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

ROC (Receiver Operating Characteristic) curve and AUC (Area Under the Curve) are important evaluation metrics used in machine learning classification problems:

  • ROC curve is a probability curve that plots the True Positive Rate (Sensitivity) vs False Positive Rate at various classification thresholds.

  • It provides an indication of model performance independent of class distribution or error costs.

  • AUC represents the entire two-dimensional area underneath the entire ROC curve from (0,0) to (1,1).

  • It measures how well a model can distinguish between classes – an AUC of 0.5 means the model performs no better than random chance, while 1.0 is perfect classification.

  • AUC has a clear probabilistic interpretation and is a discriminative measure – provides information on how well models can discriminate between classes.

  • Unlike accuracy, it is not influenced by class imbalance or error distribution.

  • Commonly used for binary and multi-class classification problems to compare models and select the best performing one.

  • Higher AUC indicates better discriminative ability even at various thresholds and more robust model not dependant on single classification threshold.

So in summary, ROC-AUC is a powerful evaluation metric to assess and compare classification models independent of various data characteristics or thresholds.

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Muhammad Tufail 4 months ago