This repository contains the code and documentation for our entry to the Kaggle House Prices competition.
The objective of this competition is to predict the sale price of houses based on various features such as the number of bedrooms, bathrooms, and square footage. Our goal is to develop an accurate model using linear regression and random trees.
The dataset consists of two files: train.csv and test.csv. The train.csv file contains the training set, which includes both the features and the target variable (sale price). The test.csv file contains only the features, and our task is to predict the sale price for each house in the test set.
I used a combination of linear regression and random trees to predict the sale price of houses. First, I performed exploratory data analysis to gain insights into the data and identify any potential issues such as missing values or outliers.
Next, I performed feature engineering to transform and preprocess the data. This involved handling missing values, encoding categorical variables, and scaling the data.