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Understanding the problem statement
Importing the dataset from Amazon AWS
How to analyze the result of the summary function from R and basic EDA
Using ggplot and Correlation Plot to find similarities between variables
Checking for variables with null values and handling them
Checking skewness of the target variable using Histogram
Checking contribution of different variables to the target variable
Finding the best feature and eliminating the least significant ones
Defining the evaluation metric 'log_error' and understanding it's significance
Selecting Boosting model XGBoost and converting dataset into DMatrix
Applying XGBoost model on the Dataset
Defining parameters for Hyperparameter tuning
Using Cross Folds Validation to prevent overfitting
Visualizing important features for XGboost model
Training the final model using the selected features
Making final predictions and Saving in CSV format
Zillow is asking you to predict the log-error between their Zestimate and the actual sale price, given all the features of a home. The log error is defined as:
and it is recorded in the transactions file train.csv. In this project, you are going to predict the log error for the months in Fall 2017.
"Zestimates" are estimated home values based on 7.5 million statistical and machine learning models that analyze hundreds of data points on each property. And, by continually improving the median margin of error (from 14% at the onset to 5% today), Zillow has since become established as one of the largest, most trusted marketplaces for real estate information in the U.S. and a leading example of impactful machine learning.
In this data science project, we will develop a machine learning algorithm that makes predictions about the future sale prices of homes. We will also build a model to improve the Zestimate residual error. And finally, we'll build a home valuation algorithm from the ground up, using external data sources.
