The dataset I used for this personal project is from Kaggle uploaded by aariyan panchal.
Link of Dataset : https://www.kaggle.com/aariyan101/usa-housingcsv.
File Python/main.py contains the code that used for doing the prediction of House Price using Linear Regression Method.
File Dataset/USA_Housing.csv is the dataset that I used for this research.
File Model/housePredictionModel.pickle is the result of model that I trained in Pickle Format with highest Accuracy.
File Output/.. is the output of this research. The data is plotted using Matplotlib.
Linear regression analysis is used to predict the value of a variable based on the value of another variable.
The variable you want to predict is called the dependent variable.
The variable you are using to predict the other variables value is called the independent variable.
Linear Regression is one of the method Supervised Learning.
[[IBM](https://www.ibm.com/topics/linear-regression)]. Supervised learning is a machine learning approach that’s defined by its use of labeled datasets.
These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately.
Using labeled inputs and outputs, the model can measure its accuracy and learn over time.
[[IBM](https://www.ibm.com/cloud/blog/supervised-vs-unsupervised-learning)].
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Picture 1.1 Result of Linear Regression (Avg. Area Income, Price)
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Picture 1.2 Result of Linear Regression (Area Population, Price)