Bangladesh Real Estate Market Dataset Analysis

Overview

This dataset offers a detailed collection of house listings from various cities and regions in Bangladesh, with a specific focus on Dhaka and Chittagong. It encompasses essential details such as location, property type, size, amenities, and #. The dataset is consistently updated to provide an accurate and current portrayal of the housing market in Bangladesh.

Purpose

The dataset caters to diverse purposes, serving as a valuable resource for researchers, data scientists, real estate professionals, and investors. Some key use cases include:

• Analyzing regional price trends
• Identifying popular neighborhoods and amenities
• Training machine learning models for predicting housing prices

Machine Learning Models

The following regression models have been applied to analyze and predict housing prices using this dataset:

1. Linear Regression:

   • A fundamental model assuming a linear relationship between input features and housing prices.

2. XGBRegressor (Extreme Gradient Boosting):

   • A powerful gradient boosting algorithm known for its speed and performance, applied specifically for regression tasks.

3. LGBMRegressor (LightGBM):

   • Another gradient boosting framework, recognized for efficiency and speed, utilized for regression on this dataset.

4. Random Forest:

   • A versatile ensemble learning model that leverages multiple decision trees to make predictions, often robust and effective for various datasets.

Dataset Structure

The dataset is structured with the following columns:

• Location: The geographical location of the property.
• Property Type: Categorization of the property (e.g., apartment, house).
• Size: Size or area of the property.
• Amenities: Features and facilities associated with the property.
• Price: The listed price of the property.

Preprocessing Steps

Before applying the regression models, the dataset underwent the following preprocessing steps:

1. Handling Missing Data:

   • Any missing data in crucial columns was addressed through imputation or removal.

2. Encoding Categorical Variables:

   • Categorical variables like "Property Type" were encoded to make them suitable for the regression models.

3. Feature Scaling:

   • To ensure consistent model performance, numerical features were scaled.

How to Use

1. Dataset Access:

   • Download the dataset in CSV format for your analysis.

2. Run the Models:

   • Explore the implementation of Linear Regression, Random Forest, XGBRegressor, and LGBMRegressor in the notebook here.

3. Customization:

   • Customize models or dataset features based on specific research questions or objectives.

Potential Challenges

• Heterogeneity: The dataset may exhibit variations in property listings, requiring careful consideration during analysis.
• Outliers: Addressing outliers in # or property size may impact model performance.

Contributing

If you encounter issues or have suggestions for improvement, please open an issue or submit a pull request.

Happy analyzing!