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  • in others
  • 2 min read

Learn Web Scraping

web scraping - real python

Source: https://realpython.com/python-web-scraping-practical-introduction

Web scraping is a very essential tool for programmers to learn to gather data from websites. Specifically, for Data Scientists web scraping is goto tool to gather data from websites. We can use bs4.BeautifulSoup or selenium in Python to scrape any website.

You can see some of my projects where I scraped websites like 99acres.com, flipkart.com, housing.com and gather useful data for my Data Science projects like arv-anshul/campusx-real-estate.

I have learned Web Scraping from YouTube only.

🌴 Tree VS Regression Models

tree-vs-regression-models

Source: www.freecodecamp.org

Tree based models and Regression models are widely used Machine Learning models. So more you know about them is better for you. Also, many concepts from these models are borrowed by advance Machine Learning models like Gradient Boosting, XGBoost, etc.

These models are also great choice for :fontawesome-user-tie: interviewers so from these models they ask many interview questions. This blog mainly focuses on tree based models.

  • in mlops
  • 5 min read

Learn Docker

Docker is a platform designed to simplify the process of creating, deploying, and managing applications using containers. Containers enable developers to package an application with all its dependencies into a standardized unit for seamless deployment across different environments.

🛠 Components of Docker

  1. Dockerfile
  2. .dockerignore
  3. docker-compose.yaml
  • in others
  • 2 min read

Learn FastAPI

FastAPI Logo

A better framework than Flask. Get production-ready code and API. With automatic interactive documentation. Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.

  • 4 min read

📆 Handle Outliers - Univariate

Handling outlier is a big task for data scientist. To handle the outliers we have many different methods to handle them i.e. IQR, Z-score, Mean-Median Imputation, Winsorization, etc. We are going to discuss only univariate methods to handle outliers.

📆 I have written this page as notes very time ago; so if there is any mistake please let me know I'll fix it. Thanks 🤗