Home Value Hero

Project Overview

This project focuses on predicting house sale prices using a comprehensive dataset from a Kaggle competition. The objective is to leverage advanced regression techniques to accurately forecast prices based on a multitude of house characteristics.

Dataset Description

The dataset, sourced from a ongoing Kaggle competition, comprises a diverse range of features that describe various aspects of houses. Example features are:

Type of Road Access: Describes the type of road access to the property.
Slope of Property: Indicates the slope or gradient of the property land.
Number of Bedrooms above Basement Level: A count of bedrooms in the house, excluding any in the basement.
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Link to Dataset: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data

Methodology

We intend to apply a variety of sophisticated regression methods to this dataset, including:

1. Tree Algorithms: Techniques like Random Forests, LightGBM, and XGBoost to capture non-linear relationships.
2. Deep Neural Networks (DNN): Utilizing the power of deep learning to model complex patterns in the data.
3. Regularized Linear Regression: Implementing models like Ridge, Lasso to serve as a base line.

Goals and Objectives

The primary goal of this project is to develop a model that can accurately predict house prices based on the features provided in the dataset. Our objectives include:

1. Data Exploration: Thoroughly examine and understand the dataset, including feature distributions and correlations.
2. Feature Engineering: Enhance the dataset by creating new features and selecting the most relevant ones for our models.
3. Model Development: Build and train various regression models, tuning them for optimal performance.
4. Evaluation and Comparison: Assess the performance of each model using appropriate metrics and compare their effectiveness.

Potential Challenges

We anticipate several challenges, including handling missing data, dealing with outliers, and feature selection. Our approach will involve rigorous data preprocessing and experimentation with different techniques to address these challenges effectively.