bank data analysis

In this project, I cleaned and prepared data for analysis using a structured pipeline:

Data Exploration: Loaded a CSV file, displayed samples, dropped the ID column, checked data types, and converted categorical features.

Missing Values: Dropped columns with high missing ratios, filled categorical nulls with mode, and handled skewed numerical data using the median.

Outliers: Identified using Box Plots; replaced extreme values with upper/lower bounds and rare categorical occurrences with mode.

Duplicates & Encoding: Removed duplicates, split data into X & y, and applied Label Encoding, Binary Encoding, or One-Hot Encoding based on category counts.