This repo contains only Jupyter Notebooks in which I did analysis related to real estate of Gurgaon mainly. I used data from 99acres.com which I scrapped using my Streamlit web-app. You can see that app's codes in my GitHub repo here.
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You can see that I did versioning of my notebooks like notebooks_v1, notebooks_v2 and so on. This means that in every new version of notebooks I upgraded the data which is more suitable for Data Analysis.
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I also did versioning on Jupyter Notebooks individually which shows that you have to follow that version to get insights respectively.
I also follows a naming convention for my notebooks which helps me to identify my notebooks easily. I follow below naming convention:
| Name | Description | Exporting |
|---|---|---|
_OVERVIEW.ipynb |
Perform a very comprehensive overview of the dataset. | False |
<ver>_PREPROCESSING.ipynb |
Perform Preprocessing | True |
<ver>_EDA.ipynb |
Perform Exploratory Data Analysis | False |
<ver>_FEAT_ENG.ipynb |
Perform Feature Engineering | True |
<ver>_FEAT_SELECTION.ipynb |
Calculate Feature Importance & Perform Feature Selection | False |
- 🧑🏫 @campusx: I follow CampusX's DSMP course's Capstone project to do this analysis. And now I am getting more and many different types of insights related to this data and all thanks to Nitish Sir to make me aware of this kind of project.
- 🌐 99acres.com: I scrapped data from 99acres.com and I will only use this data to enhance my knowledge in Data Analysis.
- 💻 @arv-anshul/99acres-scrape: This Streamlit Web-App is being used to scrape data from 99acres.com website.
- 🤝 @arv-anshul/campusx-real-estate: The analysis performed in this repo is being used in this Real Estate project made by @me with 🧠 & ❤️.