This project focuses on predicting property prices based on the area using a simple linear regression model. The dataset used in this project contains information about different areas and their corresponding property prices. The goal is to understand the relationship between the area and prices and create a predictive model for property prices.
Data Loading and DataFrame Creation: The data is loaded into a Pandas DataFrame from a CSV file hosted on GitHub.
Data Visualization: A scatter plot is created using Matplotlib to visualize the relationship between the area and prices.
Data Preprocessing: No data preprocessing is needed as the dataset is already cleaned.
Data Splitting: The dataset is divided into input (x) and output (y) variables.
Model Training: A linear regression model is applied to the data using scikit-learn.
Individual Prediction: The trained model is used to predict the price for a specific area (2000 sqft).
Model Evaluation: The model's predictions are compared to the actual output values.
Coefficients and Intercept: The coefficients (slope) and intercept of the linear regression model are obtained.
Best Fit Line Visualization: A scatter plot of actual values is created, and the best-fit line is plotted using the linear regression model.