An End-to-End Data Science Project: Predicting House Prices

1. Introduction

In this article, we’ll walk through an end-to-end data science project to predict house prices using the popular Ames Housing dataset. Our primary objective is to build a model that accurately predicts the sale price of houses based on their features.

my_data_science_project/
│
├── README.md                           # Project overview and instructions
│
├── data/                               # Folder containing raw and processed data
│   ├── raw_data/                       # Raw data files
│   │   ├── dataset1.csv
│   │   └── dataset2.json
│   │
│   └── processed_data/                 # Processed data files
│       ├── cleaned_data.csv
│       └── feature_engineered_data.csv
│
├── notebooks/                          # Jupyter notebooks for analysis and modeling
│   ├── 01_data_collection.ipynb        # Data collection notebook
│   ├── 02_data_preprocessing.ipynb     # Data preprocessing notebook
│   ├── 03_exploratory_data_analysis.ipynb  # EDA notebook
│   ├── 04_feature_engineering.ipynb    # Feature engineering and selection notebook
│   ├── 05_model_selection.ipynb        # Model selection and training notebook
│   ├── 06_model_evaluation.ipynb       # Model evaluation notebook
│   └── 07_interpretation_and_reporting.ipynb  # Interpretation and reporting notebook
│
├── src/…