Mobile_Price_Classification.ipynb

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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Anupa-123/Bike-sharing-demand-prediction/blob/main/Mobile_Price_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Mobile Price Range Prediction**"
      ],
      "metadata": {
        "id": "xaSps88xhKQq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**In the competitive mobile phone market companies want\n",
        "to understand sales data of mobile phones and factors which drive the prices.\n",
        "The objective is to find out some relation between features of a mobile phone(eg:- RAM,\n",
        "Internal Memory, etc) and its selling price. In this problem, we do not have to predict the\n",
        "actual price but a price range indicating how high the price is.**\n",
        "\n",
        "Data Description -\n",
        "Battery_power - Total energy a battery can store in one time measured in mAh\n",
        "\n",
        "Blue - Has bluetooth or not\n",
        "\n",
        "Clock_speed - speed at which microprocessor executes instructions\n",
        "\n",
        "Dual_sim - Has dual sim support or not\n",
        "\n",
        "Fc - Front Camera mega pixels\n",
        "\n",
        "Four_g - Has 4G or not\n",
        "\n",
        "Int_memory - Internal Memory in Gigabytes\n",
        "\n",
        "M_dep - Mobile Depth in cm\n",
        "\n",
        "Mobile_wt - Weight of mobile phone\n",
        "\n",
        "N_cores - Number of cores of processor\n",
        "\n",
        "Pc - Primary Camera mega pixels\n",
        "\n",
        "Px_height - Pixel Resolution Height\n",
        "\n",
        "Px_width - Pixel Resolution Width\n",
        "\n",
        "Ram - Random Access Memory in Mega Bytes\n",
        "\n",
        "Sc_h - Screen Height of mobile in cm\n",
        "\n",
        "Sc_w - Screen Width of mobile in cm\n",
        "\n",
        "Talk_time - longest time that a single battery charge \n",
        "will last when you are\n",
        "\n",
        "Three_g - Has 3G or not\n",
        "\n",
        "Touch_screen - Has touch screen or not\n",
        "\n",
        "Wifi - Has wifi or not\n",
        "\n",
        "Price_range - This is the target variable with value of 0(low cost), 1(medium cost),\n",
        "2(high cost) and 3(very high cost)."
      ],
      "metadata": {
        "id": "95KLyqcx16_K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Import the libraries**"
      ],
      "metadata": {
        "id": "iQaKvIgiWuAd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "KG37YExh1r-I"
      },
      "outputs": [],
      "source": [
        "# importing important libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "# setting max display column limit to 30\n",
        "pd.set_option(\"display.max_columns\", 30)\n",
        "\n",
        "#set style\n",
        "# plt.style.use('seaborn')\n",
        "sns.set()\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Load The Dataset**"
      ],
      "metadata": {
        "id": "YAE-CVa4W1sF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mounting drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffpTquw4O5VI",
        "outputId": "4a623a47-9389-45f6-c080-ed38ed4b9398"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CSV file loaction path\n",
        "df = pd.read_csv('/content/drive/MyDrive/data_mobile_price_range.csv')"
      ],
      "metadata": {
        "id": "SqJMKDA4YKp9"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#shape of the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "id": "0sYaar-vYzud",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f07bd224-16e8-480e-f5d0-5b9f9bfa7fad"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our data set has 2000 rows and 21 columns"
      ],
      "metadata": {
        "id": "-LNrW9scaeJ8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Dispaly all columns information\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08VVVirvoUAH",
        "outputId": "72eae8e2-fc5e-434e-95f3-8a02b9f2cd73"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2000 entries, 0 to 1999\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   battery_power  2000 non-null   int64  \n",
            " 1   blue           2000 non-null   int64  \n",
            " 2   clock_speed    2000 non-null   float64\n",
            " 3   dual_sim       2000 non-null   int64  \n",
            " 4   fc             2000 non-null   int64  \n",
            " 5   four_g         2000 non-null   int64  \n",
            " 6   int_memory     2000 non-null   int64  \n",
            " 7   m_dep          2000 non-null   float64\n",
            " 8   mobile_wt      2000 non-null   int64  \n",
            " 9   n_cores        2000 non-null   int64  \n",
            " 10  pc             2000 non-null   int64  \n",
            " 11  px_height      2000 non-null   int64  \n",
            " 12  px_width       2000 non-null   int64  \n",
            " 13  ram            2000 non-null   int64  \n",
            " 14  sc_h           2000 non-null   int64  \n",
            " 15  sc_w           2000 non-null   int64  \n",
            " 16  talk_time      2000 non-null   int64  \n",
            " 17  three_g        2000 non-null   int64  \n",
            " 18  touch_screen   2000 non-null   int64  \n",
            " 19  wifi           2000 non-null   int64  \n",
            " 20  price_range    2000 non-null   int64  \n",
            "dtypes: float64(2), int64(19)\n",
            "memory usage: 328.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display first top 5 rows\n",
        "df.head()"
      ],
      "metadata": {
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        },
        "id": "FIKu_HioosaM",
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      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
              "0            842     0          2.2         0   1       0           7    0.6   \n",
              "1           1021     1          0.5         1   0       1          53    0.7   \n",
              "2            563     1          0.5         1   2       1          41    0.9   \n",
              "3            615     1          2.5         0   0       0          10    0.8   \n",
              "4           1821     1          1.2         0  13       1          44    0.6   \n",
              "\n",
              "   mobile_wt  n_cores  pc  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
              "0        188        2   2         20       756  2549     9     7         19   \n",
              "1        136        3   6        905      1988  2631    17     3          7   \n",
              "2        145        5   6       1263      1716  2603    11     2          9   \n",
              "3        131        6   9       1216      1786  2769    16     8         11   \n",
              "4        141        2  14       1208      1212  1411     8     2         15   \n",
              "\n",
              "   three_g  touch_screen  wifi  price_range  \n",
              "0        0             0     1            1  \n",
              "1        1             1     0            2  \n",
              "2        1             1     0            2  \n",
              "3        1             0     0            2  \n",
              "4        1             1     0            1  "
            ],
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    {
      "cell_type": "code",
      "source": [
        "# Display last 5 rows\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
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          "height": 270
        },
        "id": "4AV9IxWio1eO",
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          "data": {
            "text/plain": [
              "      battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  \\\n",
              "1995            794     1          0.5         1   0       1           2   \n",
              "1996           1965     1          2.6         1   0       0          39   \n",
              "1997           1911     0          0.9         1   1       1          36   \n",
              "1998           1512     0          0.9         0   4       1          46   \n",
              "1999            510     1          2.0         1   5       1          45   \n",
              "\n",
              "      m_dep  mobile_wt  n_cores  pc  px_height  px_width   ram  sc_h  sc_w  \\\n",
              "1995    0.8        106        6  14       1222      1890   668    13     4   \n",
              "1996    0.2        187        4   3        915      1965  2032    11    10   \n",
              "1997    0.7        108        8   3        868      1632  3057     9     1   \n",
              "1998    0.1        145        5   5        336       670   869    18    10   \n",
              "1999    0.9        168        6  16        483       754  3919    19     4   \n",
              "\n",
              "      talk_time  three_g  touch_screen  wifi  price_range  \n",
              "1995         19        1             1     0            0  \n",
              "1996         16        1             1     1            2  \n",
              "1997          5        1             1     0            3  \n",
              "1998         19        1             1     1            0  \n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Discription of dataset\n",
        "df.describe()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "F0fq9CkAo5dc",
        "outputId": "bc792f1d-e1d9-438f-f675-f35d01e54afe"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       battery_power       blue  clock_speed     dual_sim           fc  \\\n",
              "count    2000.000000  2000.0000  2000.000000  2000.000000  2000.000000   \n",
              "mean     1238.518500     0.4950     1.522250     0.509500     4.309500   \n",
              "std       439.418206     0.5001     0.816004     0.500035     4.341444   \n",
              "min       501.000000     0.0000     0.500000     0.000000     0.000000   \n",
              "25%       851.750000     0.0000     0.700000     0.000000     1.000000   \n",
              "50%      1226.000000     0.0000     1.500000     1.000000     3.000000   \n",
              "75%      1615.250000     1.0000     2.200000     1.000000     7.000000   \n",
              "max      1998.000000     1.0000     3.000000     1.000000    19.000000   \n",
              "\n",
              "            four_g   int_memory        m_dep    mobile_wt      n_cores  \\\n",
              "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
              "mean      0.521500    32.046500     0.501750   140.249000     4.520500   \n",
              "std       0.499662    18.145715     0.288416    35.399655     2.287837   \n",
              "min       0.000000     2.000000     0.100000    80.000000     1.000000   \n",
              "25%       0.000000    16.000000     0.200000   109.000000     3.000000   \n",
              "50%       1.000000    32.000000     0.500000   141.000000     4.000000   \n",
              "75%       1.000000    48.000000     0.800000   170.000000     7.000000   \n",
              "max       1.000000    64.000000     1.000000   200.000000     8.000000   \n",
              "\n",
              "                pc    px_height     px_width          ram         sc_h  \\\n",
              "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
              "mean      9.916500   645.108000  1251.515500  2124.213000    12.306500   \n",
              "std       6.064315   443.780811   432.199447  1084.732044     4.213245   \n",
              "min       0.000000     0.000000   500.000000   256.000000     5.000000   \n",
              "25%       5.000000   282.750000   874.750000  1207.500000     9.000000   \n",
              "50%      10.000000   564.000000  1247.000000  2146.500000    12.000000   \n",
              "75%      15.000000   947.250000  1633.000000  3064.500000    16.000000   \n",
              "max      20.000000  1960.000000  1998.000000  3998.000000    19.000000   \n",
              "\n",
              "              sc_w    talk_time      three_g  touch_screen         wifi  \\\n",
              "count  2000.000000  2000.000000  2000.000000   2000.000000  2000.000000   \n",
              "mean      5.767000    11.011000     0.761500      0.503000     0.507000   \n",
              "std       4.356398     5.463955     0.426273      0.500116     0.500076   \n",
              "min       0.000000     2.000000     0.000000      0.000000     0.000000   \n",
              "25%       2.000000     6.000000     1.000000      0.000000     0.000000   \n",
              "50%       5.000000    11.000000     1.000000      1.000000     1.000000   \n",
              "75%       9.000000    16.000000     1.000000      1.000000     1.000000   \n",
              "max      18.000000    20.000000     1.000000      1.000000     1.000000   \n",
              "\n",
              "       price_range  \n",
              "count  2000.000000  \n",
              "mean      1.500000  \n",
              "std       1.118314  \n",
              "min       0.000000  \n",
              "25%       0.750000  \n",
              "50%       1.500000  \n",
              "75%       2.250000  \n",
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              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>clock_speed</th>\n",
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              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>2000.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1238.518500</td>\n",
              "      <td>0.4950</td>\n",
              "      <td>1.522250</td>\n",
              "      <td>0.509500</td>\n",
              "      <td>4.309500</td>\n",
              "      <td>0.521500</td>\n",
              "      <td>32.046500</td>\n",
              "      <td>0.501750</td>\n",
              "      <td>140.249000</td>\n",
              "      <td>4.520500</td>\n",
              "      <td>9.916500</td>\n",
              "      <td>645.108000</td>\n",
              "      <td>1251.515500</td>\n",
              "      <td>2124.213000</td>\n",
              "      <td>12.306500</td>\n",
              "      <td>5.767000</td>\n",
              "      <td>11.011000</td>\n",
              "      <td>0.761500</td>\n",
              "      <td>0.503000</td>\n",
              "      <td>0.507000</td>\n",
              "      <td>1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>439.418206</td>\n",
              "      <td>0.5001</td>\n",
              "      <td>0.816004</td>\n",
              "      <td>0.500035</td>\n",
              "      <td>4.341444</td>\n",
              "      <td>0.499662</td>\n",
              "      <td>18.145715</td>\n",
              "      <td>0.288416</td>\n",
              "      <td>35.399655</td>\n",
              "      <td>2.287837</td>\n",
              "      <td>6.064315</td>\n",
              "      <td>443.780811</td>\n",
              "      <td>432.199447</td>\n",
              "      <td>1084.732044</td>\n",
              "      <td>4.213245</td>\n",
              "      <td>4.356398</td>\n",
              "      <td>5.463955</td>\n",
              "      <td>0.426273</td>\n",
              "      <td>0.500116</td>\n",
              "      <td>0.500076</td>\n",
              "      <td>1.118314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>501.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>256.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>851.750000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>109.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>282.750000</td>\n",
              "      <td>874.750000</td>\n",
              "      <td>1207.500000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.750000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>1226.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1247.000000</td>\n",
              "      <td>2146.500000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1615.250000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>170.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>947.250000</td>\n",
              "      <td>1633.000000</td>\n",
              "      <td>3064.500000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1998.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>1960.000000</td>\n",
              "      <td>1998.000000</td>\n",
              "      <td>3998.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking Statistic of the data\n",
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "ejMKkDFGqE8q",
        "outputId": "573233f1-c3d7-42f0-8dea-2a1407f4593b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                count        mean          std    min      25%     50%  \\\n",
              "battery_power  2000.0  1238.51850   439.418206  501.0   851.75  1226.0   \n",
              "blue           2000.0     0.49500     0.500100    0.0     0.00     0.0   \n",
              "clock_speed    2000.0     1.52225     0.816004    0.5     0.70     1.5   \n",
              "dual_sim       2000.0     0.50950     0.500035    0.0     0.00     1.0   \n",
              "fc             2000.0     4.30950     4.341444    0.0     1.00     3.0   \n",
              "four_g         2000.0     0.52150     0.499662    0.0     0.00     1.0   \n",
              "int_memory     2000.0    32.04650    18.145715    2.0    16.00    32.0   \n",
              "m_dep          2000.0     0.50175     0.288416    0.1     0.20     0.5   \n",
              "mobile_wt      2000.0   140.24900    35.399655   80.0   109.00   141.0   \n",
              "n_cores        2000.0     4.52050     2.287837    1.0     3.00     4.0   \n",
              "pc             2000.0     9.91650     6.064315    0.0     5.00    10.0   \n",
              "px_height      2000.0   645.10800   443.780811    0.0   282.75   564.0   \n",
              "px_width       2000.0  1251.51550   432.199447  500.0   874.75  1247.0   \n",
              "ram            2000.0  2124.21300  1084.732044  256.0  1207.50  2146.5   \n",
              "sc_h           2000.0    12.30650     4.213245    5.0     9.00    12.0   \n",
              "sc_w           2000.0     5.76700     4.356398    0.0     2.00     5.0   \n",
              "talk_time      2000.0    11.01100     5.463955    2.0     6.00    11.0   \n",
              "three_g        2000.0     0.76150     0.426273    0.0     1.00     1.0   \n",
              "touch_screen   2000.0     0.50300     0.500116    0.0     0.00     1.0   \n",
              "wifi           2000.0     0.50700     0.500076    0.0     0.00     1.0   \n",
              "price_range    2000.0     1.50000     1.118314    0.0     0.75     1.5   \n",
              "\n",
              "                   75%     max  \n",
              "battery_power  1615.25  1998.0  \n",
              "blue              1.00     1.0  \n",
              "clock_speed       2.20     3.0  \n",
              "dual_sim          1.00     1.0  \n",
              "fc                7.00    19.0  \n",
              "four_g            1.00     1.0  \n",
              "int_memory       48.00    64.0  \n",
              "m_dep             0.80     1.0  \n",
              "mobile_wt       170.00   200.0  \n",
              "n_cores           7.00     8.0  \n",
              "pc               15.00    20.0  \n",
              "px_height       947.25  1960.0  \n",
              "px_width       1633.00  1998.0  \n",
              "ram            3064.50  3998.0  \n",
              "sc_h             16.00    19.0  \n",
              "sc_w              9.00    18.0  \n",
              "talk_time        16.00    20.0  \n",
              "three_g           1.00     1.0  \n",
              "touch_screen      1.00     1.0  \n",
              "wifi              1.00     1.0  \n",
              "price_range       2.25     3.0  "
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>battery_power</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>1238.51850</td>\n",
              "      <td>439.418206</td>\n",
              "      <td>501.0</td>\n",
              "      <td>851.75</td>\n",
              "      <td>1226.0</td>\n",
              "      <td>1615.25</td>\n",
              "      <td>1998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>blue</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>0.49500</td>\n",
              "      <td>0.500100</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>clock_speed</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>1.52225</td>\n",
              "      <td>0.816004</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.20</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dual_sim</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>0.50950</td>\n",
              "      <td>0.500035</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fc</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>4.30950</td>\n",
              "      <td>4.341444</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.00</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>four_g</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>0.52150</td>\n",
              "      <td>0.499662</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int_memory</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>32.04650</td>\n",
              "      <td>18.145715</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.00</td>\n",
              "      <td>32.0</td>\n",
              "      <td>48.00</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>m_dep</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>0.50175</td>\n",
              "      <td>0.288416</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mobile_wt</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>140.24900</td>\n",
              "      <td>35.399655</td>\n",
              "      <td>80.0</td>\n",
              "      <td>109.00</td>\n",
              "      <td>141.0</td>\n",
              "      <td>170.00</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n_cores</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>4.52050</td>\n",
              "      <td>2.287837</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pc</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>9.91650</td>\n",
              "      <td>6.064315</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.00</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.00</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>px_height</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>645.10800</td>\n",
              "      <td>443.780811</td>\n",
              "      <td>0.0</td>\n",
              "      <td>282.75</td>\n",
              "      <td>564.0</td>\n",
              "      <td>947.25</td>\n",
              "      <td>1960.0</td>\n",
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              "    <tr>\n",
              "      <th>px_width</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>1251.51550</td>\n",
              "      <td>432.199447</td>\n",
              "      <td>500.0</td>\n",
              "      <td>874.75</td>\n",
              "      <td>1247.0</td>\n",
              "      <td>1633.00</td>\n",
              "      <td>1998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ram</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>2124.21300</td>\n",
              "      <td>1084.732044</td>\n",
              "      <td>256.0</td>\n",
              "      <td>1207.50</td>\n",
              "      <td>2146.5</td>\n",
              "      <td>3064.50</td>\n",
              "      <td>3998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sc_h</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>12.30650</td>\n",
              "      <td>4.213245</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.00</td>\n",
              "      <td>12.0</td>\n",
              "      <td>16.00</td>\n",
              "      <td>19.0</td>\n",
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              "    <tr>\n",
              "      <th>sc_w</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>5.76700</td>\n",
              "      <td>4.356398</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.00</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>talk_time</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>11.01100</td>\n",
              "      <td>5.463955</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>11.0</td>\n",
              "      <td>16.00</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>three_g</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>0.76150</td>\n",
              "      <td>0.426273</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>touch_screen</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>0.50300</td>\n",
              "      <td>0.500116</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>wifi</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>0.50700</td>\n",
              "      <td>0.500076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>price_range</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>1.50000</td>\n",
              "      <td>1.118314</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.25</td>\n",
              "      <td>3.0</td>\n",
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              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The nunique () method returns the number of unique values for each column\n",
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JaDyZJhInxx3",
        "outputId": "e07fd4d4-ba06-4a46-c92d-d5291e2ecc23"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "battery_power    1094\n",
              "blue                2\n",
              "clock_speed        26\n",
              "dual_sim            2\n",
              "fc                 20\n",
              "four_g              2\n",
              "int_memory         63\n",
              "m_dep              10\n",
              "mobile_wt         121\n",
              "n_cores             8\n",
              "pc                 21\n",
              "px_height        1137\n",
              "px_width         1109\n",
              "ram              1562\n",
              "sc_h               15\n",
              "sc_w               19\n",
              "talk_time          19\n",
              "three_g             2\n",
              "touch_screen        2\n",
              "wifi                2\n",
              "price_range         4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking null values**"
      ],
      "metadata": {
        "id": "yCLsN2z8ba2r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " #This function return sum of null values of columns\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sf0e8eZ7Q0-2",
        "outputId": "a660c0d9-bd48-4d11-a55f-c90785c612eb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "battery_power    0\n",
              "blue             0\n",
              "clock_speed      0\n",
              "dual_sim         0\n",
              "fc               0\n",
              "four_g           0\n",
              "int_memory       0\n",
              "m_dep            0\n",
              "mobile_wt        0\n",
              "n_cores          0\n",
              "pc               0\n",
              "px_height        0\n",
              "px_width         0\n",
              "ram              0\n",
              "sc_h             0\n",
              "sc_w             0\n",
              "talk_time        0\n",
              "three_g          0\n",
              "touch_screen     0\n",
              "wifi             0\n",
              "price_range      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking duplicate values**"
      ],
      "metadata": {
        "id": "QYJJKPJYcI26"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " #Returns is any duplicates rows has in our dataset.\n",
        "df.duplicated().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4y7V4AGRcOGy",
        "outputId": "7bf656c4-114e-4f1f-fd3f-5ce0f4bccf20"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "\n",
        "\n",
        "*   There is no null/missing value in this training data set.\n",
        "*  There is no duplicate value in this data set. \n",
        "\n"
      ],
      "metadata": {
        "id": "c9DrPblKdx6L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exploratory Data Analysis(EDA)**"
      ],
      "metadata": {
        "id": "dBMne4cCDlFc"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ttM0J3KGEXHT"
      },
      "execution_count": 12,
      "outputs": []
    }
  ]
}