# 🚀 ML-MODELS  
_Transform Data Into Actionable Insights Instantly_

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## 📑 Table of Contents
- [Overview](#overview)
- [Getting Started](#getting-started)
  - [Prerequisites](#prerequisites)
  - [Installation](#installation)
  - [Usage](#usage)
  - [Testing](#testing)

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## 🧠 Overview  
**ML-Models** is a modular toolkit designed to streamline machine learning experimentation and documentation. It supports a wide range of algorithms and emphasizes interpretability, visualization, and reproducibility.

### 🔧 Core Features
- 🧩 **Modular Notebooks**: Includes reusable components for NLP preprocessing (e.g., stemming, lemmatization).
- 🎯 **Model Visualization**: Interactive plots for decision boundaries, confusion matrices, and ROC curves.
- 🚀 **Diverse Algorithms**: SVM, KNN, Logistic Regression, Naive Bayes, Decision Trees, Random Forests, SVR.
- 📚 **Progress Documentation**: Tracks learning milestones and model iterations.
- 🔍 **Data Analysis & Visualization**: Built-in tools for EDA across domains like customer segmentation and water quality.

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## 🧰 Getting Started

### ✅ Prerequisites
- **Language**: Python (Jupyter Notebook)
- **Recommended**: Python ≥ 3.8, scikit-learn, pandas, matplotlib, seaborn

### ⚙️ Installation
Clone the repository and install dependencies:

```bash
git clone https://github.com/AmanDeepSinghH2/ML-Models
cd ML-Models
pip install -r requirements.txt