Autoplay
Autocomplete
Previous Lesson
Complete and Continue
SQL + Tableau + Python
Software Integration
Introduction to Data, Servers, Clients, Requests, and Responses (4:43)
Introduction to Data Connectivity, APIs, and Endpoints (7:05)
More on APIs (8:05)
Exchanging Information using Text Files (4:20)
Software Integration: Python-SQL-Tableau (5:25)
What's Next in the Course?
What's next in the course? (4:08)
Defining the Task: Absenteeism at Work (2:48)
The Data Set (3:18)
Preprocessing the 'Absenteeism_data'
Organization of the Content in the Next Sections
Importing the Data Set in Python (3:23)
Eyeballing the Data (5:53)
Introduction to Terms with Multiple Meanings (3:27)
ARTICLE - A Refresher on Regression Analysis
An Analytical Approach to Solving the Task (2:17)
Dropping the "ID" Column (6:27)
Dropping the "ID" Column - Solution
Analysis of the "Reason for Absence" Column (5:04)
Converting a Feature into Multiple Dummy Variables (8:37)
Converting a Feature into Multiple Dummy Variables - Solution
ARTICLE - Dropping a Dummy Variable
Working with Dummy Variables from a Statistical Perspective (1:28)
Grouping the Various Reasons for Absence (8:35)
Concatenating Column Values (4:35)
Concatenating Column Values - Solution
Reordering Columns (1:43)
Reordering Columns - Solution
Creating Checkpoints in Jupyter (2:52)
Creating Checkpoints in Jupyter - Solution
Working on the "Date" Column (7:48)
Extracting the Month Value (7:00)
Creating the "Day of the Week" Column (3:36)
ARTICLE/EXERCISE - Dropping the "Date" Column
Modifying "Education" and discussing "Children" and "Pets" (4:38)
Analyzing the Next 5 Columns in our DataFrame (3:17)
Final Remarks on the Data Preprocessing Part of the Exercise (1:59)
A Note on Exporting Your Data as a *.csv File
Applying Machine Learning to the Preprocessed Data
Exploring the Problem from a Machine Learning Point of View (3:20)
Creating the Targets for the Regression (6:32)
Selecting the Inputs for the Regression (2:41)
Standardizing the Dataset for Better Results (3:26)
Train-Test Split (6:12)
Training and evaluating the model (5:39)
Extracting the Intercept and Coefficients (5:16)
Interpreting the Coefficients (6:14)
Creating a Custom Scaler to Standardize Only Numerical Features (4:12)
Interpreting the (Important) Coefficients (5:10)
Simplifying the Model (Backward Elimination) (4:02)
Testing the Logistic Regression Model (4:43)
Saving the Logistic Regression Model (4:06)
ARTICLE - More about 'pickling'
Creating a module for later use of the model (4:04)
Connecting Python and SQL
Loading the 'absenteeism_module' (3:50)
Working with the 'absenteeism_module' (6:23)
Creating a Database Structure in MySQL (6:12)
Installing and Importing 'pymysql' (2:44)
Setting up a Connection and Creating a Cursor (2:54)
Creating the 'predicted_outputs' table in MySQL (4:52)
Executing an SQL Query from Python (3:04)
Moving Data from Python to SQL - Part I (6:15)
Moving Data from Python to SQL - Part II (6:35)
Moving Data from Python to SQL - Part III (2:45)
Analyzing the Obtained Data in Tableau
Tableau Analysis: Age vs Probability (8:49)
Tableau Analysis: Reasons vs Probability (7:49)
Tableau Analysis: Transportation Expense vs Probability (6:00)
Final Remarks on the Data Preprocessing Part of the Exercise
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock