English | Size: 257 MB
Genre: eLearning
Build a Logistic Regression Model in Python from Scratch, Predicting a qualitative response for observation can be referred to as classifying that observation since it involves assigning the observation to a category or class. Classification forms the basis for Logistic Regression. Logistic Regression is a supervised algorithm used to predict a dependent variable that is categorical or discrete. Logistic regression models the data using the sigmoid function. Churned Customers are those who have decided to end their relationship with their existing company. In our case study, we will be working on a churn dataset. XYZ is a service-providing company that provides customers with a one-year subscription plan for their product. The company wants to know if the customers will renew the subscription for the coming year or not.
What you’ll learn
Understanding the basics of classification
Introduction to Logistics regression
Understanding the logit function
Coefficients in logistics regression
Concept of maximum log-likelihood
Performance metrics like confusion metric, recall, accuracy, precision, f1-score, AUC, and ROC
Importing the dataset and required libraries.
Performing basic Exploratory Data Analysis (EDA).
Using python libraries such as matplotlib and seaborn for data interpretation and advanced visualizations.
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