prathams0ni/Data_Pre-Processing_Using_House_Rent_Dataset
Data Pre Processing in Python using a House Rent Project. Run in Jupyter Notebook. In this Project I do Data Preprocessing in that I clean Data & shows it in relevant manner
Data Pre-Processing Using House Rent Project in Python.
House Rent Data Pre-Processing & Analysis:
Project Overview:
This project focuses on processing and analyzing house rent data to uncover insights into property rental trends across different cities and property types.
It involves cleaning, transforming, and visualizing the dataset to better understand the relationship between rent amount, location, BHK type, furnishing status, and other key variables.
The analysis helps property seekers, investors, and real estate analysts identify the major factors influencing rental prices.
Objectives:
- Data Cleaning: Handle missing values, remove duplicates, and standardize column names.
- Data Transformation: Convert categorical data into meaningful numerical values.
- Exploratory Data Analysis (EDA): Identify trends and correlations in rental pricing.
- City-wise Comparison: Understand which cities and property types have higher average rents.
- Visualization: Create graphical insights for better interpretation of housing market trends.
Tools and Technologies:
| Tool / Library | Purpose |
|---|---|
| Python | Programming language for analysis |
| Jupyter Notebook | Interactive coding environment |
| Pandas | Data manipulation and cleaning |
| NumPy | Numerical computations |
| Matplotlib | Data visualization |
| Seaborn | Statistical data visualization |
Dataset Information:
The dataset includes details about rental properties, such as:
- Posted On — Date when the property was listed
- BHK — Number of bedrooms, halls, and kitchens
- Rent — Monthly rent amount (target variable)
- Size — Total area of the property (in sq.ft.)
- Floor — Floor number of the property
- Area Type — Measurement unit (e.g., Super Area, Carpet Area)
- City — City where the property is located
- Furnishing Status — Type of furnishing (Furnished / Semi-Furnished / Unfurnished)
- Tenant Preferred — Ideal tenant type (Family, Bachelor, Company)
- Bathroom — Number of bathrooms
- Point of Contact — Agent or owner contact info
Key Analysis and Insights:
- Average Rent by City: Identified which cities have higher median and mean rental rates.
- Impact of BHK and Size: Analyzed how the number of rooms and area size affect rent.
- Furnishing Status Comparison: Compared rents among furnished and unfurnished houses.
- Tenant Preferences: Explored which tenant types are most preferred across cities.
- Correlation Study: Examined how different numerical and categorical features influence rent.
Visualizations:
Some of the visualizations in the notebook include:
- Bar charts showing average rent by city and furnishing type
- Box plots for rent distribution based on BHK and area type
- Heatmaps highlighting feature correlations
- Line plots for rent trends over time (if timestamp data available)
- Scatter plots for size vs. rent relationships
Run This Project:
To Run this project you need a Jupyter Notebook in your system. also I uploaded a dataset file for the same first download it in your system & copy the path of dataset file & paste it in importing dataset column after pd.read_csv you have to paste the path of dataset file. after that you can run each & every cell to run it.
Conclusion:
This project demonstrates the complete workflow of data preprocessing and exploratory data analysis (EDA) on real estate rental data.
The insights can assist landlords, tenants, and analysts in understanding market dynamics and making data-driven decisions.