Umer Haddii

How to upload/import notebook on Kaggle

How to upload/import notebook on Kaggle In this video you will learn, how to upload notebook on Kaggle, how to attach dataset, how to select GPU/TPU on Kaggle and how to add secret API Key in Kaggl...

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Python Libraries and Frameworks for Data Science, Machine Learning and Generative AI.

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⚡ Capabilities of Generative AI 🧠

With Irfan Malik – I just got recognised as one of their top fans! 🎉

𝗗𝗮𝘁𝗮 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀

In the realm of data science, data acquisition plays a pivotal role. It encompasses the process of collecting raw data from various sources and transforming it into a format suitable for analysis. Without robust data acquisition practices, the entire data science pipeline would falter.

𝐌𝐞𝐭𝐡𝐨𝐝𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧

Data can be acquired through multiple avenues. Firstly, existing datasets from databases, repositories, or open data sources can be utilized. Alternatively, data can be freshly collected through surveys, experiments, or sensors. Each method has its own set of advantages and challenges.

𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧

Despite its importance, data acquisition is riddled with challenges. Ensuring data quality, dealing with the sheer volume of data generated daily, and navigating privacy and ethical considerations are among the primary hurdles data scientists face.

𝐓𝐨𝐨𝐥𝐬 𝐚𝐧𝐝 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧

Various tools and technologies aid in the data acquisition process. Web scraping tools like BeautifulSoup and Scrapy help extract data from websites. APIs provide structured access to data from online platforms. IoT devices continuously generate streams of data, offering valuable insights.

𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐟𝐨𝐫 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞 𝐃𝐚𝐭𝐚 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧

To navigate the complexities of data acquisition, adhering to best practices is crucial. This includes clearly defining objectives, selecting appropriate data sources, and meticulously cleaning and preprocessing data to ensure accuracy.

𝐃𝐚𝐭𝐚 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠

In the realm of machine learning, the quality of data directly impacts model performance. Acquiring labeled data for supervised learning tasks is particularly challenging but essential for training accurate models.

𝐑𝐞𝐚𝐥-𝐰𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧

Data acquisition finds applications across various industries. In healthcare, it aids in patient monitoring and diagnosis. In marketing, it enables targeted advertising and customer segmentation. Financial institutions leverage data acquisition for risk assessment and fraud detection.

𝐅𝐮𝐭𝐮𝐫𝐞 𝐓𝐫𝐞𝐧𝐝𝐬 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧

The future of data acquisition is poised for innovation. AI-powered data collection methods promise greater efficiency and accuracy. Blockchain technology offers secure and transparent data transactions, addressing concerns regarding data privacy and integrity.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧

Data acquisition forms the foundation of the data science process. By leveraging diverse methods, tools, and best practices, organizations can harness the power of data to drive informed decision-making and innovation.

𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲

1. Python for everybody specialization, university of Michigan -- Coursera
2. Python 3 programming specialization, university of Michigan -- Coursera
3. Applied Data Science with python specialization university of Michigan
-- Coursera
4. Python Data Products for predictive analytics specialization, US San Diego
-- Coursera
5. Programming for Data Science with Python -- Udacity
6. Learn Python 2 -- CodeAcademy
7. Learn Python for Data Science from scratch -- Data36

Source: Data Science Book(Sir Zeeshan Usmani)

𝐈 𝐰𝐨𝐧 𝐭𝐡𝐞 𝐀𝐈 𝐢𝐦𝐚𝐠𝐞 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐜𝐨𝐧𝐭𝐞𝐬𝐭 𝐚𝐧𝐝 𝐭𝐡𝐞 𝐢𝐦𝐚𝐠𝐞 𝐢𝐬:

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐊𝐚𝐠𝐠𝐥𝐞 𝐊𝐞𝐫𝐧𝐞𝐥𝐬

𝗟𝗲𝗮𝗿𝗻, 𝗖𝗼𝗱𝗲, 𝗣𝘂𝗯𝗹𝗶𝘀𝗵, 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗮𝗻𝗱 𝗪𝗶𝗻!
Kaggle Kernels were formally referred to as Scripts. The kernel simply refers to the Kaggle’s analysis, coding and collaboration product. According to the founder Anthony Goldbloom, this new name is more fitting because kernels are no longer short scripts that help in performing small tasks. They have been improvised and enhanced to create a product that is a combination of code, input, and output all stored together to cater for any version you choose to use. Since kernels enable you to store different attributes together, they are naturally reproducible, very simple to learn and extremely easy to share.

In Kaggle the kernel is an indispensable tool, foundation and core of your work as it contains the code required for analysis. Kaggle kernels contain code that helps make the entire model reproducible and enable you to invite collaborators when needed. It’s a one key solution for data science projects from code to comments and from environment variables to required input files. In future, we hope to see kernel’s integration with our local machine environment and more of an open collaboration tool where friends, employees, and teams can come across the world and contribute. We have also seen Kaggle kernel use in academic papers and research.

The indispensable Kaggle kernel runs exclusively on docker containers. For each Kaggle user, a kernel works by mounting the input into the containers that feature docker images that are already pre-loaded with the most common data science libraries and languages. In plain terms, a kernel is essentially a notebook or a script with data. It offers a number of advantages including, the containerization comes in handy in allowing contributors to set up their Kaggle projects, the users do not have to download data because it is already mounted in the docker container and the kernel code can be easily shared. It also offers transparency of shared code and makes it more accessible for beginners and experts alike.

𝗛𝗼𝘄 𝘁𝗼 𝗧𝗮𝗸𝗲 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝗞𝗲𝗿𝗻𝗲𝗹𝘀
Go through the top ranking kernels on a regular basis to get an idea of the thought process of other Kaggle contributors. Kaggle is a platform for learning; you should take advantage of any information and ideas you can get to improve yours skills. Overtime you will realize that you can easily increase your chances of winning if you use and combine the ideas. Use these kernels to improve your skills set and advance your knowledge in data sciences.

Kernels are a great way to boost transparency and also share code with other Kaggle contributors. This eliminates the chance that any contributor is left out of a piece of code buried somewhere else, it levels the playing field for all who like to learn, explore and improve their data science skills.

𝗤𝘂𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗼𝗳 𝗮 𝗚𝗼𝗼𝗱 𝗞𝗲𝗿𝗻𝗲𝗹
On Thursday of every week, the Kaggle team comes together to select the best kernel using datasets available on the platform for the previous fourteen days. When choosing a winning kernel, there are two main considerations – Quality, the code of high quality consists of both a code and narrative that shares invaluable insights and also makes an impact that helps other Kagglers to learn, and the Quantity, the number of comments, UpVotes, and forks (the copies of your kernel made by other Kagglers). The winner is revealed on social media weekly using the hashtag .

𝗣𝘂𝗯𝗹𝗶𝘀𝗵 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗞𝗲𝗿𝗻𝗲𝗹
Ask yourself what insights or perspectives are you trying to educate the data science community about. Be creative, do you have something unique to share, a tool, some perspective, or new ways to explore data. Feel free to create a tutorial that helps you share your knowledge and expertise, visualize data or reveal the hidden patterns. Here are examples of some great kernels that have been featured on Kaggle – Generation Unemployed? Interactive Plotly Visuals by Anisotropic using data from World Bank youth unemployment rates, Analyzing soccer player faces by SelfishGene using data from the Complete FIFA 2017 player dataset, and Traffic Fatalities in 2015 by Abigail Larion using data from 2015 Traffic Fatalities.

Now, the next step is to publish your own kernel. Simply click on New Kernel then select the data sources to use and a notebook or script to use. Publish both your narrative and code. Make sure to make your kernel public so other users can see and play with it. It will also get their feedback, comments, forks, and UpVotes, and you are automatically in the run to be selected as a winner.

The next step is to broadcast and publicize your work; it does not stop at sharing your kennel to the public. One of the most reliable ways to demonstrate the impact of your kernel is by sharing it widely within the Kaggle community. Broadcasting entails encouraging your connections on Kaggle to fork your kernel, UpVote, and comment and write a post and blog about it. Some effective ways to broadcast your kernel include sharing on social media accounts with proper hashtags like etc.

You should also share your insights and motivations to write your kernel on a blog post and then share it with Kaggle and social media community.

Since it’s all about learning on Kaggle, you do not have to participate by creating your Kernel. You can also participate by being an active spectator. Keep up to date by checking out the latest kernels then comment and UpVote the ones you like. Fork your favorite kernel and see what changes you can make to improve its efficiency and performance. By doing this one day you will able to publish your own kernel.

𝘚𝑜𝘶𝑟𝘤𝑒: 𝘒𝑎𝘨𝑔𝘭𝑒(𝑍𝘦𝑒𝘴ℎ𝘢𝑛 𝑈𝘴𝑚𝘢𝑛𝘪)

Welcome!