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https://github.com/ashishsingh789/data_visualization
Data visualization project using Python to analyze categorical and continuous variables. Includes bar charts, histograms, and scatter plots. Libraries used: pandas, matplotlib, and seaborn.
https://github.com/ashishsingh789/data_visualization
analysis barchart data data-science data-visualization histogram matplotlib pandas-dataframe scatter-plot seaborn
Last synced: 2 days ago
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Data visualization project using Python to analyze categorical and continuous variables. Includes bar charts, histograms, and scatter plots. Libraries used: pandas, matplotlib, and seaborn.
- Host: GitHub
- URL: https://github.com/ashishsingh789/data_visualization
- Owner: AshishSingh789
- Created: 2024-10-01T16:39:50.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-10-01T16:51:58.000Z (about 2 months ago)
- Last Synced: 2024-10-19T12:04:27.614Z (29 days ago)
- Topics: analysis, barchart, data, data-science, data-visualization, histogram, matplotlib, pandas-dataframe, scatter-plot, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 760 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Data Visualization Task - Prodigy Infotech Internship #
This repository contains the analysis and visualizations created as part of my internship at Prodigy Infotech. The objective was to explore and visualize data to understand the distribution of various categorical and continuous variables.
# Problem Statement
Task:
Create 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.
# Solution Overview
In 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:
# 1. Data Cleaning
Removed missing or null values to ensure a clean dataset for visualization.
Converted data types where necessary (e.g., integers for categorical values such as 'Survived' or 'Gender').
2. Data Analysis
I performed the following types of analyses:
Categorical Data Visualization: Created bar charts to show distributions of variables like gender, survival status, etc.
Continuous Data Visualization: Visualized the distribution of numerical variables like age and fare using histograms and scatter plots.
# 3. Visualizations Created
Bar Chart: Visualization of categorical variables like 'Gender' and 'Survival Status'.
Box Plot: Analysis of continuous data like 'Fare' based on survival rates.
Scatter Plot: To observe relationships between 'Fare' and survival.
Each visualization provides insights into the data patterns and allows for a deeper understanding of the distribution of different variables.
# 4. Tools & Libraries Used
Python: For data manipulation and visualization.
Libraries:
pandas for data handling.
matplotlib and seaborn for creating visualizations.
Files
train.csv: The dataset used for analysis.
visualization_code.py: Python script with all the code used for data analysis and generating visualizations.
fare_vs_survival.png: Image showing the relationship between fare and survival.
Additional visualizations such as bar charts and histograms.
# Data Analysis and Visualisation
