Advanced statistical analysis and visualization of key factors influencing rental prices in the real estate market.
Overview Understanding the factors influencing rental prices is crucial in the dynamic and diverse real estate rental market. This project utilizes a comprehensive dataset of over 545 residential properties to provide actionable insights into key determinants of rental prices, such as property size, number of bedrooms and bathrooms, and the presence of various amenities. The goal is to enhance decision-making for both renters and landlords by predicting rental prices with greater accuracy.
Problem Statement The real estate rental market exhibits considerable variability influenced by various property attributes. We aim to analyze these attributes to predict rental prices more accurately and provide valuable insights that can significantly aid stakeholders in making informed decisions.
Data The dataset, sourced from Kaggle(https://www.kaggle.com/datasets/harishkumardatalab/housing-price-prediction), encompasses a broad spectrum of property attributes including area, number of bedrooms, furnishing status, and more. This rich dataset serves as the foundation for our analytical endeavors.
Methodology
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Statistical Analysis: We employ methods such as Simple Linear Regression to quantify the impact of property area on rental prices and use Chi-Square tests to assess relationships between categorical variables like air conditioning presence and furnishing status.
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Data Visualization: We create visualizations such as heatmaps and scatter plots to represent our data and support our analytical conclusions effectively.
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Data Transformation: We perform transformations, including logarithmic adjustments, to address skewed data and ensure the robustness of our regression models.