🧪🔬 DataFlow Solubility Analysis

Welcome to the DataFlow Solubility Project! This project focuses on analyzing and predicting solubility using data science techniques. By leveraging modern tools and methods, it uncovers patterns and insights into the solubility of chemical compounds, an essential aspect of chemical and pharmaceutical industries.

🎯 Project Objective

The aim of this project is to:

• Analyze datasets related to solubility and chemical properties.
• Use machine learning to predict solubility based on chemical features.
• Provide visualizations and insights for better understanding and decision-making.

📂 Project Structure

• DataFlow_Solubility.ipynb: The main Jupyter Notebook containing all data analysis, modeling, and visualizations.
• data/: Folder containing datasets used in the project (if included).
• README.md: This file, documenting the project.

📊 Key Insights

This project includes:

• Exploration of chemical datasets to identify patterns.
• Feature engineering to enhance model performance.
• Predictive modeling using machine learning algorithms.
• Visualizations showcasing solubility trends and model performance.

🚀 How to Run the Project

1. Clone the repository:

   git clone https://github.com/ay0788/DataFlow-Solubility.git
   cd DataFlow-Solubility