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PRODIGY_ML_01

House Price Prediction Project

Welcome to the House Price Prediction project repository! In this project, I ventured into the fascinating realm of real estate data to develop models that predict house prices accurately. Predicting house prices is a critical task with applications ranging from assessing mortgage risks to making informed investment decisions.

Objective

The primary objective of this project was to explore and implement machine learning techniques to create reliable house price prediction models. These models can assist potential buyers, sellers, and investors in the housing market by providing estimates of property values based on various input features.

Key Techniques

I experimented with several machine learning algorithms, but the main focus was on the following key techniques:

  • Linear Regression: This method uses a linear model to estimate house values. It's a straightforward approach and well-suited for scenarios where there's a linear relationship between input features and house prices.

  • Random Forest: Leveraging decision trees, the Random Forest algorithm provides more complexity and can capture non-linear relationships in the data.

  • Gradient Boosting: This technique combines the power of multiple models to make robust and accurate predictions. It excels in situations where fine-tuning is necessary to improve predictive performance.

The accuracy of house price prediction models can vary depending on the quality of the data and the method that is used. However, these models can be a valuable tool for making informed decisions about the housing market.

Dataset contains 7 columns and 5000 rows with CSV extension. The data contains the following columns :

  • 'Avg. Area Income': Avg. Income of householder of the city house is located in.
  • 'Avg. Area House Age': Avg. Age of Houses in same city.
  • 'Avg. Area Number of Rooms': Avg. Number of Rooms for Houses in same city.
  • 'Avg. Area Number of Bedrooms': Avg. Number of Bedrooms for Houses in same city.
  • 'Area Population': Population of city.
  • 'Price': Price that the house sold at.
  • 'Address': Address of the houses.