This project aims to implement and compare variant machine learning techniques for predicting house prices. It is an expanded project from **Kaggle** House Prices: Advanced Regression Techniques, such that the project is going to use the same dataset provided under this Kaggle competition. The machine learning techniques in this project include Linear Regression, Random Forest, Adaptive Boosting, Support Vector Regression, Gradient Boosted Decision Tree, K Nearest Neighbors, and Neural Network.

*Trie* is an ordered tree data structure which stores words in it so that users can search a word or a prefix quickly. Time complexity for adding a word to a Trie is $O(l)$, and for searching a word or a prefix is $O(l)$ too ($l$ is the length of the word or the prefix being added or searched). So it’s why we prefer Trie rather than *Set* and *Hash Table* in some situations, such as *Dynamic Set*, *Associative Array* and a predictive text or autocomplete *Dictionary*.

Though hash table is one of the most efficient data structures, it has some weaknesses.

## Randomized QuickSort

Randomized QuickSort is an efficient sort algorithm requiring only $\Theta(n\log n)$ expected time on any input.

1 | function RANDOMIZED-QUICKSORT(Array, low, high) |

## Randomized Select

Randomized Select is an efficient algorithm to find $i$th smallest element of Array[low…high] with only linear expected time on any input.

1 | function RANDOMIZED-SELECT(Array, low, high, i) |