Learn how to apply the logistic regression model.
- [Narrator] Logistic regression is a simple…machine learning method that you can use to predict…an observation's category based on the relationship…between the target feature and independent…categorical predictive features in the data set.…For example, imagine you're a marketing data scientist…for a major telecom service provider.…You've got a customer data set that describes…each customer with variables like age, income,…average call duration, interaction history…with customer support, leftover minutes per month…and customer status.…
Customer status is a variable that describes whether…a customer is active or has canceled services.…Based on the predictive features in this data set,…in their relationship with a customer status variable,…you could build a logistic regression model…that predicts whether a customer is likely to cancel…services in the near future.…This is called a customer churn model.…Logistic regression differs from linear regression…in that, with logistic regression, you're predicting…categories for ordinal variables, in linear progression…
AuthorLillian Pierson, P.E.
- Getting started with Jupyter Notebooks
- Visualizing data: basic charts, time series, and statistical plots
- Preparing for analysis: treating missing values and data transformation
- Data analysis basics: arithmetic, summary statistics, and correlation analysis
- Outlier analysis: univariate, multivariate, and linear projection methods
- Introduction to machine learning
- Basic machine learning methods: linear and logistic regression, Naïve Bayes
- Reducing dataset dimensionality with PCA
- Clustering and classification: k-means, hierarchical, and k-NN
- Simulating a social network with NetworkX
- Creating Plot.ly charts
- Scraping the web with Beautiful Soup
Skill Level Beginner
1. Data Munging Basics
2. Data Visualization Basics
3. Basic Math and Statistics
4. Dimensionality Reduction
Explanatory factor analysis6m 39s
5. Outlier Analysis
6. Cluster Analysis
7. Network Analysis with NetworkX
8. Basic Algorithmic Learning
9. Web-based Data Visualizations with Plotly
10. Web Scraping with Beautiful Soup
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