Global-cases-analysis

Hey! in this we are analysing data of "Covid 19" and ,Making data clean

Data Visualization Project

📌 Overview

This project is all about exploring and visualizing data to uncover meaningful insights. We take a dataset containing global case statistics and clean, transform, and visualize it to better understand trends and patterns.

📂 About the Dataset

The dataset includes information on:

• Country/Region: Name of the country.
• Total Cases: The number of reported cases.
• Deaths: Total deaths recorded.
• Recovered: Number of recovered cases.
• Active: Currently active cases.
• WHO Region: The WHO classification of each country.
• Confirmed: Officially confirmed cases.

🔍 What We Did

Here’s a breakdown of the tasks we tackled: 1️⃣ Loaded and displayed the first few rows of the dataset.
2️⃣ Set the dataset index and renamed it for clarity.
3️⃣ Reformatted column names to remove spaces.
4️⃣ Handled missing values by filling them with the column mean.
5️⃣ Checked for duplicate country entries and removed them if necessary.
6️⃣ Calculated statistics like mean, median, and standard deviation of total cases.
7️⃣ Cleaned up the Deaths column by replacing string values with the column mean.
8️⃣ Converted the Deaths column into the correct numeric data type.
9️⃣ Calculated total deaths and recoveries worldwide.
🔟 Identified countries with more than 1 million cases.
1️⃣1️⃣ Found countries where the recovery rate is above 95%.
1️⃣2️⃣ Dropped unnecessary columns (WHO Region and Confirmed).
1️⃣3️⃣ Identified the country with the most deaths.
1️⃣4️⃣ Sorted countries by total deaths in descending order.
1️⃣5️⃣ Created a new column Total_Cases by summing Deaths, Recovered, and Active.
1️⃣6️⃣ Calculated the Death Rate and stored it in a new column.
1️⃣7️⃣ Looked for countries where cases are rising but death rates remain low.
1️⃣8️⃣ Displayed only the Country/Region and Death Rate columns.
1️⃣9️⃣ Created a scatter plot to compare Total Cases and Deaths.
2️⃣0️⃣ Saved the cleaned dataset as a CSV file.

🚀 How to Run This Project

What You’ll Need:

• Python 3.x
• Pandas
• Matplotlib
• Seaborn

Steps to Run:

1. Install the required libraries:

   pip install pandas matplotlib seaborn

2. Make sure the dataset is in the project directory.
3. Open the Jupyter Notebook (.ipynb) and run the cells to see the analysis and visualizations.

📊 What We Discovered

• The dataset was cleaned to remove missing values and duplicates.
• We calculated key statistics for total cases, deaths, and recoveries.
• We identified countries with high recovery rates and low death rates.
• A scatter plot was created to visualize the relationship between Total Cases and Deaths.
• The cleaned dataset was saved as a CSV file for further analysis.

📌 What’s Next?

• Adding interactive visualizations for better data exploration.
• Using machine learning models to predict trends.
• Building a dashboard for real-time tracking.

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✨ Created for Data Science & Visualization Enthusiasts 🚀