Python Data Analysis Pipeline

Create a Python data analysis script for [dataset description]. IMPORTS: ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import StandardScaler ``` DATA LOADING: ```python df = pd.read_csv('[file_path]') df.head(), df.info(), df.describe() ``` CLEANING: • Handle missing values: [dropna / fillna with median/mean] • Remove duplicates • Fix data types (dates, categories, numerics) • Outlier detection using IQR method EXPLORATORY ANALYSIS: • Univariate: histograms, box plots for each feature • Bivariate: correlation matrix heatmap, scatter plots • Categorical: bar charts, count plots FEATURE ENGINEERING: • Create date features (day of week, month, quarter) • Binning continuous variables • One-hot encoding for categorical variables VISUALIZATION: • 3 key charts that tell the story • Annotated findings on each chart