you to find out some relation between features of a mobile phone(eg:- RAM,Internal Memory etc) and its selling price.\n",
    "You have to predict a price range indicating how high the price is."
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Data Definition\n",
    "\n",
    "**battery_power:** Total energy a battery can store in one time measured in mAh<br> \n",
    "\n",
    "**clock_speed:** Speed at which microprocessor executes instructions<br>\n",
    "\n",
    "**fc:** Front Camera mega pixels<br>\n",
    "\n",
    "**int_memory:** Internal Memory in Gigabytes<br>\n",
    "\n",
    "**m_dep:** Mobile Depth in cm<br>\n",
    "\n",
    "**mobile_wt:** Weight of mobile phone<br>\n",
    "\n",
    "**n_cores:** Number of cores of processor<br>\n",
    "\n",
    "**pc:** Primary Camera mega pixels<br>\n",
    "\n",
    "**px_height:** Pixel Resolution Height<br>\n",
    "\n",
    "**px_width:** Pixel Resolution Width<br>\n",
    "\n",
    "**ram:** Random Access Memory in Mega Bytes<br>\n",
    "\n",
    "**sc_h:** Screen Height of mobile in cm<br>\n",
    "\n",
    "**sc_w:** Screen Width of mobile in cm<br>\n",
    "\n",
    "**talk_time:** longest time that a single battery charge will last when you are<br>\n",
    "\n",
    "**price_range:** This is the target variable with value of 0(low cost), 1(medium cost), 2(high cost) and 3(very high cost)."
   ]
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   "source": [
    "## Icon Legends\n",
    "<table>\n",
    "  <tr>\n",
    "    <th width=\"25%\"> <img src=\"infer.png\" style=\"width:25%;\"></th>\n",
    "    <th width=\"25%\"> <img src=\"alsoreadicon.png\" style=\"width:25%;\"></th>\n",
    "    <th width=\"25%\"> <img src=\"todo.png\" style=\"width:25%;\"></th>\n",
    "    <th width=\"25%\"> <img src=\"quicktip.png\" style=\"width:25%;\"></th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Inferences from outcome</b></font></div>\n",
    "    </td>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Additional Reads</b></font></div>\n",
    "    </td>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Lets do it</b></font></div>\n",
    "    </td>\n",
    "    <td><div align=\"center\" style=\"font-size:120%\">\n",
    "        <font color=\"#21618C\"><b>Quick Tips</b></font></div>\n",
    "    </td>\n",
    "\n",
    "</tr>\n",
    "\n",
    "</table>"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Table of Content\n",
    "\n",
    "1. **[Import Libraries](#import_lib)**\n",
    "2. **[Set Options](#set_options)**\n",
    "3. **[Read Data](#RD)**\n",
    "4. **[Data Analysis and Preparation](#data_preparation)**\n",
    "    - 4.1 - **[Understand the Dataset](#Data_Understanding)**\n",
    "        - 4.1.1 - **[Data Type](#Data_Types)**\n",
    "        - 4.1.2 - **[Summary Statistics](#Summary_Statistics)**\n",
    "        - 4.1.3 - **[Distribution of Variables](#distribution_variables)**\n",
    "        - 4.1.4 - **[Correlation](#correlation)**\n",
    "        - 4.1.5 - **[Discover Outliers](#outliers)**\n",
    "        - 4.1.6 - **[Missing Values](#Missing_Values)**      \n",
    "    - 4.2 - **[Prepare the Data](#Data_Preparation)**\n",
    "5. **[KNN](#knn)**"
   ]
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   "source": [
    "<a id='import_lib'></a>\n",
    "# 1. Import Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Import the required libraries and functions</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
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    "# suppress display of warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 'Pandas' is used for data manipulation and analysis\n",
    "import pandas as pd \n",
    "\n",
    "# 'Numpy' is used for mathematical operations on large, multi-dimensional arrays and matrices\n",
    "import numpy as np\n",
    "\n",
    "# 'Matplotlib' is a data visualization library for 2D and 3D plots, built on numpy\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# 'Seaborn' is based on matplotlib; used for plotting statistical graphics\n",
    "import seaborn as sns\n",
    "\n",
    "# import various functions to perform classification\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score "
   ]
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    "# set the plot size using 'rcParams'\n",
    "# once the plot size is set using 'rcParams', it sets the size of all the forthcoming plots in the file\n",
    "# pass width and height in inches to 'figure.figsize' \n",
    "plt.rcParams['figure.figsize'] = [15,8]"
   ]
  },
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    "id": "Ll0ntaGOebng"
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   "source": [
    "<a id='set_options'></a>\n",
    "# 2. Set Options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Now we make necessary changes to :<br><br>\n",
    "1. Display complete data frames<br>\n",
    "2. To avoid the exponential number<br>\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    "colab_type": "code",
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    "# display all columns of the dataframe\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "# display all rows of the dataframe\n",
    "pd.options.display.max_rows = None\n",
    "\n",
    "# use below code to convert the 'exponential' values to float\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yPb9_if1ebnl"
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   "source": [
    "<a id='RD'></a>\n",
    "# 3. Read Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Read and display data to get an insight into the data.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
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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>clock_speed</th>\n",
       "      <th>fc</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</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",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>2</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>6</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>6</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>9</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1.2</td>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>14</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",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   battery_power  clock_speed  fc  int_memory  m_dep  mobile_wt  n_cores  pc  \\\n",
       "0            842          2.2   1           7    0.6        188        2   2   \n",
       "1           1021          0.5   0          53    0.7        136        3   6   \n",
       "2            563          0.5   2          41    0.9        145        5   6   \n",
       "3            615          2.5   0          10    0.8        131        6   9   \n",
       "4           1821          1.2  13          44    0.6        141        2  14   \n",
       "\n",
       "   px_height  px_width   ram  sc_h  sc_w  talk_time  price_range  \n",
       "0         20       756  2549     9     7         19            1  \n",
       "1        905      1988  2631    17     3          7            2  \n",
       "2       1263      1716  2603    11     2          9            2  \n",
       "3       1216      1786  2769    16     8         11            2  \n",
       "4       1208      1212  1411     8     2         15            1  "
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     "metadata": {},
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   "source": [
    "# read csv file using pandas\n",
    "df_mobile = pd.read_csv('Mobile_data.csv')\n",
    "\n",
    "# display the top 5 rows of the dataframe\n",
    "df_mobile.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lets take a glance at our dataframe and see how it looks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dimensions of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'shape' function gives the total number of rows and columns in the data\n",
    "df_mobile.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SSHIYKY2ebns"
   },
   "source": [
    "<a id='data_preparation'></a>\n",
    "# 4. Data Analysis and Preparation"
   ]
  },
  {
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    "id": "l48tGdoxebnt"
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   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Data preparation is the process of cleaning and transforming raw data before building predictive models. <br><br>\n",
    "                        Here, we analyze and prepare data to perform classification techniques:<br>\n",
    "                        1. Check data types. Ensure your data types are correct. Refer data definitions to validate <br>\n",
    "                        2. If data types are not as per business definition, change the data types as per requirement <br>\n",
    "                        3. Study summary statistics<br>\n",
    "                        4. Distribution of variables<br>\n",
    "                        5. Study correlation<br>\n",
    "                        6. Detect outliers<br>\n",
    "                        7. Check for missing values<br><br>\n",
    "                        Note: It is an art to explore data, and one needs more and more practice to gain expertise in this area\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
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   "source": [
    "<a id='Data_Understanding'></a>\n",
    "## 4.1 Understand the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<a id='Data_Types'></a>\n",
    "### 4.1.1 Data Type\n",
    "The main data types in Pandas dataframes are the object, float, int64, bool, and datetime64. To understand each attribute of our data, it is always good for us to know the data type of each column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>In our dataset, we have numerical and categorical variables. The numeric variables should have data type 'int'/'float' while categorical variables should have data type 'object'.<br><br> \n",
    "                        1. Check for the data type <br>\n",
    "                        2. For any incorrect data type, change the data type with the appropriate type<br>\n",
    "                        3. Recheck for the data type\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Check for the data type**"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "battery_power      int64\n",
       "clock_speed      float64\n",
       "fc                 int64\n",
       "int_memory         int64\n",
       "m_dep            float64\n",
       "mobile_wt          int64\n",
       "n_cores            int64\n",
       "pc                 int64\n",
       "px_height          int64\n",
       "px_width           int64\n",
       "ram                int64\n",
       "sc_h               int64\n",
       "sc_w               int64\n",
       "talk_time          int64\n",
       "price_range        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'dtypes' gives the data type for each column\n",
    "df_mobile.dtypes"
   ]
  },
  {
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    "id": "uvOvxensebn6"
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   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>From the above output, it is clear that the data type of all the variables except 'clock_speed' and 'm_dep' is int64. The data type of 'clock_speed' and 'm_dep' is float64.<br><br></b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['battery_power', 'clock_speed', 'fc', 'int_memory', 'm_dep',\n",
       "       'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h',\n",
       "       'sc_w', 'talk_time', 'price_range'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mobile.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>For convenience, we shall split the target variable from the data frame.\n",
    "Let dataframe 'df_target' be the dataframe containing the dependent variable and dataframe 'df_features' be the set of all independent variables.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# splitting features and the target variable\n",
    "# consider all the columns except 'RGD' using 'iloc'\n",
    "df_features = df_mobile.iloc[:, df_mobile.columns != 'price_range']\n",
    "\n",
    "# consider the target variable\n",
    "df_target = df_mobile.iloc[:, df_mobile.columns == 'price_range']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the dataframe containing features (df_features) for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BFOkFedXeboC"
   },
   "source": [
    "<a id='Summary_Statistics'></a>\n",
    "### 4.1.2 Summary Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> In our dataset, we have both numerical and categorical variables. Now we check for summary statistics of all the variables.<br><br>\n",
    "                        1. For numerical variables, use the describe()<br>\n",
    "                        2. For categorical variables, use the describe(include=object) \n",
    "                        </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. For numerical variables, use the describe()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BCq-Il09eboC",
    "outputId": "a7837b7a-f994-4ad1-d962-be936a04043e",
    "scrolled": true
   },
   "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>clock_speed</th>\n",
       "      <th>fc</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</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>count</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1238.518500</td>\n",
       "      <td>1.522250</td>\n",
       "      <td>4.309500</td>\n",
       "      <td>32.046500</td>\n",
       "      <td>0.501750</td>\n",
       "      <td>140.249000</td>\n",
       "      <td>4.520500</td>\n",
       "      <td>9.916500</td>\n",
       "      <td>645.108000</td>\n",
       "      <td>1251.515500</td>\n",
       "      <td>2124.213000</td>\n",
       "      <td>12.306500</td>\n",
       "      <td>5.767000</td>\n",
       "      <td>11.011000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>439.418206</td>\n",
       "      <td>0.816004</td>\n",
       "      <td>4.341444</td>\n",
       "      <td>18.145715</td>\n",
       "      <td>0.288416</td>\n",
       "      <td>35.399655</td>\n",
       "      <td>2.287837</td>\n",
       "      <td>6.064315</td>\n",
       "      <td>443.780811</td>\n",
       "      <td>432.199447</td>\n",
       "      <td>1084.732044</td>\n",
       "      <td>4.213245</td>\n",
       "      <td>4.356398</td>\n",
       "      <td>5.463955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>501.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>851.750000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>282.750000</td>\n",
       "      <td>874.750000</td>\n",
       "      <td>1207.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1226.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1247.000000</td>\n",
       "      <td>2146.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1615.250000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>947.250000</td>\n",
       "      <td>1633.000000</td>\n",
       "      <td>3064.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>3998.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       battery_power  clock_speed           fc   int_memory        m_dep  \\\n",
       "count    2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean     1238.518500     1.522250     4.309500    32.046500     0.501750   \n",
       "std       439.418206     0.816004     4.341444    18.145715     0.288416   \n",
       "min       501.000000     0.500000     0.000000     2.000000     0.100000   \n",
       "25%       851.750000     0.700000     1.000000    16.000000     0.200000   \n",
       "50%      1226.000000     1.500000     3.000000    32.000000     0.500000   \n",
       "75%      1615.250000     2.200000     7.000000    48.000000     0.800000   \n",
       "max      1998.000000     3.000000    19.000000    64.000000     1.000000   \n",
       "\n",
       "         mobile_wt      n_cores           pc    px_height     px_width  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean    140.249000     4.520500     9.916500   645.108000  1251.515500   \n",
       "std      35.399655     2.287837     6.064315   443.780811   432.199447   \n",
       "min      80.000000     1.000000     0.000000     0.000000   500.000000   \n",
       "25%     109.000000     3.000000     5.000000   282.750000   874.750000   \n",
       "50%     141.000000     4.000000    10.000000   564.000000  1247.000000   \n",
       "75%     170.000000     7.000000    15.000000   947.250000  1633.000000   \n",
       "max     200.000000     8.000000    20.000000  1960.000000  1998.000000   \n",
       "\n",
       "               ram         sc_h         sc_w    talk_time  \n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  \n",
       "mean   2124.213000    12.306500     5.767000    11.011000  \n",
       "std    1084.732044     4.213245     4.356398     5.463955  \n",
       "min     256.000000     5.000000     0.000000     2.000000  \n",
       "25%    1207.500000     9.000000     2.000000     6.000000  \n",
       "50%    2146.500000    12.000000     5.000000    11.000000  \n",
       "75%    3064.500000    16.000000     9.000000    16.000000  \n",
       "max    3998.000000    19.000000    18.000000    20.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the describe() returns the statistical summary of the variables\n",
    "# by default, it returns the summary of numerical variables\n",
    "df_features.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BvaxGBkKeboF"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "<b>The above output illustrates the summary statistics of all the numeric variables namely mean, median (50%), standard deviation, minimum, and maximum values, along with the first and third quantiles.<br>\n",
    "For example, the average battery life of the mobile phones is around 1238 mAh<br><br>\n",
    "It can be seen that the variables have count equal to the number of observations. This means that there are no missing values in any of the column.</b>  \n",
    "                    </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='distribution_variables'></a>\n",
    "### 4.1.3 Distribution of Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Check the distribution of all the variables <br><br>\n",
    "                        1. Distribution of numeric independent variables<br>\n",
    "                        2. Distribution of categoric independent variables<br>\n",
    "                        3. Distribution of dependent variable\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-tWX9ptGeboe"
   },
   "source": [
    "#### 1. Distribution of numeric independent variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the independent numeric variables, we plot the histogram to check the distribution of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ne5rQHn5ebof",
    "outputId": "d38f97d9-c277-4991-f1e9-0cdc0b8c8a72"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the histogram of numeric independent variables\n",
    "# the hist() function considers the numeric variables only, by default\n",
    "df_features.hist()\n",
    "\n",
    "# adjust the subplots\n",
    "plt.tight_layout()\n",
    "\n",
    "# display the plot\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> It can be seen that the variables 'fc', 'px_height', and 'sc_w' are right-skewed.<br> <br> \n",
    "                        Other variables show no particular pattern of distribution.  \n",
    "                     </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Distribution of dependent variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In section 4.1.1, we have split the dependent variable (RGD) and created a dataframe 'df_target'. Use this dataframe to check the distribution of RGD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    500\n",
       "2    500\n",
       "1    500\n",
       "0    500\n",
       "Name: price_range, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get counts of 0's, 1's, 2's and 3's in the 'price_range' variable using 'value_counts()'\n",
    "# store the values in 'class_frequency'\n",
    "class_frequency = df_target.price_range.value_counts()\n",
    "class_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uApquB+ySfntfS3cl0vH33unprgD7l31NN6cLRNut6T3Vz/+TdD2/N6f7rHw9ycvXsv97pAvaD5pg+89OF4p/mm547BtGj9E0931JuuG238sv348fns7rO4XXcsLP4gxfx7tss3+P/Uu6fxt+nOT/9uteP7befdJdHGr1Z3xluovo7D3SZuN0w7UPns7nz83NbTi3am2dT9EAgAWjqh6bLgg8sbV22nzXs9hV1XlJPt5aO6Kf3j7deXd/2Fpbl9+FHJSq+q8km7TWHjfN9fZJd/GoB7b+vFCAUYaAArAoVdU/pbv4xw+TPCzJa9P1CH5pTesxaw5PckRVvaP98hxIJlBVT0jXc/uNdFfa/eMkT8rdz7edir9O8g7hD5iMAAjAYrVpuvM5t0k33PAL6YYuTutcM9bZJ9MNe942k/9cxLwa+S29ibTWXfhoLtyY7rzE16QbDnpRuiHin5zORvqLHn01yTvW1hYYLkNAAYBBqqo1fQn6Umvt8XNVC8Bc0QMIAAzVnmtYtqafuQBYsPQAAgAADMSi6wHcaqut2vbbbz/fZQAAAMyLc84550ettaUTLVt0AXD77bfP2WefPd9lAAAAzIuqmvTiWxvNZSEAAADMHwEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEACZNZdffnme8IQnZOedd84uu+ySd73rXUmS17/+9dl2222z++67Z/fdd8/JJ5884fqf+9zn8rCHPSwPechD8pa3vOXO+X/zN3+T3XbbLc95znPunPexj33szu3DYuEzBDPncwQz4zM0AK21RXV71KMe1ZgfV155ZTvnnHNaa63dcMMNbaeddmrnn39+e93rXteOOOKINa57++23tx133LFdcskl7ZZbbmm77bZbO//889v111/fHvvYx7bWWnvWs57VvvnNb7abbrqpPfGJT2y33nrret8nmEs+QzBzPkcwMz5Di0OSs9skeUkPILNm2bJl+Y3f+I0kyRZbbJGdd945V1xxxZTWPfPMM/OQhzwkO+64Y+5xj3vkoIMOygknnJCNNtoot956a1prufnmm7PJJpvkiCOOyEte8pJssskm63N3YM75DMHM+RzBzPgMLX4CIOvF97///Zx77rl59KMfnSQ58sgjs9tuu+V5z3tefvzjH9+t/RVXXJHtttvuzunly5fniiuuyBZbbJH9998/j3zkI7PDDjvkPve5T84666ysWLFizvYF5oPPEMyczxHMjM/Q4jTnAbCqvl9V36qq86rq7H7ellV1SlVd1N/fb6T9a6rq4qq6sKr2met6mb4bb7wx+++/f975znfm3ve+d17wghfkkksuyXnnnZdly5blsMMOu9s6XU/1XVVVkuRVr3pVzjvvvLz97W/Pa1/72rzxjW/MBz7wgRx44IF585vfvN73B+aazxDMnM8RzIzP0OI1Xz2AT2it7d5a26OffnWSU1trOyU5tZ9OVT08yUFJdkmyb5L3VtWS+SiYqbntttuy//7759nPfnae8YxnJEm22WabLFmyJBtttFH+/M//PGeeeebd1lu+fHkuv/zyO6dXrlyZBz7wgXdpc+655yZJHvrQh+ajH/1ojjvuuHz729/ORRddtB73COaWzxDMnM8RzIzP0OK2oQwBXZHk6P7x0Un2G5l/bGvtltbapUkuTrLX3JfHVLTWcsghh2TnnXfOy1/+8jvnX3XVVXc+Pv744/OIRzzibuvuueeeueiii3LppZfm1ltvzbHHHpunPe1pd2mz+q9Ft912W+64444kyUYbbZSbbrppPe0RzC2fIZg5nyOYGZ+hxW/jeXjOluQLVdWSHNVae1+SbVprVyVJa+2qqtq6b7ttkq+NrLuyn8cG6IwzzsjHPvax7Lrrrtl9992TJP/wD/+QY445Juedd16qKttvv32OOuqoJMmVV16Z5z//+Tn55JOz8cYb58gjj8w+++yTO+64I8973vOyyy673Lntz3zmM9lzzz3v/CvSYx7zmOy6667Zbbfd8uu//utzvq+wPvgMwcz5HMHM+AwtfjXRWN31+oRVD2ytXdmHvFOS/FWSE1tr9x1p8+PW2v2q6j1Jvtpa+7d+/geTnNxa+9TYNg9NcmiSPOhBD3rUD37wg1mt+VGv/Oisbg9m0zlHPGftjTYAl71x1/kuASb0oL//1nyXMCV7v3vv+S4BJnTGX50x3yVMyZd+53HzXQJM6HFf/tKsb7Oqzhk53e4u5nwIaGvtyv7+miTHpxvSeXVVLUuS/v6avvnKJNuNrL48yZUTbPN9rbU9Wmt7LF26dH2WDwAAsGDNaQCsqntW1RarHyf5vSTfTnJikoP7ZgcnOaF/fGKSg6pq06raIclOSe5+xikAAABrNdfnAG6T5Pj+crAbJ/l/rbXPVdVZSY6rqkOSXJbkgCRprZ1fVccluSDJ7Ule1Fq7Y45rBgAAWBTmNAC21r6X5G5neLbWrk3ypEnWOTzJ4eu5NAAAgEVvQ/kZCAAAANYzARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAg5iUAVtWSqjq3qk7qp7esqlOq6qL+/n4jbV9TVRdX1YVVtc981AsAALAYzFcP4EuTfGdk+tVJTm2t7ZTk1H46VfXwJAcl2SXJvkneW1VL5rhWAACARWHOA2BVLU/ylCQfGJm9IsnR/eOjk+w3Mv/Y1totrbVLk1ycZK85KhUAAGBRmY8ewHcmeVWSX4zM26a1dlWS9Pdb9/O3TXL5SLuV/by7qKpDq+rsqjp71apV66VoAACAhW5OA2BVPTXJNa21c6a6ygTz2t1mtPa+1toerbU9li5dOqMaAQAAFquN5/j59k7ytKr6gySbJbl3Vf1bkqurallr7aqqWpbkmr79yiTbjay/PMmVc1oxAADAIjGnPYCttde01pa31rZPd3GXL7bW/iTJiUkO7psdnOSE/vGJSQ6qqk2raockOyU5cy5rBgAAWCzmugdwMm9JclxVHZLksiQHJElr7fyqOi7JBUluT/Ki1tod81cmAADAwjVvAbC1dnqS0/vH1yZ50iTtDk9y+JwVBgAAsEjN1+8AAgAAMMcEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIGY0wBYVZtV1ZlV9T9VdX5VvaGfv2VVnVJVF/X39xtZ5zVVdXFVXVhV+8xlvQAAAIvJXPcA3pLkia21X0+ye5J9q+o3k7w6yamttZ2SnNpPp6oenuSgJLsk2TfJe6tqyRzXDAAAsCjMaQBsnRv7yU36W0uyIsnR/fyjk+zXP16R5NjW2i2ttUuTXJxkr7mrGAAAYPGY83MAq2pJVZ2X5Jokp7TWvp5km9baVUnS32/dN982yeUjq6/s541v89CqOruqzl61atV6rR8AAGChmvMA2Fq7o7W2e5LlSfaqqkesoXlNtIkJtvm+1toerbU9li5dOkuVAgAALC7zdhXQ1tr1SU5Pd27f1VW1LEn6+2v6ZiuTbDey2vIkV85dlQAAAIvHXF8FdGlV3bd/vHmSJyf5bpITkxzcNzs4yQn94xOTHFRVm1bVDkl2SnLmXNYMAACwWGw8x8+3LMnR/ZU8N0pyXGvtpKr6apLjquqQJJclOSBJWmvnV9VxSS5IcnuSF7XW7pjjmgEAABaFOQ2ArbVvJnnkBPOvTfKkSdY5PMnh67k0AACARW/ezgEEAABgbgmAAAAAAyEAAgAADIQACAAAMBACIAAAwEBMOQBW1XOq6v6TLNuyqp4ze2UBAAAw26bTA/jhJL86ybId+uUAAABsoKYTAGsNy+6f5IYZ1gIAAMB6tMYfgq+qFUlWjMx6bVWtGmu2WZLfTnLWLNcGAADALFpjAEyydZJdR6Z/NckDxtrcmuQLSd48i3UBAAAwy9YYAFtr70/y/iSpqtOSvKC19t25KAwAAIDZtbYewDu11p6wPgsBAABg/ZpyAEySqnpgkqcmWZ7u3L9RrbX2N7NVGAAAALNrygGwqp6e5JgkS5Jck+7cv1EtiQAIAACwgZpOD+A/pLvYy3Nba9etp3oAAABYT6YTALdL8lfCHwAAwMI0nR+C/+8kD1tfhQAAALB+TacH8OVJPl5VNyY5Jcn14w1aazfNUl0AAADMsukEwG/29x9Od8GXiSyZWTkAAACsL9MJgM/L5MEPAACADdx0fgj+I+uxDgAAANaz6VwEBgAAgAVsOj8EvyprGQLaWtt6xhUBAACwXkznHMD35O4BcMskT0xy7yQfnK2iAAAAmH3TOQfw9RPNr6pKclyS22epJgAAANaDGZ8D2FprST6Q5MUzLwcAAID1ZbYuArNjknvM0rYAAABYD6ZzEZgXTjD7Hkl2TvLsJJ+YraIAAACYfdO5CMyRE8y7JcnKJO9N8oZZqQgAAID1YjoXgfGbgQAAAAuYUAcAADAQ0wqAVbVjVf1LVX2rqq7o799bVTuurwIBAACYHdO5CMyjkpyW5OdJTkpydZJtkuyf5NlV9YTW2jfWS5UAAADM2HQuAvO2JOcm+f3W2k2rZ1bVryQ5uV/+xNktDwAAgNkynSGgeyV562j4S5J++m1JHj2bhQEAADC7phMAb05y/0mWbZluaCgAAAAbqOkEwP9I8paqeuzozH76H5N8djYLAwAAYHZN5xzAlyc5IcmXqmpVuovAbJ3uQjBnJDls9ssDAABgtkznh+CvTfLYqto3yZ5JliW5KsnXW2tfWE/1AQAAMEvWOAS0qu5fVZ+qqn1Wz2utfa619qbW2gtba2/qmtWnqmrr9V4tAAAA62xt5wC+LMmOSdbUw/eFJDvEEFAAAIAN2toC4IFJ/rW11iZr0C87KsmK2SwMAACA2bW2APjgJBdMYTvfSbL9jKsBAABgvVlbALw5yb2nsJ179W0BAADYQK0tAH4jydOmsJ0VfVsAAAA2UGsLgO9JckhVHTxZg6p6TpI/S3LkbBYGAADA7Frj7wC21j5dVe9K8uGqenGSzyW5LElL8qAk+yTZI8k7WmvHr+9iAQAAWHdr/SH41tphVXV6up+EeEWSTftFtyQ5I8mK1tpJ66tAAAAAZsdaA2CStNY+m+SzVbVxkvv3s69trd2+3ioDAABgVk0pAK7WB76r11MtAAAArEdruwgMAAAAi4QACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQMxpAKyq7arqtKr6TlWdX1Uv7edvWVWnVNVF/f39RtZ5TVVdXFUXVtU+c1kvAADAYjLXPYC3JzmstbZzkt9M8qKqeniSVyc5tbW2U5JT++n0yw5KskuSfZO8t6qWzHHNAAAAi8KcBsDW2lWttW/0j3+a5DtJtk2yIsnRfbOjk+zXP16R5NjW2i2ttUuTXJxkr7msGQAAYLGYt3MAq2r7JI9M8vUk27TWrkq6kJhk677ZtkkuH1ltZT8PAACAaZqXAFhV90ryqSQva63dsKamE8xrE2zv0Ko6u6rOXrVq1WyVCQAAsKjMeQCsqk3Shb+Pt9Y+3c++uqqW9cuXJbmmn78yyXYjqy9PcuX4Nltr72ut7dFa22Pp0qXrr3gAAIAFbK6vAlpJPpjkO621fx5ZdGKSg/vHByc5YWT+QVW1aVXtkGSnJGfOVb0AAACLycZz/Hx7J/nTJN+qqvP6ef8nyVuSHFdVhyS5LMkBSdJaO7+qjktyQboriL6otXbHHNcMAACwKMxpAGytfSUTn9eXJE+aZJ3Dkxy+3ooCAAAYiHm7CigAAABzSwAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYCAEQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQAAAgIEQAAEAAAZCAAQAABgIARAAAGAgBEAAAICBEAABAAAGQgAEAAAYiDkNgFX1oaq6pqq+PTJvy6o6paou6u/vN7LsNVV1cVVdWFX7zGWtAAAAi81c9wB+JMm+Y/NeneTU1tpOSU7tp1NVD09yUJJd+nXeW1VL5q5UAACAxWVOA2Br7ctJrhubvSLJ0f3jo5PsNzL/2NbaLa21S5NcnGSvuagTAABgMdoQzgHcprV2VZL091v387dNcvlIu5X9PAAAANbBhhAAJ1MTzGsTNqw6tKrOrqqzV61atZ7LAgAAWJg2hAB4dVUtS5L+/pp+/sok2420W57kyok20Fp7X2ttj9baHkuXLl2vxQIAACxUG0IAPDHJwf3jg5OcMDL/oKratKp2SLJTkjPnoT4AAIBFYeO5fLKqOibJ45NsVVUrk7wuyVuSHFdVhyS5LMkBSdJaO7+qjktyQZLbk7yotXbHXNYLAACwmMxpAGytPXOSRU+apP3hSQ5ffxUBAAAMx4YwBBQAAIA5IAACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADIQACAAAMBACIAAAwEAIgAAAAAMhAAIAAAyEAAgAADAQAiAAAMBACIAAAAADIQACAAAMxIIIgFW1b1VdWFUXV9Wr57seAACAhWiDD4BVtSTJe5L8fpKHJ3lmVT18fqsCAABYeDb4AJhkryQXt9a+11q7NcmxSVbMc00AAAALzkIIgNsmuXxkemU/DwAAgGnYeL4LmIKaYF67S4OqQ5Mc2k/eWFUXrveqmImtkvxovotYLOptB893Ccw9n6HZ9LqJ/pthkfMZmkX1Ep+hgfI5mi21Xj5DD55swUIIgCuTbDcyvTzJlaMNWmvvS/K+uSyKdVdVZ7fW9pjvOmCh8hmCmfEZgpnzOVq4FsIQ0LOS7FRVO1TVPZIclOTEea4JAABgwdngewBba7dX1YuTfD7JkiQfaq2dP89lAQAALDgbfABMktbayUlOnu86mDWG68LM+AzBzPgMwcz5HC1Q1VpbeysAAAAWvIVwDiAAAACzQABkzlTVvlV1YVVdXFWvnu96YKGpqg9V1TVV9e35rgUWoqrarqpOq6rvVNX5VfXS+a4JFpKq2qyqzqyq/+k/Q2+Y75qYPkNAmRNVtSTJ/yb53XQ/7XFWkme21i6Y18JgAamq30lyY5KPttYeMd/1wEJTVcuSLGutfaOqtkhyTpL9/F8EU1NVleSerbUbq2qTJF9J8tLW2tfmuTSmQQ8gc2WvJBe31r7XWrs1ybFJVsxzTbCgtNa+nOS6+a4DFqrW2lWttW/0j3+a5DtJtp3fqmDhaJ0b+8lN+pvepAVGAGSubJvk8pHplfGfLgDzpKq2T/LIJF+f51JgQamqJVV1XpJrkpzSWvMZWmAEQOZKTTDPX4wAmHNVda8kn0rystbaDfNdDywkrbU7Wmu7J1meZK+qckrCAiMAMldWJtluZHp5kivnqRYABqo/b+lTST7eWvv0fNcDC1Vr7fokpyfZd34rYboEQObKWUl2qqodquoeSQ5KcuI81wTAgPQXsPhgku+01v55vuuBhaaqllbVffvHmyd5cpLvzmtRTJsAyJxord2e5MVJPp/upPvjWmvnz29VsLBU1TFJvprkYVW1sqoOme+aYIHZO8mfJnliVZ3X3/5gvouCBWRZktOq6pvp/rh/SmvtpHmuiWnyMxAAAAADoQcQAABgIARAAACAgRAAAQAABkIABAAAGAgBEAAAYCAEQADmVFW1KdwevwHUeWhV7beWNnv09e4/yfJtqur2qnrVDGt5fVX9aB3Wa1X14rW0eW7f7l7rXiEAC8XG810AAIPzmJHHmyf5YpI3J/mPkfkXzGlFEzs0ybeTfGayBq21s6vqoiQHJfnUBE0OSPfH1n+fYS0fSPLZGW4DAARAAOZWa+1rqx+P9DpdMjp/uqpqSZIlrbVbZ1rfOjg2ySuq6l6ttRvHlh2U5L9baz9Ylw1X1SZJftFaW5lk5QzrBABDQAHYsFTVc6rqK1V1XVX9uKpOq6o9xtp8pKrOrqr9qur8JD9P8uh+2Yur6vKq+llVfaaqnjQ+rLSqNqqqV1fVxVV1S1X9b1UdPLL89CSPSnLwyLDU505S8jHpejJXjNW4XZLf6penqg6rqrOq6idVdXVVfbaqHjK2zulV9cl++Okl/X49cHwIaFXds6qOrKoLq+qmqrq0qt5TVfeeoL57VNW7+uN5fVW9u6ruMfkrkFTVZlX11v443lJV/1NVf7CmdQBYGPQAArCh2T7JR5NckuQeSZ6V5MtV9YjW2vfG2r01yRuTXJ3k0qp6epJ3J3lvkhOSPDbJByd4jncnObhf9xtJfjfJh6rq2tbaSUlemG5I5/eSvKlf55KJim2tfaeq/iddb9/HRxb9cZJfJPlEP708yZFJfpDk3kn+MskZVfXQ1tpPRtbbO8mvJvmbJDclGV222q8kWZLkb5OsSrJd//gTSfYZa3tYkq8leXaSXZIcni5YvnKi/el9MsleSV7X7/eBSU6sqj1aa+etYT0ANnACIAAblNbaG1c/rqqNkpySZM8kf5IusK12/yRPHg0kVXV8kpNbay/qZ32hqrZK8oKRNg/pp/+stXZ0P/u/qmpZusBzUmvtgqr6WZJVUxyaekySN1bV/VprP+7nHZTk1NbaNf1+/fVIDUv6/bomXc/hR0e2dd8kj2yt/XCk/V2erLW2amyfNk5yaZKvVNWDWmuXjTT/aZIDWmu/SPKfVbVpkr+tqn9srV03viNV9aQkT0ny+Nbal/rZX6iqh6YLmQdM4XgAsIEyBBSADUpV7VxVx1fV1UnuSHJbkocleehY0yvGwt+SJLsnOXGs3fj0k9L1zB1fVRuvviU5Ncnu/Xam69gkmyR5el/Lr6YbQnrMSH2/WVWnVNW1SW5P17t3rwn265zR8DeZqvrTqjq3qm5Md4y+0i8a394Jffhb7dPphqw+YpJNPznJD9P1To4fnz0mWQeABUIPIAAbjKraIskX0g3pfHm64ZI/T3cVzM3Gml89Nr003f9rq8bmj09vlW745ERDK5NkWaZ5wZXW2g+q6qvpev0+1N/fkuT4JKmqB6XbrzOT/EWSK5Pcmu7Kp2vbr7vph7p+NMm/JPk/Sa7r6z5+gu1dM8n0skk2v1WSB6QLlePuWFttAGzYBEAANiSPSXeu3O+21r67emZV3WeCtm1selW6nrWlY/PHp6/r2+2dridw3HhgmqpjkryzqrZOFwBPHjm3b9905+2taK39LLlz2OaWE2xnfL8mckCSr7fWXrh6RlU9bpK2W08yfdUk7a9LckWS/aZQBwALjCGgAGxINu/vb1k9o6p+K90FX9aotXZHkvMydjXOJE8bm/5iuh7A+7TWzp7gtvqnJG7N3XvT1uS4/v7v0w2vPGZk2ebpwubtI/MOzLr/IXbzjByj3rMnabuiP5dytWckuTndbxxO5NR0PYA3TnR81rFeADYQegAB2JB8LcmNSd5fVW9N1xv4+nQ9UlPxD0k+XVVHpjv3b+90FzRJ+t6+1tqFVfWvSY7tn+PsdEFvlyQPba09v2//3ST7VNU+Sa5Ncmlr7drJnri1dk1VfTHdFURvTHLSyOLVofPDVfXB/rlekeT6Ke7XuFOSvKeq/jbJ15P8QbpzGyeyRZJPVNX7++f9+yRHTnQBmJFtfz7JKVX1T0nOT3fV0t2TbNZae8061gzABkAPIAAbjNba1emGNz4g3c84vCzdzyVcPMX1j0/yknTDFz+T7uqhr+gX3zDS9EXpft7hOUlOTvKRdEHxyyNt3pzkO+l69s5K8odTKOGYJJXuwis3j9T1rSR/lu63Ck9K99MWB2Ty8xDX5qgkb0/y0nQXdXlwv82JvD3dcM9j0oW/D6Q7b3BCrbWWrpfwQ+mO/+f753tMfnmhGQAWqOr+nQeAxamq/i7dzxdsORrKAGCIDAEFYNGoqqVJXpPktHQ/s/Db6X5Q/YPCHwAIgAAsLrcm+bV0Qzvvk27o47uSvHY+iwKADYUhoAAAAAPhIjAAAAADIQACAAAMhAAIAAAwEAIgAADAQAiAAAAAAyEAAgAADMT/DwPIFP0MDJR4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the countplot of the variable 'price_range'\n",
    "sns.countplot(x = df_target.price_range)\n",
    "\n",
    "# use below code to print the values in the graph\n",
    "# 'x' and 'y' gives position of the text\n",
    "# 's' is the text on the plot\n",
    "plt.text(x = -0.05, y = df_target.price_range.value_counts()[0], s = str((class_frequency[0])*100/len(df_target.price_range)) + '%')\n",
    "plt.text(x = 0.95, y = df_target.price_range.value_counts()[1], s = str((class_frequency[1])*100/len(df_target.price_range)) + '%')\n",
    "plt.text(x = 1.95, y = df_target.price_range.value_counts()[2], s = str((class_frequency[2])*100/len(df_target.price_range)) + '%')\n",
    "plt.text(x = 2.95, y = df_target.price_range.value_counts()[3], s = str((class_frequency[3])*100/len(df_target.price_range)) + '%')\n",
    "\n",
    "# add plot and axes labels\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Count Plot for Target Variable (price_range)', fontsize = 15)\n",
    "plt.xlabel('Target Variable', fontsize = 15)\n",
    "plt.ylabel('Count', fontsize = 15)\n",
    "\n",
    "# to show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>There are equal observations in each class thus we see that there is a perfect balance between the four classes of the target variable.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group the dataframe by the price range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generally, customers mainly look for ram while purchasing mobile. So, lets see what is the relation between ram and price_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "price_group = df_mobile.groupby('price_range')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price_range\n",
       "0     785.314\n",
       "1    1679.490\n",
       "2    2582.816\n",
       "3    3449.232\n",
       "Name: ram, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the average ram in each price group\n",
    "price_group['ram'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='price_range', ylabel='ram'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a barplot for price_range and ram\n",
    "sns.barplot('price_range', 'ram', data=df_mobile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** The price of the mobile increases as the ram increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vkZahcS9eboQ"
   },
   "source": [
    "<a id='correlation'></a>\n",
    "### 4.1.4 Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sAagGX-keboT"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b> To check the correlation between numerical variables, we perform the following steps:<br><br>\n",
    "                    1. Compute a correlation matrix  <br>\n",
    "                    2. Plot a heatmap for the correlation matrix\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Compute a correlation matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_eqWwTr8eboZ",
    "outputId": "99bc4636-e7d5-49b5-f413-84110b856e8d"
   },
   "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>clock_speed</th>\n",
       "      <th>fc</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</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>battery_power</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011482</td>\n",
       "      <td>0.033334</td>\n",
       "      <td>-0.004004</td>\n",
       "      <td>0.034085</td>\n",
       "      <td>0.001844</td>\n",
       "      <td>-0.029727</td>\n",
       "      <td>0.031441</td>\n",
       "      <td>0.014901</td>\n",
       "      <td>-0.008402</td>\n",
       "      <td>-0.000653</td>\n",
       "      <td>-0.029959</td>\n",
       "      <td>-0.021421</td>\n",
       "      <td>0.052510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock_speed</th>\n",
       "      <td>0.011482</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000434</td>\n",
       "      <td>0.006545</td>\n",
       "      <td>-0.014364</td>\n",
       "      <td>0.012350</td>\n",
       "      <td>-0.005724</td>\n",
       "      <td>-0.005245</td>\n",
       "      <td>-0.014523</td>\n",
       "      <td>-0.009476</td>\n",
       "      <td>0.003443</td>\n",
       "      <td>-0.029078</td>\n",
       "      <td>-0.007378</td>\n",
       "      <td>-0.011432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fc</th>\n",
       "      <td>0.033334</td>\n",
       "      <td>-0.000434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.029133</td>\n",
       "      <td>-0.001791</td>\n",
       "      <td>0.023618</td>\n",
       "      <td>-0.013356</td>\n",
       "      <td>0.644595</td>\n",
       "      <td>-0.009990</td>\n",
       "      <td>-0.005176</td>\n",
       "      <td>0.015099</td>\n",
       "      <td>-0.011014</td>\n",
       "      <td>-0.012373</td>\n",
       "      <td>-0.006829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_memory</th>\n",
       "      <td>-0.004004</td>\n",
       "      <td>0.006545</td>\n",
       "      <td>-0.029133</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006886</td>\n",
       "      <td>-0.034214</td>\n",
       "      <td>-0.028310</td>\n",
       "      <td>-0.033273</td>\n",
       "      <td>0.010441</td>\n",
       "      <td>-0.008335</td>\n",
       "      <td>0.032813</td>\n",
       "      <td>0.037771</td>\n",
       "      <td>0.011731</td>\n",
       "      <td>-0.002790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m_dep</th>\n",
       "      <td>0.034085</td>\n",
       "      <td>-0.014364</td>\n",
       "      <td>-0.001791</td>\n",
       "      <td>0.006886</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021756</td>\n",
       "      <td>-0.003504</td>\n",
       "      <td>0.026282</td>\n",
       "      <td>0.025263</td>\n",
       "      <td>0.023566</td>\n",
       "      <td>-0.009434</td>\n",
       "      <td>-0.025348</td>\n",
       "      <td>-0.018388</td>\n",
       "      <td>0.017003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mobile_wt</th>\n",
       "      <td>0.001844</td>\n",
       "      <td>0.012350</td>\n",
       "      <td>0.023618</td>\n",
       "      <td>-0.034214</td>\n",
       "      <td>0.021756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018989</td>\n",
       "      <td>0.018844</td>\n",
       "      <td>0.000939</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>-0.002581</td>\n",
       "      <td>-0.033855</td>\n",
       "      <td>-0.020761</td>\n",
       "      <td>0.006209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_cores</th>\n",
       "      <td>-0.029727</td>\n",
       "      <td>-0.005724</td>\n",
       "      <td>-0.013356</td>\n",
       "      <td>-0.028310</td>\n",
       "      <td>-0.003504</td>\n",
       "      <td>-0.018989</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001193</td>\n",
       "      <td>-0.006872</td>\n",
       "      <td>0.024480</td>\n",
       "      <td>0.004868</td>\n",
       "      <td>-0.000315</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>0.013148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pc</th>\n",
       "      <td>0.031441</td>\n",
       "      <td>-0.005245</td>\n",
       "      <td>0.644595</td>\n",
       "      <td>-0.033273</td>\n",
       "      <td>0.026282</td>\n",
       "      <td>0.018844</td>\n",
       "      <td>-0.001193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018465</td>\n",
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       "      <td>0.028984</td>\n",
       "      <td>0.004938</td>\n",
       "      <td>-0.023819</td>\n",
       "      <td>0.014657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>px_height</th>\n",
       "      <td>0.014901</td>\n",
       "      <td>-0.014523</td>\n",
       "      <td>-0.009990</td>\n",
       "      <td>0.010441</td>\n",
       "      <td>0.025263</td>\n",
       "      <td>0.000939</td>\n",
       "      <td>-0.006872</td>\n",
       "      <td>-0.018465</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.510664</td>\n",
       "      <td>-0.020352</td>\n",
       "      <td>0.059615</td>\n",
       "      <td>0.043038</td>\n",
       "      <td>-0.010645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>px_width</th>\n",
       "      <td>-0.008402</td>\n",
       "      <td>-0.009476</td>\n",
       "      <td>-0.005176</td>\n",
       "      <td>-0.008335</td>\n",
       "      <td>0.023566</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.024480</td>\n",
       "      <td>0.004196</td>\n",
       "      <td>0.510664</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004105</td>\n",
       "      <td>0.021599</td>\n",
       "      <td>0.034699</td>\n",
       "      <td>0.006720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram</th>\n",
       "      <td>-0.000653</td>\n",
       "      <td>0.003443</td>\n",
       "      <td>0.015099</td>\n",
       "      <td>0.032813</td>\n",
       "      <td>-0.009434</td>\n",
       "      <td>-0.002581</td>\n",
       "      <td>0.004868</td>\n",
       "      <td>0.028984</td>\n",
       "      <td>-0.020352</td>\n",
       "      <td>0.004105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015996</td>\n",
       "      <td>0.035576</td>\n",
       "      <td>0.010820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sc_h</th>\n",
       "      <td>-0.029959</td>\n",
       "      <td>-0.029078</td>\n",
       "      <td>-0.011014</td>\n",
       "      <td>0.037771</td>\n",
       "      <td>-0.025348</td>\n",
       "      <td>-0.033855</td>\n",
       "      <td>-0.000315</td>\n",
       "      <td>0.004938</td>\n",
       "      <td>0.059615</td>\n",
       "      <td>0.021599</td>\n",
       "      <td>0.015996</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506144</td>\n",
       "      <td>-0.017335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sc_w</th>\n",
       "      <td>-0.021421</td>\n",
       "      <td>-0.007378</td>\n",
       "      <td>-0.012373</td>\n",
       "      <td>0.011731</td>\n",
       "      <td>-0.018388</td>\n",
       "      <td>-0.020761</td>\n",
       "      <td>0.025826</td>\n",
       "      <td>-0.023819</td>\n",
       "      <td>0.043038</td>\n",
       "      <td>0.034699</td>\n",
       "      <td>0.035576</td>\n",
       "      <td>0.506144</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>talk_time</th>\n",
       "      <td>0.052510</td>\n",
       "      <td>-0.011432</td>\n",
       "      <td>-0.006829</td>\n",
       "      <td>-0.002790</td>\n",
       "      <td>0.017003</td>\n",
       "      <td>0.006209</td>\n",
       "      <td>0.013148</td>\n",
       "      <td>0.014657</td>\n",
       "      <td>-0.010645</td>\n",
       "      <td>0.006720</td>\n",
       "      <td>0.010820</td>\n",
       "      <td>-0.017335</td>\n",
       "      <td>-0.022821</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "               battery_power  clock_speed        fc  int_memory     m_dep  \\\n",
       "battery_power       1.000000     0.011482  0.033334   -0.004004  0.034085   \n",
       "clock_speed         0.011482     1.000000 -0.000434    0.006545 -0.014364   \n",
       "fc                  0.033334    -0.000434  1.000000   -0.029133 -0.001791   \n",
       "int_memory         -0.004004     0.006545 -0.029133    1.000000  0.006886   \n",
       "m_dep               0.034085    -0.014364 -0.001791    0.006886  1.000000   \n",
       "mobile_wt           0.001844     0.012350  0.023618   -0.034214  0.021756   \n",
       "n_cores            -0.029727    -0.005724 -0.013356   -0.028310 -0.003504   \n",
       "pc                  0.031441    -0.005245  0.644595   -0.033273  0.026282   \n",
       "px_height           0.014901    -0.014523 -0.009990    0.010441  0.025263   \n",
       "px_width           -0.008402    -0.009476 -0.005176   -0.008335  0.023566   \n",
       "ram                -0.000653     0.003443  0.015099    0.032813 -0.009434   \n",
       "sc_h               -0.029959    -0.029078 -0.011014    0.037771 -0.025348   \n",
       "sc_w               -0.021421    -0.007378 -0.012373    0.011731 -0.018388   \n",
       "talk_time           0.052510    -0.011432 -0.006829   -0.002790  0.017003   \n",
       "\n",
       "               mobile_wt   n_cores        pc  px_height  px_width       ram  \\\n",
       "battery_power   0.001844 -0.029727  0.031441   0.014901 -0.008402 -0.000653   \n",
       "clock_speed     0.012350 -0.005724 -0.005245  -0.014523 -0.009476  0.003443   \n",
       "fc              0.023618 -0.013356  0.644595  -0.009990 -0.005176  0.015099   \n",
       "int_memory     -0.034214 -0.028310 -0.033273   0.010441 -0.008335  0.032813   \n",
       "m_dep           0.021756 -0.003504  0.026282   0.025263  0.023566 -0.009434   \n",
       "mobile_wt       1.000000 -0.018989  0.018844   0.000939  0.000090 -0.002581   \n",
       "n_cores        -0.018989  1.000000 -0.001193  -0.006872  0.024480  0.004868   \n",
       "pc              0.018844 -0.001193  1.000000  -0.018465  0.004196  0.028984   \n",
       "px_height       0.000939 -0.006872 -0.018465   1.000000  0.510664 -0.020352   \n",
       "px_width        0.000090  0.024480  0.004196   0.510664  1.000000  0.004105   \n",
       "ram            -0.002581  0.004868  0.028984  -0.020352  0.004105  1.000000   \n",
       "sc_h           -0.033855 -0.000315  0.004938   0.059615  0.021599  0.015996   \n",
       "sc_w           -0.020761  0.025826 -0.023819   0.043038  0.034699  0.035576   \n",
       "talk_time       0.006209  0.013148  0.014657  -0.010645  0.006720  0.010820   \n",
       "\n",
       "                   sc_h      sc_w  talk_time  \n",
       "battery_power -0.029959 -0.021421   0.052510  \n",
       "clock_speed   -0.029078 -0.007378  -0.011432  \n",
       "fc            -0.011014 -0.012373  -0.006829  \n",
       "int_memory     0.037771  0.011731  -0.002790  \n",
       "m_dep         -0.025348 -0.018388   0.017003  \n",
       "mobile_wt     -0.033855 -0.020761   0.006209  \n",
       "n_cores       -0.000315  0.025826   0.013148  \n",
       "pc             0.004938 -0.023819   0.014657  \n",
       "px_height      0.059615  0.043038  -0.010645  \n",
       "px_width       0.021599  0.034699   0.006720  \n",
       "ram            0.015996  0.035576   0.010820  \n",
       "sc_h           1.000000  0.506144  -0.017335  \n",
       "sc_w           0.506144  1.000000  -0.022821  \n",
       "talk_time     -0.017335 -0.022821   1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the corr() function to generate the correlation matrix of the numeric variables\n",
    "corrmat = df_features.corr()\n",
    "\n",
    "# print the correlation matrix\n",
    "corrmat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Plot the heatmap for the diagonal correlation matrix**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A correlation matrix is a symmetric matrix. Plot only the lower triangular entries using a heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 231
    },
    "colab_type": "code",
    "id": "oeXcIgFyebob",
    "outputId": "b98fffd7-08ab-4cd6-9b22-e377631cdfea"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use 'mask' to plot a lower triangular correlation matrix \n",
    "# 'triu_indices_from' returns the indices for the upper-triangle of matrix\n",
    "mask = np.zeros_like(corrmat)\n",
    "mask[np.triu_indices_from(mask, k=1)] = True\n",
    "\n",
    "# plot the heat map\n",
    "# corr: give the correlation matrix\n",
    "# cmap: color code used for plotting\n",
    "# vmax: gives a maximum range of values for the chart\n",
    "# vmin: gives a minimum range of values for the chart\n",
    "# annot: prints the correlation values in the chart\n",
    "# annot_kws: sets the font size of the annotation\n",
    "# mask: mask the upper traingular matrix values\n",
    "sns.heatmap(corrmat, cmap = 'YlGnBu', vmax = 1.0, vmin = -1.0, annot = True, annot_kws = {\"size\": 12}, mask = mask)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DiZ_ts_Debod"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>From the above heatmap, it can see that most of the continuous variables are not correlated with each other since the correlation coefficients are close to zero. The maximum positive correlation is 0.64 between 'fc' and 'pc'. SImilarly 'px_height' and 'px_weight' have the correlation of 0.51 and 'sc_h' and 'sc_w' also havve the same correlation value of 0.51.<br><br>\n",
    "Note: The diagonal values are always 1 because it is the correlation of the variable with itself.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pPwoQF_Hebod"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"quicktip.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Correlation does not imply causation. In other words, if two variables are correlated, it does not mean that one variable caused the other.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OET43huSeboe"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"alsoreadicon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>I love to know more:</b> <br><br>\n",
    "                    <a href=\"https://bit.ly/2PBvA8T\">Why correlation does not imply causation </a>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FjtDatMReboh"
   },
   "source": [
    "<a id='outliers'></a>\n",
    "### 4.1.5 Discover Outliers "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>To detect outliers in numeric data, we perform the following:<br><br>\n",
    "                    1. Plot the boxplot for numeric data<br>\n",
    "                    2. Note the variables in which outliers are present<br>\n",
    "                    3. Remove outliers by IQR method<br> \n",
    "                    4. Plot the boxplot to recheck for outliers</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Plot the boxplot for numeric data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WGaqwSezeboj",
    "outputId": "82c7cd79-e3a5-40ff-c010-32c38b054e64"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a boxplot to visualize the outliers in all the numeric variables\n",
    "df_features.boxplot()\n",
    "\n",
    "# set plot label\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Distribution of all Numeric Variables', fontsize = 15)\n",
    "\n",
    "# xticks() returns the x-axis ticks\n",
    "# 'rotation = vertical' rotates the x-axis labels vertically\n",
    "plt.xticks(rotation = 'vertical', fontsize = 15)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some boxplots of variables are not seen because of the extreme values im other variables. So let's plot a separate boxplot for these variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WGaqwSezeboj",
    "outputId": "82c7cd79-e3a5-40ff-c010-32c38b054e64"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a boxplot to visualize the outliers in all the numeric variables\n",
    "df_features.drop(['battery_power', 'mobile_wt', 'px_height', 'px_width', 'ram'], axis = 1).boxplot()\n",
    "\n",
    "# set plot label\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Distribution of the Numeric Variables', fontsize = 15)\n",
    "\n",
    "# xticks() returns the x-axis ticks\n",
    "# 'rotation = vertical' rotates the x-axis labels vertically\n",
    "plt.xticks(rotation = 'vertical', fontsize = 15)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can see that only 'fc' and 'px_height' have a few outliers. But they are not visible clearly. So we will plot separate boxplots for these two. Otherwise all the other variables do not have any outliers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot boxplots for fc\n",
    "sns.boxplot(df_features['fc'])\n",
    "\n",
    "# set plot label\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Distribution of fc (front camera megapixels)', fontsize = 15)\n",
    "\n",
    "# xticks() returns the x-axis ticks\n",
    "# 'rotation = vertical' rotates the x-axis labels vertically\n",
    "plt.xticks(rotation = 'vertical', fontsize = 15)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot boxplots for fc\n",
    "sns.boxplot(df_features['px_height'])\n",
    "\n",
    "# set plot label\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Distribution of px_height', fontsize = 15)\n",
    "\n",
    "# xticks() returns the x-axis ticks\n",
    "# 'rotation = vertical' rotates the x-axis labels vertically\n",
    "plt.xticks(rotation = 'vertical', fontsize = 15)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Note the variables for which outliers are present**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AXWCnG8yebol"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>From the above plot, we notice that for the variables 'fc' and 'px_height' there are points above the upper extreme or below the lower extreme - these points are outliers in the data. However, the exact location of these points is not precisely seen.<br><br>\n",
    "                        Let us use the IQR method to remove the outliers.<br><br>\n",
    "                        Note: The variables have not been scaled. Thus the boxplots are not visualized efficiently.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gqK11WzYebom"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"quicktip.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Outliers can also be detected using the standard deviation method if we know that the distribution of values in the sample is Gaussian or Gaussian-like.</b> \n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BudwXJ6Kebou"
   },
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"alsoreadicon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>I love to know more:  </b><a href=\"https://bit.ly/33bgNpq\">How to use statistics to identify outliers in data</a>\n",
    "</font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Remove outliers by IQR method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HBc-FXUpebom",
    "outputId": "6d8246f0-fb48-4be6-e268-c0b66788af8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "battery_power     763.50\n",
      "clock_speed         1.50\n",
      "fc                  6.00\n",
      "int_memory         32.00\n",
      "m_dep               0.60\n",
      "mobile_wt          61.00\n",
      "n_cores             4.00\n",
      "pc                 10.00\n",
      "px_height         664.50\n",
      "px_width          758.25\n",
      "ram              1857.00\n",
      "sc_h                7.00\n",
      "sc_w                7.00\n",
      "talk_time          10.00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# calculate the first quartile\n",
    "Q1 = df_features.quantile(0.25)\n",
    "\n",
    "# calculate the third quartile\n",
    "Q3 = df_features.quantile(0.75)\n",
    "\n",
    "# Interquartile Range (IQR) is defined as the difference between the third and first quartile\n",
    "# calculate IQR\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# print the IQR\n",
    "print(IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "OGb5EZBpebop",
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# remove the outliers from the dataframe 'df_mobile'\n",
    "# retrieve the dataframe without the outliers\n",
    "# '~' returns the values that do not satisfy the given conditions \n",
    "# i.e. it returns values between the range [Q1-1.5*IQR, Q3+1.5*IQR]\n",
    "# '|' is used as 'OR' operator on multiple conditions   \n",
    "# 'any(axis=1)' checks the entire row for atleast one 'True' entry (those rows represents outliers in the data)\n",
    "df_mobile = df_mobile[~((df_mobile < (Q1 - 1.5 * IQR)) | (df_mobile > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
    "\n",
    "# reset the index of the dataframe without outliers\n",
    "df_mobile = df_mobile.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4iuSrkGhebor"
   },
   "source": [
    "To confirm that the outliers have been removed; let us visualize the boxplot again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Plot the boxplot to recheck for outliers**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the boxplots for all variables except for the variable `white corpuscle` for better visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2ZmatCI-ebos",
    "outputId": "38229e72-93c9-42ab-d048-fd685c3e384d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot multiple boxplots\n",
    "df_mobile.boxplot()\n",
    "\n",
    "# set plot label\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Distribution of Independent Variables', fontsize = 15)\n",
    "\n",
    "# xticks() returns the x-axis ticks\n",
    "# 'rotation = vertical' rotates the x-axis labels vertically\n",
    "plt.xticks(rotation = 'vertical', fontsize = 15)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observing the range of the boxplot, we say that the outliers are removed from the original data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A crude way to know whether the outliers have been removed or not is to check the dimensions of the data. If the dimensions are reduced that implies outliers are removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ERHiDR7tebov",
    "outputId": "258d0da1-e1e9-433b-9ce7-3306b5a47d41",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1980, 15)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of the data after removal of outliers \n",
    "df_mobile.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "           <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>The output shows a reduction in the number of rows. Thus we may say that the potential outliers have been removed.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G-aTB0DseboJ"
   },
   "source": [
    "<a id='Missing_Values'></a>\n",
    "### 4.1.6 Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>First, run a check for the presence of missing values and their percentage for each column. Then choose the right approach to remove them.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XsY-ydhgeboM",
    "outputId": "1287fead-9adc-4382-a939-bdba17105d0f"
   },
   "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>Total</th>\n",
       "      <th>Percentage of Missing Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price_range</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>talk_time</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sc_w</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sc_h</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>px_width</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>px_height</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pc</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_cores</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mobile_wt</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m_dep</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_memory</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fc</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock_speed</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>battery_power</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Total  Percentage of Missing Values\n",
       "price_range        0                           0.0\n",
       "talk_time          0                           0.0\n",
       "sc_w               0                           0.0\n",
       "sc_h               0                           0.0\n",
       "ram                0                           0.0\n",
       "px_width           0                           0.0\n",
       "px_height          0                           0.0\n",
       "pc                 0                           0.0\n",
       "n_cores            0                           0.0\n",
       "mobile_wt          0                           0.0\n",
       "m_dep              0                           0.0\n",
       "int_memory         0                           0.0\n",
       "fc                 0                           0.0\n",
       "clock_speed        0                           0.0\n",
       "battery_power      0                           0.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the variables on the basis of total null values in the variable\n",
    "# 'isnull().sum()' returns the number of missing values in each variable\n",
    "# 'ascending = False' sorts values in the descending order\n",
    "# the variable with highest number of missing values will appear first\n",
    "Total = df_mobile.isnull().sum().sort_values(ascending = False)          \n",
    "\n",
    "# calculate the percentage of missing values\n",
    "# 'ascending = False' sorts values in the descending order\n",
    "# the variable with highest percentage of missing values will appear first\n",
    "Percent = (df_mobile.isnull().sum()*100/df_mobile.isnull().count()).sort_values(ascending = False)   \n",
    "\n",
    "# concat the 'Total' and 'Percent' columns using 'concat' function\n",
    "# 'keys' is the list of column names\n",
    "# 'axis = 1' concats along the columns\n",
    "missing_data = pd.concat([Total, Percent], axis = 1, keys = ['Total', 'Percentage of Missing Values'])    \n",
    "missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"infer.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>There are no missing values.</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot a heatmap for visualization of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heatmap to check null values\n",
    "# 'cbar = False' does not show the color axis \n",
    "sns.heatmap(df_mobile.isnull(), cbar=False)\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The horizontal lines in the heatmap correspond to the missing values. But there are no such line. This means there are no missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Separate the target variable from the independent variables</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# store the target variable in the variable 'y'\n",
    "y = df_mobile.price_range\n",
    "\n",
    "# drop the target variable from the dataframe\n",
    "df_mobile = df_mobile.drop('price_range', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Data_Preparation'></a>\n",
    "## 4.2 Prepare the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Scale the numeric variables</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We scale the variables to get all the variables in the same range. With this, we can avoid a problem in which some features come to dominate solely because they tend to have larger values than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>battery_power</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>fc</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</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>0</th>\n",
       "      <td>-0.897248</td>\n",
       "      <td>0.830652</td>\n",
       "      <td>-0.765253</td>\n",
       "      <td>-1.382209</td>\n",
       "      <td>0.338657</td>\n",
       "      <td>1.351754</td>\n",
       "      <td>-1.103004</td>\n",
       "      <td>-1.300985</td>\n",
       "      <td>-1.411916</td>\n",
       "      <td>-1.143741</td>\n",
       "      <td>0.390818</td>\n",
       "      <td>-0.787936</td>\n",
       "      <td>0.281378</td>\n",
       "      <td>1.465078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.490030</td>\n",
       "      <td>-1.249531</td>\n",
       "      <td>-1.005152</td>\n",
       "      <td>1.155467</td>\n",
       "      <td>0.685011</td>\n",
       "      <td>-0.117795</td>\n",
       "      <td>-0.666564</td>\n",
       "      <td>-0.636909</td>\n",
       "      <td>0.599639</td>\n",
       "      <td>1.710709</td>\n",
       "      <td>0.466425</td>\n",
       "      <td>1.112614</td>\n",
       "      <td>-0.636461</td>\n",
       "      <td>-0.733510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.531961</td>\n",
       "      <td>-1.249531</td>\n",
       "      <td>-0.525354</td>\n",
       "      <td>0.493465</td>\n",
       "      <td>1.377719</td>\n",
       "      <td>0.136550</td>\n",
       "      <td>0.206317</td>\n",
       "      <td>-0.636909</td>\n",
       "      <td>1.413354</td>\n",
       "      <td>1.080506</td>\n",
       "      <td>0.440608</td>\n",
       "      <td>-0.312799</td>\n",
       "      <td>-0.865920</td>\n",
       "      <td>-0.367079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.413663</td>\n",
       "      <td>1.197743</td>\n",
       "      <td>-1.005152</td>\n",
       "      <td>-1.216708</td>\n",
       "      <td>1.031365</td>\n",
       "      <td>-0.259098</td>\n",
       "      <td>0.642758</td>\n",
       "      <td>-0.138852</td>\n",
       "      <td>1.306525</td>\n",
       "      <td>1.242690</td>\n",
       "      <td>0.593666</td>\n",
       "      <td>0.875045</td>\n",
       "      <td>0.510837</td>\n",
       "      <td>-0.000648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.329935</td>\n",
       "      <td>-0.392985</td>\n",
       "      <td>2.113534</td>\n",
       "      <td>0.658966</td>\n",
       "      <td>0.338657</td>\n",
       "      <td>0.023508</td>\n",
       "      <td>-1.103004</td>\n",
       "      <td>0.691243</td>\n",
       "      <td>1.288342</td>\n",
       "      <td>-0.087224</td>\n",
       "      <td>-0.658459</td>\n",
       "      <td>-1.025505</td>\n",
       "      <td>-0.865920</td>\n",
       "      <td>0.732215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  clock_speed        fc  int_memory     m_dep  mobile_wt  \\\n",
       "0      -0.897248     0.830652 -0.765253   -1.382209  0.338657   1.351754   \n",
       "1      -0.490030    -1.249531 -1.005152    1.155467  0.685011  -0.117795   \n",
       "2      -1.531961    -1.249531 -0.525354    0.493465  1.377719   0.136550   \n",
       "3      -1.413663     1.197743 -1.005152   -1.216708  1.031365  -0.259098   \n",
       "4       1.329935    -0.392985  2.113534    0.658966  0.338657   0.023508   \n",
       "\n",
       "    n_cores        pc  px_height  px_width       ram      sc_h      sc_w  \\\n",
       "0 -1.103004 -1.300985  -1.411916 -1.143741  0.390818 -0.787936  0.281378   \n",
       "1 -0.666564 -0.636909   0.599639  1.710709  0.466425  1.112614 -0.636461   \n",
       "2  0.206317 -0.636909   1.413354  1.080506  0.440608 -0.312799 -0.865920   \n",
       "3  0.642758 -0.138852   1.306525  1.242690  0.593666  0.875045  0.510837   \n",
       "4 -1.103004  0.691243   1.288342 -0.087224 -0.658459 -1.025505 -0.865920   \n",
       "\n",
       "   talk_time  \n",
       "0   1.465078  \n",
       "1  -0.733510  \n",
       "2  -0.367079  \n",
       "3  -0.000648  \n",
       "4   0.732215  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scale all the numerical columns\n",
    "# standardize all the columns of the dataframe 'df_feature'\n",
    "df_num = df_mobile.apply(lambda rec: (rec - rec.mean()) / rec.std())\n",
    "\n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"8%\">\n",
    "            <img src=\"todo.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>Rename the dataframes (df_mobile_dummy and df_target)</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "X = df_num.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "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>clock_speed</th>\n",
       "      <th>fc</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</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>0</th>\n",
       "      <td>-0.897248</td>\n",
       "      <td>0.830652</td>\n",
       "      <td>-0.765253</td>\n",
       "      <td>-1.382209</td>\n",
       "      <td>0.338657</td>\n",
       "      <td>1.351754</td>\n",
       "      <td>-1.103004</td>\n",
       "      <td>-1.300985</td>\n",
       "      <td>-1.411916</td>\n",
       "      <td>-1.143741</td>\n",
       "      <td>0.390818</td>\n",
       "      <td>-0.787936</td>\n",
       "      <td>0.281378</td>\n",
       "      <td>1.465078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.490030</td>\n",
       "      <td>-1.249531</td>\n",
       "      <td>-1.005152</td>\n",
       "      <td>1.155467</td>\n",
       "      <td>0.685011</td>\n",
       "      <td>-0.117795</td>\n",
       "      <td>-0.666564</td>\n",
       "      <td>-0.636909</td>\n",
       "      <td>0.599639</td>\n",
       "      <td>1.710709</td>\n",
       "      <td>0.466425</td>\n",
       "      <td>1.112614</td>\n",
       "      <td>-0.636461</td>\n",
       "      <td>-0.733510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.531961</td>\n",
       "      <td>-1.249531</td>\n",
       "      <td>-0.525354</td>\n",
       "      <td>0.493465</td>\n",
       "      <td>1.377719</td>\n",
       "      <td>0.136550</td>\n",
       "      <td>0.206317</td>\n",
       "      <td>-0.636909</td>\n",
       "      <td>1.413354</td>\n",
       "      <td>1.080506</td>\n",
       "      <td>0.440608</td>\n",
       "      <td>-0.312799</td>\n",
       "      <td>-0.865920</td>\n",
       "      <td>-0.367079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.413663</td>\n",
       "      <td>1.197743</td>\n",
       "      <td>-1.005152</td>\n",
       "      <td>-1.216708</td>\n",
       "      <td>1.031365</td>\n",
       "      <td>-0.259098</td>\n",
       "      <td>0.642758</td>\n",
       "      <td>-0.138852</td>\n",
       "      <td>1.306525</td>\n",
       "      <td>1.242690</td>\n",
       "      <td>0.593666</td>\n",
       "      <td>0.875045</td>\n",
       "      <td>0.510837</td>\n",
       "      <td>-0.000648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.329935</td>\n",
       "      <td>-0.392985</td>\n",
       "      <td>2.113534</td>\n",
       "      <td>0.658966</td>\n",
       "      <td>0.338657</td>\n",
       "      <td>0.023508</td>\n",
       "      <td>-1.103004</td>\n",
       "      <td>0.691243</td>\n",
       "      <td>1.288342</td>\n",
       "      <td>-0.087224</td>\n",
       "      <td>-0.658459</td>\n",
       "      <td>-1.025505</td>\n",
       "      <td>-0.865920</td>\n",
       "      <td>0.732215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  clock_speed        fc  int_memory     m_dep  mobile_wt  \\\n",
       "0      -0.897248     0.830652 -0.765253   -1.382209  0.338657   1.351754   \n",
       "1      -0.490030    -1.249531 -1.005152    1.155467  0.685011  -0.117795   \n",
       "2      -1.531961    -1.249531 -0.525354    0.493465  1.377719   0.136550   \n",
       "3      -1.413663     1.197743 -1.005152   -1.216708  1.031365  -0.259098   \n",
       "4       1.329935    -0.392985  2.113534    0.658966  0.338657   0.023508   \n",
       "\n",
       "    n_cores        pc  px_height  px_width       ram      sc_h      sc_w  \\\n",
       "0 -1.103004 -1.300985  -1.411916 -1.143741  0.390818 -0.787936  0.281378   \n",
       "1 -0.666564 -0.636909   0.599639  1.710709  0.466425  1.112614 -0.636461   \n",
       "2  0.206317 -0.636909   1.413354  1.080506  0.440608 -0.312799 -0.865920   \n",
       "3  0.642758 -0.138852   1.306525  1.242690  0.593666  0.875045  0.510837   \n",
       "4 -1.103004  0.691243   1.288342 -0.087224 -0.658459 -1.025505 -0.865920   \n",
       "\n",
       "   talk_time  \n",
       "0   1.465078  \n",
       "1  -0.733510  \n",
       "2  -0.367079  \n",
       "3  -0.000648  \n",
       "4   0.732215  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a generalized function to calculate the performance metrics for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# create a generalized function to calculate the performance metrics values for test set\n",
    "def get_test_report(model):\n",
    "    \n",
    "    # for test set:\n",
    "    # test_pred: prediction made by the model on the test dataset 'X_test'\n",
    "    # y_test: actual values of the target variable for the test dataset\n",
    "\n",
    "    # predict the output of the target variable from the test data \n",
    "    test_pred = model.predict(X_test)\n",
    "\n",
    "    # return the classification report for test data\n",
    "    return(classification_report(y_test, test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define a function to plot the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "Av39nL7SebpS",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "eb72d174-cce1-49f2-abcc-b8a6111b2008"
   },
   "outputs": [],
   "source": [
    "# define a to plot a confusion matrix for the model\n",
    "def plot_confusion_matrix(model):\n",
    "    \n",
    "    # predict the target values using X_test\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # create a confusion matrix\n",
    "    # pass the actual and predicted target values to the confusion_matrix()\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "    # label the confusion matrix  \n",
    "    # pass the matrix as 'data'\n",
    "    # pass the required column names to the parameter, 'columns'\n",
    "    # pass the required row names to the parameter, 'index'\n",
    "    conf_matrix = pd.DataFrame(data = cm,columns = ['Predicted:0','Predicted:1', 'Predicted:2','Predicted:3'],\\\n",
    "                               index = ['Actual:0','Actual:1', 'Actual:2','Actual:3'])\n",
    "\n",
    "    # plot a heatmap to visualize the confusion matrix\n",
    "    # 'annot' prints the value of each grid \n",
    "    # 'fmt = d' returns the integer value in each grid\n",
    "    # 'cmap' assigns color to each grid\n",
    "    # as we do not require different colors for each grid in the heatmap,\n",
    "    # use 'ListedColormap' to assign the specified color to the grid\n",
    "    # 'cbar = False' will not return the color bar to the right side of the heatmap\n",
    "    # 'linewidths' assigns the width to the line that divides each grid\n",
    "    # 'annot_kws = {'size':25})' assigns the font size of the annotated text \n",
    "    sns.heatmap(conf_matrix, annot = True, fmt = 'd', cmap = ListedColormap(['lightskyblue']), cbar = False, \n",
    "                linewidths = 0.1, annot_kws = {'size':25})\n",
    "\n",
    "    # set the font size of x-axis ticks using 'fontsize'\n",
    "    plt.xticks(fontsize = 20)\n",
    "\n",
    "    # set the font size of y-axis ticks using 'fontsize'\n",
    "    plt.yticks(fontsize = 20)\n",
    "\n",
    "    # display the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"knn\"></a>\n",
    "# 5. K Nearest Neighbors (KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "KNN is a classification machine learning algorithm used to identify the class of the observation. This algorithm search for `K` nearest points to determine the class of an observation. To identify the nearest points, it considers the distance metrics like `Euclidean`, `Manhattan`, `Chebyshev`, `Hamming`, and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Split the dataset into train and test sets**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9XWUmyXrebo_",
    "outputId": "7dcfd65a-f799-464c-d257-c7df8d4b7a06",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (1386, 14)\n",
      "y_train (1386,)\n",
      "X_test (594, 14)\n",
      "y_test (594,)\n"
     ]
    }
   ],
   "source": [
    "# split data into train subset and test subset\n",
    "# set 'random_state' to generate the same dataset each time you run the code \n",
    "# 'test_size' returns the proportion of data to be included in the test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state = 10)\n",
    "\n",
    "# check the dimensions of the train & test subset using 'shape'\n",
    "# print dimension of train set\n",
    "print(\"X_train\",X_train.shape)\n",
    "print(\"y_train\",y_train.shape)\n",
    "\n",
    "# print dimension of test set\n",
    "print(\"X_test\",X_test.shape)\n",
    "print(\"y_test\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build a knn model on a training dataset using euclidean distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# instantiate the 'KNeighborsClassifier'\n",
    "# n_neighnors: number of neighbors to consider\n",
    "# default metric is minkowski, and with p=2 it is equivalent to the euclidean metric\n",
    "knn_classification = KNeighborsClassifier(n_neighbors = 3)\n",
    "\n",
    "# fit the model using fit() on train data\n",
    "knn_model = knn_classification.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build a confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the function to plot the confusion matrix\n",
    "# pass the knn model to the function\n",
    "plot_confusion_matrix(knn_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate performance measures on the test set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UP5DA9_qebpC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.78      0.70       144\n",
      "           1       0.38      0.39      0.38       150\n",
      "           2       0.45      0.44      0.45       144\n",
      "           3       0.79      0.59      0.68       156\n",
      "\n",
      "    accuracy                           0.55       594\n",
      "   macro avg       0.56      0.55      0.55       594\n",
      "weighted avg       0.56      0.55      0.55       594\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# compute the performance measures on test data\n",
    "# call the function 'get_test_report'\n",
    "# pass the knn model to the function\n",
    "test_report = get_test_report(knn_model)\n",
    "\n",
    "# print the performace measures\n",
    "print(test_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** The accuracy is 55% for this model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"hyper\"></a>\n",
    "## 5.1 Optimal Value of K (using GridSearchCV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for KNN Classifier:  {'metric': 'manhattan', 'n_neighbors': 21} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create a dictionary with hyperparameters and its values\n",
    "# n_neighnors: number of neighbors to consider\n",
    "# usually, we consider the odd value of 'n_neighnors' to avoid the equal number of nearest points with more than one class\n",
    "# pass the different distance metrics to the parameter, 'metric'\n",
    "tuned_paramaters = {'n_neighbors': np.arange(1, 25, 2),\n",
    "                   'metric': ['hamming','euclidean','manhattan','Chebyshev']}\n",
    " \n",
    "# instantiate the 'KNeighborsClassifier' \n",
    "knn_classification = KNeighborsClassifier()\n",
    "\n",
    "# use GridSearchCV() to find the optimal value of the hyperparameters\n",
    "# estimator: pass the knn model\n",
    "# param_grid: pass the list 'tuned_parameters'\n",
    "# cv: number of folds in k-fold i.e. here cv = 5\n",
    "# scoring: pass the scoring parameter 'accuracy'\n",
    "knn_grid = GridSearchCV(estimator = knn_classification, \n",
    "                        param_grid = tuned_paramaters, \n",
    "                        cv = 5, \n",
    "                        scoring = 'accuracy')\n",
    "\n",
    "# fit the model on X_train and y_train using fit()\n",
    "knn_grid.fit(X_train, y_train)\n",
    "\n",
    "# get the best parameters\n",
    "print('Best parameters for KNN Classifier: ', knn_grid.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Draw a line plot to see the error rate for each value of K using euclidean distance as a metric of KNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# consider an empty list to store error rate\n",
    "error_rate = []\n",
    "\n",
    "# use for loop to build a knn model for each K\n",
    "for i in np.arange(1,30,2):\n",
    "    \n",
    "    # setup a knn classifier with k neighbors\n",
    "    # use the 'euclidean' metric \n",
    "    knn = KNeighborsClassifier(i, metric = 'manhattan')\n",
    "   \n",
    "    # fit the model using 'cross_val_score'\n",
    "    # pass the knn model as 'estimator'\n",
    "    # use 5-fold cross validation\n",
    "    score = cross_val_score(knn, X_train, y_train, cv = 5)\n",
    "    \n",
    "    # calculate the mean score\n",
    "    score = score.mean()\n",
    "    \n",
    "    # compute error rate \n",
    "    error_rate.append(1 - score)\n",
    "\n",
    "# plot the error_rate for different values of K \n",
    "plt.plot(range(1,30,2), error_rate)\n",
    "\n",
    "# add plot and axes labels\n",
    "# set text size using 'fontsize'\n",
    "plt.title('Error Rate', fontsize = 15)\n",
    "plt.xlabel('K', fontsize = 15)\n",
    "plt.ylabel('Error Rate', fontsize = 15)\n",
    "\n",
    "# set the x-axis labels\n",
    "plt.xticks(np.arange(1, 30, step = 2))\n",
    "\n",
    "# plot a vertical line across the minimum error rate\n",
    "plt.axvline(x = 21, color = 'red')\n",
    "\n",
    "# display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** We can see that the optimal value of K (= 21) obtained from the GridSearchCV() results in a lowest error rate. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculate performance measures on the test set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report for test set: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.87      0.82       144\n",
      "           1       0.64      0.60      0.62       150\n",
      "           2       0.59      0.67      0.62       144\n",
      "           3       0.87      0.71      0.78       156\n",
      "\n",
      "    accuracy                           0.71       594\n",
      "   macro avg       0.72      0.71      0.71       594\n",
      "weighted avg       0.72      0.71      0.71       594\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print the performance measures for test set for the model with best parameters\n",
    "# call the function 'get_test_report'\n",
    "# pass the knn model using GridSearch to the function\n",
    "print('Classification Report for test set: \\n', get_test_report(knn_grid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** The accuracy is 71% for this model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Kappa score for knn_model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "y_pred = knn_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kappa value: 0.40144018477842447\n"
     ]
    }
   ],
   "source": [
    "# compute the kappa value\n",
    "kappa = cohen_kappa_score(y_test, y_pred)\n",
    "\n",
    "# print the kappa value\n",
    "print('kappa value:',kappa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** As, the kappa score for the KNN model is 0.4014, we can say that there is fair agrrement between the actual and predicted values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Kappa score tuned KNN model i.e. knn_grid**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "y_pred = knn_grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kappa value: 0.6121164081078021\n"
     ]
    }
   ],
   "source": [
    "# compute the kappa value\n",
    "kappa = cohen_kappa_score(y_test, y_pred)\n",
    "\n",
    "# print the kappa value\n",
    "print('kappa value:',kappa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** As, the kappa score for the tuned KNN model is 0.6121, we can say that there is substantial agrrement between the actual and predicted values. The tuned knn model with k=21 is better than the knn model with k=3."
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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