Course Content
Day-2: How to use VScode (an IDE) for Python?
Day-3: Basics of Python Programming
This section will train you for Python programming language
Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
Day-10: Data Wrangling and Data Visualization
Data Wrangling and Visualization is an important part of Exploratory Data Analysis, and we are going to learn this.
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
Day-12,13: Exploratory Data Analysis (EDA)
EDA stands for Exploratory Data Analysis. It refers to the initial investigation and analysis of data to understand the key properties and patterns within the dataset.
Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
Day-39: git and github
We will learn about git and github
Day-40: Prompt Engineering (ChatGPT for Social Media Handling)
Social media per activae rehna hi sab kuch hy, is main ap ko wohi training milay ge.
Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

Key points about logistic regression:

  • It is used when the dependent variable is categorical (binary or multiclass classification) rather than continuous.

  • It predicts the probability of an observation belonging to a specific class rather than the class itself.

  • The relationship between independent variables X and dependent variable Y is modelled using a logistic/sigmoid curve rather than linear function.

  • The output is a probability value between 0 and 1, via the logistic function formula:

P(Y=1|X) = 1/(1+e^-(b0 + b1X1 + b2X2 +…))

  • The coefficients b have similar interpretations as linear regression – effect of one unit change in X on log odds of Y.

  • It can handle both continuous and categorical independents.

  • Can be extended for multiclass classification problems using techniques like one-vs-rest.

  • Evaluated using classification metrics like accuracy, AUC-ROC, confusion matrix etc. rather than R-squared.

  • Commonly used for problems like prediction, recommendation systems, sentiment analysis etc.

So in summary, logistic regression applies when we want to model and predict categorical outcomes using linear regression techniques for multiple variables.

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Muhammad Tufail 4 months ago
Logistic Regression