jusepi-y / real_estate-

Read Me for: Exploring Real Estate Rents with the Manthan Dataset

This repository delves into the fascinating world of real estate rents, using the rich Manthan dataset. Join me as I uncover intriguing insights and trends hidden within the intricate tapestry of attributes like building age, area, and proximity to metro stations.

Dataset: Manthan - a comprehensive collection of real estate rental data.

Tasks:

Exploratory Data Analysis (EDA) to understand the data distribution, relationships between variables, and identify potential outliers. Data Visualization to reveal patterns and trends in a visually compelling manner.

Key Findings:

Uncover correlations between rent and factors like building age, area, and metro distance. Identify potential clusters or segments within the rental market. Discover interesting patterns and outliers that contribute to rent variations. Explore the effectiveness of different visualization techniques in communicating insights.

Tools Used:

Python programming language with libraries like Pandas, NumPy, and Matplotlib. Data visualization tools like Seaborn and Tableau (optional).

Navigating the Repository:

EDA.ipynb: The Jupyter notebook showcasing the exploration process, data cleaning, and analysis. visualization.ipynb: (Optional) Additional visualizations and insights, potentially using dedicated tools like Tableau. data: Folder containing the Manthan dataset (if publicly available) and any processed versions used in the analysis. README.md: This very document, providing an overview and key takeaways.

Target Audience:

Data analysts and enthusiasts interested in real estate market trends. Individuals seeking to learn and apply data analysis techniques on a practical dataset. Anyone curious about the factors influencing rental prices and exploring insights through data visualization.

Contributions and Feedback:

I encourage you to explore the code, analyze the results, and share your thoughts or insights. Feel free to fork the repository, contribute your own findings, or ask questions – let's delve deeper into the world of real estate rents together!

I hope this helps you create a compelling Read Me for your data analysis portfolio!