House price prediction

Story One: Data Collection

Initially, I started by gathering comprehensive data on house prices from multiple sources, including real estate websites, government records, and surveys. Information was collected on location, size, number of rooms, amenities, and other factors influencing house prices. Afterward, I cleaned and processed the data to ensure its quality and accuracy

Story Two: Data Analysis

Using statistical analysis tools, I examined data patterns and discovered relationships between different variables. I employed techniques such as exploratory data analysis and linear regression to understand how various factors affect house prices. This phase helped me build a preliminary model to grasp the main factors influencing prices.

Story Three: Model Development

After analyzing the data, I developed a machine learning model to predict house prices. I utilized algorithms such as linear regression, decision trees, and neural networks. I trained the model on a large dataset and tested it on another dataset to verify its accuracy. Ultimately, I was able to provide accurate price predictions