1. Library Imports;

In this code snippet we import libraries to assist with data analysis and visualization. We use the pandas library, for data manipulation and analysis matplotlib.pyplot and seaborn for creating visualization, numpy for numerical operations and array handling warnings to suppress any unnecessary warnings during the analysis process.

Beginning my Exploratory Data Analysis, starting with inspecting the header with (20) to give me the first 20 rows of data.

Inspecting the last five rows to give me a brief overview of the dataset.With 171 features, my first impression was that there is a lot of noise in this data set and the pre-processing and prepartion phase of this project will be key to my success. I have also noticed that my data conatins na values, which I will need to remove or change as part of my preprocessing.

To gain an understanding of the data we display the 20 rows of the dataframe using the afs.head(20) command, afs.tail(This will display the most recent 5 rows in the dataframe) and to iover view tehd atset I used afs.info()

The assignment stated that:

"The dataset’s positive class consists of component failures for a specific component of the APS system.The negative class consists of trucks with failures for components not related to the APS. The data consists of a subset of all available data, selected by experts. This analysis will help determine the investment strategy for the company in the upcoming year."

Therefore, my first act is to delete the unneccessary 'negative class' contained in the dataset as it is not relevent.

df_filtered = df[df['class'] != 'neg']

df_filtered.to_csv('afs-filtered.csv', index=False)

df_filtered.head(100)

This code filters out any rows where the value in the 'class column is 'neg'. The resulting filtered data is then saved to a CSV file named 'afs filtered.csv'.

Reference: https://sparkbyexamples.com/pandas/pandas-filter-rows-by-conditions/