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Scrape 99scrape.com with python using curl command and export data as CSV.

Home Page: https://arv-anshul-99acres-2a.streamlit.app/

License: MIT License

Python 42.82% Jupyter Notebook 55.74% Makefile 1.44%

99acres api asyncio curl-parser curler project python3 real-estate real-estate-website streamlit web-scraping

99acres-scrape's Introduction

I'm Anshul Raj Verma

Currently, I am in 12th standard but dedicated to mastering Data Science and ML for the past two years. I've built a very good knowledge in Python and Data Science libraries. I've developed a solid foundation and applied it to various projects, you can see them on my website.

🤝 Connect with Me 🤝

🖥️ My Personal Website

👨 Portfolio: Showcase of my skills, achievements, and projects to provide an overview of my professional journey.
🗂️ Project Index: A comprehensive index highlighting the details of various projects I've undertaken, including descriptions, technologies used, and outcomes.
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YouTube Watch History Analyser	Recommendation	yt-watch-history-v2
My Data Science Notebooks	EDA Notebooks	notebooks
Indian Real Estate Project	Real Estate	campusx-real-estate
E-commerce Scrapper API - FastAPI	E-commerce	ecommerce-scrapper-api
PW Course Assignment Solution	NLP	pw-impact-batch
FreeAPI in Python using FastAPI	FastAPI	freeapi-py

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nehasatya krish-01 yigitsasmaz

99acres-scrape's Issues

[FEATURE]: A page to parse curl command and make request with it.

This helps user to make requests with custom parameters.
Maybe this helps user to bypass to blocking of IP Address by the website.
Use curler library to parse curl command.

[REFACTOR]: Data validation of properties.

As you know the fetched data is not well type hinted that's why the streamlit app throw ValidationError which is annoying. So remove the pydantic validation on the fetched data.

😎 A better approach!

# Whole fetched data converted as pd.DataFrame
df = fetched_data_as_dataframe

imp_cols = [
    'PROP_ID', 'PREFERENCE', 'DESCRIPTION', 'PROPERTY_TYPE', 'CITY', 'TRANSACT_TYPE',
    'OWNTYPE', 'CLASS_LABEL', 'BEDROOM_NUM', 'PRICE_PER_UNIT_AREA', 'FURNISH',
    'FACING', 'AGE', 'TOTAL_FLOOR', 'FEATURES', 'PROP_NAME', 'PRICE_SQFT', 'MAP_DETAILS',
    'AMENITIES', 'AREA', 'PRICE', 'PROP_HEADING', 'SECONDARY_TAGS', 'TOTAL_LANDMARK_COUNT',
    'FORMATTED_LANDMARK_DETAILS', 'SOCIETY_NAME', 'BUILDING_NAME', 'location', 'BALCONY_NUM',
    'FLOOR_NUM', 'CARPET_SQFT', 'SUPERBUILTUP_SQFT', 'BUILTUP_SQFT', 'SUPER_SQFT',
]

# Filter the data using the most important columns
df = df[imp_cols]  # Now df contains only important columns

Now, you can provide a multiselect option in the app from that users can choose other important data (column/feature) related to properties. As specified above this method remove the pydantic validation on the fetched data.

[REFACTOR]: Create a separate module to deal with DataFrames.

Refactor the Acres99 class and separate the DataFrame operations.
Move the DataFrame operations and make it more flexible to use.
Add more features and make it modular.

Web App not working

Does not seem to load

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