Housing Prices Dataset

Dataset Features:

• House Area: The total square footage of the property.
• Number of Bedrooms: The total number of bedrooms in the property.
• Furnishing Status: Whether the property is furnished or unfurnished.
• Nearness to Main Road: The proximity of the property to the nearest main road.
• Additional Features: Other factors that may impact pricing, such as the presence of a garage or garden.

Challenges:

• Multicollinearity: Several features are highly correlated with each other, which can complicate the prediction task.
• Limited Dataset Size: The small size of the dataset presents an additional challenge for robust modeling.
• Feature Selection: Carefully selecting the most relevant features to avoid overfitting and improve model performance.

Objectives:

1. Data Understanding and Cleaning:
   Perform an exploratory analysis of the dataset to identify missing values, outliers, or any inconsistencies. Clean and prepare the data accordingly for better model performance.
2. Model Building:
   Develop regression models using both simple linear regression and multivariate regression techniques. Explore relationships between house prices and individual features, and then with multiple features simultaneously to improve predictions.
3. Advanced Models for Multicollinearity:
   Use advanced techniques like Ridge and Lasso regression to address the multicollinearity issue and improve model stability.

Goals:

• Feature Importance Analysis:
  Use statistical methods or machine learning algorithms like Random Forest or Decision Trees to determine the most important features that impact housing prices.
• Hyperparameter Tuning:
  Fine-tune your model’s parameters (such as the regularization strength in Ridge/Lasso) to optimize predictive performance.
• Predictive Performance Comparisons:
  Compare the performance of different models (linear regression vs. Ridge vs. Lasso) using cross-validation scores to understand their respective strengths and weaknesses.