Refine README formatting and content

Updated headings and descriptions for clarity and consistency.

Author: Daniel15568

README.md

@@ -1,15 +1,15 @@
-#                                              % Percentify %
+#                                               Percentify
 [![PyPI Downloads](https://static.pepy.tech/personalized-badge/percentify?period=total&units=INTERNATIONAL_SYSTEM&left_color=BLACK&right_color=GREEN&left_text=downloads)](https://pepy.tech/projects/percentify)
 [![PyPI version](https://img.shields.io/pypi/v/percentify.svg?style=flat&color=blue)](https://pypi.org/project/percentify/)
 [![Python Versions](https://img.shields.io/pypi/pyversions/percentify.svg?style=flat&color=green)](https://pypi.org/project/percentify/)
 [![License](https://img.shields.io/pypi/l/percentify.svg?style=flat&color=orange)](LICENSE)
 [![Build Status](https://github.com/data-centt/percentify/actions/workflows/python-app.yml/badge.svg)](https://github.com/data-centt/percentify/actions/workflows/python-app.yml)
 
-**Percentify** — one import, one line, the stats you actually need.
+**Percentify** is a one import, one line code, that covers all stats you need for your data analysis and codebase.
 
 Stop digging through scipy, statsmodels, and sklearn for operations you run every day. Percentify surfaces the most common percentage and statistical calculations into simple, readable function calls.
 
-**Beyond percentages** — percentify gives you easy access to deeply buried, everyday-useful statistical operations in just one import.
+
 
 ---
 
@@ -20,9 +20,9 @@ pip install percentify
 
 ---
 
-## ✨ Core — Percentage Toolkit
+## ✨ Core Percentage Toolkit
 
-### `percent` — Part of a Whole
+### `percent`: Part of a Whole
 ```python
 from percentify import percent
 
@@ -31,31 +31,31 @@ percent(1, 3)             # → 33.33
 percent(5, 0)             # → 0.0  (safe division by zero)
 ```
 
-### `change` — Percentage Increase or Decrease
+### `change`: Percentage Increase or Decrease
 ```python
 from percentify import change
 
 change(100, 150)  # → 50.0   (50% increase)
 change(200, 150)  # → -25.0  (25% decrease)
 ```
 
-### `difference` — Difference Between Two Values
+### `difference`: Difference Between Two Values
 ```python
 from percentify import difference
 
 difference(10, 20)  # → 66.67
 difference(50, 50)  # → 0.0
 ```
 
-### `split` — Split a Total by Weights
+### `split`: Split a Total by Weights
 ```python
 from percentify import split
 
 split(200, [1, 3])       # → [50.0, 150.0]
 split(100, [1, 1, 1])    # → [33.33, 33.33, 33.33]
 ```
 
-### `display` — Format as a String
+### `display`: Format as a String
 ```python
 from percentify import display
 
@@ -70,11 +70,11 @@ display(change(100, 20))              # → "-80.0%"
 
 ---
 
-## 📊 Beyond Percentages — Data Science & Analytics
+## 📊 Beyond Percentages; Data Science & Analytics
 
 The functions below replace multi-step, hard-to-remember imports from scipy, statsmodels, and sklearn with a single line.
 
-### `vif` — Variance Inflation Factor
+### `vif`: Variance Inflation Factor (MultiCollinearity)
 Currently buried in `statsmodels.stats.outliers_influence`. One line instead of six.
 ```python
 from percentify import vif
@@ -86,7 +86,7 @@ vif(df, flag=5.0)
 # → only columns with VIF > 5 (multicollinearity warnings)
 ```
 
-### `missing` — Missing Data Profiling
+### `missing`: Easy Missing Data Profiling
 No more typing `df.isnull().sum() / len(df) * 100` every time.
 ```python
 from percentify import missing
@@ -95,7 +95,7 @@ missing(df)
 # → {"salary": 12.4, "age": 3.1, "name": 0.0}
 ```
 
-### `cv` — Coefficient of Variation
+### `cv`:  Coefficient of Variation
 Not built-in anywhere — one line instead of `df.std() / df.mean() * 100`.
 ```python
 from percentify import cv
@@ -104,7 +104,7 @@ cv(df["salary"])  # → 34.2
 cv(df)            # → all numeric columns at once
 ```
 
-### `outliers` — Percentage of Outliers (IQR Method)
+### `outliers`: Percentage of Outliers (IQR Method)
 Stop rewriting the IQR calculation from scratch.
 ```python
 from percentify import outliers
@@ -113,14 +113,14 @@ outliers(df["salary"])  # → 4.7
 outliers(df)            # → all numeric columns
 ```
 
-### `r_squared` — R-Squared
+### `r_squared`: R-Squared
 ```python
 from percentify import r_squared
 
 r_squared(y_true, y_pred)  # → 87.3
 ```
 
-### `variance_explained` — PCA Variance Breakdown
+### `variance_explained`: PCA Variance Breakdown
 ```python
 from percentify import variance_explained