Malik Zayn Awan - Invidious

Malik Zayn Awan

Welcome to my coding YouTube channel! My name is Zayn Malik and I'm a student of computer science with a passion for teaching others. On this channel, you'll find a range of programming tutorials and code demos covering a variety of languages and technologies. You're just starting out with coding or you're an experienced developer looking to deepen your skills, I hope you'll find something here that's helpful and informative.
I specialize in different programming languages and have a particular interest in Data science. In my videos, I'll cover everything from the basics of programming languages to more advanced concepts like DSA.
If you're new to my channel, be sure to check out my courses playlist to get started. And if you have any questions or suggestions for future videos, don't hesitate to reach out in the comments or on social media.
I hope you'll join me on this journey of learning and exploration. Don't forget to subscribe to stay up-to-date on all the latest content!

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Malik Zayn Awan

🚀 I’m Back!
Hey everyone! After a short break, I’m finally back on YouTube — this time with even better skills, fresh knowledge, and exciting new content for you all. 🙌

I’ve been learning, growing, and preparing so I can bring you more valuable videos, better quality, and engaging topics.

Stay tuned — the best is yet to come! 💡🔥

1 month ago | [YT] | 0

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Malik Zayn Awan

Introduction to Pandas Profiling

Hello AI engineer,
Once you have gathered data, the first task is to analyze what kind of data you have. To start this analysis, you can use the pandas-profiling module. This powerful tool provides a comprehensive overview of your dataset with just a few lines of code, making it an essential part of any data scientist's toolkit.

Introduction to Pandas Profiling

pandas-profiling is an open-source library that generates detailed reports of a DataFrame's statistics. These reports include a variety of summary statistics, visualizations, and warnings about potential issues in the data, such as missing values, duplicates, and correlations. This helps you quickly understand the structure and quality of your data.

Installation

Before you can use pandas-profiling, you need to install it. You can do this easily using pip:
pip install ydata-profilingimport pandas as pd

Generating a Report

To generate a profile report, you first need to load your data into a Pandas DataFrame. Here is a basic example of how to create a profile report:
from ydata_profiling import ProfileReport
df=pd.read_csv("datasets/placement.csv")
pf=ProfileReport(df)
pf.to_file(output_file="out.html")
This code snippet will create a comprehensive report that you can open in a web browser.

Features of Pandas Profiling

Overview

The report begins with an overview section that includes essential information such as the number of variables, observations, missing cells, and memory usage. This gives you a quick snapshot of your dataset.

Variable Descriptions

Each variable in your dataset is analyzed in detail. This section includes:
Type Inference: Identifies if the variable is numerical, categorical, boolean, etc.
Descriptive Statistics: Provides measures such as mean, median, standard deviation, and quartiles for numerical variables. For categorical variables, it lists the most frequent categories.
Missing Values: Highlights the number and percentage of missing values.
Unique Values: Indicates the number of unique values in the variable.
Histograms: Displays the distribution of the data for numerical variables.
Bar Charts: Shows the frequency of categories for categorical variables.

Correlations

Understanding the relationships between variables is crucial in any data analysis. The correlation section provides various correlation matrices such as Pearson, Spearman, Kendall, and Phi_k, helping you identify potential dependencies and multicollinearity issues.
Interactions
This feature allows you to explore interactions between variables. You can create scatter plots and other visualizations to better understand how variables influence each other.

Missing Values

The missing values section provides a detailed analysis of where and how much data is missing. It also offers visualizations like heatmaps and dendrograms to help you understand patterns in missing data.

Samples

The report includes a few samples of your data, showing the first and last rows. This can be useful for a quick inspection of what your raw data looks like.

Conclusion

Pandas Profiling is an invaluable tool for any data scientist or ML engineer. It accelerates the data exploration phase by providing a thorough analysis of your dataset with minimal effort. By using pandas-profiling, you can quickly identify potential issues, understand the distribution and relationships in your data, and make informed decisions about how to preprocess and model your data.
Incorporating pandas-profiling into your workflow will undoubtedly save you time and provide deeper insights into your data, ultimately leading to more robust and accurate machine learning models. So, the next time you start a new data project, remember to profile your data first!

1 year ago | [YT] | 1

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Malik Zayn Awan

How to Frame a Machine Learning Problem | How to Plan a Data Science Project Effectively

If you are working as a junior data scientist and your team is working on a very important project, in the beginner phase, you might be assigned a small part of the company project to complete. Suppose the company is working on a recommendation system or a prediction system, then you might be assigned to preprocess the data, etc. But after gaining some experience and leading the team, you will be the leader of your team. You have to plan everything while getting the project.

Here are 7 main steps to follow to become a good data scientist and a good team lead in the data science department:

1. Business Problem
2. Types of Problem
3. Current Solution
4. Getting Data
5. Metrics to Measure
6. Online or Batch Training
7. Checking Assumptions

Business Problem:

Suppose you are the head of the data science department at Netflix. You are in a meeting discussing how to generate more revenue for Netflix. All department heads ask for your opinion on increasing revenue. You will discuss, from your point of view, three points to gain more profit: first, bringing more users to Netflix using marketing; second, decreasing the prices of Netflix plans; and third, decreasing the churn rate, meaning focusing on existing users so they don’t quit the platform. There is a 4% churn rate, and you create a meeting with your team to discuss the existing churn rate and your 6-month target to decrease this churn rate with full effort.

Type of Problem:

After setting your goal, you now discuss what type of problem this is. Is it a regression problem or a classification problem? You analyze that this user might leave the platform next month, and you are giving a 50% discount to the leaving user. This is a classification problem because a user either leaves or not (yes or no). If a user has a 10% chance of leaving the platform or some users have a 100% chance of leaving the platform, then you focus on discounts: if a user has a 10% chance of leaving, you give a lesser discount compared to a 100% chance of leaving.

Current Solution:

In the current solution, you connect with the CTO to ask if there is any model predicting user behavior, etc. Suppose there is a model predicting the churn rate of Netflix, so it is easy to check the working of this model and connect with the team that made this model.

Getting Data:

This is a crucial step. You can connect with the data engineering team and share the details that you need certain features for this problem. For example, you need users' movie watching time and user searches, etc.

Metrics to Measure:

You have to define metrics to measure whether the model is predicting correctly or not. For instance, if you are sure that this user will leave the platform, are you giving a discount or not?

Online or Batch Training:

After your model works well, you need to train the model on upcoming data. Online training means you directly connect with the warehouse and train the model online, while batch training means you train the model later using a batch of data.

Check Assumptions:

Now you check assumptions: is this one model good for all regions or not? Definitely not, because all regions have different preferences and dislikes, so you have to check assumptions accordingly.

1 year ago | [YT] | 4

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Malik Zayn Awan

🖼️🚀 Just launched my latest project! Introducing an AI Image Generator frontend website crafted with HTML, Tailwind CSS, and CSS. 💻✨ As a Junior Python Developer at Robx.ai, I've explored the fascinating intersection of AI and frontend development.

Check out my AI Image Generator here:

6574470462d1154dcb6a80b6--ubiquitous-bubblegum-c18…

source code:

github.com/Zainisrar/AI-image-generator

and witness the magic of technology-driven design. I'm thrilled about this blend of AI and frontend, and I can't wait to delve deeper into more cutting-edge projects! 🎨🤖 #AI #FrontendDevelopment #ImageGenerator #Innovation