Advanced-House-Price-Predictions

This is my first project and also participated in kaggle competition

Project Overview

The Housing Price Prediction project aims to forecast house prices using advanced regression techniques. This project is based on the Kaggle competition "House Prices - Advanced Regression Techniques," leveraging detailed property data to build robust predictive models.

Key Features

• Predict house prices using machine learning algorithms.
• Exploratory Data Analysis (EDA) for insights into the data.
• Feature engineering and selection to improve model performance.
• Model evaluation using appropriate metrics.

Technologies Used

• Languages: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
• Tools: Jupyter Notebook

Dataset Details

• Source: Kaggle's "House Prices - Advanced Regression Techniques" competition.
• Description: The dataset includes 80 features related to property characteristics, such as dwelling type, zoning, lot area, utilities, and sale conditions.
• Objective: Predict the sale price of houses based on their attributes.

Results

Achieved a competitive score/rank of 3521 out of 6261 teams in the competition. Visualization and performance metrics demonstrate the effectiveness of the implemented models. Usage

Model Details

Models Implemented:

• Linear Regression
• Random Forest Regressor

Techniques:

• Hyperparameter tuning for improved accuracy.
• Handling missing data and outliers.

Acknowledgments

Special thanks to Kaggle for providing the dataset and fostering the competitive spirit!