Data Analysis of KCSE results for KamiLimu ICT Track

A dataset of KCSE results over a couple of years was presented and required to be analysed.

I analysed the data using Python Programming Language using PyCharm. Pandas library and Matplotlib for Data Visualization.

This is what I did withthe data:-

1. Checked the data if it had any missing values or if the data had wrongly named values and cleaned it.
2. Sorted the data based on gender and year and compared the results based on these parameters.
3. Compared the results based on those who qualified to join University (Grades A - C+) and those who did not (Grades C - E)
4. Compared the number of candidates over the years.
5. Found the most common grades among both genders.

1. There was a value named FEMLAE instead of FEMALE in Gender.

The most common grade among males over the years is D

The most common grade among females over the years is D

Over the years, only 25.91% of students achieved quality grades while 74.09% failed to qualify for University.

In 2011, there was the least gap between quality grades and non-quality grades. In 2016, there was the largest gap between quality grades and non-quality grades.

Found difficulty creating pie charts to visualize some of the data. Found out that Jupyter Notebook would be more effective for Data Analysis

1. Practice plotting pie charts
2. Practice Data Visualiztion Techniques
3. Practice and compare efficiency of Jupyter Notebook to PyCharm.