This repository contains the price_regression.ipynb notebook, which demonstrates a regression analysis for predicting house prices using advanced techniques.
In this notebook, we explore various regression techniques to predict house prices. We start by analyzing the dataset, performing data preprocessing, feature engineering, and model selection. Then, we train and evaluate different regression models to find the best one for our task.
The dataset used in this analysis is the House Prices: Advanced Regression Techniques dataset from Kaggle. It contains various features related to residential homes in Ames, Iowa, and the goal is to predict the sale price of each house.
To run the notebook, you need to have the following dependencies installed:
- Python 3.x
- Jupyter Notebook
- NumPy
- Pandas
- Scikit-learn
- Matplotlib
- Seaborn
You can install these dependencies using pip: