House Price Prediction - Kaggle Competition

Overview

This project is an in-depth analysis and machine learning modeling exercise for the Kaggle competition "House Price Prediction." The goal is to predict residential home prices in Ames, Iowa, using various explanatory variables.

Features of the Notebook

• Data Exploration: Analysis of the distribution of data, identification of missing values, and understanding relationships between features and the target variable.
• Data Preprocessing: Handling missing values, feature engineering, and data scaling/transformation.
• Model Selection: Evaluation of multiple machine learning models to identify the most effective ones.
• Hyperparameter Tuning: Optimization of the chosen models for improved performance.
• Model Evaluation: Use of cross-validation and other techniques to assess model performance.
• Prediction and Submission: Generating predictions for the test dataset and preparing a submission for the Kaggle competition.

Technologies Used

• Data Wrangling and Exploration: Pandas, NumPy, Scipy
• Visualization: Matplotlib, Seaborn
• Machine Learning: Scikit-learn, XGBoost, LightGBM, CatBoost

Dataset

The dataset comprises 79 explanatory variables detailing various aspects of residential homes. More details can be found in the dataset description.

Objectives

• Perform a thorough exploratory data analysis.
• Prepare the data for machine learning modeling.
• Build and tune ensemble model to predict house prices accurately.
• Assess model performance with suitable metrics.
• Create a final prediction for competition submission.