House Sales Price Analysis (USA)

Overview

This project analyzes house sales data in the USA to identify pricing trends and relationships between housing features and sale prices using Python.

Tools & Technologies

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn
• Jupyter Notebook

Key Features

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Feature scaling using StandardScaler
• Polynomial regression using Scikit-learn Pipeline
• Data visualization for insights and trends

Results

• Identified key factors influencing house prices
• Built regression models to analyze non-linear relationships
• Visualized correlations and pricing patterns

How to Run

1. Clone the repository
2. Install dependencies: pip install pandas numpy matplotlib seaborn scikit-learn
3. Open the notebook: jupyter notebook

Author

Rajat Dungarwal