https://github.com/augustine-aj/entri-elevate-python-projects
https://github.com/augustine-aj/entri-elevate-python-projects
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/augustine-aj/entri-elevate-python-projects
- Owner: augustine-aj
- Created: 2024-05-06T15:38:02.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-29T22:58:29.000Z (almost 2 years ago)
- Last Synced: 2025-04-23T17:13:17.736Z (about 1 year ago)
- Language: Python
- Size: 45.9 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# EntriElevate Projects
# Banking-system
# EmployeeDataAnalysis-EDA
Employee Data Analysis Project
## Overview
This project aims to analyze a dataset containing information about employees, including their team, position, age, salary, and height. The analysis includes preprocessing steps, exploratory data analysis, and visualizations to gain insights into the dataset.
## Dataset
The dataset is stored in a CSV file (myexcel - myexcel.csv.csv) and contains the following columns:
- Team - The team to which the employee belongs.
- Position - The position of the employee.
- Age - The age of the employee.
- Salary - The salary of the employee.
## Preprocessing Steps
1. Reading the CSV file into a Pandas DataFrame.
2. Adding a new column 'height' with random values between 150 and 180.
3. Checking for missing values and dropping rows with missing values if any are found.
## Analysis Tasks
1. **Team Distribution**: Analyzing the distribution of employees across teams.
2. **Position Distribution**: Analyzing the distribution of employees across positions.
3. **Age Group Distribution**: Categorizing employees into age groups and analyzing the distribution.
4. **Team and Position Salary Expenditure**: Analyzing the total salary expenditure of each team and position.
5. **Correlation between Age and Salary**: Calculating the correlation coefficient between age and salary.
## Insights Gained
- The distribution of employees across teams and positions.
- The most common age group and position.
- The team and position with the highest total salary expenditure.
- The correlation between age and salary.
## Graphical Representations
Count plots and bar charts showing the distribution of employees across teams, positions, and age groups.
Scatter plot showing the relationship between age and salary.
## Additional Information:
The project includes visualizations created using Matplotlib and Seaborn to enhance the analysis.
The insights gained from the analysis can help in making informed decisions related to team management, hiring, and salary adjustments.
# FIFA Players Data Analysis-EDA
## Overview
This project analyzes the FIFA players dataset to extract key insights about players' distribution across countries and clubs, salary ranges, physical attributes, and preferences.
## Dataset
The dataset contains the following columns:
- Nationality: Country of the player
- Wage: Player's salary
- Height: Player's height
- Club: Player's club
- Preferred Foot: Dominant foot of the player
## Preprocessing Steps
1. Reading the CSV file into a Pandas DataFrame.
2. Converting 'Wage' from string to float.
3. Converting 'Height' from string to float.
4. Handling missing values if any.
## Analysis Tasks and Insights
1. **Country with the Most Players**: Identified the country with the highest number of players.
2. **Top 5 Countries with Most Players**: Visualized using a bar chart.
3. **Player with the Highest Salary**: Identified the highest-paid player.
4. **Salary Range of Players**: Visualized using a histogram.
5. **Tallest Player in FIFA**: Identified the tallest player.
6. **Club with Most Players**: Identified the club with the highest number of players.
7. **Preferred Foot of Players**: Visualized using a bar chart.
## Visualizations
- Bar chart for the top 5 countries with the most players.
- Histogram for the salary range of players.
- Bar chart for the preferred foot of players.
## Conclusion
The analysis provides valuable insights into the distribution, salaries, physical attributes, and preferences of FIFA players. These insights can help in making informed decisions in the sports industry.
Attached video explanation
https://drive.google.com/file/d/1ppoT29eKCR_ZkoKBRbukutoGvpO8dSR7/view?usp=sharing