sanjayfuloria / machine_learning_assignment

This is an assignment to the MA Economics Machine Learning Class

Basic Machine Learning Task: Regression

Objective

The objective of this assignment is to write a Python program to perform a basic machine learning task using regression. You will train a regression model on a small dataset and make predictions.

Instructions

1. Fork this repository to your GitHub account.
2. Clone the forked repository to your local machine.
3. Install the required libraries using pip install -r requirements.txt.
4. Run the script data/generate_data.py to create a small dataset.
5. Implement the regression model in regression.py.
6. Ensure your model passes the unit tests provided in tests/test_regression.py.
7. Commit your changes and push them to your forked repository.

Requirements

• Use the libraries specified in requirements.txt.
• Your code should create a dataset, train a regression model, and evaluate it using Mean Squared Error (MSE).

Example

Here is a basic structure of the regression.py script:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Load the dataset
data = pd.read_csv('data/dataset.csv')

# Split the dataset into features and target variable
X = data[['feature1', 'feature2']]
y = data['target']

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train the regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict on the testing set
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')