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

R-squared is not commonly used as an evaluation metric for logistic regression models because it works differently for linear and non-linear models. Here are some key points about R-squared in logistic regression:

  • In linear regression, R-squared represents the proportion of variance in the dependent variable that is explained by the independent variables. It ranges from 0 to 1.

  • However, for logistic regression the dependent variable is binary/categorical so the concept of variance doesn’t apply directly.

  • Logistic regression predicts probabilities rather than actual values, so the interpretation of R-squared is different.

  • Some variations of R-squared have been adapted for logistic regression but they do not have the same probabilistic interpretation as in linear models.

  • Pseudo R-squared metrics like Cox & Snell, Nagelkerke can range from 0 to 1 but may exceed 1 which is not desirable.

  • AUC (Area Under the ROC Curve) is a more robust metric to assess logistic regression performance as it doesn’t rely on assumptions behind R-squared.

  • Other classification metrics like accuracy, precision, recall are also more suitable than R-squared.

So in summary, while R-squared can be calculated for logistic regression, it doesn’t have the same straightforward interpretation and probabilistic meaning as for linear regression models. Alternative metrics are preferred for evaluation.

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Muhammad Tufail 4 months ago
You have explain well