mobile-like-dislike-model-mp-neuron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder,MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, ParameterGrid\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, mean_squared_error, log_loss\n",
    "import operator\n",
    "import json\n",
    "from IPython import display\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "np.random.seed(0)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "THRESHOLD = 4\n",
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train: 355\n",
      "Number of features in train: 99\n",
      "Number of data points in test: 119\n",
      "Number of features in test: 98\n"
     ]
    }
   ],
   "source": [
    "# read data from file\n",
    "train = pd.read_csv(\"../input/train.csv\") \n",
    "test = pd.read_csv(\"../input/test.csv\")\n",
    "\n",
    "# check the number of features and data points in train\n",
    "print(\"Number of data points in train: %d\" % train.shape[0])\n",
    "print(\"Number of features in train: %d\" % train.shape[1])\n",
    "\n",
    "# check the number of features and data points in test\n",
    "print(\"Number of data points in test: %d\" % test.shape[0])\n",
    "print(\"Number of features in test: %d\" % test.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_clean(data):\n",
    "    \n",
    "    # Let's first remove all missing value features\n",
    "    columns_to_remove = ['Also Known As','Applications','Audio Features','Bezel-less display'\n",
    "                         'Browser','Build Material','Co-Processor','Browser'\n",
    "                         'Display Colour','Mobile High-Definition Link(MHL)',\n",
    "                         'Music', 'Email','Fingerprint Sensor Position',\n",
    "                         'Games','HDMI','Heart Rate Monitor','IRIS Scanner', \n",
    "                         'Optical Image Stabilisation','Other Facilities',\n",
    "                         'Phone Book','Physical Aperture','Quick Charging',\n",
    "                         'Ring Tone','Ruggedness','SAR Value','SIM 3','SMS',\n",
    "                         'Screen Protection','Screen to Body Ratio (claimed by the brand)',\n",
    "                         'Sensor','Software Based Aperture', 'Special Features',\n",
    "                         'Standby time','Stylus','TalkTime', 'USB Type-C',\n",
    "                         'Video Player', 'Video Recording Features','Waterproof',\n",
    "                         'Wireless Charging','USB OTG Support', 'Video Recording','Java']\n",
    "\n",
    "    columns_to_retain = list(set(data.columns)-set(columns_to_remove))\n",
    "    data = data[columns_to_retain]\n",
    "\n",
    "    #Features having very low variance \n",
    "    columns_to_remove = ['Architecture','Audio Jack','GPS','Loudspeaker','Network','Network Support','VoLTE']\n",
    "    columns_to_retain = list(set(data.columns)-set(columns_to_remove))\n",
    "    data = data[columns_to_retain]\n",
    "\n",
    "    # Multivalued:\n",
    "    columns_to_remove = ['Architecture','Launch Date','Audio Jack','GPS','Loudspeaker','Network','Network Support','VoLTE', 'Custom UI']\n",
    "    columns_to_retain = list(set(data.columns)-set(columns_to_remove))\n",
    "    data = data[columns_to_retain]\n",
    "\n",
    "    # Not much important\n",
    "    columns_to_remove = ['Bluetooth', 'Settings','Wi-Fi','Wi-Fi Features']\n",
    "    columns_to_retain = list(set(data.columns)-set(columns_to_remove))\n",
    "    data = data[columns_to_retain]\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Removing Features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data_clean(train)\n",
    "test = data_clean(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "removing all those data points in which more than 15 features are missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train[(train.isnull().sum(axis=1) <= 15)]\n",
    "# You shouldn't remove data points from test set\n",
    "#test = test[(test.isnull().sum(axis=1) <= 15)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train: 341\n",
      "Number of features in train: 47\n",
      "Number of data points in test: 119\n",
      "Number of features in test: 46\n"
     ]
    }
   ],
   "source": [
    "# check the number of features and data points in train\n",
    "print(\"Number of data points in train: %d\" % train.shape[0])\n",
    "print(\"Number of features in train: %d\" % train.shape[1])\n",
    "\n",
    "# check the number of features and data points in test\n",
    "print(\"Number of data points in test: %d\" % test.shape[0])\n",
    "print(\"Number of features in test: %d\" % test.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filling Missing values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def for_integer(test):\n",
    "    try:\n",
    "        test = test.strip()\n",
    "        return int(test.split(' ')[0])\n",
    "    except IOError:\n",
    "           pass\n",
    "    except ValueError:\n",
    "        pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "def for_string(test):\n",
    "    try:\n",
    "        test = test.strip()\n",
    "        return (test.split(' ')[0])\n",
    "    except IOError:\n",
    "        pass\n",
    "    except ValueError:\n",
    "        pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "def for_float(test):\n",
    "    try:\n",
    "        test = test.strip()\n",
    "        return float(test.split(' ')[0])\n",
    "    except IOError:\n",
    "        pass\n",
    "    except ValueError:\n",
    "        pass\n",
    "    except:\n",
    "        pass\n",
    "def find_freq(test):\n",
    "    try:\n",
    "        test = test.strip()\n",
    "        test = test.split(' ')\n",
    "        if test[2][0] == '(':\n",
    "            return float(test[2][1:])\n",
    "        return float(test[2])\n",
    "    except IOError:\n",
    "        pass\n",
    "    except ValueError:\n",
    "        pass\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "    \n",
    "def for_Internal_Memory(test):\n",
    "    try:\n",
    "        test = test.strip()\n",
    "        test = test.split(' ')\n",
    "        if test[1] == 'GB':\n",
    "            return int(test[0])\n",
    "        if test[1] == 'MB':\n",
    "#             print(\"here\")\n",
    "            return (int(test[0]) * 0.001)\n",
    "    except IOError:\n",
    "           pass\n",
    "    except ValueError:\n",
    "        pass\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "def find_freq(test):\n",
    "    try:\n",
    "        test = test.strip()\n",
    "        test = test.split(' ')\n",
    "        if test[2][0] == '(':\n",
    "            return float(test[2][1:])\n",
    "        return float(test[2])\n",
    "    except IOError:\n",
    "        pass\n",
    "    except ValueError:\n",
    "        pass\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_clean_2(x):\n",
    "    data = x.copy()\n",
    "    \n",
    "    data['Capacity'] = data['Capacity'].apply(for_integer)\n",
    "\n",
    "    data['Height'] = data['Height'].apply(for_float)\n",
    "    data['Height'] = data['Height'].fillna(data['Height'].mean())\n",
    "\n",
    "    data['Internal Memory'] = data['Internal Memory'].apply(for_Internal_Memory)\n",
    "\n",
    "    data['Pixel Density'] = data['Pixel Density'].apply(for_integer)\n",
    "\n",
    "    data['Internal Memory'] = data['Internal Memory'].fillna(data['Internal Memory'].median())\n",
    "    data['Internal Memory'] = data['Internal Memory'].astype(int)\n",
    "\n",
    "    data['RAM'] = data['RAM'].apply(for_integer)\n",
    "    data['RAM'] = data['RAM'].fillna(data['RAM'].median())\n",
    "    data['RAM'] = data['RAM'].astype(int)\n",
    "\n",
    "    data['Resolution'] = data['Resolution'].apply(for_integer)\n",
    "    data['Resolution'] = data['Resolution'].fillna(data['Resolution'].median())\n",
    "    data['Resolution'] = data['Resolution'].astype(int)\n",
    "\n",
    "    data['Screen Size'] = data['Screen Size'].apply(for_float)\n",
    "\n",
    "    data['Thickness'] = data['Thickness'].apply(for_float)\n",
    "    data['Thickness'] = data['Thickness'].fillna(data['Thickness'].mean())\n",
    "    data['Thickness'] = data['Thickness'].round(2)\n",
    "\n",
    "    data['Type'] = data['Type'].fillna('Li-Polymer')\n",
    "\n",
    "    data['Screen to Body Ratio (calculated)'] = data['Screen to Body Ratio (calculated)'].apply(for_float)\n",
    "    data['Screen to Body Ratio (calculated)'] = data['Screen to Body Ratio (calculated)'].fillna(data['Screen to Body Ratio (calculated)'].mean())\n",
    "    data['Screen to Body Ratio (calculated)'] = data['Screen to Body Ratio (calculated)'].round(2)\n",
    "\n",
    "    data['Width'] = data['Width'].apply(for_float)\n",
    "    data['Width'] = data['Width'].fillna(data['Width'].mean())\n",
    "    data['Width'] = data['Width'].round(2)\n",
    "\n",
    "    data['Flash'][data['Flash'].isna() == True] = \"Other\"\n",
    "\n",
    "    data['User Replaceable'][data['User Replaceable'].isna() == True] = \"Other\"\n",
    "\n",
    "    data['Num_cores'] = data['Processor'].apply(for_string)\n",
    "    data['Num_cores'][data['Num_cores'].isna() == True] = \"Other\"\n",
    "\n",
    "\n",
    "    data['Processor_frequency'] = data['Processor'].apply(find_freq)\n",
    "    #because there is one entry with 208MHz values, to convert it to GHz\n",
    "    data['Processor_frequency'][data['Processor_frequency'] > 200] = 0.208\n",
    "    data['Processor_frequency'] = data['Processor_frequency'].fillna(data['Processor_frequency'].mean())\n",
    "    data['Processor_frequency'] = data['Processor_frequency'].round(2)\n",
    "\n",
    "    data['Camera Features'][data['Camera Features'].isna() == True] = \"Other\"\n",
    "\n",
    "    #simplifyig Operating System to os_name for simplicity\n",
    "    data['os_name'] = data['Operating System'].apply(for_string)\n",
    "    data['os_name'][data['os_name'].isna() == True] = \"Other\"\n",
    "\n",
    "    data['Sim1'] = data['SIM 1'].apply(for_string)\n",
    "\n",
    "    data['SIM Size'][data['SIM Size'].isna() == True] = \"Other\"\n",
    "\n",
    "    data['Image Resolution'][data['Image Resolution'].isna() == True] = \"Other\"\n",
    "\n",
    "    data['Fingerprint Sensor'][data['Fingerprint Sensor'].isna() == True] = \"Other\"\n",
    "\n",
    "    data['Expandable Memory'][data['Expandable Memory'].isna() == True] = \"No\"\n",
    "\n",
    "    data['Weight'] = data['Weight'].apply(for_integer)\n",
    "    data['Weight'] = data['Weight'].fillna(data['Weight'].mean())\n",
    "    data['Weight'] = data['Weight'].astype(int)\n",
    "\n",
    "    data['SIM 2'] = data['SIM 2'].apply(for_string)\n",
    "    data['SIM 2'][data['SIM 2'].isna() == True] = \"Other\"\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train: 341\n",
      "Number of features in train: 51\n",
      "Number of data points in test: 119\n",
      "Number of features in test: 50\n"
     ]
    }
   ],
   "source": [
    "train = data_clean_2(train)\n",
    "test = data_clean_2(test)\n",
    "\n",
    "# check the number of features and data points in train\n",
    "print(\"Number of data points in train: %d\" % train.shape[0])\n",
    "print(\"Number of features in train: %d\" % train.shape[1])\n",
    "\n",
    "# check the number of features and data points in test\n",
    "print(\"Number of data points in test: %d\" % test.shape[0])\n",
    "print(\"Number of features in test: %d\" % test.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_clean_3(x):\n",
    "    \n",
    "    data = x.copy()\n",
    "\n",
    "    columns_to_remove = ['User Available Storage','SIM Size','Chipset','Processor','Autofocus','Aspect Ratio','Touch Screen',\n",
    "                        'Bezel-less display','Operating System','SIM 1','USB Connectivity','Other Sensors','Graphics','FM Radio',\n",
    "                        'NFC','Shooting Modes','Browser','Display Colour' ]\n",
    "\n",
    "    columns_to_retain = list(set(data.columns)-set(columns_to_remove))\n",
    "    data = data[columns_to_retain]\n",
    "\n",
    "\n",
    "    columns_to_remove = [ 'Screen Resolution','User Replaceable','Camera Features',\n",
    "                        'Thickness', 'Display Type']\n",
    "\n",
    "    columns_to_retain = list(set(data.columns)-set(columns_to_remove))\n",
    "    data = data[columns_to_retain]\n",
    "\n",
    "\n",
    "    columns_to_remove = ['Fingerprint Sensor', 'Flash', 'Rating Count', 'Review Count','Image Resolution','Type','Expandable Memory',\\\n",
    "                        'Colours','Width','Model']\n",
    "    columns_to_retain = list(set(data.columns)-set(columns_to_remove))\n",
    "    data = data[columns_to_retain]\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train: 341\n",
      "Number of features in train: 18\n",
      "Number of data points in test: 119\n",
      "Number of features in test: 17\n"
     ]
    }
   ],
   "source": [
    "train = data_clean_3(train)\n",
    "test = data_clean_3(test)\n",
    "\n",
    "# check the number of features and data points in train\n",
    "print(\"Number of data points in train: %d\" % train.shape[0])\n",
    "print(\"Number of features in train: %d\" % train.shape[1])\n",
    "\n",
    "# check the number of features and data points in test\n",
    "print(\"Number of data points in test: %d\" % test.shape[0])\n",
    "print(\"Number of features in test: %d\" % test.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(460, 17)\n",
      "Index(['PhoneId', 'Screen Size', 'SIM Slot(s)', 'RAM', 'Resolution',\n",
      "       'Num_cores', 'Brand', 'Capacity', 'Sim1', 'Processor_frequency',\n",
      "       'Pixel Density', 'Weight', 'os_name', 'SIM 2', 'Internal Memory',\n",
      "       'Screen to Body Ratio (calculated)', 'Height'],\n",
      "      dtype='object')\n",
      "(460, 87)\n",
      "Index(['PhoneId', 'Screen Size', 'RAM', 'Resolution', 'Capacity',\n",
      "       'Processor_frequency', 'Pixel Density', 'Weight', 'Internal Memory',\n",
      "       'Screen to Body Ratio (calculated)', 'Height',\n",
      "       'SIM Slot(s)_Dual SIM, GSM+CDMA', 'SIM Slot(s)_Dual SIM, GSM+GSM',\n",
      "       'SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE',\n",
      "       'SIM Slot(s)_Single SIM, GSM', 'Num_cores_312', 'Num_cores_Deca',\n",
      "       'Num_cores_Dual', 'Num_cores_Hexa', 'Num_cores_Octa', 'Num_cores_Other',\n",
      "       'Num_cores_Quad', 'Num_cores_Tru-Octa', 'Brand_10.or', 'Brand_Apple',\n",
      "       'Brand_Asus', 'Brand_Billion', 'Brand_Blackberry', 'Brand_Comio',\n",
      "       'Brand_Coolpad', 'Brand_Do', 'Brand_Gionee', 'Brand_Google',\n",
      "       'Brand_HTC', 'Brand_Honor', 'Brand_Huawei', 'Brand_InFocus',\n",
      "       'Brand_Infinix', 'Brand_Intex', 'Brand_Itel', 'Brand_Jivi',\n",
      "       'Brand_Karbonn', 'Brand_LG', 'Brand_Lava', 'Brand_LeEco',\n",
      "       'Brand_Lenovo', 'Brand_Lephone', 'Brand_Lyf', 'Brand_Meizu',\n",
      "       'Brand_Micromax', 'Brand_Mobiistar', 'Brand_Moto', 'Brand_Motorola',\n",
      "       'Brand_Nokia', 'Brand_Nubia', 'Brand_OPPO', 'Brand_OnePlus',\n",
      "       'Brand_Oppo', 'Brand_Panasonic', 'Brand_Razer', 'Brand_Realme',\n",
      "       'Brand_Reliance', 'Brand_Samsung', 'Brand_Sony', 'Brand_Spice',\n",
      "       'Brand_Tecno', 'Brand_Ulefone', 'Brand_VOTO', 'Brand_Vivo',\n",
      "       'Brand_Xiaomi', 'Brand_Xiaomi Poco', 'Brand_Yu', 'Brand_iVooMi',\n",
      "       'Sim1_2G', 'Sim1_3G', 'Sim1_4G', 'os_name_Android',\n",
      "       'os_name_Blackberry', 'os_name_KAI', 'os_name_Nokia', 'os_name_Other',\n",
      "       'os_name_Tizen', 'os_name_iOS', 'SIM 2_2G', 'SIM 2_3G', 'SIM 2_4G',\n",
      "       'SIM 2_Other'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# one hot encoding\n",
    "\n",
    "train_ids = train['PhoneId']\n",
    "test_ids = test['PhoneId']\n",
    "\n",
    "cols = list(test.columns)\n",
    "cols.remove('PhoneId')\n",
    "cols.insert(0, 'PhoneId')\n",
    "\n",
    "combined = pd.concat([train.drop('Rating', axis=1)[cols], test[cols]])\n",
    "print(combined.shape)\n",
    "print(combined.columns)\n",
    "#combined = combined.drop(['Brand'],axis=1)\n",
    "combined = pd.get_dummies(combined)\n",
    "print(combined.shape)\n",
    "print(combined.columns)\n",
    "\n",
    "train_new = combined[combined['PhoneId'].isin(train_ids)]\n",
    "test_new = combined[combined['PhoneId'].isin(test_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_new = train_new.merge(train[['PhoneId', 'Rating']], on='PhoneId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points in train: 341\n",
      "Number of features in train: 88\n",
      "Number of data points in test: 119\n",
      "Number of features in test: 87\n"
     ]
    }
   ],
   "source": [
    "# check the number of features and data points in train\n",
    "print(\"Number of data points in train: %d\" % train_new.shape[0])\n",
    "print(\"Number of features in train: %d\" % train_new.shape[1])\n",
    "\n",
    "# check the number of features and data points in test\n",
    "print(\"Number of data points in test: %d\" % test_new.shape[0])\n",
    "print(\"Number of features in test: %d\" % test_new.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PhoneId</th>\n",
       "      <th>Screen Size</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Processor_frequency</th>\n",
       "      <th>Pixel Density</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Internal Memory</th>\n",
       "      <th>Screen to Body Ratio (calculated)</th>\n",
       "      <th>Height</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
       "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
       "      <th>Num_cores_312</th>\n",
       "      <th>Num_cores_Deca</th>\n",
       "      <th>Num_cores_Dual</th>\n",
       "      <th>Num_cores_Hexa</th>\n",
       "      <th>Num_cores_Octa</th>\n",
       "      <th>Num_cores_Other</th>\n",
       "      <th>Num_cores_Quad</th>\n",
       "      <th>Num_cores_Tru-Octa</th>\n",
       "      <th>Brand_10.or</th>\n",
       "      <th>Brand_Apple</th>\n",
       "      <th>Brand_Asus</th>\n",
       "      <th>Brand_Billion</th>\n",
       "      <th>Brand_Blackberry</th>\n",
       "      <th>Brand_Comio</th>\n",
       "      <th>Brand_Coolpad</th>\n",
       "      <th>Brand_Do</th>\n",
       "      <th>Brand_Gionee</th>\n",
       "      <th>Brand_Google</th>\n",
       "      <th>Brand_HTC</th>\n",
       "      <th>Brand_Honor</th>\n",
       "      <th>Brand_Huawei</th>\n",
       "      <th>Brand_InFocus</th>\n",
       "      <th>Brand_Infinix</th>\n",
       "      <th>Brand_Intex</th>\n",
       "      <th>Brand_Itel</th>\n",
       "      <th>...</th>\n",
       "      <th>Brand_Meizu</th>\n",
       "      <th>Brand_Micromax</th>\n",
       "      <th>Brand_Mobiistar</th>\n",
       "      <th>Brand_Moto</th>\n",
       "      <th>Brand_Motorola</th>\n",
       "      <th>Brand_Nokia</th>\n",
       "      <th>Brand_Nubia</th>\n",
       "      <th>Brand_OPPO</th>\n",
       "      <th>Brand_OnePlus</th>\n",
       "      <th>Brand_Oppo</th>\n",
       "      <th>Brand_Panasonic</th>\n",
       "      <th>Brand_Razer</th>\n",
       "      <th>Brand_Realme</th>\n",
       "      <th>Brand_Reliance</th>\n",
       "      <th>Brand_Samsung</th>\n",
       "      <th>Brand_Sony</th>\n",
       "      <th>Brand_Spice</th>\n",
       "      <th>Brand_Tecno</th>\n",
       "      <th>Brand_Ulefone</th>\n",
       "      <th>Brand_VOTO</th>\n",
       "      <th>Brand_Vivo</th>\n",
       "      <th>Brand_Xiaomi</th>\n",
       "      <th>Brand_Xiaomi Poco</th>\n",
       "      <th>Brand_Yu</th>\n",
       "      <th>Brand_iVooMi</th>\n",
       "      <th>Sim1_2G</th>\n",
       "      <th>Sim1_3G</th>\n",
       "      <th>Sim1_4G</th>\n",
       "      <th>os_name_Android</th>\n",
       "      <th>os_name_Blackberry</th>\n",
       "      <th>os_name_KAI</th>\n",
       "      <th>os_name_Nokia</th>\n",
       "      <th>os_name_Other</th>\n",
       "      <th>os_name_Tizen</th>\n",
       "      <th>os_name_iOS</th>\n",
       "      <th>SIM 2_2G</th>\n",
       "      <th>SIM 2_3G</th>\n",
       "      <th>SIM 2_4G</th>\n",
       "      <th>SIM 2_Other</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>6.26</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>4000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>403</td>\n",
       "      <td>182</td>\n",
       "      <td>64</td>\n",
       "      <td>80.68</td>\n",
       "      <td>157.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6.20</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>4230</td>\n",
       "      <td>1.8</td>\n",
       "      <td>271</td>\n",
       "      <td>168</td>\n",
       "      <td>32</td>\n",
       "      <td>80.85</td>\n",
       "      <td>156.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6.30</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>3500</td>\n",
       "      <td>2.1</td>\n",
       "      <td>409</td>\n",
       "      <td>168</td>\n",
       "      <td>32</td>\n",
       "      <td>83.68</td>\n",
       "      <td>157.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6.00</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>3300</td>\n",
       "      <td>2.2</td>\n",
       "      <td>411</td>\n",
       "      <td>169</td>\n",
       "      <td>64</td>\n",
       "      <td>74.78</td>\n",
       "      <td>159.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6.50</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>3750</td>\n",
       "      <td>2.2</td>\n",
       "      <td>396</td>\n",
       "      <td>175</td>\n",
       "      <td>64</td>\n",
       "      <td>84.23</td>\n",
       "      <td>160.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhoneId  Screen Size  RAM   ...    SIM 2_4G  SIM 2_Other  Rating\n",
       "0        0         6.26    4   ...           1            0     4.5\n",
       "1        1         6.20    3   ...           1            0     4.5\n",
       "2        2         6.30    3   ...           1            0     4.4\n",
       "3        4         6.00    4   ...           1            0     4.3\n",
       "4        5         6.50    4   ...           1            0     4.4\n",
       "\n",
       "[5 rows x 88 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PhoneId</th>\n",
       "      <th>Screen Size</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Processor_frequency</th>\n",
       "      <th>Pixel Density</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Internal Memory</th>\n",
       "      <th>Screen to Body Ratio (calculated)</th>\n",
       "      <th>Height</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
       "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
       "      <th>Num_cores_312</th>\n",
       "      <th>Num_cores_Deca</th>\n",
       "      <th>Num_cores_Dual</th>\n",
       "      <th>Num_cores_Hexa</th>\n",
       "      <th>Num_cores_Octa</th>\n",
       "      <th>Num_cores_Other</th>\n",
       "      <th>Num_cores_Quad</th>\n",
       "      <th>Num_cores_Tru-Octa</th>\n",
       "      <th>Brand_10.or</th>\n",
       "      <th>Brand_Apple</th>\n",
       "      <th>Brand_Asus</th>\n",
       "      <th>Brand_Billion</th>\n",
       "      <th>Brand_Blackberry</th>\n",
       "      <th>Brand_Comio</th>\n",
       "      <th>Brand_Coolpad</th>\n",
       "      <th>Brand_Do</th>\n",
       "      <th>Brand_Gionee</th>\n",
       "      <th>Brand_Google</th>\n",
       "      <th>Brand_HTC</th>\n",
       "      <th>Brand_Honor</th>\n",
       "      <th>Brand_Huawei</th>\n",
       "      <th>Brand_InFocus</th>\n",
       "      <th>Brand_Infinix</th>\n",
       "      <th>Brand_Intex</th>\n",
       "      <th>Brand_Itel</th>\n",
       "      <th>...</th>\n",
       "      <th>Brand_Lyf</th>\n",
       "      <th>Brand_Meizu</th>\n",
       "      <th>Brand_Micromax</th>\n",
       "      <th>Brand_Mobiistar</th>\n",
       "      <th>Brand_Moto</th>\n",
       "      <th>Brand_Motorola</th>\n",
       "      <th>Brand_Nokia</th>\n",
       "      <th>Brand_Nubia</th>\n",
       "      <th>Brand_OPPO</th>\n",
       "      <th>Brand_OnePlus</th>\n",
       "      <th>Brand_Oppo</th>\n",
       "      <th>Brand_Panasonic</th>\n",
       "      <th>Brand_Razer</th>\n",
       "      <th>Brand_Realme</th>\n",
       "      <th>Brand_Reliance</th>\n",
       "      <th>Brand_Samsung</th>\n",
       "      <th>Brand_Sony</th>\n",
       "      <th>Brand_Spice</th>\n",
       "      <th>Brand_Tecno</th>\n",
       "      <th>Brand_Ulefone</th>\n",
       "      <th>Brand_VOTO</th>\n",
       "      <th>Brand_Vivo</th>\n",
       "      <th>Brand_Xiaomi</th>\n",
       "      <th>Brand_Xiaomi Poco</th>\n",
       "      <th>Brand_Yu</th>\n",
       "      <th>Brand_iVooMi</th>\n",
       "      <th>Sim1_2G</th>\n",
       "      <th>Sim1_3G</th>\n",
       "      <th>Sim1_4G</th>\n",
       "      <th>os_name_Android</th>\n",
       "      <th>os_name_Blackberry</th>\n",
       "      <th>os_name_KAI</th>\n",
       "      <th>os_name_Nokia</th>\n",
       "      <th>os_name_Other</th>\n",
       "      <th>os_name_Tizen</th>\n",
       "      <th>os_name_iOS</th>\n",
       "      <th>SIM 2_2G</th>\n",
       "      <th>SIM 2_3G</th>\n",
       "      <th>SIM 2_4G</th>\n",
       "      <th>SIM 2_Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>6.20</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4230</td>\n",
       "      <td>1.8</td>\n",
       "      <td>271</td>\n",
       "      <td>168</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>6.20</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>402</td>\n",
       "      <td>205</td>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>6.30</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>3500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>409</td>\n",
       "      <td>169</td>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>6.30</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>3500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>409</td>\n",
       "      <td>169</td>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>156.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>5.99</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>4000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>403</td>\n",
       "      <td>181</td>\n",
       "      <td>64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhoneId  Screen Size  RAM     ...       SIM 2_3G  SIM 2_4G  SIM 2_Other\n",
       "0        3         6.20    2     ...              0         1            0\n",
       "1       11         6.20    4     ...              0         1            0\n",
       "2       13         6.30    6     ...              0         1            0\n",
       "3       16         6.30    4     ...              0         1            0\n",
       "4       19         5.99    4     ...              0         1            0\n",
       "\n",
       "[5 rows x 87 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create staging DataFrames**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create X_train_new which will hold all columns except PhoneID and Rating using train_new\n",
    "# Create Y_train_new which will only hold the Rating column present in train_new, note that the dataframe maintains integrity of the PhoneID \n",
    "# which is very essential\n",
    "\n",
    "# Create X_test_new which will hold all columns except PhoneID\n",
    "# There is no Y_test_new for obvious reasons as this is what you will be predicting \n",
    "\n",
    "X_train_new = train_new.drop(['PhoneId','Rating'],axis=1)\n",
    "Y_train_new_rating_discrete = train_new['Rating'] \n",
    "Y_train_new_rating_binary   = train_new['Rating'].map(lambda x: 1 if x >= 4 else 0)\n",
    "\n",
    "X_test_new = test_new.drop(['PhoneId'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Perform a decribe function on X_train_new and X_test_new to check std, mean and other parameters which will help you understand the data better in terms of dummy variables, null values, possible outliers etc.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Screen Size</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Processor_frequency</th>\n",
       "      <th>Pixel Density</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Internal Memory</th>\n",
       "      <th>Screen to Body Ratio (calculated)</th>\n",
       "      <th>Height</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
       "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
       "      <th>Num_cores_312</th>\n",
       "      <th>Num_cores_Deca</th>\n",
       "      <th>Num_cores_Dual</th>\n",
       "      <th>Num_cores_Hexa</th>\n",
       "      <th>Num_cores_Octa</th>\n",
       "      <th>Num_cores_Other</th>\n",
       "      <th>Num_cores_Quad</th>\n",
       "      <th>Num_cores_Tru-Octa</th>\n",
       "      <th>Brand_10.or</th>\n",
       "      <th>Brand_Apple</th>\n",
       "      <th>Brand_Asus</th>\n",
       "      <th>Brand_Billion</th>\n",
       "      <th>Brand_Blackberry</th>\n",
       "      <th>Brand_Comio</th>\n",
       "      <th>Brand_Coolpad</th>\n",
       "      <th>Brand_Do</th>\n",
       "      <th>Brand_Gionee</th>\n",
       "      <th>Brand_Google</th>\n",
       "      <th>Brand_HTC</th>\n",
       "      <th>Brand_Honor</th>\n",
       "      <th>Brand_Huawei</th>\n",
       "      <th>Brand_InFocus</th>\n",
       "      <th>Brand_Infinix</th>\n",
       "      <th>Brand_Intex</th>\n",
       "      <th>Brand_Itel</th>\n",
       "      <th>Brand_Jivi</th>\n",
       "      <th>...</th>\n",
       "      <th>Brand_Lyf</th>\n",
       "      <th>Brand_Meizu</th>\n",
       "      <th>Brand_Micromax</th>\n",
       "      <th>Brand_Mobiistar</th>\n",
       "      <th>Brand_Moto</th>\n",
       "      <th>Brand_Motorola</th>\n",
       "      <th>Brand_Nokia</th>\n",
       "      <th>Brand_Nubia</th>\n",
       "      <th>Brand_OPPO</th>\n",
       "      <th>Brand_OnePlus</th>\n",
       "      <th>Brand_Oppo</th>\n",
       "      <th>Brand_Panasonic</th>\n",
       "      <th>Brand_Razer</th>\n",
       "      <th>Brand_Realme</th>\n",
       "      <th>Brand_Reliance</th>\n",
       "      <th>Brand_Samsung</th>\n",
       "      <th>Brand_Sony</th>\n",
       "      <th>Brand_Spice</th>\n",
       "      <th>Brand_Tecno</th>\n",
       "      <th>Brand_Ulefone</th>\n",
       "      <th>Brand_VOTO</th>\n",
       "      <th>Brand_Vivo</th>\n",
       "      <th>Brand_Xiaomi</th>\n",
       "      <th>Brand_Xiaomi Poco</th>\n",
       "      <th>Brand_Yu</th>\n",
       "      <th>Brand_iVooMi</th>\n",
       "      <th>Sim1_2G</th>\n",
       "      <th>Sim1_3G</th>\n",
       "      <th>Sim1_4G</th>\n",
       "      <th>os_name_Android</th>\n",
       "      <th>os_name_Blackberry</th>\n",
       "      <th>os_name_KAI</th>\n",
       "      <th>os_name_Nokia</th>\n",
       "      <th>os_name_Other</th>\n",
       "      <th>os_name_Tizen</th>\n",
       "      <th>os_name_iOS</th>\n",
       "      <th>SIM 2_2G</th>\n",
       "      <th>SIM 2_3G</th>\n",
       "      <th>SIM 2_4G</th>\n",
       "      <th>SIM 2_Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.0</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.0</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.0</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>341.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.462317</td>\n",
       "      <td>11.120235</td>\n",
       "      <td>9.592375</td>\n",
       "      <td>3191.307918</td>\n",
       "      <td>1.792551</td>\n",
       "      <td>344.211144</td>\n",
       "      <td>161.343109</td>\n",
       "      <td>46.369501</td>\n",
       "      <td>72.353343</td>\n",
       "      <td>149.946394</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.759531</td>\n",
       "      <td>0.152493</td>\n",
       "      <td>0.085044</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.029326</td>\n",
       "      <td>0.035191</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>0.017595</td>\n",
       "      <td>0.325513</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.055718</td>\n",
       "      <td>0.023460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014663</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029326</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.017595</td>\n",
       "      <td>0.049853</td>\n",
       "      <td>0.017595</td>\n",
       "      <td>0.008798</td>\n",
       "      <td>0.008798</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.043988</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.043988</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.061584</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.041056</td>\n",
       "      <td>0.008798</td>\n",
       "      <td>0.017595</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.129032</td>\n",
       "      <td>0.026393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023460</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.076246</td>\n",
       "      <td>0.061584</td>\n",
       "      <td>0.008798</td>\n",
       "      <td>0.011730</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.958944</td>\n",
       "      <td>0.906158</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.005865</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.055718</td>\n",
       "      <td>0.211144</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.683284</td>\n",
       "      <td>0.085044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.742529</td>\n",
       "      <td>61.320855</td>\n",
       "      <td>5.953827</td>\n",
       "      <td>932.533527</td>\n",
       "      <td>0.451598</td>\n",
       "      <td>88.161129</td>\n",
       "      <td>22.305556</td>\n",
       "      <td>46.435453</td>\n",
       "      <td>9.357839</td>\n",
       "      <td>9.192305</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.427996</td>\n",
       "      <td>0.360026</td>\n",
       "      <td>0.279357</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.076471</td>\n",
       "      <td>0.168965</td>\n",
       "      <td>0.184532</td>\n",
       "      <td>0.494179</td>\n",
       "      <td>0.131668</td>\n",
       "      <td>0.469255</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.076471</td>\n",
       "      <td>0.229714</td>\n",
       "      <td>0.151583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.120375</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.076471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.168965</td>\n",
       "      <td>0.107827</td>\n",
       "      <td>0.131668</td>\n",
       "      <td>0.217962</td>\n",
       "      <td>0.131668</td>\n",
       "      <td>0.093520</td>\n",
       "      <td>0.093520</td>\n",
       "      <td>0.107827</td>\n",
       "      <td>0.107827</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.076471</td>\n",
       "      <td>0.205370</td>\n",
       "      <td>0.107827</td>\n",
       "      <td>0.205370</td>\n",
       "      <td>0.076471</td>\n",
       "      <td>0.240751</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.198711</td>\n",
       "      <td>0.093520</td>\n",
       "      <td>0.131668</td>\n",
       "      <td>0.107827</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.107827</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.335728</td>\n",
       "      <td>0.160536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.151583</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.265782</td>\n",
       "      <td>0.240751</td>\n",
       "      <td>0.093520</td>\n",
       "      <td>0.107827</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.142006</td>\n",
       "      <td>0.142006</td>\n",
       "      <td>0.198711</td>\n",
       "      <td>0.292037</td>\n",
       "      <td>0.076471</td>\n",
       "      <td>0.076471</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.142006</td>\n",
       "      <td>0.054153</td>\n",
       "      <td>0.229714</td>\n",
       "      <td>0.408720</td>\n",
       "      <td>0.142006</td>\n",
       "      <td>0.465879</td>\n",
       "      <td>0.279357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1020.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>115.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2900.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>68.800000</td>\n",
       "      <td>146.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3055.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>151.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>3750.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>403.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>77.480000</td>\n",
       "      <td>156.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.590000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>13000.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>87.660000</td>\n",
       "      <td>174.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Screen Size         RAM     ...         SIM 2_4G  SIM 2_Other\n",
       "count   341.000000  341.000000     ...       341.000000   341.000000\n",
       "mean      5.462317   11.120235     ...         0.683284     0.085044\n",
       "std       0.742529   61.320855     ...         0.465879     0.279357\n",
       "min       2.400000    1.000000     ...         0.000000     0.000000\n",
       "25%       5.200000    2.000000     ...         0.000000     0.000000\n",
       "50%       5.500000    3.000000     ...         1.000000     0.000000\n",
       "75%       6.000000    4.000000     ...         1.000000     0.000000\n",
       "max       6.590000  512.000000     ...         1.000000     1.000000\n",
       "\n",
       "[8 rows x 86 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_new.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Screen Size</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Processor_frequency</th>\n",
       "      <th>Pixel Density</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Internal Memory</th>\n",
       "      <th>Screen to Body Ratio (calculated)</th>\n",
       "      <th>Height</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+CDMA</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM</th>\n",
       "      <th>SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE</th>\n",
       "      <th>SIM Slot(s)_Single SIM, GSM</th>\n",
       "      <th>Num_cores_312</th>\n",
       "      <th>Num_cores_Deca</th>\n",
       "      <th>Num_cores_Dual</th>\n",
       "      <th>Num_cores_Hexa</th>\n",
       "      <th>Num_cores_Octa</th>\n",
       "      <th>Num_cores_Other</th>\n",
       "      <th>Num_cores_Quad</th>\n",
       "      <th>Num_cores_Tru-Octa</th>\n",
       "      <th>Brand_10.or</th>\n",
       "      <th>Brand_Apple</th>\n",
       "      <th>Brand_Asus</th>\n",
       "      <th>Brand_Billion</th>\n",
       "      <th>Brand_Blackberry</th>\n",
       "      <th>Brand_Comio</th>\n",
       "      <th>Brand_Coolpad</th>\n",
       "      <th>Brand_Do</th>\n",
       "      <th>Brand_Gionee</th>\n",
       "      <th>Brand_Google</th>\n",
       "      <th>Brand_HTC</th>\n",
       "      <th>Brand_Honor</th>\n",
       "      <th>Brand_Huawei</th>\n",
       "      <th>Brand_InFocus</th>\n",
       "      <th>Brand_Infinix</th>\n",
       "      <th>Brand_Intex</th>\n",
       "      <th>Brand_Itel</th>\n",
       "      <th>Brand_Jivi</th>\n",
       "      <th>...</th>\n",
       "      <th>Brand_Lyf</th>\n",
       "      <th>Brand_Meizu</th>\n",
       "      <th>Brand_Micromax</th>\n",
       "      <th>Brand_Mobiistar</th>\n",
       "      <th>Brand_Moto</th>\n",
       "      <th>Brand_Motorola</th>\n",
       "      <th>Brand_Nokia</th>\n",
       "      <th>Brand_Nubia</th>\n",
       "      <th>Brand_OPPO</th>\n",
       "      <th>Brand_OnePlus</th>\n",
       "      <th>Brand_Oppo</th>\n",
       "      <th>Brand_Panasonic</th>\n",
       "      <th>Brand_Razer</th>\n",
       "      <th>Brand_Realme</th>\n",
       "      <th>Brand_Reliance</th>\n",
       "      <th>Brand_Samsung</th>\n",
       "      <th>Brand_Sony</th>\n",
       "      <th>Brand_Spice</th>\n",
       "      <th>Brand_Tecno</th>\n",
       "      <th>Brand_Ulefone</th>\n",
       "      <th>Brand_VOTO</th>\n",
       "      <th>Brand_Vivo</th>\n",
       "      <th>Brand_Xiaomi</th>\n",
       "      <th>Brand_Xiaomi Poco</th>\n",
       "      <th>Brand_Yu</th>\n",
       "      <th>Brand_iVooMi</th>\n",
       "      <th>Sim1_2G</th>\n",
       "      <th>Sim1_3G</th>\n",
       "      <th>Sim1_4G</th>\n",
       "      <th>os_name_Android</th>\n",
       "      <th>os_name_Blackberry</th>\n",
       "      <th>os_name_KAI</th>\n",
       "      <th>os_name_Nokia</th>\n",
       "      <th>os_name_Other</th>\n",
       "      <th>os_name_Tizen</th>\n",
       "      <th>os_name_iOS</th>\n",
       "      <th>SIM 2_2G</th>\n",
       "      <th>SIM 2_3G</th>\n",
       "      <th>SIM 2_4G</th>\n",
       "      <th>SIM 2_Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.0</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>119.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.463445</td>\n",
       "      <td>11.983193</td>\n",
       "      <td>9.294118</td>\n",
       "      <td>3233.764706</td>\n",
       "      <td>1.768403</td>\n",
       "      <td>334.731092</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>48.336134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.277679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.789916</td>\n",
       "      <td>0.184874</td>\n",
       "      <td>0.025210</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.630252</td>\n",
       "      <td>0.025210</td>\n",
       "      <td>0.319328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.050420</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.025210</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.033613</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>0.050420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.042017</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>0.042017</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.033613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.050420</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>0.025210</td>\n",
       "      <td>0.033613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.184874</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.016807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.067227</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.025210</td>\n",
       "      <td>0.042017</td>\n",
       "      <td>0.932773</td>\n",
       "      <td>0.957983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.033613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008403</td>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.025210</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.025210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.759304</td>\n",
       "      <td>65.670335</td>\n",
       "      <td>5.618301</td>\n",
       "      <td>772.097887</td>\n",
       "      <td>0.471133</td>\n",
       "      <td>102.821278</td>\n",
       "      <td>16.698726</td>\n",
       "      <td>49.842001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.516004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.389837</td>\n",
       "      <td>0.157426</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129090</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.484778</td>\n",
       "      <td>0.157426</td>\n",
       "      <td>0.468188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.219736</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.157426</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.180994</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.129090</td>\n",
       "      <td>0.219736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129090</td>\n",
       "      <td>0.129090</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.201476</td>\n",
       "      <td>0.129090</td>\n",
       "      <td>0.201476</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.180994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.219736</td>\n",
       "      <td>0.129090</td>\n",
       "      <td>0.157426</td>\n",
       "      <td>0.180994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.389837</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.129090</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.251473</td>\n",
       "      <td>0.236289</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.157426</td>\n",
       "      <td>0.201476</td>\n",
       "      <td>0.251473</td>\n",
       "      <td>0.201476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.425976</td>\n",
       "      <td>0.157426</td>\n",
       "      <td>0.453664</td>\n",
       "      <td>0.157426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1020.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>269.500000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146.550000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3050.000000</td>\n",
       "      <td>1.770000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151.700000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.995000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>3725.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>402.000000</td>\n",
       "      <td>173.500000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>155.900000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>512.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>2.960000</td>\n",
       "      <td>745.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>173.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Screen Size         RAM     ...         SIM 2_4G  SIM 2_Other\n",
       "count   119.000000  119.000000     ...       119.000000   119.000000\n",
       "mean      5.463445   11.983193     ...         0.714286     0.025210\n",
       "std       0.759304   65.670335     ...         0.453664     0.157426\n",
       "min       1.800000    1.000000     ...         0.000000     0.000000\n",
       "25%       5.100000    2.000000     ...         0.000000     0.000000\n",
       "50%       5.500000    3.000000     ...         1.000000     0.000000\n",
       "75%       5.995000    4.000000     ...         1.000000     0.000000\n",
       "max       6.500000  512.000000     ...         1.000000     1.000000\n",
       "\n",
       "[8 rows x 86 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_new.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Understanding Correlation***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Screen Size</th>\n",
       "      <th>RAM</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Capacity</th>\n",
       "      <th>Processor_frequency</th>\n",
       "      <th>Pixel Density</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Internal Memory</th>\n",
       "      <th>Screen to Body Ratio (calculated)</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.26</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>4000</td>\n",
       "      <td>1.8</td>\n",
       "      <td>403</td>\n",
       "      <td>182</td>\n",
       "      <td>64</td>\n",
       "      <td>80.68</td>\n",
       "      <td>157.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.20</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>4230</td>\n",
       "      <td>1.8</td>\n",
       "      <td>271</td>\n",
       "      <td>168</td>\n",
       "      <td>32</td>\n",
       "      <td>80.85</td>\n",
       "      <td>156.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.30</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>3500</td>\n",
       "      <td>2.1</td>\n",
       "      <td>409</td>\n",
       "      <td>168</td>\n",
       "      <td>32</td>\n",
       "      <td>83.68</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.00</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>3300</td>\n",
       "      <td>2.2</td>\n",
       "      <td>411</td>\n",
       "      <td>169</td>\n",
       "      <td>64</td>\n",
       "      <td>74.78</td>\n",
       "      <td>159.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.50</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>3750</td>\n",
       "      <td>2.2</td>\n",
       "      <td>396</td>\n",
       "      <td>175</td>\n",
       "      <td>64</td>\n",
       "      <td>84.23</td>\n",
       "      <td>160.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Screen Size  RAM   ...    Screen to Body Ratio (calculated)  Height\n",
       "0         6.26    4   ...                                80.68   157.9\n",
       "1         6.20    3   ...                                80.85   156.2\n",
       "2         6.30    3   ...                                83.68   157.0\n",
       "3         6.00    4   ...                                74.78   159.8\n",
       "4         6.50    4   ...                                84.23   160.4\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for correlation only for the first 10 discrete variables\n",
    "X_train_corr = X_train_new.iloc[:,[0,1,2,3,4,5,6,7,8,9]]\n",
    "X_train_corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Weight</td>\n",
       "      <td>Height</td>\n",
       "      <td>0.642595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Height</td>\n",
       "      <td>Weight</td>\n",
       "      <td>0.642595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Capacity</td>\n",
       "      <td>Weight</td>\n",
       "      <td>0.687891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Weight</td>\n",
       "      <td>Capacity</td>\n",
       "      <td>0.687891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Screen to Body Ratio (calculated)</td>\n",
       "      <td>Height</td>\n",
       "      <td>0.744119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Height</td>\n",
       "      <td>Screen to Body Ratio (calculated)</td>\n",
       "      <td>0.744119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Screen Size</td>\n",
       "      <td>Height</td>\n",
       "      <td>0.861143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Height</td>\n",
       "      <td>Screen Size</td>\n",
       "      <td>0.861143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Screen to Body Ratio (calculated)</td>\n",
       "      <td>Screen Size</td>\n",
       "      <td>0.930492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Screen Size</td>\n",
       "      <td>Screen to Body Ratio (calculated)</td>\n",
       "      <td>0.930492</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              level_0    ...            0\n",
       "80                             Weight    ...     0.642595\n",
       "81                             Height    ...     0.642595\n",
       "82                           Capacity    ...     0.687891\n",
       "83                             Weight    ...     0.687891\n",
       "84  Screen to Body Ratio (calculated)    ...     0.744119\n",
       "85                             Height    ...     0.744119\n",
       "86                        Screen Size    ...     0.861143\n",
       "87                             Height    ...     0.861143\n",
       "88  Screen to Body Ratio (calculated)    ...     0.930492\n",
       "89                        Screen Size    ...     0.930492\n",
       "\n",
       "[10 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations = X_train_corr.corr().abs().unstack().sort_values(kind=\"quicksort\").reset_index()\n",
    "correlations = correlations[correlations['level_0'] != correlations['level_1']]\n",
    "\n",
    "correlations.tail(10)\n",
    "\n",
    "# The tail of the dataframe has the information about highest correlation between features,\n",
    "# where as the head has details about features that have least correlation\n",
    "\n",
    "# Observe that Screen Size and Screen to Body Ratio are highly correlated, We don't know if they are\n",
    "# positively or negatively correlated yet. We will find that out visually\n",
    "# Similarly Height and Screen Size are correlated.\n",
    "\n",
    "# Intution tells me that they will be positvely correlated, meaning any increase in Height of the \n",
    "# phone will result in an increase of the Screen Size and vice versa. \n",
    "\n",
    "#A bigger phone screen (Screen Size) means the phone is lenghtier (Height). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Visual Interpretation of correlation***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = X_train_new.corr()\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches(20,20)\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(corr,cmap='coolwarm', vmin=-1, vmax=1)\n",
    "fig.colorbar(cax)\n",
    "ticks = np.arange(0,len(X_train_new.columns),1)\n",
    "ax.set_xticks(ticks)\n",
    "plt.xticks(rotation=90)\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_xticklabels(X_train_new.columns)\n",
    "ax.set_yticklabels(X_train_new.columns)\n",
    "plt.show()\n",
    "\n",
    "# We can clearly see that Screen Size is positively correlated with Height and Screen to body Ratio\n",
    "# There are other positively correlated variables too. Another good easy to understand example is \n",
    "# Brand_Apple and os_name_iOS.\n",
    "\n",
    "# Look against the line of 'Brand_Apple' and compare it with all the top columns \n",
    "# It's obvious isn't it that an Apple iPhone / product user will have an iOS operating \n",
    "# system on his/her device. He/she cannot have an Android on his Apple iPhone. \n",
    "# This now introduces to you negative correlation. Notice how Brand_Apple and os_name_Andriod are\n",
    "# negatively correlated (dark blue), which means Apple folks cannot have Android OS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Let's plot some interesting graphs***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Assigning a dataframe to capture visuals on the discrete data, note that how we have two dataframes where the Y_train_new has been desgined to \n",
    "#hold discrete value as well as binary value for the ease of interpretation\n",
    "X_train_visual_rating_binary = pd.concat([X_train_new,Y_train_new_rating_binary],axis=1)\n",
    "X_train_visual_rating_discrete = pd.concat([X_train_new,Y_train_new_rating_discrete],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*RAM vs Capacity - Box Plot¶*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The plot is indicating me that phones with high RAM have larger Capacity values ( for the first few bars atleast ) or vice versa, may be the phones > 16GB have good design where capacity is optimized to a lower value but the trend remains the same. But did the users like the phone ?? Note that there is no correlation between RAM and Capacity so they are good variables/features to decide on if the user would have liked the phone or not.****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1996e4f60>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.barplot(x='RAM',y='Capacity',data=X_train_visual_rating_binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***RAM vs Capacity - Scatter Plot***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> I was not able to derive any inference from this plot for the RAM values of 2 GB to 64 GB as data is too much stacked. But it is very evident that folks with 512 units of RAM have disliked the phone, so that is intellgent data and I cannot treat those users / phones as outliers. It has to be 512 MB of RAM and not GB that's why they have disliked it. We don't have a phone with 512 GB of RAM yet or do we ? Samsung and 1+ , Are you guys listening ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa199633cf8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.scatterplot(x='RAM',y='Capacity',hue='Rating',data=X_train_visual_rating_binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Screen Size vs Pixel Density - Box Plot***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The plot is indicating me that if Screen Size increases then Pixel Density also seems to be increasing ( atleast for majority of the cases ), but its very hard to draw a trend here. In reality we would not like the phone which has large screen size and low pixel density. So what about those phones with Screen Size of 5.34, 6.22 and 5.84 ? Did the users like them ? The correlation heatmap of these two columns is at 0.5 ( balanced )> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa199633240>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.barplot(x='Screen Size',y='Pixel Density',data=X_train_visual_rating_binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> > Screen Size vs Pixel Density - Scatter Plot\n",
    "*Screen Size vs Pixel Density - Scatter Plot*\n",
    "Most of the folks seem to like phones with Screen Size greater than 5 and Pixel Density greater than 300 the bottom left quadrant of points. That quadrant has fewer phones. If you look at the box plot above most of the phones have Screen Size greater than 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa194057cf8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.scatterplot(x='Screen Size',y='Pixel Density',hue='Rating',data=X_train_visual_rating_binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ***Resolution vs Pixel Density - Box Plot***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In reality high resoultion needs more pixles per square block. But it seems like Resolution 7, 13, 25 have similar Pixel Density. There is no linear trend observed. Correlation seems to be < 0.5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa199f4dc88>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.barplot(x='Resolution',y='Pixel Density',data=X_train_visual_rating_binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Resolution vs Pixel Density - Scatter Plot\n",
    "This is a hard scatter plot to interpret, Resolution < 15 and Pixel Density < 325 - take a look at the quadrant. It may have a pattern of dislikes. But again, this is very hard to judge.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa18f4d2470>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.scatterplot(x='Resolution',y='Pixel Density',hue='Rating',data=X_train_visual_rating_binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Internal Memory vs Weight - Scatter Plot**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This is a hard scatter plot to interpret, because the value of Internal Memory is in multiples of 2 we don't really have a good plot. I feel < 32 GB of space and weight range of < 150 have not liked the phone. It actually makes literal sense in todays world. But we cannot go by intituions in real life data driven problems*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa18f43aac8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.scatterplot(x='Internal Memory',y='Weight',hue='Rating',data=X_train_visual_rating_binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Capacity vs Weight - Bar Plot**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> A bigger battery Capacity leads to inrease in its weight, its obvious in the real world and evident in the trend. There are phones with low Capacity numbers and higher weights - Will the users like it ? Let's look at it in our next plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa18f428ac8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "plt.xticks(rotation='vertical')\n",
    "sns.barplot(x='Capacity',y='Weight',data=X_train_visual_rating_binary)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**> **The MP Neuron Model**\n",
    "*Some Key Points which we need to understand with respect to building a model*\n",
    "> 1. The idea is to get the Train accuracy close to Test accuracy so that the model follows a generalized behaviour and I can use it on any type of data that comes in, if at all I happen to deploy it.\n",
    "> 2. The model is very primitive, it only takes binary as inputs and outputs a binary value. We will have to convert the discrete variables to binary to begin with.\n",
    "> 3. There are several ways to do it, after multiple iterations using mean(), median() functions over the features, I decided to go full visual using the help of above scatter plots wherever possible and kept the values to what I feel makes best sense and gives me better accuracy.\n",
    "**Key Factors & Observation for High Accuracy Score**\n",
    "*1. Dealing with correlation -*\n",
    "> I removed one of the columns in the group of columns which were correlated. You can see that I removed Weight, Height, Screen to Body Ratio & Resolution. Removing these columns helped me in increasing the accuracy to a greater extent, having them in my model would have resulted in bias situation where I would have predicted more 1's or more 0's ! I have run the code multiple times in combination logic to see which column arrangement & its values among discrete variables gives me best accuracy. It's time consuming, well so is the model ( primitive )\n",
    "*2. Accounting for the direction using Scatter plot -*\n",
    "> If you see Brand_Apple and os_name_iOS though they are correlated I felt I should not remove one of the columns as the intellegence is lost about the column. In the model where intellegence is the key, I should keep as many columns as I can and this logic worked for me. I would need some experts to vouch as this is still under my scanner if this is the right way. There are discussion in MP Neuron if correlation really matters or not with respect to binary variables.\n",
    "*3. Binary Columns -*\n",
    "> If I were to remove binary columns which are correlated ( Apple_Brand & os_name_iOS ) I obeserved a dip in accuracy.\n",
    "> Evaluate direction of the column using Scatter plot\n",
    "> I will give two examples and the rest you can figure it out by yourself as to what is happening and how the lambda function is used\n",
    "**Example 1 - Brand_Apple vs Rating , Hue = Rating**\n",
    "> Notice how all the Apple users have liked the phone. Now since they are Apple users they have the column Brand_Apple as '1' , That column is now intellegent enough as other rows in that column are '0' - I call this as positve direction which helps in the final score when the 1s across the rows are summed to check against the 'b' / threshold value parameter.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa18f0c7470>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.scatterplot(x='Rating',y='Brand_Apple',hue='Rating',data=X_train_visual_rating_discrete)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example 2 - Brand_Blackberry vs Rating , Hue = Rating**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " *Notice how most of the Blackberry users have disliked the phone. Now since they are Blackberry users they have the column Brand_Blackberry as '1' , That column is now intellegent enough as other rows in that column are '0', but the rows which are '1' in the Brand_Blackberry column are going in the negative direction, it is brining down the rating value. We can also say that for rows which contain '0' we are certain that the user corresponding to that row would have liked some other phone*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*****Hence I am converting the '0's in Brand_blackberry column to '1' and making the '1s' as '0', this would eventually imply that \" Most Blackberry users who had the phone disliked it \"*****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Did you see what I did there ? I simply swapped the binaries so that now it makes more sense or in other words I am brininging in more intellegence for that column so that my predictions are accurate*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa18f07d710>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.scatterplot(x='Rating',y='Brand_Blackberry',hue='Rating',data=X_train_visual_rating_discrete)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[X_train_binarised,Y_train_binarised](http://)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train_binarised = np.array([\n",
    "#X_train_new['Weight'].map(lambda x: 1 if x > 153 else 0), # Removed due to correlation and accuracy increased\n",
    "#X_train_new['Height'].map(lambda x: 1 if x > 151 else 0), # Removed due to correlation and accuracy increased\n",
    "#X_train_new['Screen to Body Ratio (calculated)'].map(lambda x: 1 if x >= 55  else 0), # Removed due to correlation and accuracy increased\n",
    "X_train_new['Pixel Density'].map(lambda x: 1 if x > 270 else 0),\n",
    "X_train_new['Processor_frequency'].map(lambda x: 1 if x > 2.1 else 0),\n",
    "X_train_new['Screen Size'].map(lambda x: 1 if x > 4.8 else 0),\n",
    "X_train_new['RAM'].map(lambda x: 1 if x > 4 else 0),\n",
    "#X_train_new['Resolution'].map(lambda x: 1 if x > 20 else 0), # Removed due to correlation and accuracy increased\n",
    "X_train_new['Internal Memory'].map(lambda x: 1 if x > 64 else 0),\n",
    "X_train_new['Capacity'].map(lambda x: 1 if x > 2100 else 0),\n",
    "X_train_new['Brand_10.or'].map(lambda x: 0 if x >= 1 else 1), # 1 user has this phone and has not liked it. so its flipped\n",
    "X_train_new['Brand_Apple'],#.map(lambda x: 0 if x=1), # 4 users have this phone and have liked it, hence no changes\n",
    "X_train_new['Brand_Asus'],#.map(lambda x: 0 if x=1), # 4 users like this phone and 1 dislike, keep the field \n",
    "#X_train_new['Brand_Billion'].map(lambda x: 0 if x=1), # No Rows found, Removed to increase train accuracy due to scope of assignment\n",
    "X_train_new['Brand_Blackberry'].map(lambda x: 0 if x>=1 else 1), # 3 Userd have disliked the phone hence its flipped\n",
    "X_train_new['Brand_Comio'],#.map(lambda x: 0 if x=1), No changes 1 has liked it keep it\n",
    "X_train_new['Brand_Coolpad'].map(lambda x: 0 if x >= 1 else 1), #flipped as 2 of them have disliked\n",
    "#X_train_new['Brand_Do'].map(lambda x: 0 if x=1), # No Rows found, Removed to increase train accuracy due to scope of assignment\n",
    "X_train_new['Brand_Gionee'],#.map(lambda x: 0 if x=1), # 50-50 column, as of now removed as there is no intellegence \n",
    "X_train_new['Brand_Google'],#.map(lambda x: 0 if x=1), Everyone has liked it\n",
    "X_train_new['Brand_HTC'].map(lambda x: 0 if x>=1 else 1), #Max users dislike it\n",
    "X_train_new['Brand_Honor'],#.map(lambda x: 0 if x=1), Max like it - 1 dislike, retained\n",
    "X_train_new['Brand_Huawei'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_InFocus'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['Brand_Infinix'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Intex'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['Brand_Itel'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Jivi'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['Brand_Karbonn'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['Brand_LG'].map(lambda x: 0 if x>=1 else 1), # Many LG users don't like it\n",
    "X_train_new['Brand_Lava'],#.map(lambda x: 0 if x=1), 50-50 user base, discared\n",
    "X_train_new['Brand_LeEco'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Lenovo'],#.map(lambda x: 0 if x=1), 50-50 user base, discarded\n",
    "X_train_new['Brand_Lephone'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['Brand_Lyf'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['Brand_Meizu'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Micromax'].map(lambda x: 0 if x>=1 else 1), # Many micromax users don't like the phone\n",
    "X_train_new['Brand_Mobiistar'].map(lambda x: 0 if x>=1 else 1), # On the line, 2 of them don't like 1 likes\n",
    "X_train_new['Brand_Moto'],#.map(lambda x: 0 if x=1), 4 of them like, 3 of them don't\n",
    "X_train_new['Brand_Motorola'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Nokia'],#.map(lambda x: 0 if x=1),  50 - 50 User Base\n",
    "X_train_new['Brand_Nubia'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['Brand_OPPO'],#.map(lambda x: 0 if x=1),\n",
    "X_train_new['Brand_OnePlus'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Oppo'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Panasonic'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['Brand_Razer'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['Brand_Realme'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Reliance'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['Brand_Samsung'],#.map(lambda x: 0 if x=1),\n",
    "X_train_new['Brand_Sony'].map(lambda x: 0 if x>=1 else 1),\n",
    "#X_train_new['Brand_Spice'].map(lambda x: 0 if x=1), # No Rows found, Removed to increase train accuracy due to scope of assignment\n",
    "X_train_new['Brand_Tecno'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Ulefone'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_VOTO'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['Brand_Vivo'],#.map(lambda x: 0 if x=1), , \n",
    "X_train_new['Brand_Xiaomi'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Xiaomi Poco'],#.map(lambda x: 0 if x=1), \n",
    "X_train_new['Brand_Yu'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['Brand_iVooMi'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['SIM Slot(s)_Dual SIM, GSM+CDMA'],#.map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['SIM Slot(s)_Dual SIM, GSM+GSM'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE'],#.map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['SIM Slot(s)_Single SIM, GSM'],#.map(lambda x: 0 if x>=1 else 1)\n",
    "X_train_new['Num_cores_312'],#.map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['Num_cores_Deca'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['Num_cores_Dual'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['Num_cores_Hexa'],#.map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['Num_cores_Octa'].map(lambda x: 0 if x>=1 else 1), # Slightly more than 50% dislike it\n",
    "X_train_new['Num_cores_Other'],#.map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['Num_cores_Quad'].map(lambda x: 0 if x>=1 else 1),  # Slightly more than 50% dislike it\n",
    "X_train_new['Num_cores_Tru-Octa'],#.map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['Sim1_2G'],#.map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['Sim1_3G'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['Sim1_4G'],#.map(lambda x: 1 if x>=0 else 0), # Equal Spread\n",
    "X_train_new['SIM 2_2G'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['SIM 2_3G'],#.map(lambda x: 0 if x>=1 else 1),  # Equal Spread\n",
    "X_train_new['SIM 2_4G'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['SIM 2_Other'],#.map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['os_name_Android'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_train_new['os_name_Blackberry'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['os_name_KAI'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['os_name_Nokia'],#.map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['os_name_Other'],#.map(lambda x: 0 if x>=1 else 1), # Equal Spread\n",
    "X_train_new['os_name_Tizen'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_train_new['os_name_iOS'],#.map(lambda x: 0 if x>=1 else 1)\n",
    "    \n",
    "]) \n",
    "X_train_binarised = X_train_binarised.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_binarised = np.array([\n",
    "#X_test_new['Weight'].map(lambda x: 1 if x > 153 else 0), # Removed due to correlation and accuracy increased\n",
    "#X_test_new['Height'].map(lambda x: 1 if x > 151 else 0), # Removed due to correlation and accuracy increased\n",
    "#X_test_new['Screen to Body Ratio (calculated)'].map(lambda x: 1 if x >= 55  else 0), # Removed due to correlation and accuracy increased\n",
    "X_test_new['Pixel Density'].map(lambda x: 1 if x > 270 else 0),\n",
    "X_test_new['Processor_frequency'].map(lambda x: 1 if x > 2.1 else 0),\n",
    "X_test_new['Screen Size'].map(lambda x: 1 if x > 4.8 else 0),\n",
    "X_test_new['RAM'].map(lambda x: 1 if x > 4 else 0),\n",
    "#X_test_new['Resolution'].map(lambda x: 1 if x > 20 else 0), # Removed due to correlation and accuracy increased\n",
    "X_test_new['Internal Memory'].map(lambda x: 1 if x > 64 else 0),\n",
    "X_test_new['Capacity'].map(lambda x: 1 if x > 2100 else 0),\n",
    "X_test_new['Brand_10.or'].map(lambda x: 0 if x >= 1 else 1), # 1 user has this phone and has not liked it. so its flipped\n",
    "X_test_new['Brand_Apple'],#.map(lambda x: 0 if x=1), # 4 users have this phone and have liked it, hence no changes\n",
    "X_test_new['Brand_Asus'],#.map(lambda x: 0 if x=1), # 4 users like this phone and 1 dislike, keep the field \n",
    "#X_test_new['Brand_Billion'].map(lambda x: 0 if x=1), # No Rows found, Removed to increase train accuracy due to scope of assignment\n",
    "X_test_new['Brand_Blackberry'].map(lambda x: 0 if x>=1 else 1), # 3 Userd have disliked the phone hence its flipped\n",
    "X_test_new['Brand_Comio'],#.map(lambda x: 0 if x=1), No changes 1 has liked it keep it\n",
    "X_test_new['Brand_Coolpad'].map(lambda x: 0 if x >= 1 else 1), #flipped as 2 of them have disliked\n",
    "#X_test_new['Brand_Do'].map(lambda x: 0 if x=1), # No Rows found, Removed to increase train accuracy due to scope of assignment\n",
    "X_test_new['Brand_Gionee'],#.map(lambda x: 0 if x=1), # 50-50 column, as of now removed as there is no intellegence \n",
    "X_test_new['Brand_Google'],#.map(lambda x: 0 if x=1), Everyone has liked it\n",
    "X_test_new['Brand_HTC'].map(lambda x: 0 if x>=1 else 1), #Max users dislike it\n",
    "X_test_new['Brand_Honor'],#.map(lambda x: 0 if x=1), Max like it - 1 dislike, retained\n",
    "X_test_new['Brand_Huawei'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_InFocus'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['Brand_Infinix'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Intex'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['Brand_Itel'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Jivi'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['Brand_Karbonn'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['Brand_LG'].map(lambda x: 0 if x>=1 else 1), # Many LG users don't like it\n",
    "X_test_new['Brand_Lava'],#.map(lambda x: 0 if x=1), 50-50 user base, discared\n",
    "X_test_new['Brand_LeEco'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Lenovo'],#.map(lambda x: 0 if x=1), 50-50 user base, discarded\n",
    "X_test_new['Brand_Lephone'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['Brand_Lyf'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['Brand_Meizu'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Micromax'].map(lambda x: 0 if x>=1 else 1), # Many micromax users don't like the phone\n",
    "X_test_new['Brand_Mobiistar'].map(lambda x: 0 if x>=1 else 1), # On the line, 2 of them don't like 1 likes\n",
    "X_test_new['Brand_Moto'],#.map(lambda x: 0 if x=1), 4 of them like, 3 of them don't\n",
    "X_test_new['Brand_Motorola'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Nokia'],#.map(lambda x: 0 if x=1),  50 - 50 User Base\n",
    "X_test_new['Brand_Nubia'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['Brand_OPPO'],#.map(lambda x: 0 if x=1),\n",
    "X_test_new['Brand_OnePlus'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Oppo'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Panasonic'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['Brand_Razer'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['Brand_Realme'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Reliance'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['Brand_Samsung'],#.map(lambda x: 0 if x=1),\n",
    "X_test_new['Brand_Sony'].map(lambda x: 0 if x>=1 else 1),\n",
    "#X_test_new['Brand_Spice'].map(lambda x: 0 if x=1), # No Rows found, Removed to increase train accuracy due to scope of assignment\n",
    "X_test_new['Brand_Tecno'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Ulefone'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_VOTO'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['Brand_Vivo'],#.map(lambda x: 0 if x=1), , \n",
    "X_test_new['Brand_Xiaomi'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Xiaomi Poco'],#.map(lambda x: 0 if x=1), \n",
    "X_test_new['Brand_Yu'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['Brand_iVooMi'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['SIM Slot(s)_Dual SIM, GSM+CDMA'],#.map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['SIM Slot(s)_Dual SIM, GSM+GSM'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE'],#.map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['SIM Slot(s)_Single SIM, GSM'],#.map(lambda x: 0 if x>=1 else 1)\n",
    "X_test_new['Num_cores_312'],#.map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['Num_cores_Deca'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['Num_cores_Dual'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['Num_cores_Hexa'],#.map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['Num_cores_Octa'].map(lambda x: 0 if x>=1 else 1), # Slightly more than 50% dislike it\n",
    "X_test_new['Num_cores_Other'],#.map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['Num_cores_Quad'].map(lambda x: 0 if x>=1 else 1),  # Slightly more than 50% dislike it\n",
    "X_test_new['Num_cores_Tru-Octa'],#.map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['Sim1_2G'],#.map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['Sim1_3G'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['Sim1_4G'],#.map(lambda x: 1 if x>=0 else 0), # Equal Spread\n",
    "X_test_new['SIM 2_2G'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['SIM 2_3G'],#.map(lambda x: 0 if x>=1 else 1),  # Equal Spread\n",
    "X_test_new['SIM 2_4G'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['SIM 2_Other'],#.map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['os_name_Android'].map(lambda x: 0 if x>=1 else 1),\n",
    "X_test_new['os_name_Blackberry'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['os_name_KAI'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['os_name_Nokia'],#.map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['os_name_Other'],#.map(lambda x: 0 if x>=1 else 1), # Equal Spread\n",
    "X_test_new['os_name_Tizen'].map(lambda x: 0 if x>=1 else 1), \n",
    "X_test_new['os_name_iOS'],#.map(lambda x: 0 if x>=1 else 1)\n",
    "    \n",
    "]) \n",
    "\n",
    "X_test_binarised = X_test_binarised.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***The MP Neuron Model Class*****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MPNeuron:\n",
    "    def __init__self(self):\n",
    "        self.b = None\n",
    "    def model(self,x):\n",
    "        return(sum(x) >= self.b)\n",
    "    def predict(self,X):\n",
    "        Y=[]\n",
    "        for x in X:\n",
    "            result = self.model(x)\n",
    "            Y.append(result)\n",
    "        return np.array(Y)\n",
    "    def fit(self,X,Y):\n",
    "        accuracy = {}\n",
    "        \n",
    "        for b in range(X.shape[1]+1):\n",
    "            self.b = b\n",
    "            Y_pred = self.predict(X)\n",
    "            accuracy[b] = accuracy_score(Y_pred, Y)\n",
    "            \n",
    "        best_b = max(accuracy, key = accuracy.get)\n",
    "        self.b = best_b\n",
    "        \n",
    "        print('Optimal value of b is', best_b)\n",
    "        print('Highest accuracy is', accuracy[best_b])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal value of b is 33\n",
      "Highest accuracy is 0.8475073313782991\n"
     ]
    }
   ],
   "source": [
    "mp_neuron = MPNeuron()\n",
    "mp_neuron.fit(X_train_binarised,Y_train_new_rating_binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test_pred = mp_neuron.predict(X_test_binarised)\n",
    "    \n",
    "# Convert True, False to 1,0\n",
    "Y_test_pred = Y_test_pred.astype(int)\n",
    "Y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>PhoneId</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PhoneId  Class\n",
       "0        3      1\n",
       "1       11      1\n",
       "2       13      1\n",
       "3       16      1\n",
       "4       19      1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.DataFrame({'PhoneId':test_new['PhoneId'], 'Class':Y_test_pred})\n",
    "submission = submission[['PhoneId', 'Class']]\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv(\"submission_final.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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