updated

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9123d8808c50fe91e2aeb83907ef24a5a0bf70f6b163591cd79991c35187605
+size 419

model.py

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+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import pickle
+
+
+# Importing the dataset
+# dataset = pd.read_csv('dataset/Sales_Salary_Data.csv')
+dataset = pd.read_csv('dataset/Sales_Salary_Data_IDR.csv')
+
+# seprate feature & target
+X = dataset.iloc[:, :-1].values
+y = dataset.iloc[:, 1].values
+
+# Splitting the dataset into the Training set and Test set
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
+
+
+# Fitting Simple Linear Regression to the Training set
+regressor = LinearRegression()
+regressor.fit(X_train, y_train)
+
+
+# Predicting the Test set results
+y_pred = regressor.predict(X_test)
+
+# Saving serialized model to disk
+pickle.dump(regressor, open('model.pkl','wb'))
+#joblib.dump(regressor, 'model.pkl')
+
+
+# Loading model to compare the results
+model = pickle.load(open('model.pkl','rb'))
+#model = joblib.load('model.pkl')
+
+print("Regressor model output", regressor.predict([[1.8]]))
+print("Saved  model output", model.predict([[1.8]]))
+

requirements.txt

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-joblib==0.13.2
-Flask==2.0.3
-numpy==1.17.2
-pandas==0.25.1
-scikit_learn==0.21.3
+joblib==1.1.
+Flask==2.2.2
+numpy==1.23.5
+pandas==1.5.3
+scikit_learn==1.2.1
 gunicorn==19.9.0
 
 uvicorn[standard]==0.17.*
-requests==2.27.*
+requests==2.28.1