test upload

README.md

@@ -1,8 +1,12 @@
----
-datasets:
-- maxcembalest/boston_housing
-metrics:
-- accuracy
-base_model:
-- pinkieseb/carb_regression_model
----
+# Linear Regression Model
+
+This is a simple linear regression model that returns coefficients and intercept when provided with column names.
+
+## Usage
+
+### Input
+The model takes JSON input with the following structure:
+```json
+{
+  "columns": ["feature1", "feature2", "feature3"]
+}

linear_regression_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c16213ea0ddeef8034d48cb914e7bf3e85e2c0c491c8c823e41a2a93e98fb062
+size 559

model.py

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+# model.py
+import joblib
+
+class LinearRegressionModel:
+    def __init__(self):
+        # Load the model
+        self.model = joblib.load("linear_regression_model.joblib")
+
+    def get_coefficients(self, columns):
+        # Return coefficients as a dictionary with column names as keys
+        coefficients = dict(zip(columns, self.model.coef_.tolist()))
+        intercept = self.model.intercept_
+        return {"coefficients": coefficients, "intercept": intercept}
+
+# Instantiate the model for inference
+model = LinearRegressionModel()
+
+# Sample usage function to demonstrate
+def predict(inputs):
+    # Assume 'columns' is a key in the inputs dict with a list of column names
+    columns = inputs.get("columns", [])
+    return model.get_coefficients(columns)

train_and_save_model.py

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+# train_and_save_model.py
+from sklearn.linear_model import LinearRegression
+from sklearn.datasets import make_regression
+import joblib
+
+# Generate example data (replace with your own dataset)
+X, y = make_regression(n_samples=100, n_features=3, noise=0.1, random_state=42)
+
+# Train the model
+model = LinearRegression()
+model.fit(X, y)
+
+# Save the model to a file
+joblib.dump(model, "linear_regression_model.joblib")
+print("Model saved as 'linear_regression_model.joblib'")