Data-Preprocessing-Air-Quality-Analyisis

This project demonstrates the fundamentals of Data Preprocessing, a crucial first step in Machine Learning and Deep Learning applications and projects. It covers the following domains:

1. Data Cleaning
2. Outlier Detection
3. Feature Scaling
4. Exploratory Data Analysis (EDA)

This project extensively uses the Numpy and Pandas libraries of Python for all the above steps. Additionally, it uses Matplotlib for visual representation of data and Scikit-learn for Feature scaling.