This project demonstrates how to perform data analysis using Python, leveraging essential libraries such as pandas, matplotlib, and seaborn. The focus is on analyzing real-world datasets, applying statistical techniques, and visualizing meaningful insights.
*Key Features:
-Data Cleaning: Handling missing values, filtering data, and correcting inconsistencies.
-Exploratory Data Analysis (EDA): Uncovering patterns, correlations, and trends using visualizations.
-Data Visualization: Creating impactful graphs and charts using matplotlib and seaborn.
-Statistical Analysis: Applying basic statistical methods to summarize and interpret the data.
*Libraries Used: -pandas -matplotlib -seaborn -numpy
*Dataset: We used a publicly available dataset for this project, demonstrating how to manipulate and analyze the data effectively to draw conclusions.
*How It Works: -The script loads a dataset, cleans the data, and generates visualizations that help in understanding key trends and patterns. -Each step of the analysis is broken down into well-documented sections for clarity and ease of replication.
*Project Motivation: -The aim of this project is to help beginners understand the process of data analysis and visualization using Python. By walking through a structured workflow, ----users can learn how to approach data-centric projects with confidence.
*Contributions: Contributions are welcome! Feel free to fork this repository and submit pull requests.