Mobile price prdiction.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHTMuIF_rSZb"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sX5pqsD_7VKy"
      },
      "source": [
        "from sklearn import metrics"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXQOMenfSYO_"
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      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnMM-5DnrgUk"
      },
      "source": [
        "df=pd.read_csv(\"train.csv\")"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
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        "outputId": "fa60dd2f-141e-4e9e-9745-94f5c9614bcf"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>clock_speed</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>fc</th>\n",
              "      <th>four_g</th>\n",
              "      <th>int_memory</th>\n",
              "      <th>m_dep</th>\n",
              "      <th>mobile_wt</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>pc</th>\n",
              "      <th>px_height</th>\n",
              "      <th>px_width</th>\n",
              "      <th>ram</th>\n",
              "      <th>sc_h</th>\n",
              "      <th>sc_w</th>\n",
              "      <th>talk_time</th>\n",
              "      <th>three_g</th>\n",
              "      <th>touch_screen</th>\n",
              "      <th>wifi</th>\n",
              "      <th>price_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842</td>\n",
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              "      <td>2.2</td>\n",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>756</td>\n",
              "      <td>2549</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1021</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>0.7</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>905</td>\n",
              "      <td>1988</td>\n",
              "      <td>2631</td>\n",
              "      <td>17</td>\n",
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              "      <td>0.5</td>\n",
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              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>0.9</td>\n",
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              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1263</td>\n",
              "      <td>1716</td>\n",
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              "      <td>11</td>\n",
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              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>615</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.8</td>\n",
              "      <td>131</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>1216</td>\n",
              "      <td>1786</td>\n",
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              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.6</td>\n",
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              "      <td>1411</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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            "text/plain": [
              "   battery_power  blue  clock_speed  ...  touch_screen  wifi  price_range\n",
              "0            842     0          2.2  ...             0     1            1\n",
              "1           1021     1          0.5  ...             1     0            2\n",
              "2            563     1          0.5  ...             1     0            2\n",
              "3            615     1          2.5  ...             0     0            2\n",
              "4           1821     1          1.2  ...             1     0            1\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
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    {
      "cell_type": "code",
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      },
      "source": [
        "df.info()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
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      "cell_type": "code",
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        "outputId": "ef3b5cf6-d644-467e-9259-2a69cfd6307e"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": 141,
      "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": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
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      "source": [
        "df.duplicated().sum()"
      ],
      "execution_count": 142,
      "outputs": [
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              "0"
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        "id": "te5cq1cQsEdP",
        "outputId": "811801d6-5710-4bd1-994b-31e577889a4b"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g',\n",
              "       'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height',\n",
              "       'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g',\n",
              "       'touch_screen', 'wifi', 'price_range'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLUEcUQ7Qwxr"
      },
      "source": [
        "**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 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), 2(high cost) and 3(very high cost).\n"
      ]
    },
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      "source": [
        "\n",
        "fig=plt.figure(figsize=(18,15))\n",
        "sns.heatmap(df.corr())"
      ],
      "execution_count": 144,
      "outputs": [
        {
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb20eae860>"
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\n",
            "text/plain": [
              "<Figure size 1296x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xm0byH754H0o"
      },
      "source": [
        "##Price Range"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "iZc6NpV_4MVP",
        "outputId": "73513833-ad30-45ce-9002-c6a3c3b31766"
      },
      "source": [
        "sns.countplot(df['price_range'])"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1cc69a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GF6qfQLo5fdu"
      },
      "source": [
        "this tells us that count of mobile in each of price range are "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cyVtYUly14PQ"
      },
      "source": [
        "##Battery power\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tcnZVIu8tBcH",
        "outputId": "36f04f86-8b44-499f-d1e6-7f4ff75eb808"
      },
      "source": [
        "len(df['battery_power'].unique())#can be considered as real values"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1094"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "GZ3q2rh1V8R8",
        "outputId": "54d97a74-34e4-48b2-c441-2c02db6d4007"
      },
      "source": [
        "df.boxplot(column='battery_power')"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1cbca208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "yGhgMwVBs29u",
        "outputId": "1ea6505f-8d8f-4af0-bdce-d6521232e75b"
      },
      "source": [
        "sns.violinplot(df['price_range'],df['battery_power'])"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1cba4b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dYJrDrSgaFuJ"
      },
      "source": [
        "we can see mobiles phones of higher cost have greater battery capacities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4m0R-iQ0hNmK"
      },
      "source": [
        "##Bluetooth"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "5rABAfPCc3pC",
        "outputId": "04b22fcd-8a47-4d31-ffda-3c8e8ac717b3"
      },
      "source": [
        "df[['blue','price_range','battery_power']].groupby(['blue','price_range']).count()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "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></th>\n",
              "      <th>battery_power</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>blue</th>\n",
              "      <th>price_range</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">0</th>\n",
              "      <th>0</th>\n",
              "      <td>257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
              "      <th>0</th>\n",
              "      <td>243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>259</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  battery_power\n",
              "blue price_range               \n",
              "0    0                      257\n",
              "     1                      255\n",
              "     2                      257\n",
              "     3                      241\n",
              "1    0                      243\n",
              "     1                      245\n",
              "     2                      243\n",
              "     3                      259"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "P9K0zE3FhJQq",
        "outputId": "f1a42222-8508-4002-fb92-f596a3401c59"
      },
      "source": [
        "sns.countplot(x=df['blue'],hue=df['price_range'])"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1cb26e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "DCh2_h8tvPaz",
        "outputId": "3ec481d4-b3a5-4f90-da60-9b867621aee1"
      },
      "source": [
        "sns.pointplot(y=df['blue'],x=df['price_range'])"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1caeada0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQtJba2BmaCS"
      },
      "source": [
        "mobile phones of higher costs have higher tendency to have bluetooth"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Wna547PnBOI"
      },
      "source": [
        "##clock_speed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RlTGNJR5mWjf",
        "outputId": "358ea8bd-92b9-4908-affa-8bcdddbde28b"
      },
      "source": [
        "df['clock_speed'].unique()#can be considered as categorical variable"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.2, 0.5, 2.5, 1.2, 1.7, 0.6, 2.9, 2.8, 2.1, 1. , 0.9, 1.1, 2.6,\n",
              "       1.4, 1.6, 2.7, 1.3, 2.3, 2. , 1.8, 3. , 1.5, 1.9, 2.4, 0.8, 0.7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 895
        },
        "id": "RKB8BS3PnEjX",
        "outputId": "fcf139f2-63f9-42c7-e2d8-9ca18ab81d44"
      },
      "source": [
        "fig=plt.figure(figsize=[15,15])\n",
        "sns.countplot(x=df['clock_speed'],hue=df['price_range'])"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1ca0fbe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "easFc6Lgvy5K",
        "outputId": "a73d20ae-1688-45b7-fae2-c4a9e3b1b804"
      },
      "source": [
        "sns.pointplot(y=df['clock_speed'],x=df['price_range'])"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1ca69dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jMnctpQmmfYtU0tMakZCgBBMvYb4h7iTySPbOZvYMMAi4uppzphBZ7OnJKuq85+7l+06KgZ+4+8JgOdoFZvaOu68Ijt/v7veGiF0qcHK7Zrxy4yD+9NYqHpsdyd8vLtzMvPW7eGBUJmd3aRXnCCXWducV8vgH63nig/VlOsATE4yvnNKOhRt3k10umTROTmTCd85mUK/0Sq9bWFzC9twCtuVGEktpctmWmx9JMnsL2L7vYKX9bQVFJazbkce6HXmVvkdyotG2WdA01iKVDofvXBofTjBtmzUiOVHPb42FGicPd3/HzBYC5wEG3OLuVQ74dvdZZtYtbFDuvhXYGmzvM7OVQEdgRZUnSmipyYn88pJTGXpyBj95fjE5+w6ycdcBvjVxDjdf2Jsbh/ckSf/4Gpxtewt49L21/P2jjRyIegZaSlICV2V1ZtwFPejcugl7DhTy9482Mv5fn3GwuITmqUm8ftMQurRpUuX1U5IS6Ny6CZ1bV17vUImzY//B4M4lP5JgcqMSzd5I8iksLqnw/KJDzpY9+WzZk1/pe5hBRtNGh/tgIncujY/ok9GCauGFbZsYCgwm0pSUDLxcCzEMDNYq/xz4qbsvjz4YJJ+zgLlRxT8ys+8C84ncoeyu6MJmNg4YB9ClS5eKqkjggpMzeOvWC/ifF5bwr5XbOVTi3P+vT5n1WQ4PXJVZ5ZeA1B/rd+Tx11lreHHBFgoPffGl3LRREt85rwtjB3enbbPUw+Utm6Tww2G9eH7+ZtbtyKNN00bVJo6aSkww2jWPfHnTuWWFddyd3QeK2FqaXKKaxiL7kfK8Sh4C6g7Z+w6Sve8gizdXPqe5VZPkMkkluv+ltF+mNppyF2zYzePvr2PTrgNA5M5vV14hrdNSjvnax1uNfxtmNgHoBUwNiq4zsy+7+43H8P4Lga7uvt/MLgZeAXpHvWdT4EUiw4NLe+AeAX5DJIH9BriPSh4N7+6TgEkAWVlZGo9ajdZpKfztu+fw94828pvXV1BQVMKCDbu5ePx7/Oby07n8rI7xDlGO0sqtuTwycw2vL/mc6JaiVk2SuWZQd747sBstgmei1SVmRuu0FFqnpXDaSS0qrbevoKjMHUtpE9kX2wXsOVD5vJTdB4rYfaCIlVV09DdtlFSm7+Vwc1mLVNo3jySelk0q7+h/bt4mbn9xSZk1vffkF/H1B2fz/PUDOamejXYMk0ovBE5x99IO8yeA5VWfUrWohIC7TzezCWaW7u47zCyZSOJ4xt1fiqp3eLEKM/sbGipcq8yM75zblXO7t+GWaR+z/PNc9h0s5tZnFzFjVTa/ufx0mqfWvS8ZqdiCDbuZMGM1//4ku0x5++apXHtBD0YP6EyTlPo/OKJZajLNUpPp3a5ZpXXyCw990QcT9L1U1NHvlfw3c//BYlZn72d1FR39jZISDt+5RI8kS01O4OcvL6WiS2/Zk89dry3nr2OyQn7q+Arzt2Y10AXYEOx3DsqOmpm1B7a7u5vZACKTFncGQ4AfA1a6+5/LndMh6BMBuAJYdiwxSMV6tW3Kyz8cxH3vrGLSrLW4w6uLPmf++t08MCqT/t1axztEqYS7M3v1Dh6esZoP1+4qc6xbmyZcP7QnV5zdkUZJJ1Y7f+OURLqnp9E9Pa3SOoXFJWTv+6K/pezdTD7bcw+yLbeg0o7+g8UlbNh5gA07D4SK7Z0V29mx/yDpTRuFOi+ewiSPZsBKM/so2O8PzDezfwC4+6XlTzCzqcAwIN3MNhMZsZUc1J8IjARuMLNiIB8YFSSSwcAYYKmZLQouVzok9x4zyyTSbLWeyKNSJAZSkhL42UWnMLR3Bj9+bjHbcgvYsiefq/46hxuH9+LmL/XWSJY6pKTEeXvFdibMXM2Scu37fds348bhvbj4jA4kanhrpVKSEujUqgmdWlXd0b8z6Ogv2weTf7iJbNveAg5W0tFfkRKH7bkFDTZ5/Crsxd19dDXHHyIylLd8+WwiI7oqOmdM2Djk2JzfK503bhnCz19eyhvLtlHi8OB/VvPeZzsYPyqTrm0q/5+cxF7RoRJeW/w5E2auOaJJ5ewuLfnRhb0Y3qetJt3VksQEo23zVNo2T6Vf54rruDt7DhQd7nt59qNNvFXF8tAJRpmBCvVBmOQxH8h39xIzOxnoC7zh7no62gmgVVoKE75zNs/P38yvX1vOgcJDLNq0h4vHv8evLz2Nked00pfTcVZQdIjn529i4rtrjxiuOqR3OjcO78W53VvrzyUOzIxWaSm0Skvh1JOac2qHFvz7k2yKK2nuurBvOzKa1Z+7DgiXPGYBQ8ysFfA2MA+4CvhOLAKTusfMuLJ/Z/p3b82t0z5m8ea95BUe4rYXljBzVQ6/v+KMOjlip6HZV1DEM3M38uh768rM5DaDEae154fDenFGp8pHJsnx175FKndfdjo/f3npEcc6tEjl15eeGoeojk2Y5GHB86zGAhPc/Z5gfoacYLqnp/HCDecz/l+f8fDM1bjDP5duZeHG3fz5ykwG9mwT7xAbpF15hUx5fx1TPlhPbsEX67EkJhiXZ3bkhmE96NW28tFGEl/fPrcLvdo2ZfLsdbwTzKVq0TiZ124aXK/6OkqFSh5mNpDIncbYoEy9pSeo5MQEfvq1Pgzpnc6Pn1vMlj2RzsJvP/oh113Qkx9/5WRSkvTXozZs3ZvP32atY+pHG8mPeux5o6QErurfmWuH9NAkznpiQPfWDOjemuH3zmTdjjxap6XUy8QB4ZLHLcDPgJfdfbmZ9QBmxCYsqS/O7dGG6bcM4RcvL+X1JVtxh4nvruH91Tt4YFQmPTOaxjvEemvdjjz++u4aXly4maJDX7SVN22UxJiBXblmUPd6104uDUeYZ1vNItLvUbq/Fri5dN/MHnR3LUt7AmrROJkHR5/FhX3b8qtXl7P/YDFLt+zlkr/M5ldfP5VR/Tur0zaEFZ/nMmHmaqYv3VpmNnjrtBSuGdSNMQO70aKx+pYkvmpzaumgWryW1DNmxjfO7kRW19bc+uzHLNy4h/yiQ/zspaXM+CSbP3zzzHr5/J7jacGGXTw8Yw3/qWA2+LgLejCqgcwGl4ZBfxOlVnVp04TnrhvIQzNW85d/f0aJw9srtrNo0yzuu7IfQ3pnxDvEOsXdmfXZDibMWM3cdWVng3dPT+P6oT244qxO6j+SOkfJQ2pdUmICt375ZIb0TueWaYvYvDuf7H0HGfPYR/xgcHduG9HnhHs0RnklJc5by7fx8MzVLNtS9mF8p3Rozo3De3LR6ZoNLnVXbSYP/S2XMs7p2po3bhnCna8u56WPtwDw6Ox1vL9mJ38ZlVnlQ+waqqJDJby66HMembmaNTllFzrK6tqKG4f3YlifDPURSZ0X5pHs3d19Xbmy/u4+L9gdX6uRSYPQLDWZP1+VydA+GfzvK8vYV1DMyq25XPLgbP73v07hv8/rekJ8URYUHeLZeZuYNOvI2eBDT87gxuG9GNBdD5uU+iPMnceLZvZ1d98CYGZDiTyX6gwAd59S++FJQ3FZZkfO6dqKHz+7mI/W7+JgcQm/fHU5M1blcM/IM+vtWPfq5BYU8fSHG5g8ex079hceLjeDi06PzAY/vaNmg0v9EyZ5XAe8YmZfB84G/g+4OCZRSYPUqVUTpo47j4nvruHP73zKoRLnP59kM+KB97j3W2cyrE/beIdYa3buP8jj76/niTnr2Rc1Gzwpwbj8rI5cP7QnvdpqDozUX2Hmecwzs5uJPNeqAPiyu+fELDJpkBITjBuH92JQr3RumfYxG3YeYMf+g1z9+DyuPr8bd1zUt16vJ/35nnz+9t5apn60kYKiLx7J3SgpgdEDunDtBT3oWM9WjBOpSLXJw8xegzILYDUB9gKPmVmF63iIVCezc0um3zyEu15bznPzNwMw5YP1zFmzk/GjM+nbvnmcIwxnbc5+Jr67hpc/3lJmNnizYDb49zUbXBqYmtx53Hu0FzezycAlQLa7n17B8WHAq0BpR/xL7n53cGwEkU74ROBRd/9DUN4dmAa0ARYAY9y9EKl30holcc/Ifgzr05afvbSUvflFrNq+j0sfep87RvTl6vO7kVDHh6ou/3wvE2auYfrSrWWWL22dlsLYwd357/O6aja4NEjVJg93fxcOf2lvdfeCYL8x0K6a06cQ6VR/soo677n7JdEFZpYIPAx8BdgMzDOzf7j7CuCPwP3uPs3MJhJ5SOMj1X0OqbsuPqMDZ3VpyY+fXcyctTspLC7h7tdXMPPTHO4deSZtm9e9RXLmrd/FhBmrmbGqbMvtSS0ia4OP6t+Fxin1t/lNpDphpq0+D0Svq3goKKtU8DysXVXVqcQAYLW7rw3uKqYBlwVrm18IvBDUewK4/CiuL3VMhxaNefoH53LHRX1JTozcbcz6NIcR49/jX1WswHY8uTszV2Vz5cQ5fGvinDKJo0d6GveMPJOZtw3n+4O6K3FIgxdmtFVSdPOQuxeaWW08rGhgsC7I58BP3X050BHYFFVnM3AukaaqPe5eHFXesbILm9k4YBxAly5daiFUiaXEBOP6oT0Z1DOdW579mLU5eezKK+QHT87nv8/rwi8uPjUuX8qHSmeDz1jN8s/LzgY/tUNzbhzeixGnt9dscDmhhEkeOWZ2qbv/A8DMLgN2HOP7LwS6uvt+M7sYeAXofYzXPMzdJwGTALKysipe/1HqnDM6teD1mwbz23+u5O9zNwLw9IcbI53po846bvMiCotLeGXRFibOXMPaHWVngw/o1pofDu/J0JM1G1xOTGGSx/XAM2b2cLC/CRhzLG/u7rlR29PNbIKZpQNbgOil5TsFZTuBlmaWFNx9lJZLA9MkJYnfX3EGw07O4PYXl7D7QBFrcvK4YsL73Pa1PvxgcI+YdabnFx7i2XkbmTRrLZ/vLShzbFifDH44TLPBRcLM81gDnGdmTYP9/cf65mbWHtju7m5mA4j0wewE9gC9g076LcAo4NtBvRnASCL9IN8jMlpLGqivntaezM4t+cnzi3nvsx0UHXJ+P/0T3v00h/u+lUn7FrXXmZ5bUMRTcyKzwXfmlZ0NfvHpHbhhWE/NBhcJhHm2VQvgTuCCYP9d4G5331vFOVOBYUC6mW0Ozk8GcPeJRJLADWZWDOQDo9zdgWIz+xHwFpGhupODvhCA24FpZvZb4GPgsZp/XKmP2jZP5YnvD2Dy++u4581VFB4q4f3VOxkxfhZ/+MaZjDi9/TFdf8f+g0yevY6n5mxg38Gys8GvOKsj1w/rqRURRcoJ02w1GVgGXBnsjwEeB75R2QnuPrqqC7r7Q0SG8lZ0bDowvYLytURGY8kJJCHB+MGQHpzfMzIz/bPs/ew5UMT1Ty9gVP/O/PKSU0lrFO4h0Vv25PO3WZHZ4AeLvxhImJqcwKj+mg0uUpUw/9p6uvs3o/bvMrNFtR2QSFVOPak5r900mP+bvpIn5mwAYNq8Tcxdt4sHrsqkX+eWQGS9DIASP3KcxOrsyGzwVz7eQnFJ2dng3z0/Mhu8oT6oUaS2hEke+WY22N1nA5jZICJNTSLHVWpyIndddjrD+rTlthcWs2N/Iet25PHNRz7gmsHd2bQrjw27DgCwcecBfvXqMm77Wh827DzAhJmreWPZtjKzwdukpTB2SGQ2ePNUzQYXqYkwyeMG4Img78OITP67OhZBidTE8L5teeOWC7jthcXMXJVDcYkzadbaMnUceHLOBl5euKVMfwZEZoNfN7QnV2Z11qQ+kZDCjLZaBPQzs+bBfm41p4jEXEazRjx+dX+enLOBu15bTkkls3miE0ePjDRuGNqTyzI7am1wkaNUk6fq/riScgDc/c+1HJNIKGbG987vxn3vrCI3v7jSemmNEvnTyH587TTNBhc5VjW58yhdaNo5cp1yzdqWOqOgsKTK4z0zmnLxGR2OUzQiDVtNnqp7F4CZPQHc4u57gv1WwH2xDU+k5k45qTmLN+2p9Lgm+InUnjANvmeWJg4Ad98NnFX7IYkcnWsGdav0WILBmPO6Hr9gRBq4MMkjIbjbAMDMWhNutJZITF3a7ySuH9rziPKkRONPI/txSof6tTqhSF0W5sv/PmCOmZWu4fEt4He1H5LI0TEz7rioL5dlnsRVk+aQm19MyybJ/Ep0xTkAAAr5SURBVPPmIZopLlLLwgzVfdLM5hNZjAngG8HKfiJ1yikdmtMmrRG5+cW0apKixCESA6GanYJkoYQhInKC0wwpEREJTclDRERCU/IQEZHQYpo8zGyymWWb2bJq6vU3s2IzGxnsDzezRVGvAjO7PDg2xczWRR3LjOVnEBGRI8V6nsYUIos9PVlZBTNLBP4IvF1a5u4zgMzgeGtgdfRx4DZ3fyEG8YqISA3E9M7D3WcReXR7VW4CXgSyKzk+EnjD3Q/UZmwiInL04trnYWYdgSuAR6qoNgqYWq7sd2a2xMzuN7NKl3wzs3FmNt/M5ufk5NRCxCIiAvHvMH8AuN3dK3wcqpl1AM4A3ooq/hnQF+gPtAZur+zi7j7J3bPcPSsjI6P2ohYROcHF+9lUWcC0YG2QdOBiMyt291eC41cCL7t7UekJ7r412DxoZo8DPz2eAYuISJyTh7t3L902synA61GJA2A0kTsNoup1cPetFsk4lwNVjuQSEZHaF9PkYWZTgWFAupltBu4EkgHcfWI153YDOgPvljv0jJllEFmYahFwfa0GLSIi1Ypp8nD30SHqXl1ufz3QsYJ6F5Yvayg6tWpc5qeISF0V7z4PifLU2HPjHYKISI3Ee7SViIjUQ0oeIiISmpKHiIiEpuQhIiKhKXmIiEhoGm0lItXSMHIpT8lDRKqlYeRSnpqtREQkNCUPEREJTclDRERCU/IQEZHQlDxERCQ0JQ8REQlNyUNEREKLafIws8lmlm1mVa72Z2b9zazYzEZGlR0ys0XB6x9R5d3NbK6ZrTazZ80sJZafQUREjhTrO48pwIiqKphZIvBH4O1yh/LdPTN4XRpV/kfgfnfvBewGxtZivCIiUgMxTR7uPgvYVU21m4AXgezqrhesW34h8EJQ9ASRdcxFROQ4imufh5l1BK4AHqngcKqZzTezD82sNEG0Afa4e3Gwv5kKlqoVEZHYivezrR4Abnf3kshNRRld3X2LmfUA/mNmS4G9YS5uZuOAcQBdunSpjXhFRIT4j7bKAqaZ2XpgJDCh9C7D3bcEP9cCM4GzgJ1ASzMrTXqdgC2VXdzdJ7l7lrtnZWRkxOxDiIicaOKaPNy9u7t3c/duRPoxfujur5hZKzNrBGBm6cAgYIW7OzCDSKIB+B7wahxCFxE5ocW02crMpgLDgHQz2wzcCSQDuPvEKk49BfirmZUQSXB/cPcVwbHbidyt/Bb4GHgsRuGLiEglYpo83H10iLpXR21/AJxRSb21wIBjDk5ERI5avDvMRUROOA1hZUYlDxGR46whrMwY79FWIiJSDyl5iIhIaEoeIiISmpKHiIiEpuQhIiKhKXmIiEhoSh4iIhKakoeIiISm5CEiIqEpeYiISGhKHiIiEpqSh4iIhKbkISIioSl5iIhIaDFNHmY22cyyzWxZNfX6m1mxmY0M9jPNbI6ZLTezJWZ2VVTdKWa2zswWBa/MWH4GERE5UqzvPKYAI6qqYGaJwB+Bt6OKDwDfdffTgvMfMLOWUcdvc/fM4LWolmMWEZFqxDR5uPssYFc11W4CXgSyo8771N0/C7Y/D45lxCpOEREJJ659HmbWEbgCeKSKOgOAFGBNVPHvguas+82sURXnjjOz+WY2Pycnp9biFhE50cW7w/wB4HZ3L6nooJl1AJ4Cvh9V52dAX6A/0Bq4vbKLu/skd89y96yMDN24iIjUlnivYZ4FTDMzgHTgYjMrdvdXzKw58E/gF+7+YekJ7r412DxoZo8DPz3eQYuInOjimjzcvXvptplNAV4PEkcK8DLwpLu/EH2OmXVw960WyTiXA1WO5BIRkdoX0+RhZlOBYUC6mW0G7gSSAdx9YhWnXglcALQxs6uDsquDkVXPmFkGYMAi4PrYRC8iIpWJafJw99Eh6l4dtf008HQl9S489shERORYxLvDXERE6iElDxERCU3JQ0REQlPyEBGR0JQ8REQkNCUPEREJLd4zzEViolOrxmV+ikjtUvKQBumpsefGOwSRBk3NViIiEpqSh4iIhKbkISIioSl5iIhIaEoeIiISmpKHiIiEpuQhIiKhmbvHO4bjwsxygA3xjqMG0oEd8Q6igdDvsnbp91m76svvs6u7Z5QvPGGSR31hZvPdPSvecTQE+l3WLv0+a1d9/32q2UpEREJT8hARkdCUPOqeSfEOoAHR77J26fdZu+r171N9HiIiEpruPEREJDQlDxERCU3Jo44wsxFmtsrMVpvZHfGOpz4zs8lmlm1my+IdS0NgZp3NbIaZrTCz5WZ2S7xjqq/MLNXMPjKzxcHv8q54x3S01OdRB5hZIvAp8BVgMzAPGO3uK+IaWD1lZhcA+4En3f30eMdT35lZB6CDuy80s2bAAuBy/f0Mz8wMSHP3/WaWDMwGbnH3D+McWmi686gbBgCr3X2tuxcC04DL4hxTveXus4Bd8Y6joXD3re6+MNjeB6wEOsY3qvrJI/YHu8nBq17+D17Jo27oCGyK2t+M/nFKHWRm3YCzgLnxjaT+MrNEM1sEZAPvuHu9/F0qeYhIjZhZU+BF4FZ3z413PPWVux9y90ygEzDAzOpl06qSR92wBegctd8pKBOpE4L2+ReBZ9z9pXjH0xC4+x5gBjAi3rEcDSWPumEe0NvMuptZCjAK+EecYxIBDnfyPgasdPc/xzue+szMMsysZbDdmMggmU/iG9XRUfKoA9y9GPgR8BaRzsjn3H15fKOqv8xsKjAH6GNmm81sbLxjqucGAWOAC81sUfC6ON5B1VMdgBlmtoTIfxrfcffX4xzTUdFQXRERCU13HiIiEpqSh4iIhKbkISIioSl5iIhIaEoeIiISmpKHiIiEpuQhcpTM7G4z+3K84xCJB83zEDkKZpbo7odidO2kYOKoSJ2lOw+Rcsysm5l9YmbPmNlKM3vBzJqY2Xoz+6OZLQS+ZWZTzGxkcE5/M/sgWOTnIzNrFjw99U9mNs/MlpjZdVW85zAze8/M/gGsCMpeMbMFwaJB46Lq7jez3wXv9aGZtQvKewb7S83st2a2P+qc26LiqLcLEEndoeQhUrE+wAR3PwXIBX4YlO9097PdfVppxeB5ZM8SWdSnH/BlIB8YC+x19/5Af+BaM+texXueHVzj5GD/Gnc/B8gCbjazNkF5GvBh8F6zgGuD8vHAeHc/g8hj/Uvj+yrQm8i6MZnAOcGCWSJHTclDpGKb3P39YPtpYHCw/WwFdfsAW919HoC75wbNTl8Fvhus3TAXaEPkS7wyH7n7uqj9m81sMfAhkacul55bCJQ+D2kB0C3YHgg8H2z/Peo6Xw1eHwMLgb7VxCFSraR4ByBSR5XvDCzdzwtxDQNucve3alj/8LXNbBiRO5iB7n7AzGYCqcHhIv+is/IQ1f87NuD/3P2vNQ1cpDq68xCpWBczGxhsf5vIWtOVWQV0MLP+AEF/RxKRpyTfEKyFgZmdbGZpNXz/FsDuIHH0Bc6rwTkfAt8MtkdFlb8FXBMs5oSZdTSztjWMQ6RCSh4iFVsF3GhmK4FWwCOVVQzWnb8KeDBoZnqHyF3Co0Q6vxea2TLgr9T8bv9NICl4/z8QSQzVuRX4cfC4717A3iC+t4k0Y80xs6XAC0CzGsYhUiEN1RUpJ1in+3V3r1fLg5pZEyDf3d3MRgGj3f2yeMclDZP6PEQajnOAh4KV//YA18Q5HmnAdOchchyZ2RnAU+WKD7r7ufGIR+RoKXmIiEho6jAXEZHQlDxERCQ0JQ8REQlNyUNEREL7fyMmAUPsAFbEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jLTeUeyq56I"
      },
      "source": [
        "clock speed is not much effecting the price beacuse as we can see from the chart that all 4 price categories lie in same frequency or iregular frequency range at every clock speed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vpt43qjMwj_1"
      },
      "source": [
        "##dual_sim - categorical variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "sp4iyy2qwaA-",
        "outputId": "69389126-887b-44b7-acc6-cbe692df7775"
      },
      "source": [
        "sns.pointplot(y=df['dual_sim'],x=df['price_range'])"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c81db70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AZGZPzCB/YiZz8jKYNT5D+x3HQNhGQB+eEdkI6JQ0bQTUG3VlFdhzgR8B04jsN50M1JxoPwnpX/Yfqg26i4Iuo52VlFYc6fC6zGEDmZGdzszstOZAyMkYEtol9KEzxvD8597Hw38tZsnfd9LQ6KQNTuGlL1xI9ojIcxKzJmQwa0IGiy6KzJrZuv8QK4ojgVGwrZwd5S111dQ18Jd39vOXd/YDkT7xGdnpzWMac3IzyRw2sBu+Q/1XexsBjQs2AvqgNgLq1boycH0/kZ3lfg3kA58GpsSiKOnd3J291bWsLY3cHTSNIeyuOtrhtaNTBzEzO50zstOZMS6NmTnpjEkb3OUxghnZ6fzffzib1TsOUry/hpHDBzUHRFtJScZpo1M5bXQqN8ybAMCuyiMUllQ0321s3F3V3N1V3+is3nGQ1TsO8tO/RMY1Ts0axty8TPInZjI3L/OEISYt6uob+clr7/KjV1s2AkoyWHh+HrddPkUbASWALv0bcvctZpbs7g3Az4JVYI/bMEj6DndnZ+VR1pZWRo0hVLH/UG2H145LHxzpLspOjwTDuDRG95IuhbHpQ7jirCFccdY4ILIvwartwZ1GcQWrSw82/1ADeHdfDe/uq+HJFTsAOCVtUPOYxpzcTKaOSSVZfemtFJaUc8eza3lnb+uNgO65diZn5mgjoETRlZA4HOw7vdrMvgvsAjTa14e4O9vLDzd3FTV1HUX3H4eZkDmUGdlpnDGuJRAS6eGn9CEDuGTqaC6ZOhqA2voG1pZWUhDcbRSWlLd6PmNPVS2/XbOL366JbM+eOiiF2RMzgruNDM4aP6LfTt+sPHKMe363kSdXtN4I6LbLpvCP78vVJIEE05WQuJHIOMRi4IvAeEAL+iWoxkan+EAN68oqIwPLQddR9QkeVGsyadQwzogaQzhjXHqfW6Z5UEoy+bmZ5Odm8i+cSmOjs3lvdSQ0isspKClnV2VL91p1bT2vbd7Ha5v3AZFF6GbmpJOfm8Hc3EzOmZjBiKF9e1zD3Xlp7S6+/uL6VhsBXTI1i7u0EVDC6soqsE2znI4AX49NORIL9Q2NbN0fCYS1ZZXN009r6hpOeF2SwalZw1uNIUwfl0bq4L4VCJ2RlGScPiaN08ekceO5EwEoO3ikOTAKSspb7a9c19DIym0VrNxWwU9e2wrA1FNSI6GRFwmfsDGURFRacZj/eX4dr27a19w2avggvrZgOh+dqY2AEllXZjcV085Dde6u1V97kWMNjbyz51Cr7qL1u6paPTfQnuQkY/LoSCBExhHSmDY2jaEDNbAYJnvEELJnZXPVrGwADh6uY+W2ClaUlFNYUsGa0tbPc2zaU82mPdX8ItiPeVz6YOYEgTE3N5PJo4cn3DMC9Q2N/Oz1Eu5dro2A+qqu/ATIj3o9GLgO0G4fcVRb38Dm3YdaPYOwYXd1qwHX9gxMTmLqmNRWYwhTx6T22z707jJi6EA+MO0UPjAtMqXz6LEG/r7jYPMMqlXbKlotXLez8igvrN7JC6t3ApFxkfyJTdNuM5iZk96r90tYW1rJ7c+uoWhny1Ipk0cP51vaCKhP6Up304E2Tf/PzFYCd3ZvSX1XRU0dh45GBoFrj524q6et9tYx2rynut3VUKMNSkli2tg0ZmSnBQPK6Uw5JZWBKRo8jLXBA5KZN2kk8yaNBKCh0dm4u4rCksjdRkFxOXuj+u4rjxzjlY17eSXYu3lgShJn54xgTl4kOM6ZmEFaL+jqa3cjoJQkPn/Jadzy/lP131Yf05XuptlRh0lE7izUF9EJ7s6P//QuP3zlHWqD3/J3Vh7lEz95g/s/Oeu4aaEnu47R0IHJTB+b1jztdEZ2GqdlDddskl4iOck4Ixjov+n8XNydHeVHIrOntpWzoricd/fVNJ9fV9/IipJyVpSUA+9iBqePSWNu1EN+Y9J7dkrxH9bv4c4X1rEzatD+vEkjufvqGdoIqI/qyg/5H9AyJlEPlBDpcjohM5sP3EdkZtRD7n5Pm/cXAt8DmhZyud/dHwreuwn4StD+TXd/rAv19hpPvLWd7y3bdFz7iuJybnx4Bf/zsWls3F3dpXWMUgelMH1cWqsxhLxRwzVXP4GYGRNGDmXCyKFce04OEFnXqHBb5HmNFSUVFJVVNt8tukd25Nuwq4rH3ojMIxmfOYQ5EzODxQszODVreEwGifdUHeVrS4qalzIBbQTUX3RmFdjbgpe/JRISTf81OJHF/+49wbXJwAPAZUApUGBmS4L9sqM97e6L21ybCXyVyB2LAyuDays6/Fv1IvUNjfz41S2h72/aU83/eXjFCb9G+pABwQyjyJTTmdnpTAjWMZK+ZeTwQXzojDF86IwxQGSTpdXbD0bWoNpWzsptFRyOmpW2o/wIO8rLmhfLyxg6oHlMY05uJmeMS+90909tfQP1Da3Hs8I2ArpmVjZf1kZA/UJn7iSadm+ZCswBXiASFFcAJ/7pBnOBLe6+FcDMngKuBNqGRHs+BCx39/Lg2uXAfODJTlzba5QcqGk1n74jI4N1jGZ0ch0j6duGDkzh/NNGcf5po4DILx0bdlUHM6giU2/3H6prPr/i8DGWr9/D8vV7ABg8IIlZ4zMioZGXyawJGccthVFTW8/3X97ErwtLORQEwd6qo7y2aS/3vfLOcRsB3X3VTC6YPCrWf3XpJTqzCuzXAczsz8Bsd68Ojr8GvNTB5dnAjqjjUmBeO+dda2YXAZuBL7r7jpBrs9teaGaLgEUAEyZM6Oiv0+M688P97PHpfO6SyczITjupdYyk/0gJHtKbmZPOzRfk4e6UHDgcmUFVXE7htgqK97eMaxw91sgbWw/wxtbIvJMkg+nj0pqXEzkrZwS3PvU2K7e1vkGvqWvgpp8VtHxuknHL+yfx+Uu1EVB/05UxiVOAuqjjuqDtvXoReNLda83sFuAx4NLOXuzuDwIPAuTn53d9h5oYyx05jPGZQ1qtOtrWFz4whUtOH92DVUlfYWbkjRpG3qhhfCJ/PAB7q4+ysqTleY2inZXNs5AanWAyRBU/e72kU58xe8IIvn3NmdoStp/qSkj8HFhhZs8Fx1cBj3ZwTRmR5Tua5NAyQA0cN7X2IeC7Udde3ObaP3Wh3l4hOcm49dLJ/Mcza9p9f9aEEbx/ivbwle4zOnUwH545lg/PHAvAodp63t5e0bykyNs7Kjp8uLLJ4AFJPPPZ8zX+1Y915TmJu83sd8CFQdM/uvvbHVxWAEw2szwiP/SvB26IPsHMxrr7ruBwAbAheL0M+JaZZQTHl5OgK85elz+ew3UNfP/lTa3WRrr09NH84Lqz9D+gxNTwQSlcODmLCydHfhk51tDIurJKCksquP/VLVQeCV/A8ViD67/Pfq6rS4WvAlZ14fx6M1tM5Ad+MvCIuxeZ2V1AobsvAW41swVEptWWAwuDa8vN7BtEggbgrqZB7ER00/m5XJefw8Xf+xN7q2vJyRjCIwvnxLss6YcGJCc1b8pUcqCmeZmQ9kwbqy6m/i7mD8O5+1JgaZu2O6Ne30HIHYK7PwI8EtMCe9DQgSkMG5QC1bUM0ANu0gvceN5EnlyxnbDnNBeen9ezBUmvo59UIv3Y6WPS+P51Z5HSTpfSzRfkce3s4yYUSj+jkBDp566ZncNr/3kJt35gMsMGRqa3jhsxmP/52HRNxxaFhIhElj2/7bIpzeuI9ebVZ6VnKSRERCSUQkJEREIpJEREJJRCQkREQikkREQklEJCRERCKSRERCSUQkJEREIpJEREJJRCQkREQikkREQklEJCRERCxXw/CRGR/ignY0irfyYqhYSISAw8fvO8eJfQLdTdJCIioRQSIiISSiEhIiKhFBIiIhJKISEiIqEUEiIiEkohISIioWIeEmY238w2mdkWM7v9BOdda2ZuZvnB8QAze8zM1prZBjO7I9a1iohIazENCTNLBh4APgxMBz5pZtPbOS8V+ALwVlTzdcAgd58JnAPcYma5saxXRERai/WdxFxgi7tvdfc64CngynbO+wbwHeBoVJsDw8wsBRgC1AFVMa5XRESixDoksoEdUcelQVszM5sNjHf3l9pc+wxQA+wCtgPfd/fyth9gZovMrNDMCvft29etxYuI9HdxHbg2syTgXuBL7bw9F2gAxgF5wJfMbFLbk9z9QXfPd/f8rKysmNYrItLfxHqBvzJgfNRxTtDWJBWYAfzJzADGAEvMbAFwA/B7dz8G7DWz14F8YGuMaxYRkUCs7yQKgMlmlmdmA4HrgSVNb7p7pbuPcvdcd88F3gQWuHshkS6mSwHMbBhwLrAxxvWKiEiUmIaEu9cDi4FlwAbgV+5eZGZ3BXcLJ/IAMNzMioiEzc/cfU0s6xURkdZivp+Euy8FlrZpuzPk3IujXh8iMg1WRETiRE9ci4hIKIWEiIiEUkiIiEgohYSIiIRSSIiISCiFhIiIhFJIiIhIKIWEiIiEUkiIiEgohYSIiIRSSIiISCiFhIiIhFJIiIhIKIWEiIiEUkiIiEgohYSIiIRSSIiISCiFhIiIhIr59qUisZSTMaTVP0WkeykkJKE9fvO8eJcg0qepu0lEREIpJEREJJRCQkREQikkREQkVMxDwszmm9kmM9tiZref4LxrzczNLD+q7Uwze8PMisxsrZkNjnW9IiLSIqazm8wsGXgAuAwoBQrMbIm7r29zXirwBeCtqLYU4AngRnf/u5mNBI7Fsl4REWkt1ncSc4Et7r7V3euAp4Ar2znvG8B3gKNRbZcDa9z97wDufsDdG2Jcr4iIRIl1SGQDO6KOS4O2ZmY2Gxjv7i+1uXYK4Ga2zMxWmdl/tvcBZrbIzArNrHDfvn3dWbuISL8X14FrM0sC7gW+1M7bKcAFwKeCf15tZh9oe5K7P+ju+e6en5WVFdN6RUT6m1iHRBkwPuo4J2hrkgrMAP5kZiXAucCSYPC6FPizu+9398PAUmB2jOsVEZEosQ6JAmCymeWZ2UDgemBJ05vuXunuo9w9191zgTeBBe5eCCwDZprZ0GAQ+/3A+uM/QkREYiWmIeHu9cBiIj/wNwC/cvciM7vLzBZ0cG0Fka6oAmA1sKqdcQsREYmhmC/w5+5LiXQVRbfdGXLuxW2OnyAyDVZEROJAT1yLiEgohYSIiIRSSIiISCiFhIiIhFJIiIhIKG1f2sO0J7OIJBKFRA/TnswikkjU3SQiIqF0JyEizdQdKm0pJESkmbpDpS11N4mISCiFhIiIhFJIiIhIKIWEiIiEUkiIiEgohYSIiIRSSIiISChz93jX0G3MbB+wLd51dMIoYH+8i+hD9P3sXvp+dp9E+V5OdPes9t7oUyGRKMys0N3z411HX6HvZ/fS97P79IXvpbqbREQklEJCRERCKSTi48F4F9DH6PvZvfT97D4J/73UmISIiITSnYSIiIRSSIiISCiFRA8zs/lmtsnMtpjZ7fGuJ5GZ2SNmttfM1sW7lkRnZuPN7FUzW29mRWb2hXjXlMjMbLCZrTCzvwffz6/Hu6aTpTGJHmRmycBm4DKgFCgAPunu6+NaWIIys4uAQ8DP3X1GvOtJZGY2Fhjr7qvMLBVYCVyl/zZPjpkZMMzdD5nZAOCvwBfc/c04l9ZlupPoWXOBLe6+1d3rgKeAK+NcU8Jy9z8D5fGuoy9w913uvip4XQ1sALLjW1Xi8ohDweGA4E9C/kaukOhZ2cCOqONS9D+i9DJmlgvMAt6KbyWJzcySzWw1sBdY7u4J+f1USIhIMzMbDvwG+Dd3r4p3PYnM3Rvc/WwgB5hrZgnZJaqQ6FllwPio45ygTSTugr7z3wC/cPdn411PX+HuB4FXgfnxruVkKCR6VgEw2czyzGwgcD2wJM41iTQNtD4MbHD3e+NdT6IzsywzGxG8HkJkssrG+FZ1chQSPcjd64HFwDIiA4O/cvei+FaVuMzsSeANYKqZlZrZzfGuKYG9D7gRuNTMVgd/PhLvohLYWOBVM1tD5JfD5e7+2zjXdFI0BVZERELpTkJEREIpJEREJJRCQkREQikkREQklEJCRERCKSRERCSUQkLkBMzsLjP7YLzrEIkXPSchEsLMkt29IUZfOyV4uFKkV9OdhPRLZpZrZhvN7BdmtsHMnjGzoWZWYmbfMbNVwHVm9pzlDGQAAAJsSURBVKiZfTy4Zo6Z/S3YSGaFmaUGK31+z8wKzGyNmd1ygs+82Mz+YmZLgPVB2/NmtjLYmGZR1LmHzOzu4LPeNLNTgvZTg+O1ZvZNMzsUdc1/RNWRsJvcSO+ikJD+bCrwY3efBlQB/xq0H3D32e7+VNOJwVpbTxPZOOYs4IPAEeBmoNLd5wBzgH82s7wTfObs4GtMCY4/4+7nAPnArWY2MmgfBrwZfNafgX8O2u8D7nP3mUSWmm+q73JgMpE9S84Gzgk2ZRJ5TxQS0p/tcPfXg9dPABcEr59u59ypwC53LwBw96qgu+hy4NPBvgFvASOJ/LAOs8Ldi6OObzWzvwNvElkhuOnaOqBprZ+VQG7w+jzg18HrX0Z9ncuDP28Dq4DTO6hDpFNS4l2ASBy1HZBrOq7pwtcw4PPuvqyT5zd/bTO7mMgdyXnuftjM/gQMDt4+5i0Dhg10/P+qAd929590tnCRztCdhPRnE8zsvOD1DUT2IQ6zCRhrZnMAgvGIFCIr+v5LsBcDZjbFzIZ18vPTgYogIE4Hzu3ENW8C1wavr49qXwZ8Jtg0CDPLNrPRnaxDJJRCQvqzTcDnzGwDkAH8b9iJwZ7k/wD8KOgeWk7kt/6HiAxCrzKzdcBP6Pwd+u+BlODz7yESAB35N+C2YAnq04DKoL6XiXQ/vWFma4FngNRO1iESSlNgpV8K9nH+rbsn1JaSZjYUOOLubmbXA5909yvjXZf0XRqTEEks5wD3BzvJHQQ+E+d6pI/TnYRINzOzmcDjbZpr3X1ePOoReS8UEiIiEkoD1yIiEkohISIioRQSIiISSiEhIiKh/j8gweitBNVuRgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zEhwn0m_w4iE"
      },
      "source": [
        "we can say that dual sim phones are costly as we can see a spike at 3(very high prize mobile)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2kVKWw1jxa4v"
      },
      "source": [
        "##Front Camera\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfQfFTaaw0jG",
        "outputId": "987f3997-5612-4a73-b5cf-07425c1663b8"
      },
      "source": [
        "df['fc'].unique()"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  0,  2, 13,  3,  4,  5,  7, 11, 12, 16,  6, 15,  8,  9, 10, 18,\n",
              "       17, 14, 19])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "x54F6UqSxpuF",
        "outputId": "baa19330-36f1-4d2b-9827-1db2508afe9e"
      },
      "source": [
        "plt.figure(figsize=(15,15))\n",
        "sns.countplot(x=df['fc'],hue=df['price_range'])"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c7a01d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "RiYmkGti1MZC",
        "outputId": "779f7021-a1f8-4c76-b3c8-4772c405af09"
      },
      "source": [
        "\n",
        "sns.pointplot(y=df['fc'],x=df['price_range'])"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c764518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jY_u-Ka5yfqP"
      },
      "source": [
        "comparitively we van say that costlier mobiles have high front camera resolutions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-KOjBAuzDHc"
      },
      "source": [
        "##4g & 3g"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPIHJ17QyTAb",
        "outputId": "7c70b854-75f7-4d53-f3df-622e0db87734"
      },
      "source": [
        "df[\"four_g\"].unique()"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "8D5GXKAKzHMV",
        "outputId": "9ba86748-7e38-441d-cabf-2651416e41ea"
      },
      "source": [
        "sns.barplot(y=df['four_g'],x=df['price_range'])"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1cc6a2b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "RzQD1NSY8pLr",
        "outputId": "c3f79836-20ca-43af-c2fb-dc9b1cd1040c"
      },
      "source": [
        "sns.countplot(df['four_g'])"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c8a2198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y5CtJPp/kX5K8axJ/NmlfDAvpAFXVHwL/DfwmsBr4UlU9A3gTsHWEU6wF/qmqfqGq7tpzZ5InA38BnAqcBjz9MJUuHTTDQjo0vwa8D6CqrgGOT/KEzjF3VdW1+9l/CvCZqrq/qn4IfPjwlCodPMNCGo9dPPzf1zFL1r834VqkQ2ZYSIfmP4FXACR5DvDNqvo28DXg5NZ+MrDmAM75BeA3kixPsgz43cNZsHQwlg1dgHSEuxC4NMmNwEPAhtb+EeCcJLcA1wFfGfWEVbUjyd8An2fxd6a/DDx4OIuWDpS3zkpTKMnjq+q7bWTxMeDSqvrY0HVpdjkNJU2nC5PcwOLttV8FPj5wPZpxjiykASW5DnjMHs2vqqqbhqhH2hfDQpLU5TSUJKnLsJAkdRkWkqQuw0KS1PV/Dae3S/WNAhwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "csLDOTiJzqyR",
        "outputId": "43d05d6e-9ed6-4ac0-aef4-92909f3cde46"
      },
      "source": [
        "sns.pointplot(y=df['three_g'],x=df['price_range'])"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb2032b898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jWHPrIW78dgNbd71+LcMf9h3hhvvX8vkPzuN763e4EW02TIYMCUklZP6W/6dkhnsE3B4Ruwc9MBERK4GV/dbd1W/5CynHPg6cl8vn2PjQ3R188sF1fQKix5PP7+bJ5/v+Z9nTiL5hUS3vcCPa7LgNGRIR0S3p8xGxHPjhCNRklmr1i6+xeef+IfdzI9pseOQ63PTTZOjnEeBgz8qI2JOXqsz62bX/CI1Nrdz365eG3Pfmd53OX3/gbDeizYZBriFxTfI7++qjAE4f3nLMMkNKz+9qo7FpD40vt9LYtIftew7nfPz75p/sgDAbJrnecT0334XY+HW4o4unm/eyrqmVhpf3sL6plf1HOoc+cAAnT63kotqqYa7QbPzK+aFDkt4O1GUfExEP5qEmG+NaDrSzLjlLaGhqZeOOfXR2p98HOWNKBfW1J1FfV8WC2ioee+oV/nX1ywPu+/kr51NeWpKfws3GoVyn5VgKnAFsAHruiA4yE/CZperuDl5oaaOxqbV36KjptUODHnPmyVNYWFfFgtqTqK+tonb6JLLv3Ty/5kROmVbJfU++xGsHOwAoKxFfueYCrj7/tLz+85iNN7meSdQDb82+j8FsIEeOdvFM8z4am/aw7uVW1m1rZe+h9LkgK8pKOL9mGvV1mUC4aE4VVZMrBv2MkhJxy3vO5KZ3zOXyL/+SHXsPU1M10QFhlge5hsTvgFOBnXmsxYrQa23tNDa1sq6plcaX9/Dsjn3HTH2R7aTJFSyoreo9Uzhn1glvuMlcWVZKRVlmaCm3WWLM7HgNGhKS/p3MsNJUYJOktWRNxxERV+e3PBtNIoIXWg729hMam1p5affBQY85vXoy9bVVvWcKc2dM9h/oZkVkqDOJL5O5w/ofgQ9lre9ZZ2NYe2cXzzbv6+0nrGvaQ+tgQ0elJZxbM603FBbUVnHSEENHZja6DRoSEfFLAEnlPa97JBP+2Riy52BHZtgo6Sc8s2MfHZ3pDwOsmlTOgtrMsNHCuirOmTWNCeW+P8FsLBlquOkvgVuA0yU9k7VpKvCbfBZm+RURvLT7YKafkFx19ELL4ENHc2dMZkFtVe+ZwhnVHjoyG+uGGm56GPgR8EXgzqz1BzwlR3Hp6Ozm2R37evsJ65paey8fHUh5qThnVmboaEFtZuioemrlCFZsZqPBUMNN+4B9ZJ49bUVk76GeoaPMmcLTzXtpH2ToaNrEnqGjKhbWncR5NR46MrPjuOPaRq+IYNueQzQkzeXGl1t5foCptLPVTp+UDB1l+glnVE/xNNpmdgyHRBHq6Oxm4yv7knsTMmcLu9vSHxRYViLelgwd1ddWsaCuipOnThjBis2sWDkkRlBE0NGVGfLpPo6b1/cdPsr65KqjxmTo6MjR9KGjqRPKehvMC2pP4oLZJzKxwkNHZnb8HBIj5BdbdvEPKzezozUz5fW21w5xzw838dkPzutzx3FE0Nx6mIaX9/T2E57bdYDBMqWmaiILk/sS6uuqOPvkqR46MrNh4ZAYAb98roVPPNBA9kSnAXzzyZfYtucQt7znDBqb9vb2E3YdSB86Ki0Rb515AvV1Vb0zo55ygoeOzCw/HBJ5FhF86Ue/J20m7FUbX2XVxldTj59SWcaFc05kYTKtxfmzT2Rypf+1mdnI8J82ebZ9z+GcnsncY9aJE5OzhEw/Yd6pUyn10JGZFYhDIs8OH+0acp95p07l1veeSX1dFTOnebYTMxs9HBJ5Vjt9EtMmlrPvcPrEeDe/83T+xM9CMLNRyM95zLMJ5aV8fFFt6vaZ0ybwx+fNHMGKzMxy55AYAZ9+31n86UWzjll/2rQJPPDnF3v6CzMbtRwSI6CstISvfPQCfvjpdzBtYjkA1VMreeJz72HeqVMLXJ2ZWTqHxAh622nTeh/CM6Wy7A0/ttPMbKQ4JMzMLJVDwszMUjkkzMwsVd5DQtKVkrZI2irpzgG2f1XShuTnOUl7+20/QVKzpK/nu1YzM+srrzfTSSoFvgFcATQDDZJWRMSmnn0i4o6s/W8DLuz3Nv8d+FU+6zQzs4Hl+0ziYmBrRLwYER3AMmDxIPtfC3y3Z0HSAuAU4Cd5rdLMzAaU75CYBWzPWm5O1h1DUi0wF/h5slwC/C/gs4N9gKSbJTVKamxpaRmWos3MLGM0Na6XAI9GRM+MeLcAKyOiebCDIuLeiKiPiPrq6uq8F2lmNp7ke4K/HcDsrOWaZN1AlgCfylpeBLxT0i3AFKBCUltEHNP8NjOz/Mh3SDQAZ0maSyYclgAf67+TpPlAFbC6Z11E/FnW9huBegeEmdnIyutwU0R0ArcCq4DNwPKI2CjpbklXZ+26BFgWMdiTnM3MbKTl/XkSEbESWNlv3V39lr8wxHs8ADwwzKWZWT81VRP7/DbzQ4fMrNfSmy4pdAk2yoymq5vMzGyUcUiYmVkqh4SZmaVySJiZWSqHhJmZpXJImJlZKoeEmZmlckiYmVkqh4SZmaXyHddmZnkwVqY4cUiYmeXBWJnixMNNZmaWyiFhZmapHBJmZpbKIWFmZqkcEmZmlsohYWZmqRwSZmaWyiFhZmapHBJmZpbKIWFmZqkcEmZmlsohYWZmqRwSZmaWyiFhZmapHBJmZpbKIWFmZqkcEmZmlirvISHpSklbJG2VdOcA278qaUPy85ykvcn6CyStlrRR0jOSrsl3rWZm1ldeH18qqRT4BnAF0Aw0SFoREZt69omIO7L2vw24MFk8BNwQEc9LOg1YJ2lVROzNZ81mZva6fJ9JXAxsjYgXI6IDWAYsHmT/a4HvAkTEcxHxfPL6FWAXUJ3nes3MLEu+Q2IWsD1ruTlZdwxJtcBc4OcDbLsYqABeyEONZmaWYjQ1rpcAj0ZEV/ZKSTOBpcCfR0R3/4Mk3SypUVJjS0vLCJVqZjY+5DskdgCzs5ZrknUDWUIy1NRD0gnAD4G/jYg1Ax0UEfdGRH1E1FdXezTKzGw45TskGoCzJM2VVEEmCFb030nSfKAKWJ21rgL4v8CDEfFonus0M7MB5DUkIqITuBVYBWwGlkfERkl3S7o6a9clwLKIiKx1HwXeBdyYdYnsBfms18zM+srrJbAAEbESWNlv3V39lr8wwHEPAQ/ltTgzMxvUaGpcm5nZKOOQMDOzVA4JMzNLlfeehFk+1VRN7PPbzIaXQ8KK2tKbLil0CWZjmoebzMwslUPCzMxSOSTMzCyVQ8LMzFI5JMzMLJVDwszMUjkkzMwslUPCzMxSOSTMzCyVQ8LMzFJ5Wo4R5rmGzKyYOCRGmOcaMrNi4uEmMzNL5ZAwM7NUDgkzM0vlkDAzs1QOCTMzS+WQMDOzVA4JMzNLpYgodA3DRlIL0FToOnIwA9hd6CLGEH+fw8vf5/Aplu+yNiKqB9owpkKiWEhqjIj6QtcxVvj7HF7+PofPWPguPdxkZmapHBJmZpbKIVEY9xa6gDHG3+fw8vc5fIr+u3RPwszMUvlMwszMUjkkzMwslUNihEm6UtIWSVsl3VnoeoqZpPsl7ZL0u0LXUuwkzZb0hKRNkjZKur3QNRUzSRMkrZX0dPJ9/rdC1/RGuScxgiSVAs8BVwDNQANwbURsKmhhRUrSu4A24MGIOKfQ9RQzSTOBmRGxXtJUYB3wIf+3+cZIEjA5ItoklQO/Bm6PiDUFLu24+UxiZF0MbI2IFyOiA1gGLC5wTUUrIn4F7Cl0HWNBROyMiPXJ6wPAZmBWYasqXpHRliyWJz9F+Tdyh8TImgVsz1puxv8j2igjqQ64EPhtYSspbpJKJW0AdgGPR0RRfp8OCTPrJWkK8D3gryJif6HrKWYR0RURFwA1wMWSinJI1CExsnYAs7OWa5J1ZgWXjJ1/D/hORHy/0PWMFRGxF3gCuLLQtbwRDomR1QCcJWmupApgCbCiwDWZ9TRa7wM2R8RXCl1PsZNULenE5PVEMher/L6wVb0xDokRFBGdwK3AKjKNweURsbGwVRUvSd8FVgPzJDVLuqnQNRWxy4DrgcslbUh+rip0UUVsJvCEpGfI/OXw8Yj4QYFrekN8CayZmaXymYSZmaVySJiZWSqHhJmZpXJImJlZKoeEmZmlckiYmVkqh4TZICTdLen9ha7DrFB8n4RZCkmlEdGVp/cuS26uNBvVfCZh45KkOkm/l/QdSZslPSppkqSXJf2jpPXARyQ9IOnDyTELJf1H8iCZtZKmJjN9/k9JDZKekfQXg3zmeyQ9KWkFsClZ95ikdcmDaW7O2rdN0j3JZ62RdEqy/oxk+VlJfy+pLeuYz2XVUbQPubHRxSFh49k84P9ExFuA/cAtyfrXIuKiiFjWs2My19YjZB4ccz7wfuAwcBOwLyIWAguBT0qaO8hnXpS8x9nJ8iciYgFQD3xa0vRk/WRgTfJZvwI+maz/GvC1iDiXzFTzPfV9ADiLzDNLLgAWJA9lMntTHBI2nm2PiN8krx8C3pG8fmSAfecBOyOiASAi9ifDRR8AbkieG/BbYDqZP6zTrI2Il7KWPy3paWANmRmCe47tAHrm+lkH1CWvFwH/lrx+OOt9PpD8PAWsB+YPUYdZTsoKXYBZAfVvyPUsHzyO9xBwW0SsynH/3veW9B4yZySLIuKQpF8AE5LNR+P1hmEXQ/+/KuCLEfEvuRZulgufSdh4NkfSouT1x8g8hzjNFmCmpIUAST+ijMyMvn+ZPIsBSWdLmpzj508DWpOAmA9cmsMxa4D/nLxekrV+FfCJ5KFBSJol6eQc6zBL5ZCw8WwL8ClJm4Eq4J/TdkyeSX4N8E/J8NDjZP7W/y0yTej1kn4H/Au5n6H/GChLPv9LZAJgKH8FfCaZgvpMYF9S30/IDD+tlvQs8CgwNcc6zFL5Elgbl5LnOP8gIorqkZKSJgGHIyIkLQGujYjFha7Lxi73JMyKywLg68mT5PYCnyhwPTbG+UzCbJhJOhdY2m91e0RcUoh6zN4Mh4SZmaVy49rMzFI5JMzMLJVDwszMUjkkzMws1f8HwfgzESSL2LUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "wA5vCQfB8xHe",
        "outputId": "d910890c-f555-4c19-ae94-48a8737496e6"
      },
      "source": [
        "sns.countplot(df['three_g'])"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c542c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tQ/WL211RZwGPD52ukiRNwPFT2OfZwE8Dn07yqVb7ReBXgJuTbAUeAt7Ylt0GvAZYAL4GXDrZdiVJEw+LqvozIMssPmeJ8QVcNtamJEmH5RPckqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkrqOmbBIcl6SzyZZSHL5tPuRpFlyTIRFkuOA9wHnA6cDFyU5fbpdSdLsOCbCAjgTWKiqB6rqW8BNwOYp9yRJM+P4aTcworXA7qH5PcArhgck2QZsa7NfSfLZCfU2C04GvjDtJlaD/PqWabegp/Lf50FX5Hvdwt9ZbsGxEhZdVbUd2D7tPp6OksxX1aZp9yEtxX+fk3GsnIbaC6wfml/XapKkCThWwuLjwMYkpyV5FnAhsHPKPUnSzDgmTkNV1YEkPwt8EDgO2FFV9065rVni6T2tZv77nIBU1bR7kCStcsfKaShJ0hQZFpKkLsNCh+VrVrQaJdmRZF+Se6bdy6wwLLQsX7OiVew64LxpNzFLDAsdjq9Z0apUVR8B9k+7j1liWOhwlnrNytop9SJpigwLSVKXYaHD8TUrkgDDQofna1YkAYaFDqOqDgAHX7NyH3Czr1nRapDkRuAO4CVJ9iTZOu2enu583YckqcsjC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0IaQZI1Sd7Spl+V5I+n3ZM0SYaFNJo1wFuOZIX2infpacGH8qQRJDn4evbPAt8Gvgp8AXgpcBfwpqqqJA8CHwD+KfCfGbxG+z8CJwB/DVxaVV9J8nLgN4Hntu1cUlUPL7PvfwRcA3wH2AWcX1UvHdOfKi3JIwtpNJcDf11V/xD4BeBlwNsY/CjU3wXOHhr7aFWdAfxf4JeAH2vz88DbkzwTeC9wQVW9HNgBvPsw+74W+Jm27ydW9s+SRnP8tBuQjlEfq6o9AEk+BWwA/qwt+0D7PotBmPx5EoBn0d5nxOCIZFerHwcsd1SxBnheVd3RSv8VeO0K/y1Sl2EhHZ1vDk0/wZP/W/pq+w6wq6ouGl4xyQ8D91bVK8fborRyPA0ljebLwPOOcJ07gbOTvBggyXOS/D0G1z3mkryy1Z+Z5IeW2kBVfRH4cpJXtNKFR9W99D3yyEIaQVU9muTPk9wDfB34/AjrLCa5BLgxyQmt/EtV9VdJLgCuSvICBv8d/haw3OvftwK/m+Q7wJ8Cj3+Pf450xLwbSlrlkjy3qr7Spi8HTq2qt065Lc0Yjyyk1e/Hk7yTwX+vDwGXTLcdzSKPLKRVIsn7ePItuABXVtW10+hHGmZYSJK6vBtKktRlWEiSugwLSVKXYSFJ6vp/w9XVLFajfekAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Pb-aR1BzS4o"
      },
      "source": [
        "1. costly mobiles are more likely to be 4g and 3g\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7X6r01PG0YW6"
      },
      "source": [
        "## internal memory in gb's"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxfUQqnCzOK1",
        "outputId": "e05fd2dd-4e66-4d98-9cd4-766d4902fdb9"
      },
      "source": [
        "df['int_memory'].unique()#can be considerd as continous as well as categorical"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 7, 53, 41, 10, 44, 22, 24,  9, 33, 17, 52, 46, 13, 23, 49, 19, 39,\n",
              "       47, 38,  8, 57, 51, 21,  5, 60, 61,  6, 11, 50, 34, 20, 27, 42, 40,\n",
              "       64, 14, 63, 43, 16, 48, 12, 55, 36, 30, 45, 29, 58, 25,  3, 54, 15,\n",
              "       37, 31, 32,  4, 18,  2, 56, 26, 35, 59, 28, 62])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "v-0ouILQ2Tmo",
        "outputId": "707fd13b-bab5-43cb-ded9-4db75ae7fe32"
      },
      "source": [
        "sns.violinplot(df['price_range'],df['int_memory'])"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c50ba90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "lR91q5LqO_lA",
        "outputId": "31522bcc-931a-477f-cc8a-0eefe4d02fb2"
      },
      "source": [
        "sns.pointplot(df['price_range'],df['int_memory'])"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c46fa20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FuWN_4v1OIQ"
      },
      "source": [
        "we can see in the plot that 3(costly) is high which signifies that most of costlier mobiles have higher memory compared to rest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clU2g_GP18mu"
      },
      "source": [
        "##width"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "ly631ION1_Ql",
        "outputId": "5794072d-cef0-4533-f086-025f67679c0c"
      },
      "source": [
        "sns.violinplot(df['price_range'],df['m_dep'])"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c3ca2e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2A8XQ6h02Rni"
      },
      "source": [
        "higher frequency of slimer phones are present in all 4 classes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DcDbtQHE_PuW"
      },
      "source": [
        "##mobile weight"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "wmQAMc4k_R5t",
        "outputId": "96ed7978-8811-4beb-9e70-5b9aa7c2a6c3"
      },
      "source": [
        "sns.violinplot(df['price_range'],df['mobile_wt'])"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c3684e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "FnLBbBRZ2yDu",
        "outputId": "56cb808d-eafd-4610-9b05-04babef919e1"
      },
      "source": [
        "sns.barplot(df['price_range'],df['mobile_wt'])"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c27ccf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eks4yKa5_esX"
      },
      "source": [
        "we can see that 3 is highly densed at lesser values of weight so lighter mobiles costs high"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EaAe6vAq_vhy"
      },
      "source": [
        "## Number of cores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "ITwjciDK_2nC",
        "outputId": "be37b56c-0517-43bb-9617-fee337714e73"
      },
      "source": [
        "sns.barplot(df['price_range'],df['n_cores'])"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c263358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 170
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m+y5Juzcvv5bXb25t1Pb/n1nS3ya5qm3hQBv04DGoDrJ9XLP92+o9N3NbnpK0n+15ktSMv89Wb2XQP2jWUpftd9me07L9X5T0f024Hybp/S3es1jSx5rtMyYcv13SOc0DO2R7f9vvaFkHsE0EPAbVU5LOt71c0tskfXlbJzbP0D1d0hebIZU71Ottf0W9G6bLbD8m6Sq1/6v2Nkmzm/b/Tr3w3p6LJP1Rswztr0h6oanve+oN2dxv+1FJN0ma27IOYJuYJomB0zx39NYkA/UYNdt7Sno5SWyfIenMJDy4HcUwBg9Mn2MkXdk8gWmtpHM6rgeVowcPTGD7cEn/ssXhV5K8r4t6gKkg4AGgUtxkBYBKEfAAUCkCHgAqRcADQKX+H47c27HdnlWjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xC53iB4zAkN5"
      },
      "source": [
        "rise in mean of 2 and 3 signifies us more the number of cores higher the mobile phone prices are"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMjleXxKBGxJ"
      },
      "source": [
        "##main camera"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "PJTERtR855z2",
        "outputId": "c9bd77e8-d83d-46f8-d540-60ed4143f141"
      },
      "source": [
        "sns.pointplot(y=df['pc'],x=df['price_range'])"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c1bbb70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ctx0YF7OBXi4"
      },
      "source": [
        "main camera pixels has positive corelation with price range"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DspB6Zm1B91r"
      },
      "source": [
        "##pixel resolution height and width"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "7k4aZHnAB5Fu",
        "outputId": "61d50a6d-ba05-44d1-82c3-4b789cf03170"
      },
      "source": [
        "sns.barplot(df['price_range'],df['px_height'])"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c1ab710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "SHBRVmtHCLOY",
        "outputId": "13c3c13c-0de3-45c9-e4fc-34239d32b7fc"
      },
      "source": [
        "sns.barplot(df['price_range'],df['px_width'])"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c15ee80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Din8ab02CWv8"
      },
      "source": [
        "we can see that the costly mobiles have higher pixel width and height"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7fXXw2yChn-"
      },
      "source": [
        "##RAM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZUUJs8ECkJd",
        "outputId": "44e4cf2d-54b4-4145-eaf0-b6a051528147"
      },
      "source": [
        "df['ram'].unique()"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2549, 2631, 2603, ..., 2032, 3057, 3919])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "w7LbGtCCCoBv",
        "outputId": "ca9b15de-8831-44ad-ddd9-f5826fc78be9"
      },
      "source": [
        "sns.violinplot(df['price_range'],df['ram'])"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c07ed68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gfxmu0tUCw3J"
      },
      "source": [
        "its very obious from  the plot that higher the ram is higher the cost of mobile phone will be"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNLcu8KcDECI"
      },
      "source": [
        "##screen height and width"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "9UO9vgUCDECK",
        "outputId": "af019ff4-4f03-417d-d4e0-5db537281562"
      },
      "source": [
        "sns.barplot(df['price_range'],df['sc_h'])"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1c0701d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bgXXD9FHAJ4bpf9hhO8cNP18CNgFPepAc0oOaah1AGpOFJ7O2z9+xhG0EeH1Vnb/I9X+w7STPZf4viKOq6s4knwb2HF6+p/7/ZNu9PPi/wwB/WVUfWGxw6cF45K9JdWCSo4bpX2P+Was7cw2wX5IjAYbx/inm79L628O98EnyxCR7LXL/jwT+dyj+JwHPWsR7LgVeMkyfuMPy84GTh4exkGT/JD+2yBzS/bL8NamuAX43yWbgUcD7d7bi8NzllwHvHYZpLmT+KP2DzJ+83ZTkSuADLP6v5U8BU8P+38Z8sT+YNwC/P9wu+KeA7w35LmB+GOjzSa4Azgb2XmQO6X55qacmzvCc2k9W1ap6vF6StcBdVVVJTgROqqoTWufSZHLMX1o5jgD+enjy1i3AyY3zaIJ55C8tQZKnAH+/YPH3q+qZLfJIu8ryl6QOecJXkjpk+UtShyx/SeqQ5S9JHfo/vxbcUZFyO4AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "_9MOklLnDECP",
        "outputId": "064d1fcd-7fb3-4a49-a517-7d4e1806449f"
      },
      "source": [
        "sns.barplot(df['price_range'],df['sc_w'])"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1bfc2278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LbJ/abP+GOvfN3JMnJS20fZIkNfPvQ+qsDPq7zVrqsv1W2/N7rP9GSf/bhPvbJL2jh9fcJ+lDzfbZXY/fLOm85oYdsn247Z/osQ9gjwh4DKonJX3c9jpJPy7p8j0d2NxD98OSvthMqdyqzmj7SnU+MF1t+zFJV6j3f9XeJGmoqf85dcJ7by6S9PvNMrQ/I+mHTX+3qDNlc6/tRyVdK2lBj30Ae8Rlkhg4zX1Hb0wyULdRsz1P0ktJYvtsSeck4cbtKIY5eGD6nCjpb5s7MG2RdF7L/aByjOCBLraPlfRPuzz8cpJT2ugH2B8EPABUig9ZAaBSBDwAVIqAB4BKEfAAUKn/AwucxEqIlBMQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3DRwn3tDECQ"
      },
      "source": [
        "slight correlation can be seen"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k698PdmBDhyA"
      },
      "source": [
        "##talktime"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "uc9IE0kDDhyA",
        "outputId": "fc1f15bd-8eba-42ac-90a7-ce4b5610eac3"
      },
      "source": [
        "sns.barplot(df['price_range'],df['talk_time'])"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1bf58fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8j45ZgYADwQe"
      },
      "source": [
        "a positive corelation can be seen in class 1 and talk time variables\n",
        "this can be interppreted as mobiles of that price range has more focus on talktime"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xisx-QVtEqoD"
      },
      "source": [
        "##touch screen"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "HLUdEPHTEZGt",
        "outputId": "195a4f40-4b1b-422a-f7de-d299883cd154"
      },
      "source": [
        "sns.pointplot(y=df['touch_screen'],x=df['price_range'])"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1bebb630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85OQqfYIIKAH"
      },
      "source": [
        "significance here is quit different compared to real life senario so nothing usefull or meaning is seen. negative correlation is seen"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7EaF-TkzEtuv"
      },
      "source": [
        "##wifi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "LigcBznoEjdU",
        "outputId": "e071afc0-f1c0-4646-cba8-035092037f8f"
      },
      "source": [
        "sns.pointplot(y=df['wifi'],x=df['price_range'])"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7feb1be95e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQ0h3EMCEwgQ"
      },
      "source": [
        "costly mobiles have higher tendency to have have wifi "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i8S93Fx32ukf"
      },
      "source": [
        "y=df['price_range']\n",
        "X=df.drop('price_range',axis=1)"
      ],
      "execution_count": 356,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "NWlgrxVK79to",
        "outputId": "0d81adaa-39c7-43fe-e120-80a5d7c74325"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>battery_power</th>\n",
              "      <th>blue</th>\n",
              "      <th>clock_speed</th>\n",
              "      <th>dual_sim</th>\n",
              "      <th>fc</th>\n",
              "      <th>four_g</th>\n",
              "      <th>int_memory</th>\n",
              "      <th>m_dep</th>\n",
              "      <th>mobile_wt</th>\n",
              "      <th>n_cores</th>\n",
              "      <th>pc</th>\n",
              "      <th>px_height</th>\n",
              "      <th>px_width</th>\n",
              "      <th>ram</th>\n",
              "      <th>sc_h</th>\n",
              "      <th>sc_w</th>\n",
              "      <th>talk_time</th>\n",
              "      <th>three_g</th>\n",
              "      <th>touch_screen</th>\n",
              "      <th>wifi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>842</td>\n",
              "      <td>0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.6</td>\n",
              "      <td>188</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>756</td>\n",
              "      <td>2549</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1021</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>0.7</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>905</td>\n",
              "      <td>1988</td>\n",
              "      <td>2631</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>563</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>0.9</td>\n",
              "      <td>145</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1263</td>\n",
              "      <td>1716</td>\n",
              "      <td>2603</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>615</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.8</td>\n",
              "      <td>131</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>1216</td>\n",
              "      <td>1786</td>\n",
              "      <td>2769</td>\n",
              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1821</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>0.6</td>\n",
              "      <td>141</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>1208</td>\n",
              "      <td>1212</td>\n",
              "      <td>1411</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   battery_power  blue  clock_speed  ...  three_g  touch_screen  wifi\n",
              "0            842     0          2.2  ...        0             0     1\n",
              "1           1021     1          0.5  ...        1             1     0\n",
              "2            563     1          0.5  ...        1             1     0\n",
              "3            615     1          2.5  ...        1             0     0\n",
              "4           1821     1          1.2  ...        1             1     0\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 357
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybsXw7KjJOEU"
      },
      "source": [
        "##Normalizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J0wfYa3E5zdS"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": 358,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UnJDVKMxDiww"
      },
      "source": [
        "norm=StandardScaler()"
      ],
      "execution_count": 359,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7XYCFug9Disx"
      },
      "source": [
        "X['battery_power']=norm.fit_transform(X[['battery_power']])\n",
        "X['mobile_wt']=norm.fit_transform(X[['mobile_wt']])\n",
        "X['px_height']=norm.fit_transform(X[['px_height']])\n",
        "X['px_width']=norm.fit_transform(X[['px_width']])\n",
        "X['ram']=norm.fit_transform(X[['ram']])\n",
        "\n",
        "X['int_memory']=norm.fit_transform(X[['int_memory']])"
      ],
      "execution_count": 360,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5OAbjOfk51cj"
      },
      "source": [
        "##splitting data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnwTdRdo6DpO"
      },
      "source": [
        "from sklearn import model_selection"
      ],
      "execution_count": 361,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPNGUsws535p"
      },
      "source": [
        "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size= 0.33, random_state = 101)"
      ],
      "execution_count": 362,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHpSMdaP2fRh"
      },
      "source": [
        "##Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRG2s1oO2fB3"
      },
      "source": [
        "from sklearn import linear_model"
      ],
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BhqL_ErX2fFA",
        "outputId": "d77a0ad4-c837-4edd-cd89-be71c50d9c46"
      },
      "source": [
        "lm = linear_model.LogisticRegression(multi_class='ovr', solver='liblinear')\n",
        "lm.fit(X_train, y_train)"
      ],
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='ovr', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1aRwgrAlwF5"
      },
      "source": [
        "without nomalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHTuAz2F6gtE",
        "outputId": "c137de0f-56a3-4def-f27f-8c1fcf662100"
      },
      "source": [
        "lm.score(X_train,y_train)"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7902985074626866"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1nsjGV_r3PKA",
        "outputId": "7e9bb3a0-9cad-462e-f03f-bedfca63a216"
      },
      "source": [
        "lm.score(X_test,y_test)"
      ],
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7333333333333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8HlrSOGHl8EF"
      },
      "source": [
        "with nomalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1QEc0Jdl8EG",
        "outputId": "365f781f-7998-442b-eff8-906a3fbca525"
      },
      "source": [
        "lm.score(X_train,y_train)"
      ],
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8477611940298507"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttKgKlMul8EG",
        "outputId": "4a1d2cce-fefd-4458-e93f-3c1b5995b914"
      },
      "source": [
        "lm.score(X_test,y_test)"
      ],
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8181818181818182"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "f58a2MYg7kTb",
        "outputId": "a6a772b2-9f20-4433-e2d0-6df200b7b69a"
      },
      "source": [
        "metrics.plot_confusion_matrix(lm, X_test, y_test)"
      ],
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7feb1bb11c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 281
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8U2Zmodh6eKO",
        "outputId": "f6427f07-a8da-4535-e983-928fd1a35f13"
      },
      "source": [
        "print(metrics.classification_report(y_test, lm.predict(X_test)))"
      ],
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.98      0.93       158\n",
            "           1       0.70      0.76      0.73       152\n",
            "           2       0.87      0.60      0.71       199\n",
            "           3       0.82      1.00      0.90       151\n",
            "\n",
            "    accuracy                           0.82       660\n",
            "   macro avg       0.82      0.83      0.82       660\n",
            "weighted avg       0.82      0.82      0.81       660\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vD-bdE_vFBlB"
      },
      "source": [
        "##KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dc0r9bJ5FBWS",
        "outputId": "f5654e7d-1050-42c1-fa11-c00ba17cf116"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=10)\n",
        "knn.fit(X_train,y_train)"
      ],
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=10, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jw0aI5ve7Q-q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d56cfcb2-f5d5-494f-c1fd-c24da8688c07"
      },
      "source": [
        "knn.score(X_test,y_test)"
      ],
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9212121212121213"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZaGuddNnirKh",
        "outputId": "bf078fe8-23be-4df6-8e4e-7ca51885a481"
      },
      "source": [
        "np.array(y_test)"
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 2, 0, 1, 3, 1,\n",
              "       2, 3, 2, 2, 2, 2, 0, 0, 2, 3, 0, 0, 3, 0, 0, 0, 1, 1, 1, 2, 3, 2,\n",
              "       3, 0, 1, 3, 3, 1, 0, 0, 2, 3, 3, 2, 0, 3, 2, 3, 2, 2, 3, 1, 3, 1,\n",
              "       0, 0, 0, 2, 1, 2, 3, 2, 2, 3, 3, 2, 0, 2, 0, 0, 2, 1, 2, 2, 2, 1,\n",
              "       0, 0, 3, 2, 0, 2, 0, 3, 2, 0, 2, 3, 0, 1, 3, 3, 0, 2, 0, 0, 2, 0,\n",
              "       1, 0, 3, 2, 2, 1, 1, 3, 1, 0, 3, 3, 2, 3, 1, 2, 3, 2, 1, 1, 1, 0,\n",
              "       0, 1, 0, 2, 3, 0, 2, 3, 1, 3, 0, 0, 0, 1, 1, 2, 2, 0, 3, 1, 2, 2,\n",
              "       3, 2, 2, 0, 3, 2, 2, 2, 2, 2, 1, 2, 2, 1, 3, 3, 1, 2, 0, 3, 1, 3,\n",
              "       2, 2, 3, 2, 2, 1, 0, 1, 3, 2, 1, 2, 0, 3, 1, 0, 2, 2, 0, 2, 0, 0,\n",
              "       3, 0, 0, 1, 3, 2, 1, 3, 0, 3, 2, 2, 1, 2, 1, 3, 1, 3, 1, 0, 3, 1,\n",
              "       2, 2, 3, 0, 2, 3, 3, 0, 3, 1, 1, 1, 0, 2, 1, 2, 1, 2, 2, 0, 1, 2,\n",
              "       1, 2, 0, 3, 3, 2, 3, 3, 2, 2, 1, 1, 3, 1, 0, 2, 0, 1, 1, 3, 3, 0,\n",
              "       1, 0, 1, 1, 0, 2, 3, 1, 3, 2, 2, 2, 2, 2, 0, 2, 1, 3, 1, 2, 3, 2,\n",
              "       2, 1, 2, 3, 2, 3, 0, 1, 1, 0, 2, 1, 0, 2, 3, 3, 1, 2, 3, 3, 1, 1,\n",
              "       1, 1, 2, 0, 0, 0, 1, 1, 3, 1, 3, 2, 1, 3, 1, 3, 1, 2, 2, 0, 0, 2,\n",
              "       2, 2, 2, 1, 0, 0, 2, 2, 1, 1, 2, 1, 3, 0, 0, 1, 0, 2, 3, 3, 2, 2,\n",
              "       2, 2, 0, 3, 2, 0, 0, 2, 0, 3, 2, 1, 0, 1, 0, 2, 0, 0, 1, 0, 0, 2,\n",
              "       1, 2, 1, 3, 2, 2, 2, 1, 3, 0, 0, 0, 0, 0, 1, 0, 3, 1, 3, 1, 1, 1,\n",
              "       3, 2, 0, 3, 3, 2, 0, 3, 2, 2, 1, 3, 1, 2, 1, 2, 1, 2, 2, 0, 2, 0,\n",
              "       1, 3, 1, 1, 2, 1, 1, 1, 3, 0, 3, 1, 3, 2, 3, 2, 3, 2, 3, 1, 1, 3,\n",
              "       0, 0, 2, 2, 0, 0, 1, 2, 0, 3, 0, 3, 0, 2, 0, 0, 2, 2, 3, 1, 0, 1,\n",
              "       2, 2, 0, 3, 2, 3, 2, 3, 0, 3, 2, 3, 1, 2, 2, 3, 2, 3, 1, 2, 0, 2,\n",
              "       3, 2, 3, 2, 1, 1, 3, 3, 0, 3, 0, 0, 2, 2, 3, 0, 2, 1, 3, 1, 3, 0,\n",
              "       3, 0, 0, 3, 1, 0, 3, 1, 3, 0, 3, 2, 0, 2, 2, 1, 2, 1, 3, 0, 0, 1,\n",
              "       1, 0, 1, 2, 0, 2, 0, 0, 0, 2, 3, 3, 1, 2, 1, 0, 1, 0, 3, 3, 1, 2,\n",
              "       2, 3, 0, 0, 1, 0, 2, 2, 2, 0, 2, 2, 2, 3, 3, 2, 2, 2, 3, 0, 2, 2,\n",
              "       0, 0, 3, 2, 3, 3, 3, 1, 3, 0, 3, 1, 2, 1, 3, 1, 1, 0, 3, 0, 0, 3,\n",
              "       0, 0, 2, 3, 2, 0, 2, 2, 2, 3, 3, 0, 0, 3, 2, 2, 1, 2, 0, 0, 0, 2,\n",
              "       3, 0, 2, 0, 0, 3, 1, 3, 1, 2, 2, 1, 3, 2, 3, 2, 3, 2, 0, 3, 2, 1,\n",
              "       3, 1, 1, 3, 0, 2, 1, 1, 2, 2, 1, 1, 0, 3, 0, 0, 2, 0, 2, 2, 2, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyAWdCj7FNSC"
      },
      "source": [
        "error_rate = []\n",
        "for i in range(1,20):    \n",
        "    knn = KNeighborsClassifier(n_neighbors=i)\n",
        "    knn.fit(X_train,y_train)\n",
        "    pred_i = knn.predict(X_test)\n",
        "    error_rate.append(np.mean(pred_i != np.array(y_test)))\n"
      ],
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "ipogY34hKRvD",
        "outputId": "d68b5a42-4c38-4d43-e785-ffe09e8f3d38"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(range(1,20),error_rate,color='blue', linestyle='dashed', marker='o',  markerfacecolor='red', markersize=5)\n",
        "plt.title('Error Rate vs. K Value')\n",
        "plt.xlabel('K')\n",
        "plt.ylabel('Error Rate')\n",
        "\n"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Error Rate')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lqBGqDrplZc3"
      },
      "source": [
        "without normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7txIpI8MSUP",
        "outputId": "981e8fc2-d7af-4c2a-f633-73926a454740"
      },
      "source": [
        "knn1 = KNeighborsClassifier(n_neighbors=9)\n",
        "knn1.fit(X_train,y_train)"
      ],
      "execution_count": 315,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=9, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6XuvH7SjBJy",
        "outputId": "c3c51e30-140f-493b-e186-5aee5f54ab81"
      },
      "source": [
        "knn1.score(X_test,y_test)"
      ],
      "execution_count": 316,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9287878787878788"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 316
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "meNyiLe4ldmY"
      },
      "source": [
        "after normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LQV90kRZlg1F",
        "outputId": "b8106b71-e126-4e3f-a9e1-0938466e0ea7"
      },
      "source": [
        "knn1 = KNeighborsClassifier(n_neighbors=9)\n",
        "knn1.fit(X_train,y_train)"
      ],
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=9, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 283
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-GGQvS9lg1H",
        "outputId": "0fb855ec-6660-46bf-cc7e-bfaded6c3caf"
      },
      "source": [
        "knn1.score(X_test,y_test)"
      ],
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.346969696969697"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 284
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iUkxRTIUq73g"
      },
      "source": [
        "without normalization results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "5op5GnM-q_VC",
        "outputId": "8dca6f75-3120-463f-abf2-fe8f3356dc0d"
      },
      "source": [
        "metrics.plot_confusion_matrix(knn, X_test, y_test)"
      ],
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7feb1c282470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 318
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OUoN9z4zrK93",
        "outputId": "117f1989-cee8-4374-bfef-c676fd6c48fa"
      },
      "source": [
        "print(metrics.classification_report(y_test, knn.predict(X_test)))"
      ],
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.98       158\n",
            "           1       0.87      0.94      0.91       152\n",
            "           2       0.92      0.85      0.88       199\n",
            "           3       0.93      0.92      0.93       151\n",
            "\n",
            "    accuracy                           0.92       660\n",
            "   macro avg       0.92      0.93      0.92       660\n",
            "weighted avg       0.92      0.92      0.92       660\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Peb7Ov-jLxZ"
      },
      "source": [
        "##SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5cyKuG_hjC2v"
      },
      "source": [
        "from sklearn import svm"
      ],
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ws8a7cyBk2Bo"
      },
      "source": [
        "SVM=svm.SVC()"
      ],
      "execution_count": 241,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6oegfuSk5Bn",
        "outputId": "e2df7b1a-71d7-4837-8804-9819df8ecf98"
      },
      "source": [
        "SVM.fit(X_train,y_train)"
      ],
      "execution_count": 322,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 322
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTXwri7LlHoG"
      },
      "source": [
        "before normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1MY0Aw9k9UK",
        "outputId": "c94bb494-0ef7-4413-bc43-33e6dac1ed3a"
      },
      "source": [
        "SVM.score(X_test,y_test)"
      ],
      "execution_count": 323,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.946969696969697"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 323
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0-PUU_am3aA"
      },
      "source": [
        "after normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBy0UZsflVNU",
        "outputId": "71721016-8ace-4e4a-ff7d-3dda32c83844"
      },
      "source": [
        "SVM.score(X_test,y_test)"
      ],
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.65"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFmrAu-ZrfyB"
      },
      "source": [
        "Without Normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "RBucynCGrV0X",
        "outputId": "89eda860-40c3-4f91-a3b5-22e41dffefc3"
      },
      "source": [
        "metrics.plot_confusion_matrix(SVM, X_test, y_test)"
      ],
      "execution_count": 324,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7feb1ba71be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 324
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0LgF5HfrV0Y",
        "outputId": "ac9ad521-13b5-435c-8887-a331efb14afb"
      },
      "source": [
        "print(metrics.classification_report(y_test, SVM.predict(X_test)))"
      ],
      "execution_count": 325,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.97       158\n",
            "           1       0.90      0.95      0.93       152\n",
            "           2       0.97      0.89      0.93       199\n",
            "           3       0.95      0.97      0.96       151\n",
            "\n",
            "    accuracy                           0.95       660\n",
            "   macro avg       0.95      0.95      0.95       660\n",
            "weighted avg       0.95      0.95      0.95       660\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKXultkErmct"
      },
      "source": [
        "##Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mHSs1bNlE5T"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dtree = DecisionTreeClassifier()"
      ],
      "execution_count": 326,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQc2183oo-Sq",
        "outputId": "7181014a-3cfd-42e1-98d5-309f19f71b6b"
      },
      "source": [
        "dtree.fit(X_train,y_train)"
      ],
      "execution_count": 364,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 364
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5FgRT8Szs8xC"
      },
      "source": [
        "without normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sln2IMvwo-QN",
        "outputId": "c5805aff-53c0-4083-94c3-dcdaae474af8"
      },
      "source": [
        "dtree.score(X_test,y_test)"
      ],
      "execution_count": 355,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8181818181818182"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 355
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X-Owyj-gtAK4"
      },
      "source": [
        "with normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1ajuS7Co-NK",
        "outputId": "8ae9ecd7-3316-457d-8442-93fbf87e848a"
      },
      "source": [
        "dtree.score(X_test,y_test)"
      ],
      "execution_count": 365,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8242424242424242"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 365
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "XAIDjTuIt4mc",
        "outputId": "536e0b48-1c09-4640-c49f-e9a43f9b974d"
      },
      "source": [
        "metrics.plot_confusion_matrix(dtree, X_test, y_test)"
      ],
      "execution_count": 366,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7feb21e209b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 366
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "txQsHnx4t4mc",
        "outputId": "6f273aa6-c164-4101-e352-5d83aa1b1503"
      },
      "source": [
        "print(metrics.classification_report(y_test, dtree.predict(X_test)))"
      ],
      "execution_count": 368,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.89      0.89       158\n",
            "           1       0.75      0.77      0.76       152\n",
            "           2       0.83      0.76      0.79       199\n",
            "           3       0.82      0.90      0.86       151\n",
            "\n",
            "    accuracy                           0.82       660\n",
            "   macro avg       0.82      0.83      0.83       660\n",
            "weighted avg       0.82      0.82      0.82       660\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJ4eAYtGo-KS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "El2w-45To-Hl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCQAz3Gxo9-b"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}