This project focuses on predicting house prices using various features. Key steps taken in the project include:
- Dataset Investigation: Conducted a descriptive analysis to understand the dataset.
- Exploratory Analysis: Performed exploratory analysis to uncover additional insights.
- Data Distribution: Checked the distribution of the data.
- Feature Correlation: Analyzed different features for correlations.
- Data Quality Checks: Investigated missing data and outliers.
- Data Transformation: Applied transformations to normalize the data.
- Feature Engineering: Created new features to enhance the model.
- Model Training: Trained a regression model that explains 80% of the variance in house prices.
This repository includes the code and steps to replicate the analysis and model training.