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Commit fd9c1e3

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Made Changes accordingly as said by mentor
1 parent 2104685 commit fd9c1e3

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3 files changed

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‎Salary Predictor/README.md‎

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# Salary Predictor
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This is basically a python script used to predict the average annual salary of person.
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Given the sector he/she wants to work, what are the skills of a person and also for which job post he/she is applying for, this model predicts what could be hos/her average annual salary.

‎Salary Predictor/model.py‎

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import numpy as np
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# Load dataset
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df = pd.read_csv('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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# Save the model
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import pickle
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filename = '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('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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import pickle
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# Function to let user choose from various options
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def let_user_pick(options):
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print("Enter the Sector of Work:")
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def let_user_pick(options, str):
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print(f"Enter the {str}")
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for idx, element in enumerate(options):
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print("{}) {}".format(idx+1,element))
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i = input("Enter number: ")
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# Take Sector as an input from user
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sectors = ['Information Technology','Business Services','Education','Business Services','Finance', 'Government','Travel & Tourism']
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sect = let_user_pick(sectors)
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sect = let_user_pick(sectors, 'Sector')
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df['Sector'][0] = sect
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# Take job role as an input from user
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job_role = ['Software Enginner', 'data scientist', 'data engineer', 'analyst', 'Machine Learnig Engineer', 'director', 'manager']
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job_simp = let_user_pick(job_role)
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job_simp = let_user_pick(job_role, 'Job Role')
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if job_simp == 'Software Engineer':
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job_simp = 'na'
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elif job_simp == 'Machine Learnig Engineer':
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df['dl'][0] = Deep_Learning
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#Load the model
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with open('models/random_forest2_model.sav', 'rb') as f:
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regressor = pickle.load(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('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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query_ = pd.get_dummies(pd.DataFrame(df, index = [0]), prefix=['Sector','job_sim'], columns=['Sector','job_sim'])
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query = query_.reindex(columns = model_columns, fill_value= 0)
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prediction = list(regressor.predict(query))
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prediction = list(random_forest_model.predict(query))
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final_val = round(prediction[0],2)
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print(f'Your Estimated Annual Salary will be {final_val}K Dollars')

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