NEW VERSION ||
we add new feature (Handling Outliers and some explains) on [Handling Outliers.ipynb]
1- Handling Outliers With Z-score
2- Handling Outliers With IQR [InterQuartile Range]


Data Source:
This is a competition data from Kaggle about house prices for data science students ..
link: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques

Data Contains two files -> (train dataset - test dataset)
train-dataset has 'SalePrice' labels
test-dataset want to get his labels

Goal: 
Predict sales prices and practice feature engineering, RFs, and gradient boosting

Practice Skills
Creative feature engineering
Advanced regression techniques like random forest and gradient boosting

Our Goal:
1- Data Understanding
2- Data Cleaning
3- Exploratory Data Analysis to get insights
4- Feature Engineering
5- Build Several ML models and choose the best (Linear models - decision tree - random forest - XGBoost - Clusters)