1. Mastering Exploratory Data Analysis (EDA): Key Techniques & Automation Tools 📊

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
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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