Data Analysis Project README Project Overview This project involves data analysis using Python and the pandas library. The goal of the project is to analyze a dataset containing educational results data from various districts and schools. The dataset is provided in a CSV file named 'UaceResults2011-2015.csv'.

The dataset shows the district and school performance in the Uganda Advanced Certificate of Education (UACE) administered by the Uganda National Examinations Board (UNEB) from 2011 to 2015. The performance is indicated as the percentage of candidates who scored in each range of points that include 0-5, 6-10, 11-15, 16-20, 21-25. Gender breakdown is also indicated per school. We provide some analysis to try and get a better understanding of the results. The results are listed by school and show the performance in terms of how many students passed in a particular division.

The analysis will focus on understanding trends and patterns in student performance, such as percentage distributions in different score ranges, gender-based differences, and overall district performance.

Getting Started Prerequisites To run this project, you will need:

Python (version 3.6 or higher) pandas library numpy library Setup Clone this repository to your local machine:

shell Copy code git clone https://github.com/your-username/data-analysis-project.git Navigate to the project directory:

shell Copy code cd data-analysis-project Install the required libraries using pip (Python package manager):

shell Copy code pip install pandas numpy Running the Analysis Place the 'UaceResults2011-2015.csv' file in the project directory.

Open the Python script 'data_analysis.py' using your preferred text editor or integrated development environment (IDE).

Modify the script if necessary to adapt to your specific requirements or analysis goals.

Run the script:

shell Copy code python data_analysis.py The script will load the dataset, perform analysis, and display the results in a DataFrame.

Code Explanation The provided Python script 'data_analysis.py' performs the following steps:

Imports the necessary libraries:

python Copy code import pandas as pd import numpy as np Reads the CSV dataset into a pandas DataFrame:

python Copy code df = pd.read_csv('UaceResults2011-2015.csv') Conducts data analysis on the DataFrame, focusing on trends and patterns in student performance.

Displays the analyzed data in a tabular format.

Dataset Columns The dataset contains the following columns:

District_Name: Name of the district. SCHOOL: Name of the school. Gender: Gender of the students (FEMALE or MALE). Year-wise performance columns for 2011-2015 (e.g., '2011 Total', '%0-5 Points', '%6-10 Points', ...). Results The analysis provides insights into student performance across districts, schools, and gender. It offers an overview of how students scored within different point ranges over the years.

Conclusion This data analysis project demonstrates how to use Python and pandas to analyze educational results data. It showcases the steps to load, manipulate, and analyze data, offering insights into student performance trends.

Feel free to explore and customize the code to suit your specific analysis needs.

Please customize this README template according to your project's actual details, and ensure that you provide accurate information and instructions. The README serves as a guide for others (and yourself) to understand and use your project effectively.