How to implement Logistic Regression from scratch with Python

Logistic Regression

Logistic regression is used to create probabilities instead of specific values.

The sigmoid function is used to calculate the probability distribution between 0 and 1.

The equation for logistic regression is similar to linear regression, but with the addition of the sigmoid function.

Cross entropy is used instead of mean squared error in logistic regression.

Gradient descent is used to calculate the gradient of the error function in terms of weight and bias.

·  Import required libraries: To implement logistic regression, we will need to import the necessary libraries(NumPy, Pandas, and Matplotlib)

·  Load the dataset: Load the dataset into our program using Pandas , we can either load the data from a file or use an existing dataset from a library like scikit-learn.

·  Preprocess the data: Before we can train our model, we will need to preprocess the data, this includes removing any missing values, encoding categorical variables, and scaling the data if necessary.

·    Split the dataset: Split the dataset into training and testing sets using scikit-learn's traintestsplit() function, we will use 70% of the data for training and 30% for testing.

·  Initialize the weights and bias: Initialize the weights and bias to small random values., we will initialize the weights to a vector of zeros and the bias to 0