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Generative AI for Data Analysis and Engineering with ChatGPT
Project Files and Sources
The Main Prompt Source of The Course
Prompts
Github Link
Kaggle Link
ChatGPT-4o Unleashed: Innovations in Communication and Learning
How to Use ChatGPT-4o? (5:53)
Big News: Introducing ChatGPT-4o (4:54)
Chronological Development of ChatGPT (5:21)
What Are the Capabilities of ChatGPT-4o? (4:33)
As an App: ChatGPT (3:18)
Voice Communication with ChatGPT-4o (4:50)
Instant Translation in 50+ Languages (3:03)
Interview Preparation with ChatGPT-4o (18:06)
Visual Commentary with ChatGPT-4o: Lesson 1 (4:19)
Visual Commentary with ChatGPT-4o: Lesson 2 (4:56)
Dataset Exploration and Field Knowledge
ChatGPT for Generative AI Introduction (4:21)
Accessing the Dataset (1:35)
First Task: Field Knowledge (11:12)
Continuing with Field Knowledge (5:42)
Loading the Dataset and Understanding Variables (7:55)
Delving into the Details of Variables (5:35)
Variable Analysis: Missing Data, Unique Values, and Statistics
Updating Variable Names (6:19)
Let's Perform the First Analysis (6:37)
Examining Missing Values (6:07)
Examining Unique Values (14:12)
Examining Statistics of Variables Lesson 1 (15:15)
Examining Statistics of Variables Lesson 2 (13:11)
Examining Statistics of Variables Lesson 3 (9:19)
Exploratory Data Analysis (EDA) 1
Categorical Variables (Analysis with Pie Chart) Lesson 1 (10:40)
Exploratory Data Analysis (EDA) (9:59)
Categorical Variables (Analysis with Pie Chart) Lesson 2 (9:35)
Categorical Variables (Analysis with Pie Chart) Lesson 3 (6:52)
Categorical Variables (Analysis with Pie Chart) Lesson 4 (16:44)
Categorical Variables (Analysis with Pie Chart) Lesson 5 (11:19)
Exploratory Data Analysis (EDA) 2
Numerical Variables vs Target Variable Lesson 1 (6:42)
Importance of Bivariate Analysis in Data Science (7:16)
Numerical Variables vs Target Variable Lesson 2 (9:56)
Numerical Variables vs Target Variable Lesson 3 (8:24)
Numerical Variables vs Target Variable Lesson 4 (3:36)
Categoric Variables vs Target Variable Lesson 1 (3:37)
Categoric Variables vs Target Variable Lesson 2 (5:31)
Categoric Variables vs Target Variable Lesson 3 (5:21)
Categoric Variables vs Target Variable Lesson 4 (4:45)
Categoric Variables vs Target Variable Lesson 5 (5:51)
Exploratory Data Analysis (EDA) 3
Correlation Between Numerical and Categorical Variables and the Target Variable (11:42)
Correlation Between Numerical and Categorical Variables and the Target Variable (7:57)
Examining Numeric Variables Among Themselves Lesson 1 (6:12)
Examining Numeric Variables Among Themselves Lesson 2 (6:56)
Numerical Variables - Categorical Variables Lesson 1 (18:12)
Numerical Variables - Categorical Variables Lesson 2 (6:01)
Numerical Variables - Categorical Variables Lesson 3 (5:21)
Numerical Variables - Categorical Variables Lesson 4 (5:17)
Numerical Variables - Categorical Variables Lesson 5 (5:48)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 1 (12:33)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 2 (7:19)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 3 (7:01)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 4 (4:35)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 5 (4:31)
Numerical Variables - Categorical Variables with Swarm Plot Lesson 6 (8:48)
Relationships between variables (Analysis with Heatmap) Lesson 1 (8:07)
Relationships between variables (Analysis with Heatmap) Lesson 2 (14:44)
Preparation for Modeling
Dropping Columns with Low Correlation (5:26)
Preparation for Modeling (5:23)
Struggling Outliers (9:50)
Visualizing Outliers Lesson 1 (6:30)
Visualizing Outliers Lesson 2 (4:33)
Visualizing Outliers Lesson 3 (3:51)
Dealing with Outliers Lesson 1 (9:24)
Dealing with Outliers Lesson 2 (13:50)
Dealing with Outliers Lesson 3 (6:02)
Dealing with Outliers Lesson 4 (6:38)
Dealing with Outliers Lesson 5 (10:03)
Determining Distributions (11:24)
Determining Distributions of Numeric Variables Lesson 1 (6:26)
Determining Distributions of Numeric Variables Lesson 2 (4:06)
Determining Distributions of Numeric Variables Lesson 3 (4:30)
Determining Distributions of Numeric Variables Lesson 4 (8:20)
Determining Distributions of Numeric Variables Lesson 5 (6:53)
Applying One Hot Encoding Method to Categorical Variables Lesson (5:19)
Applying One Hot Encoding Method to Categorical Variables Lesson 2 (2:31)
Feature Scaling with the RobustScaler Method for Machine Learning Algorithms (3:44)
Separating Data into Test and Training Set (4:00)
Machine Learning Algorithm – Logistic Regression
Logistic Regression Algorithm Lesson 1 (6:14)
Logistic Regression Algorithm Lesson 1 (11:15)
Cross Validation (8:44)
ROC Curve and Area Under Curve (AUC) Lesson 1 (6:56)
ROC Curve and Area Under Curve (AUC) Lesson 2 (5:48)
Hyperparameter Optimization (with GridSearchCV) (7:33)
Hyperparameter Tuning for Logistic Regression Model (8:28)
Machine Learning Algorithm – Decision Tree & SVM
Decision Tree Algorithm Lesson 1 (5:50)
Decision Tree Algorithm Lesson 2 (6:55)
Support Vector Machine Algorithm Lesson 1 (5:34)
Support Vector Machine Algorithm Lesson 2 (6:23)
Machine Learning Algorithm – Random Forest
Random Forest Algorithm Lesson 2 (3:37)
Random Forest Algorithm Lesson 1 (6:09)
Random Forest Algorithm Lesson 3 (5:03)
Random Forest Algorithm Lesson 4 (5:43)
Conclusion
Project Conclusion (10:35)
Suggestions and Closing (8:07)
Correlation Between Numerical and Categorical Variables and the Target Variable
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