Overview:
The Predict House Prices analysis aims to forecast housing prices using both Linear Regression and Multivariable Regression techniques. The dataset is cleaned, summarized, and split into training and test sets to create a reliable model for predicting house prices.
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Key Features:
1. Data Handling with Pandas:
The dataset is loaded and cleaned using pandas, ensuring that the data is free from inconsistencies before moving into modeling.
2. Regression Models:
Both Linear Regression and Multivariable Regression techniques are applied to predict house prices based on various features like size, location, and number of rooms.
3. Training and Testing Split:
The dataset is divided into training and testing sets to ensure the model’s accuracy and reliability.
4. Model Evaluation:
The coefficients of the regression model are analyzed to understand the influence of different variables, and regression residuals are evaluated to gauge model performance.
5. Visualization:
Graphs are generated using matplotlib to visually represent the relationship between features and predicted prices.
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