https://github.com/tigureis/house-rent-analysis

House Rent Data Cleaning and Preparation: Clean and preprocess house rent data for further analysis.
https://github.com/tigureis/house-rent-analysis

data-cleaning data-preparation pandas seaborn

Last synced: about 2 months ago
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House Rent Data Cleaning and Preparation: Clean and preprocess house rent data for further analysis.

• Host: GitHub
• URL: https://github.com/tigureis/house-rent-analysis
• Owner: tigureis
• Created: 2024-12-05T20:32:05.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-05T20:36:36.000Z (over 1 year ago)
• Last Synced: 2025-03-23T10:45:33.879Z (about 1 year ago)
• Topics: data-cleaning, data-preparation, pandas, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 524 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
This project analyzes house rent data to identify trends and patterns. 

It leverages Python libraries like Pandas and Seaborn to clean, process, and visualize the data extracted from the 'houses_rent.xlsx' file.

## Project Goals

The main objectives of this project are:

* **Data Cleaning and Preprocessing:** Transform the raw data into a usable format by cleaning, renaming columns, changing data types, and handling missing values.

* **Exploratory Data Analysis (EDA):** Gain insights into the data through descriptive statistics and visualizations to understand distributions and relationships between variables.

* **Data Filtering and Aggregation:** Apply various filters and aggregation techniques using Pandas to isolate specific subsets of data for analysis, such as properties within certain price ranges or with specific characteristics.

* **Data Visualization:** Utilize Seaborn to create informative visualizations, such as bar plots and histograms, to depict trends and patterns like average rent prices by city, property type, and other features.

## Key Features

* Comprehensive data cleaning and preprocessing steps to prepare the data for analysis.

* Thorough EDA to understand the characteristics of rental properties.

* Data filtering and aggregation to focus on specific segments of the market.

* Clear and concise data visualizations using Seaborn to illustrate key findings.

* Well-commented code for easy understanding and reproducibility.