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.

• 1.1 Get an overview of the features (numerical and categorical) and first look on the target variable SalePrice shape, info, head and describe Distribution of the target variable SalePrice Numerical and Categorical features List of features with missing values and Filling missing values log transform
• 1.2 Relation of all features to target SalePrice Seaborn regression plots for numerical features List of numerical features and their correlation coefficient to target Seaborn boxplots for categorical features List of categorical features and their unique values
• 1.3 Determine the columns that show strong correlation to target Correlation matrix 1 : all numerical features Determine features with largest correlation to SalePrice_New

• Dropping all columns with weak correlation to SalePrice Convert categorical columns to numerical
• Checking correlation to SalePrice for the new numerical columns use only features with strong correlation to target
• Correlation Matrix 2 (including converted categorical columns) create datasets for ML algorithms

implement GridsearchCV for Hyperparameter tuning for these models from sklearn:

• Linear Regression
• Lasso
• DecisionTreeRegressor
• Random Forest Regresso
• XgBoost