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

A confusion matrix is a table that is often used to summarize the performance of a classification model (or classifier) on a set of test data for which the true values are known.

It allows visualization of the performance of the algorithm by comparing predicted labels with actual labels. The matrix will show:

  • True Positives (TP) – Examples that were predicted positive and are actually positive.

  • True Negatives (TN) – Examples that were predicted negative and are actually negative.

  • False Positives (FP) – Examples that were predicted positive but are actually negative. Also known as ‘Type I error’.

  • False Negatives (FN) – Examples that were predicted negative but are actually positive. Also known as ‘Type II error’.

From these, key metrics like:

  • Accuracy = (TP + TN) / Total
  • Precision = TP / (TP + FP)
  • Recall = TP / (TP + FN)
  • F1 Score = 2 * (Precision * Recall) / (Precision + Recall)

Can be directly calculated.

The confusion matrix thus provides an overview of classifier performance and the types of errors being made. It is a useful tool for model evaluation, comparison and identification of bias/flaws.

Join the conversation