{"id":15131133,"url":"https://github.com/rushilsharma1/iphonesales_eda","last_synced_at":"2026-01-20T01:18:07.773Z","repository":{"id":256894368,"uuid":"856266108","full_name":"rushilsharma1/IphoneSales_EDA","owner":"rushilsharma1","description":"An exploratory data analysis of an Iphone sales in jupyter notebook with the help of python and its libraries","archived":false,"fork":false,"pushed_at":"2024-09-14T18:26:51.000Z","size":202,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T20:43:14.683Z","etag":null,"topics":["data-science","exploratory-data-analysis","jupyter-notebook","numpy","pandas","python","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rushilsharma1.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-12T09:26:35.000Z","updated_at":"2024-09-28T18:05:59.000Z","dependencies_parsed_at":"2024-12-19T07:15:38.789Z","dependency_job_id":null,"html_url":"https://github.com/rushilsharma1/IphoneSales_EDA","commit_stats":{"total_commits":7,"total_committers":1,"mean_commits":7.0,"dds":0.0,"last_synced_commit":"17827f25bb40348026a657fee1fec9fd227e5117"},"previous_names":["rushilsharma1/iphonesales_eda"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rushilsharma1%2FIphoneSales_EDA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rushilsharma1%2FIphoneSales_EDA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rushilsharma1%2FIphoneSales_EDA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rushilsharma1%2FIphoneSales_EDA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rushilsharma1","download_url":"https://codeload.github.com/rushilsharma1/IphoneSales_EDA/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247399898,"owners_count":20932876,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","exploratory-data-analysis","jupyter-notebook","numpy","pandas","python","seaborn"],"created_at":"2024-09-26T03:24:00.282Z","updated_at":"2026-01-20T01:18:07.729Z","avatar_url":"https://github.com/rushilsharma1.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":" # IphoneSales_EDA\n\nTo create a project for iPhone sales exploratory data analysis using Jupyter Notebook, follow these steps:\n\nStep 1: Import necessary libraries Import essential libraries such as Pandas, NumPy, Seaborn, and Matplotlib to read and manipulate the data.\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nStep 2: Load the dataset Load the iPhone sales dataset into a Pandas DataFrame.\n\ndiab_df = pd.read_csv(Iphone_data.csv)\n\nStep 3: Data Analysis Perform exploratory data analysis on the dataset, including:\n\nHead and Tail: View the first and last few rows of the dataset.\nShape: Check the dimensions of the dataset.\nDtypes: Verify the data types of each column.\nDescribe: Generate summary statistics for the dataset.\n\nStep 4: Data Visualization Create visualizations to understand the distribution of variables and relationships between them, including:\n\nBar chart: Visualize the distribution of the \"Outcome\" variable.\nBoxplots: Compare the distribution of each independent variable for patients with and without diabetes.\nPairplot: Visualize the relationships between all numerical variables in the dataset.\nHeatmap: Represent the correlation matrix of the dataset.\n\nStep 5: Modeling Building Build a machine learning model to predict iPhone sales based on the analyzed data.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frushilsharma1%2Fiphonesales_eda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frushilsharma1%2Fiphonesales_eda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frushilsharma1%2Fiphonesales_eda/lists"}