Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

Machine Learning with Python A Practical Beginners’ Guide by Oliver Theobald-01.png

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Mlxtend

This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.

A Practical Guide to Machine Learning with Python

# 5) Remove and fill missing values
Let’s now inspect the data frame for missing values.

#5. Remove and fill missing values
print(df.isnull().sum())

The output shows that four of the thirty six variables contain missing values: these four variables and their correlation to the y (dependent) variable (`State_successful`) are summarized in the table below.

#Code for obtaining correlation coefficients
df['State_successful'].corr(df['Facebook Friends'].astype(float)) 
df['State_successful'].corr(df['Creator - # Projects Backed'].astype(float))
df['State_successful'].corr(df['# Videos'].astype(float))
df['State_successful'].corr(df['# Words (Risks and Challenges)'].astype(float))

`Facebook Friends` and `Creator - # Projects Backed` variables have many missing values, but their correlation to the dependent variable (`State_successful`) is significant for analysis. Therefore, removing rows containing missing values from these two variables would cut the dataset in half (18,142 rows to 9,532 rows).

You can remove the missing rows given their low frequency (101). Alternatively, you could opt to remove these two variables given their low correlation with the dependent variable.

Next, let’s use the `describe()` method to inspect the standard deviation and range of the two remaining variables.

This lesson will go over steps 5-7 of logistic regression implementation.

Logistic Regression Steps: 5 to 7

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Conclusion

Appendix

Logistic Regression Steps: 5 to 7

5) Remove and fill missing values