Capstone_1_Data_Analysis_of_Play_Store_Data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aniket15031998/Play-store-data-analysis/blob/main/Capstone_1_Data_Analysis_of_Play_Store_Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Project Name**    - Play Store Data Analysis.\n",
        "\n"
      ],
      "metadata": {
        "id": "vncDsAP0Gaoa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Project Type**    - EDA\n",
        "##### **Contribution**    - Team\n",
        "##### **Team Member 1 -** Vinayak Gunjal\n",
        "##### **Team Member 2 -** Nikhil Bordekar\n",
        "##### **Team Member 3 -** Aniket Kale"
      ],
      "metadata": {
        "id": "beRrZCGUAJYm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Project Summary -**"
      ],
      "metadata": {
        "id": "FJNUwmbgGyua"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   Collecting and cleaning data: The first step in the analysis process would be to collect and clean the data from the Play Store. This could involve cleaning and preprocessing the data to prepare it for analysis.\n",
        "2.   Exploratory data analysis: Once the data has been collected and cleaned, the next step would be to perform exploratory data analysis (EDA) to understand the overall structure and patterns in the data. This could involve generating summary statistics, creating visualizations, and identifying trends and patterns in the data.\n",
        "3.    Conclusion: Summarizing the key insights and recommendations based on the data, and presenting the results in a clear and understandable way.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "F6v_1wHtG2nS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **GitHub Link -**"
      ],
      "metadata": {
        "id": "w6K7xa23Elo4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "GitHun Link :- https://github.com/aniket15031998"
      ],
      "metadata": {
        "id": "h1o69JH3Eqqn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Problem Statement**\n"
      ],
      "metadata": {
        "id": "yQaldy8SH6Dl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Understanding app usage and user behavior: This involve analyzing data on app downloads, user ratings, and other metrics to understand how users interact with different apps and what factors drive app success.\n",
        "\n",
        "Understanding the competitive landscape: Analyzing data on app pricing and other characteristics can help developers understand the competitive landscape and identify opportunities for their own apps."
      ],
      "metadata": {
        "id": "DpeJGUA3kjGy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Our Business Objective**\n"
      ],
      "metadata": {
        "id": "PH-0ReGfmX4f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are many potential business objectives that could be addressed through data analysis of the Google Play Store, including-\n",
        "\n",
        "1.   Understanding app usage and user behavior.\n",
        "2.   Identifying trends and patterns in app categories.\n",
        "3.   Understanding the competitive landscape.\n",
        "4.   Optimizing app pricing and monetization strategies.\n",
        "\n"
      ],
      "metadata": {
        "id": "UXsNCcjlq_oM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***1. Knowing Data***"
      ],
      "metadata": {
        "id": "HhfV-JJviCcP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import Libraries"
      ],
      "metadata": {
        "id": "Y3lxredqlCYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline \n",
        "import seaborn as sns\n",
        "import ast"
      ],
      "metadata": {
        "id": "M8Vqi-pPk-HR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Loading"
      ],
      "metadata": {
        "id": "3RnN4peoiCZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading Dataset\n",
        "play_df=pd.read_csv(\"/content/Play Store Data.csv\")"
      ],
      "metadata": {
        "id": "4CkvbW_SlZ_R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset First View"
      ],
      "metadata": {
        "id": "x71ZqKXriCWQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset First Look\n",
        "play_df.head()"
      ],
      "metadata": {
        "id": "LWNFOSvLl09H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "0e6dfdba-1979-4c7d-ad37-d1f865cd0e4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 App        Category  Rating  \\\n",
              "0     Photo Editor & Candy Camera & Grid & ScrapBook  ART_AND_DESIGN     4.1   \n",
              "1                                Coloring book moana  ART_AND_DESIGN     3.9   \n",
              "2  U Launcher Lite – FREE Live Cool Themes, Hide ...  ART_AND_DESIGN     4.7   \n",
              "3                              Sketch - Draw & Paint  ART_AND_DESIGN     4.5   \n",
              "4              Pixel Draw - Number Art Coloring Book  ART_AND_DESIGN     4.3   \n",
              "\n",
              "  Reviews  Size     Installs  Type Price Content Rating  \\\n",
              "0     159   19M      10,000+  Free     0       Everyone   \n",
              "1     967   14M     500,000+  Free     0       Everyone   \n",
              "2   87510  8.7M   5,000,000+  Free     0       Everyone   \n",
              "3  215644   25M  50,000,000+  Free     0           Teen   \n",
              "4     967  2.8M     100,000+  Free     0       Everyone   \n",
              "\n",
              "                      Genres      Last Updated         Current Ver  \\\n",
              "0               Art & Design   January 7, 2018               1.0.0   \n",
              "1  Art & Design;Pretend Play  January 15, 2018               2.0.0   \n",
              "2               Art & Design    August 1, 2018               1.2.4   \n",
              "3               Art & Design      June 8, 2018  Varies with device   \n",
              "4    Art & Design;Creativity     June 20, 2018                 1.1   \n",
              "\n",
              "    Android Ver  \n",
              "0  4.0.3 and up  \n",
              "1  4.0.3 and up  \n",
              "2  4.0.3 and up  \n",
              "3    4.2 and up  \n",
              "4    4.4 and up  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bdeae7c8-5272-407c-b828-e01957e89b47\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>App</th>\n",
              "      <th>Category</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Size</th>\n",
              "      <th>Installs</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Content Rating</th>\n",
              "      <th>Genres</th>\n",
              "      <th>Last Updated</th>\n",
              "      <th>Current Ver</th>\n",
              "      <th>Android Ver</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Photo Editor &amp; Candy Camera &amp; Grid &amp; ScrapBook</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.1</td>\n",
              "      <td>159</td>\n",
              "      <td>19M</td>\n",
              "      <td>10,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>January 7, 2018</td>\n",
              "      <td>1.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Coloring book moana</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>3.9</td>\n",
              "      <td>967</td>\n",
              "      <td>14M</td>\n",
              "      <td>500,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Pretend Play</td>\n",
              "      <td>January 15, 2018</td>\n",
              "      <td>2.0.0</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>U Launcher Lite – FREE Live Cool Themes, Hide ...</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.7</td>\n",
              "      <td>87510</td>\n",
              "      <td>8.7M</td>\n",
              "      <td>5,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>August 1, 2018</td>\n",
              "      <td>1.2.4</td>\n",
              "      <td>4.0.3 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sketch - Draw &amp; Paint</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.5</td>\n",
              "      <td>215644</td>\n",
              "      <td>25M</td>\n",
              "      <td>50,000,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Teen</td>\n",
              "      <td>Art &amp; Design</td>\n",
              "      <td>June 8, 2018</td>\n",
              "      <td>Varies with device</td>\n",
              "      <td>4.2 and up</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pixel Draw - Number Art Coloring Book</td>\n",
              "      <td>ART_AND_DESIGN</td>\n",
              "      <td>4.3</td>\n",
              "      <td>967</td>\n",
              "      <td>2.8M</td>\n",
              "      <td>100,000+</td>\n",
              "      <td>Free</td>\n",
              "      <td>0</td>\n",
              "      <td>Everyone</td>\n",
              "      <td>Art &amp; Design;Creativity</td>\n",
              "      <td>June 20, 2018</td>\n",
              "      <td>1.1</td>\n",
              "      <td>4.4 and up</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bdeae7c8-5272-407c-b828-e01957e89b47')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bdeae7c8-5272-407c-b828-e01957e89b47 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bdeae7c8-5272-407c-b828-e01957e89b47');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Rows & Columns count"
      ],
      "metadata": {
        "id": "7hBIi_osiCS2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Rows & Columns count\n",
        "play_df.shape"
      ],
      "metadata": {
        "id": "Kllu7SJgmLij",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60a7fb65-d81e-4917-c7f2-4f8956514e9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10841, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Information"
      ],
      "metadata": {
        "id": "JlHwYmJAmNHm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Info\n",
        "play_df.info()"
      ],
      "metadata": {
        "id": "e9hRXRi6meOf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83868368-1014-4ac6-fa3f-b40eeb0f675e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10841 entries, 0 to 10840\n",
            "Data columns (total 13 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   App             10841 non-null  object \n",
            " 1   Category        10841 non-null  object \n",
            " 2   Rating          9367 non-null   float64\n",
            " 3   Reviews         10841 non-null  object \n",
            " 4   Size            10841 non-null  object \n",
            " 5   Installs        10841 non-null  object \n",
            " 6   Type            10840 non-null  object \n",
            " 7   Price           10841 non-null  object \n",
            " 8   Content Rating  10840 non-null  object \n",
            " 9   Genres          10841 non-null  object \n",
            " 10  Last Updated    10841 non-null  object \n",
            " 11  Current Ver     10833 non-null  object \n",
            " 12  Android Ver     10838 non-null  object \n",
            "dtypes: float64(1), object(12)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Duplicate Values"
      ],
      "metadata": {
        "id": "35m5QtbWiB9F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Duplicate Value Count\n",
        "len(play_df[play_df.duplicated()])"
      ],
      "metadata": {
        "id": "1sLdpKYkmox0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4bba1c9-2a4f-4363-8314-6471ab0fcbef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "483"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Missing Values/Null Values"
      ],
      "metadata": {
        "id": "PoPl-ycgm1ru"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing Values/Null Values Count\n",
        "play_df.isnull().sum()"
      ],
      "metadata": {
        "id": "GgHWkxvamxVg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "165e52d1-1f49-4795-ddf4-9d6304a36877"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "App                  0\n",
              "Category             0\n",
              "Rating            1474\n",
              "Reviews              0\n",
              "Size                 0\n",
              "Installs             0\n",
              "Type                 1\n",
              "Price                0\n",
              "Content Rating       1\n",
              "Genres               0\n",
              "Last Updated         0\n",
              "Current Ver          8\n",
              "Android Ver          3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the missing values\n",
        "cor=play_df.isnull().corr()\n",
        "sns.heatmap(cor)"
      ],
      "metadata": {
        "id": "3q5wnI3om9sJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "7b5fa617-3640-4b26-c76e-7e99324a5ea9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa366346e50>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### What we got to know about dataset?"
      ],
      "metadata": {
        "id": "H0kj-8xxnORC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The given dataset contains 10841 rows and 13 columns.Thier are total 483 duplicates in database. Most of the null values comes from the column Rating. Most of the data type is in string form except rating."
      ],
      "metadata": {
        "id": "gfoNAAC-nUe_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***2. Understanding Variables***"
      ],
      "metadata": {
        "id": "nA9Y7ga8ng1Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Columns\n",
        "play_df.columns"
      ],
      "metadata": {
        "id": "j7xfkqrt5Ag5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74b2c689-a00f-4d8c-c1f7-b35fdd10b999"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['App', 'Category', 'Rating', 'Reviews', 'Size', 'Installs', 'Type',\n",
              "       'Price', 'Content Rating', 'Genres', 'Last Updated', 'Current Ver',\n",
              "       'Android Ver'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Describe\n",
        "play_df.describe()"
      ],
      "metadata": {
        "id": "DnOaZdaE5Q5t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "f2b76ba1-baff-42c8-cd11-96e4cf75dd1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Rating\n",
              "count  9367.000000\n",
              "mean      4.193338\n",
              "std       0.537431\n",
              "min       1.000000\n",
              "25%       4.000000\n",
              "50%       4.300000\n",
              "75%       4.500000\n",
              "max      19.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2cd19a5b-b28c-485e-a5fd-5aa477c3f028\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9367.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.193338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.537431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2cd19a5b-b28c-485e-a5fd-5aa477c3f028')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2cd19a5b-b28c-485e-a5fd-5aa477c3f028 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2cd19a5b-b28c-485e-a5fd-5aa477c3f028');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Variables Description "
      ],
      "metadata": {
        "id": "PBTbrJXOngz2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   App : Name of the Applications.\n",
        "2.   Category : Category of various Applications.\n",
        "3.   Rating : Ratings given by users (0 to 5).\n",
        "4.   Reviews : Total number of reviews.\n",
        "5.   Size  : Size of Application.\n",
        "6.   Installs : Minimum number of installs of Application.\n",
        "7.   Type : Type of Application (Free & Paid).\n",
        "8.   Price : Price for paid Applications in USD.\n",
        "9.   Content Rating : Type of users.\n",
        "10.  Genres : Genre of Application.\n",
        "11.  Last Updated : Last updated date of Application.\n",
        "12.  Current ver : Current version of Application.\n",
        "13.  Android ver : Required Android Version for Application.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "aJV4KIxSnxay"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Unique Values for each variable."
      ],
      "metadata": {
        "id": "u3PMJOP6ngxN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Unique Values for each variable.\n",
        "\n",
        "column_list=list(play_df.columns)\n",
        "dic={}\n",
        "for column in column_list:\n",
        "  b=list(play_df[column].unique())\n",
        "  dic[column]=len(b)\n",
        "\n",
        "dic\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "zms12Yq5n-jE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d50d7e22-68cb-42ae-8411-4de8f1ddf1e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'App': 9660,\n",
              " 'Category': 34,\n",
              " 'Rating': 41,\n",
              " 'Reviews': 6002,\n",
              " 'Size': 462,\n",
              " 'Installs': 22,\n",
              " 'Type': 4,\n",
              " 'Price': 93,\n",
              " 'Content Rating': 7,\n",
              " 'Genres': 120,\n",
              " 'Last Updated': 1378,\n",
              " 'Current Ver': 2833,\n",
              " 'Android Ver': 34}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. ***Data Wrangling***"
      ],
      "metadata": {
        "id": "dauF4eBmngu3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Wrangling Code"
      ],
      "metadata": {
        "id": "bKJF3rekwFvQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cleaning of Dataset\n",
        "play_df.drop(10472,inplace=True)"
      ],
      "metadata": {
        "id": "epkOLJ4xW9LD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing duplicate values\n",
        "play_df.drop_duplicates(inplace=True)\n",
        "\n",
        "play_df.shape"
      ],
      "metadata": {
        "id": "wk-9a2fpoLcV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0f6002c-96fb-4940-f024-0f425179c2d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10357, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Resetting index\n",
        "play_df.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "yrFLqUcqIXAq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Droping old index from dataset\n",
        "play_df.drop(\"index\",axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "iB148hPTKaEj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert installs which is in str into Numbers\n",
        "def int_install(column):\n",
        "  a=column.replace(\",\",\"\")\n",
        "  b=a.replace(\"+\",\"\")\n",
        "  return int(b)\n",
        "\n",
        "play_df[\"Installs in Number\"]=play_df[\"Installs\"].map(int_install)\n"
      ],
      "metadata": {
        "id": "OGb0Cu74PkCa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparing code for data of category v/s installs\n",
        "Cat_df=play_df.groupby(\"Category\")[\"Installs in Number\"].sum().reset_index()\n",
        "\n",
        "Cat_df.rename(columns={\"Installs in Number\":\"Downloads\"},inplace=True)\n",
        "Cat_df.sort_values(\"Downloads\",ascending=False,inplace=True)\n"
      ],
      "metadata": {
        "id": "3ngYDQmUKSr9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Code to get data of Game genres(Top Category) and Installs\n",
        "top_cat=play_df[play_df[\"Category\"]==\"GAME\"]\n",
        "\n",
        "top_genre=top_cat.groupby(\"Genres\")[\"Installs in Number\"].sum().reset_index()\n",
        "top_genre.rename(columns={\"Installs in Number\":\"Downloads\"},inplace=True)\n",
        "top_genre.sort_values(\"Downloads\",ascending=False,inplace=True)"
      ],
      "metadata": {
        "id": "M2CavT2khy39"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Code to get data for Content Rating and Installs\n",
        "cr_df=play_df.groupby(\"Content Rating\")[\"Installs in Number\"].sum().reset_index()"
      ],
      "metadata": {
        "id": "boYb09YCqJYD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Code to get data of Paid apps and Installs\n",
        "type_df=play_df[play_df[\"Type\"]==\"Paid\"]\n",
        "paid_df=type_df.groupby(\"Category\")[\"Installs in Number\"].sum().reset_index()\n",
        "paid_df.sort_values(\"Installs in Number\",ascending=False,inplace=True)\n",
        "\n"
      ],
      "metadata": {
        "id": "fl9nUlCLCoT5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Code for getting top 10 prices which have most number of installs.\n",
        "def price_conv(column):\n",
        "  num=column.replace(\"$\",\"\")\n",
        "  return float(num)\n",
        "\n",
        "type_df[\"Price_n\"]=type_df[\"Price\"].map(price_conv)\n",
        "reasop_df=type_df.groupby(\"Price_n\")[\"Installs in Number\"].sum().reset_index()\n",
        "reasop_df.sort_values(\"Installs in Number\",ascending=False,inplace=True)\n",
        "top=reasop_df.head(10)"
      ],
      "metadata": {
        "id": "653bos2fImti",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02357167-65bb-42ba-8e54-7b7ef6f45282"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-4c91037b6148>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  type_df[\"Price_n\"]=type_df[\"Price\"].map(price_conv)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Code for corelation between Rating, Price, Installs, Size, Reviews.\n",
        "heat_df=play_df[[\"Rating\",\"Price\",\"Installs in Number\",\"Size\",\"Reviews\"]]\n",
        "\n",
        "heat_df[\"Price in n\"]=heat_df[\"Price\"].map(price_conv)\n",
        "\n",
        "h_df=heat_df[heat_df[\"Size\"]!=\"Varies with device\"]\n",
        "\n",
        "def conv_k(column):\n",
        "  if \"k\" in column:\n",
        "    n=column[:-1]\n",
        "    return float(n)/1024\n",
        "  else:\n",
        "    return float(column[:-1])\n",
        "h_df[\"Reviews\"]=h_df[\"Reviews\"].astype(float)\n",
        "h_df[\"size in MB\"]=h_df[\"Size\"].map(conv_k)\n",
        "\n"
      ],
      "metadata": {
        "id": "MDvwkGlXOR9p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ae4d040-9fad-4798-d2b7-28e03e1261d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-845b9cc1238f>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  heat_df[\"Price in n\"]=heat_df[\"Price\"].map(price_conv)\n",
            "<ipython-input-23-845b9cc1238f>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  h_df[\"Reviews\"]=h_df[\"Reviews\"].astype(float)\n",
            "<ipython-input-23-845b9cc1238f>:15: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  h_df[\"size in MB\"]=h_df[\"Size\"].map(conv_k)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Manipulations we have done and insights that we found"
      ],
      "metadata": {
        "id": "MSa1f5Uengrz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First we have removed all duplicates from the Data Set. Then we created a new column as Installs in Number where we took a minimum installs in int datatype. As you can see prices were in str data type with currency($), we have converted it into float data type for better analysis. Reviews also converted from str to int datatype. For sizes, all sizes were in KB and MB. We converted all sizes in MB for analysis.\n",
        "All above operations we had done on the copy or slice of the original dataset.\n"
      ],
      "metadata": {
        "id": "LbyXE7I1olp8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***4. Data Vizualization, Storytelling & Experimenting with charts : Understanding the relationships between variables***"
      ],
      "metadata": {
        "id": "GF8Ens_Soomf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 1"
      ],
      "metadata": {
        "id": "0wOQAZs5pc--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 1 visualization code\n",
        "# Preparing code for data visualization between Category v/s installs\n",
        "Cat_df.plot(\"Category\", kind=\"bar\")\n",
        "plt.title(\"Downloads as per Category\")\n",
        "plt.xlabel(\"Category\")\n",
        "plt.ylabel(\"Downloads in 10 billion\")"
      ],
      "metadata": {
        "id": "7v_ESjsspbW7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "d745c0dd-dbe1-47f5-cb2b-737962478b75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Downloads in 10 billion')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why we choose this specific chart?"
      ],
      "metadata": {
        "id": "K5QZ13OEpz2H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   It can help you compare the popularity of different categories. By looking at the relative number of installs for each category, you can compare the relative popularity of different categories and see which ones are more popular.\n",
        "2.   It can help you identify opportunities for new apps. If you notice that there are certain categories with relatively low numbers of installs, this may indicate an opportunity for new apps in those categories\n",
        "\n",
        "Overall, a graph between category and installs can provide valuable insights into the popularity of different categories of apps on the Play Store, which can be useful for app developers, marketers, and other stakeholders.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "XESiWehPqBRc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2.Insight(s) found from the chart"
      ],
      "metadata": {
        "id": "lQ7QKXXCp7Bj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You may notice that certain categories of apps are more popular than others. For example, you may see that games and social media apps have the highest number of installs, while parenting or event apps have fewer installs."
      ],
      "metadata": {
        "id": "C_j1G7yiqdRP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Positive and Negative impact from Insights of chart\n"
      ],
      "metadata": {
        "id": "448CDAPjqfQr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   Identifying popular categories can help app developers create apps that are likely to be successful. By focusing on creating apps in categories that are in high demand, app developers can increase their chances of success.\n",
        "2.   Identifying trends can help businesses stay ahead of the curve. For example, if you notice that certain categories of apps are becoming more popular, you can focus your efforts on creating apps in those categories, rather than in categories that are on the decline.\n",
        "3.  Identifying opportunities for new apps can help businesses enter untapped markets. If you notice that there is demand for a certain type of app that is not being met, you may be able to create a successful app by filling that gap in the market.\n",
        "\n",
        "Overall, the insights gained from analyzing a graph between category and installs for Play Store data can potentially help businesses make informed decisions that lead to a positive impact. However, it is important to note that there are many factors that can influence the success of an app, and the insights gained from this analysis should be considered alongside other relevant data and business objectives.\n"
      ],
      "metadata": {
        "id": "3cspy4FjqxJW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 2"
      ],
      "metadata": {
        "id": "KSlN3yHqYklG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 2 visualization code\n",
        "# Preparing code for data visualization between Game genres(Top Category) v/s installs\n",
        "top_genre.plot(\"Genres\",kind=\"bar\")\n",
        "plt.title(\"Downloads as per Genres\")\n",
        "plt.xlabel(\"Genres in Game\")\n",
        "plt.ylabel(\"Downloads in 10 billion\")"
      ],
      "metadata": {
        "id": "R4YgtaqtYklH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "31cec4fb-0b2f-4472-f0fb-e67b3eb59d0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Downloads in 10 billion')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why we choose this specific chart?"
      ],
      "metadata": {
        "id": "t6dVpIINYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first chart shows that the category with the highest downloads is games. We will now explore this category in great detail in this graph. by classifying game categories according to various genres. So now by help of specific chart we can analyze the demands of market. \n",
        "\n",
        "Though game has most installs, but it totally depends on various genres also.\n",
        "\n"
      ],
      "metadata": {
        "id": "5aaW0BYyYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. Insight(s) found from the chart\n",
        "\n"
      ],
      "metadata": {
        "id": "ijmpgYnKYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You may notice that certain Genres of Games are more popular than others. For example, you can see that Action, Arcade and Casual genres have the highest number of installs, while simulation or education genres have fewer installs."
      ],
      "metadata": {
        "id": "PSx9atu2YklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3.Positive and Negative impact from Insights of chart"
      ],
      "metadata": {
        "id": "-JiQyfWJYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As an app developer or company you can focus more on popular genres for the success of your Game. Also you can try to gain market in genres which are not performing well."
      ],
      "metadata": {
        "id": "BcBbebzrYklV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 3"
      ],
      "metadata": {
        "id": "EM7whBJCYoAo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 3 visualization code\n",
        "# Code for visualization between content rating and installs\n",
        "cr_df.plot(\"Content Rating\",kind=\"bar\")\n",
        "plt.title(\"Content Rating v/s Installs\")\n",
        "plt.xlabel(\"Content Rating\")\n",
        "plt.ylabel(\"Downloads in 100 Billion\")"
      ],
      "metadata": {
        "id": "t6GMdE67YoAp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "bd9bd0a1-2578-4fd7-b61a-bfed38ed432a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Downloads in 100 Billion')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why we choose this specific chart?"
      ],
      "metadata": {
        "id": "fge-S5ZAYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can help you understand which content ratings are associated with the most popular apps. By looking at the number of installs for each content rating, you can see which ratings are associated with the highest demand. This information can be valuable for app developers who want to create apps that are appropriate for a wide audience."
      ],
      "metadata": {
        "id": "5dBItgRVYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. Insight(s) found from the chart"
      ],
      "metadata": {
        "id": "85gYPyotYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "By seeing chart we can say that the the app which has content suitable to everyone is best for business. Also you can target specific user to develop app in more effective manner."
      ],
      "metadata": {
        "id": "4jstXR6OYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 4"
      ],
      "metadata": {
        "id": "4Of9eVA-YrdM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 4 visualization code\n",
        "paid_df.plot(\"Category\",kind=\"bar\")\n",
        "plt.title(\"Paid v/s Installs\")\n",
        "plt.xlabel(\"Category\")\n",
        "plt.ylabel(\"Downloads in 10 Million\")"
      ],
      "metadata": {
        "id": "irlUoxc8YrdO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "ddff94bc-e2b2-4923-c212-05f39266bb4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Downloads in 10 Million')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why we choose this specific chart?"
      ],
      "metadata": {
        "id": "iky9q4vBYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we know most of the companies keep their application free for users to acquire maximum number of engagement. But as a case of exception we want to explore paid categories in detail. \n",
        "In this chart we can see the trend in which category where the user is more willing to spend their money to install an application.\n"
      ],
      "metadata": {
        "id": "aJRCwT6DYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. Insight(s) found from the chart\n",
        "\n"
      ],
      "metadata": {
        "id": "F6T5p64dYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that as per chart the most paid category is Family and Game. So from this we can conclude that people are mostly intrested in paying for games and Family based applications."
      ],
      "metadata": {
        "id": "Xx8WAJvtYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3.Positive and Negative impact from Insights of chart"
      ],
      "metadata": {
        "id": "y-Ehk30pYrdP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be seen that keeping applications free is best for the success of your applications. but if you want to keep it paid you should try to focus more on game or family based application to minimize the consequences."
      ],
      "metadata": {
        "id": "jLNxxz7MYrdP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 5"
      ],
      "metadata": {
        "id": "bamQiAODYuh1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 5 visualization code\n",
        "top.plot(\"Installs in Number\",kind=\"box\")"
      ],
      "metadata": {
        "id": "TIJwrbroYuh3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "f893acb4-e061-4597-b626-a63c15c72234"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa363227040>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why we choose this specific chart?"
      ],
      "metadata": {
        "id": "QHF8YVU7Yuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After analyzing paid categories now we are trying to find the most suitable pricing by comparing prices vs install data. For the above chart we have considered the top 10 prices paid by the user. Here we have used the box plot to get a more self explanatory chart."
      ],
      "metadata": {
        "id": "dcxuIMRPYuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. Insight(s) found from the chart"
      ],
      "metadata": {
        "id": "GwzvFGzlYuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "By seeing above box plot we can see that about 50 % of users are more likely to pay money in between 2.30 to 6.00 USD. "
      ],
      "metadata": {
        "id": "uyqkiB8YYuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Positive and Negative impact from Insights of chart"
      ],
      "metadata": {
        "id": "qYpmQ266Yuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "If Company wants to keep their product paid they must keep in mind about keeping the price between 2 to 6 USD.\n",
        "As you can see, keeping the price above 10 USD would be a bad decision for Application. "
      ],
      "metadata": {
        "id": "_WtzZ_hCYuh4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 6 - Correlation Heatmap"
      ],
      "metadata": {
        "id": "NC_X3p0fY2L0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Heatmap visualization code\n",
        "heat=h_df.corr()\n",
        "sns.heatmap(heat)"
      ],
      "metadata": {
        "id": "xyC9zolEZNRQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "4cb80cb9-bf61-4086-feb5-827e69174506"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3631907f0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why we choose this specific chart?"
      ],
      "metadata": {
        "id": "UV0SzAkaZNRQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To show the corelation between different numeric component from Play Store Data. It will help us to understand the how different components affects each other."
      ],
      "metadata": {
        "id": "sLKTbjArZ_aR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. Insight(s) found from the chart"
      ],
      "metadata": {
        "id": "YPEH6qLeZNRQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After visualizing the Heatmap it is apparent that price is the only independent entity which does not get affected by any other components from the data set. So there are some components which are minimally correlated with each other e.g Installs, size, Rating. Installs and Reviews are the only components which are dependent on each other."
      ],
      "metadata": {
        "id": "bfSqtnDqZNRR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **5. Solution for Business Objective**"
      ],
      "metadata": {
        "id": "JcMwzZxoAimU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### What are our suggestion for client to achieve Business Objective ? \n"
      ],
      "metadata": {
        "id": "8G2x9gOozGDZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our suggestion to client are as follows,\n",
        "1. Choosing Category :  As by studying data about categories and Installs we would like to suggest focusing more on categories which are doing good in business that are Game, Social Media and Communication.\n",
        "2. About Genres :  From top categories if you decided to go with game one. Then there will be a big question about which genre we need to go with. So for this we did an analysis as you know from that we can say that the games based on action, casual and arcade are doing better than others. Going with this genres will be beneficial for app and company also.\n",
        "3. Paid or Free : After choosing category and genre now we need to decide whether we wanna go with free or paid version of our app. So as we all know, to reach maximum users we must keep our app free. But due to any complication you have in business, you want to keep it paid, then by analyzing  the data we can say that people are more interested in paying for Family and Game categorized applications. So going with games and family will be the best option for paid application.\n",
        "4. Price : If you decided to bring a paid app then the main concern will be what should be a price for your application. From analyzing the data we can say that most people are paying around 1 to 10 USD. After  analyzing data in depth for 1 to 10 USD we found out that the best suitable range for any application will be between 2.30 USD to 6.00 USD.\n",
        "\n",
        "Above mentioned factors are the most affecting factors for any application. It will be a halfway success after choosing this factors correctly.\n"
      ],
      "metadata": {
        "id": "pASKb0qOza21"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conclusion**"
      ],
      "metadata": {
        "id": "gCX9965dhzqZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "From analyzing Play Store Data we hereby conclude that users are more interested in Games, Social media and Communication applications. For games specific users would like to have action based, arcade and casual kind of games. When it comes to money people mostly prefer free applications and about paid they go for minimal prices like form 1 to 10 USD. So these are some major factors affecting the success of any application. Their are some other factors like Size and Android version which does not have much effect on business.\n",
        "There's one more factor which is essential for success is user review. For that we need to do separate analysis based on it.\n"
      ],
      "metadata": {
        "id": "Fjb1IsQkh3yE"
      }
    }
  ]
}