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Commit c2f7432

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‎Salary Predictor/model.py‎

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@@ -3,7 +3,7 @@
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import numpy as np
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# Load dataset
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df = pd.read_csv('../Salary Predictor/dataset/cleaned_dataset.csv')
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df = pd.read_csv('./Salary Predictor/dataset/cleaned_dataset.csv')
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df_model = df[['avg_salary','Sector','python_yn','job_sim','R_yn','tableau','power bi','ml','dl']]
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@@ -44,10 +44,10 @@
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# Save the model
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import pickle
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filename = '../Salary Predictor/models/random_forest2_model.sav'
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filename = './Salary Predictor/models/random_forest2_model.sav'
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pickle.dump(gs.best_estimator_, open(filename, 'wb'))
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# saving the columns
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model_columns = list(X.columns)
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with open('../Salary Predictor/models/model_columns1.pkl','wb') as file:
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with open('./Salary Predictor/models/model_columns1.pkl','wb') as file:
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pickle.dump(model_columns, file)

‎Salary Predictor/script.py‎

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@@ -60,11 +60,11 @@ def know_language(yes_no):
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df['dl'][0] = Deep_Learning
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#Load the model
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with open('../Salary Predictor/models/random_forest2_model.sav', 'rb') as f:
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with open('./Salary Predictor/models/random_forest2_model.sav', 'rb') as f:
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random_forest_model = pickle.load(f)
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# load the columns file
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with open('../Salary Predictor/models/model_columns1.pkl', 'rb') as f:
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with open('./Salary Predictor/models/model_columns1.pkl', 'rb') as f:
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model_columns = pickle.load(f)
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# Query into the model to fet results

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