HOUSE PRICE PREDICTION MODEL

Dataset to be used : https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview

1. Exploratory Data Analysis and Data Visualization (exploratory_data_analysis)

- General View
- Categorical Variables Analysis
- Numerical Variables Analysis
- Target Analysis

2. Data Preprocessing and Feature Engineering (data_preprocessing_and_feature_engineering)

- General View - Recap - Remember Dataset
- Feature Creation
- Outlier Analysis
- Missing Values Analysis
- Rare Encoding - not handled yet
- Label and/or One Hot Encoding
- Standardization
- Save the Final Dataset --> Pickle Dataset

3. Linear Models (linear_models)

- Multiple Linear Regression
- Lasso Regression
- Ridge Regression
- ElasticNet Regression

4. Nonlinear Models (nonlinear_models)

- K-Nearest Neighbors Regression
- Support Vector Machines
- Artificial Neural Network Models
- Classification and Regression Trees - DecisionTreeRegressor
- BaggingRegressor
- RandomForestRegressor
- AdaBoostRegressor
- Gradient Boosting Regressor    
- XGBoost - XGBRegressor
- LightGBM - LGBMRegressor
- CatBoost - CatBoostRegressor
- NGBoost - NGBRegressor

5. Pickle the Models, Saving the Model for later Use

6. Comparison of Regression Metrics of each Model (all_models)

--> For each model steps to follow;

- Model and Prediction
- Evaluation of Model
- Model Tuning
- Model Visualization (Feature Importances, etc.)
- Saving the Model