House Price Prediction

Introduction:

Home value predication model is also called as House? price index(HPI) is used to measure price changes of residential housing in many countries, such as the US Federal Housing Finance Agency HPI, S&P/Case-Shiller price index.

In the home value predication model “Machine learning” & “DATA Pre -Processing” is plays in the crucial role.

Let's? know about Machine learning:

Machine Learning is a branch of artificial intelligence that develops algorithms by learning the hidden patterns of the datasets used it to make predictions on new similar type data, without being explicitly programmed for each task.

Life cycle of ML:

Types of machine learning :

Supervised Machine Learning:

Supervised machine learning is an approach to creating artificial intelligence where a computer algorithm is trained on labeled input data to make predictions or classifications.

Unsupervised Machine Learning:

Unsupervised machine learning is a type of machine learning technique that uses artificial intelligence algorithms to identify patterns in data sets that are neither labeled nor categorized.

Reinforcement Machine Learning:

Reinforcement machine learning is a type of machine learning that trains computers to make independent decisions by interacting with the environment and learning from the rewards and punishments received.

About data preprocessing:

Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.

Data preprocessing in Machine Learning:

Algorithm/Technique used in home value prediction :

REGRESSION algorithm : ????

???????? Regression algorithms belong to the realm of machine learning techniques designed for forecasting numerical outcomes based on input data. Their primary objective is to establish a connection between input variables and output variables by crafting a mathematical model that best encapsulates the data patterns.

Types of regression algorithm:

Factors in home value predication:

1. Neighborhood comps: Comparable home values in the neighborhood can significantly impact a home's value.

2. Location: The location of the house is a crucial factor in determining its value. Because public transit can all impact a home's overall value.

3. Condition of the home: The overall quality of the home.

4. Property size: The size of the property can also impact its value.

5. Interest rates: Interest rates can impact the demand and price for real estate.

6. Features of the home: Such as the number of bedrooms and bathrooms, square footage, and other factors that can affect the value of a property.

These factors are used for home value prediction model.

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Conclusion:

Our home value prediction model, developed using Regression, demonstrates high accuracy in estimating home values. It incorporates significant features and has been rigorously tested, outperforming existing models. However, it should be used as a tool alongside other factors in real estate decision-making.

Written by

VADDE KARTHIKEYA

Department of Computer Science & Engineering, K L University.

Kognitiv Club