In this assignment, you are tasked with developing a machine learning model that can predict linear equations based on a given dataset. The dataset consists of pairs of input variables and corresponding output values, representing different linear equations. Your goal is to train a model that can accurately predict the coefficients of a linear equation given new input values.
- Generate a dataset of multiple linear equations, each represented by a pair of input variables and the corresponding output value.
- Ensure that the dataset includes a variety of linear equations with different slopes and intercepts.
- You can use a random generator or predefined equations to create the dataset.
- Perform any necessary pre-processing steps on the dataset, such as data normalization or standardization, to ensure better model performance.
We generated a dataset with a variety of linear equations using Python. The dataset includes random input variables X1 and X2, and the corresponding output variable y. We have incorporated different slopes and intercepts to provide a diverse set of linear relationships.
We trained a linear regression model using the generated dataset to predict linear equations based on input values X1 and X2. The model provides coefficients for X1 and X2, as well as an intercept and the predicted output y.
To use this project, follow these steps:
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Clone the repository to your local machine: ''' git clone '''
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Navigate to the project directory: ''' cd '''
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Install the required Python packages using pip: ''' pip install pandas numpy matplotlib scikit-learn '''
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Run the dataset generation script to generate the dataset: ''' python generate_dataset.py '''
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Run the model script to train the linear regression model and make predictions: ''' python linear_regression_model.py '''
Follow these steps to set up and use the project successfully.
- It is trained on 5000 dataset points.
- It predicts coefficient (a,b) as well as dependent variable (y).
- It plots the graph of the equation.
This assignment demonstrates the generation of a diverse dataset of linear equations and the training of a linear regression model for predicting coefficients based on input values. By following the requirements and implementing the code, you can accurately predict linear equations and understand their relationships.