github-actions[bot]
commented
3 hours ago Thanks for creating the issue in ML-Nexus!🎉 Before you start working on your PR, please make sure to:
- ⭐ Star the repository if you haven't already.
- Pull the latest changes to avoid any merge conflicts.
- Attach before & after screenshots in your PR for clarity.
- Include the issue number in your PR description for better tracking. Don't forget to follow @UppuluriKalyani – Project Admin – for more updates! Tag @Neilblaze,@SaiNivedh26 for assigning the issue to you. Happy open-source contributing!☺️
spandana2004
Problem Description:
The model aims to predict housing prices based on a dataset containing various features of the properties, such as size, location, and other quantitative factors. Housing prices can fluctuate based on numerous factors, and accurately predicting them can provide significant value for real estate companies, buyers, and investors. The solution will provide an efficient way to estimate property values based on past trends and features.
Model Description:
Three models are being explored:
Linear Regression Model: This is a simple and interpretable regression model that will help predict housing prices based on a linear relationship between features (independent variables) and the target variable (SalePrice). The model will minimize the squared difference between the predicted and actual housing prices.
Logistic Regression Model: Although Logistic Regression is typically used for classification, this was likely included erroneously for a regression problem. For price prediction, this model should be excluded or replaced by an appropriate regression method.
Polynomial Curve Fitting: This extends the Linear Regression model by including polynomial terms, allowing for the modeling of more complex relationships between the features and the target variable. This model will be helpful if there is a non-linear relationship in the data.
Estimated Time for Completion:
(Data preprocessing, Model implementation (Linear Regression, Polynomial Regression), Model validation and tuning,Testing on unseen data)Total Estimated Time: 1 day
Expected Outcome:
A well-trained model that predicts housing prices with a decent degree of accuracy, measured by metrics such as the R² score. A visualization comparing predicted values to actual values to understand model performance. An improved understanding of which features contribute most to price variations. Additional Context:
The dataset used includes numerical columns only, and missing values have been handled via the dropna() function. Future improvement could involve more sophisticated handling of missing data (e.g., imputation). The model's performance is measured using the R² score, and further improvement could be explored by feature engineering, regularization techniques, and model tuning.