1. Introduction to Exploratory Data Analysis (EDA) - Key Techniques & Automation Tools (updated)

ML Lectures Playlist: https://youtube.com/playlist?list=PLGWXNgjLi7BTp_T4HU-KkbHBerAE8gRp4&si=Jc00z8S92vhNuzlN In this video, we take a deep dive into Exploratory Data Analysis (EDA), a crucial step in the data science process. Learn how to inspect, clean, and visualize your data to uncover insights before applying machine learning models. We'll cover: - Univariate, Bivariate, and Multivariate Analysis techniques to understand patterns in your data. - How to handle missing data and detect outliers. - The importance of visualization tools like Matplotlib, Seaborn, and Plotly for EDA. - Automation tools like pandas_profiling, Sweetviz, and D-Tale to speed up your EDA process. - Common EDA challenges. 📊 Dataset Used: Ames Housing Dataset 💻 Tools: Python, pandas, NumPy, Matplotlib, Seaborn, and more. ✏️ Timestamps : 0:00:00 Intro, Agenda. 0:01:38 EDA as a part of Crisp-DM. 0:03:31 What is EDA? 0:08:38 Key Statistical Measures for Numerical Data: Mean, Median, Mode; Variance, Standard Deviation; Skeweness, Kurtosis. 0:16:58 Key Statistical Measures for Categorical Data: Frequency Count, Proportion or Percentage, Mode, Unique Count, Missing Values. 0:17:40 Demo 1: Basic Statistics and Dataset Overview. 0:27:53 Univariate Analysis: What is it? Key Objectives. Univariate Analysis: Histograms, KDE, ECDF, Box Plots, Density Plots. 0:34:52 Demo 2: Univariate Analysis. 0:39:45 Bivariate Analysis: What is it? Key Objectives. Scatter Plots, Pair Plots, Heatmaps, Cross-Tabulation (Contingency Table). 0:48:11 Demo 3: Bivariate Analysis. 0:56:15 Multivariate Analysis: What is it? Key Objectives. 0:59:23 Demo 4: Multivariate Analysis. 1:01:58 EDA Automation Tools: Pandas Profiling (ydata_profiling), Sweetviz, dtale. 1:06:22 Demo 5: EDA Automation Tools: Pandas Profiling (ydata_profiling), Sweetviz, dtale. 1:11:49 Visualization Tools: Matplotlib, Seaborn, Plotly, ggplot (Python). 1:13:49 Demo 6: Visualization Tools: Matplotlib, Seaborn, Plotly, ggplot (Python). 1:18:28 Common Chanllenges in EDA. 1:20:00 Final Remarks and Recap 1:21:26 Useful Resources and References. 1:22:00 Thank you --- Whether you're new to data science or looking to refine your EDA skills, this tutorial offers a comprehensive guide to understanding and preparing your data. Don't forget to like, subscribe, and hit the notification bell for more data science tutorials! (version: Feb. 2025) #DataScience #EDA #MachineLearning #Python #DataAnalysis #ExploratoryDataAnalysis