house_price_prediction_model.txt

# 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
    - RandomForestRegressor
    - BaggingRegressor
    - Gradient Boosting Regressor
    - AdaBoostRegressor
    - 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