House Prices Prediction - Kaggle

-




Competition Description

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

Practice Skills

  • Creative feature engineering 
  • Advanced regression techniques like random forest and gradient boosting
Here, is result I has used gradient boosting regression for this project



ความคิดเห็น

แสดงความคิดเห็น

โพสต์ยอดนิยมจากบล็อกนี้

Wine Quality Prediction(Logistic Regression) - Kaggle[Dataset]

-
รูปภาพ
Overview from kaggle Context The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. For more details, consult the reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones). This dataset is also available from the UCI machine learning repository,  https://archive.ics.uci.edu/ml/datasets/wine+quality  , I just shared it to kaggle for convenience. (If I am mistaken and the public license type disallowed me from doing so, I will take this down if requested.) Content For more information, read [Cortez et al., 2009]. Input variables (based on physicochemical tests): 1 - fixed acidity
อ่านเพิ่มเติม

Titanic - Kaggle

-
รูปภาพ
Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics And here is a result I use logi
อ่านเพิ่มเติม

K Nearest Neighbors - Project

-
รูปภาพ
K Nearest Neighbors Project I've been given a classified data set from a company(fake data again..). It has hidden the feature column names but has given the data and the target classes. I'll try to use KNN to create a model that directly predicts a class for a new data point based off of the features. Import Libraries I'll import libraries that I need to use as always Get the Data Exploratory Data Analysis ¶ Since this data is artificial, I'll just do a large pairplot with seaborn. Standardize the Variables ¶ Time to standardize the variables. Train Test Split Using KNN Predictions and Evaluations Choosing a K Value after I saw accuracy is quite low, so I will pick a new K value. Retrain with new K Value   after I saw K value around 25-28 are quite okay, so I will pick K value at 27
อ่านเพิ่มเติม