{"id":20430673,"url":"https://github.com/ashishsingh789/data_visualization","last_synced_at":"2025-09-07T14:10:22.133Z","repository":{"id":258524815,"uuid":"866099396","full_name":"AshishSingh789/Data_Visualization","owner":"AshishSingh789","description":"Data visualization project using Python to analyze categorical and continuous variables. Includes bar charts, histograms, and scatter plots. 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The objective was to explore and visualize data to understand the distribution of various categorical and continuous variables.\n\n# Problem Statement\n\nTask:\n\nCreate a bar chart or histogram to visualize the distribution of a categorical or continuous variable, such as the distribution of ages or genders in a population.\n\n# Solution Overview\n\nIn this project, I worked on analyzing and visualizing the distribution of variables from a given dataset. Multiple types of charts were created to represent these distributions effectively. Below are the key steps involved in the analysis:\n\n# 1. Data Cleaning\n\nRemoved missing or null values to ensure a clean dataset for visualization.\nConverted data types where necessary (e.g., integers for categorical values such as 'Survived' or 'Gender').\n2. Data Analysis\nI performed the following types of analyses:\n\nCategorical Data Visualization: Created bar charts to show distributions of variables like gender, survival status, etc.\nContinuous Data Visualization: Visualized the distribution of numerical variables like age and fare using histograms and scatter plots.\n\n# 3. Visualizations Created\n\nBar Chart: Visualization of categorical variables like 'Gender' and 'Survival Status'.\nBox Plot: Analysis of continuous data like 'Fare' based on survival rates.\nScatter Plot: To observe relationships between 'Fare' and survival.\nEach visualization provides insights into the data patterns and allows for a deeper understanding of the distribution of different variables.\n\n# 4. Tools \u0026 Libraries Used\n\nPython: For data manipulation and visualization.\nLibraries:\npandas for data handling.\nmatplotlib and seaborn for creating visualizations.\nFiles\ntrain.csv: The dataset used for analysis.\nvisualization_code.py: Python script with all the code used for data analysis and generating visualizations.\nfare_vs_survival.png: Image showing the relationship between fare and survival.\nAdditional visualizations such as bar charts and histograms.\n\n\n# Data Analysis and Visualisation \n\n\n\n![Male vs Female Population Distribution in Rural Areas by State (Ascending Order)](https://github.com/user-attachments/assets/3f1dfb3f-dc56-49f3-8b51-244409d591dc)\n\n![Male vs Female Population Distribution in Urban  Areas by State (Ascending Order)](https://github.com/user-attachments/assets/5644abc0-5b5b-490f-a223-4a4dda0157a2)\n\n![Rural  Population Distribution by State](https://github.com/user-attachments/assets/eb31eebe-bdfd-47a3-8bb4-1aeb970270dc)\n\n![Total Female Population by State (Rural + Urban](https://github.com/user-attachments/assets/679e4586-503e-4de1-9809-3b9eee1f0472)\n\n![Total Male Population by State (Rural + Urban)](https://github.com/user-attachments/assets/541e6938-b298-4df1-8b63-65a358e2aad9)\n\n![Urban Population Distribution by State](https://github.com/user-attachments/assets/adac0642-33e8-4e24-9c0e-5a658c46b5e8)\n\n![Sex Ratio by Capital City of States](https://github.com/user-attachments/assets/dd8eb763-a010-4a27-9936-de9edc43a42a)\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashishsingh789%2Fdata_visualization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fashishsingh789%2Fdata_visualization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashishsingh789%2Fdata_visualization/lists"}