Update app.py
Browse files
app.py
CHANGED
@@ -93,40 +93,40 @@ def main():
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prediction = model.predict(input_data)
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probability = model.predict_proba(input_data)[:, 1]
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#
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if job_satisfaction == 1 or environment_satisfaction == 1:
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st.write("Enhance job and environment satisfaction through initiatives such as recognition programs and improving workplace conditions.")
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if years_since_last_promotion > 5:
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st.write("Implement a transparent promotion policy and provide opportunities for career advancement.")
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if years_with_curr_manager > 5:
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st.write("Offer opportunities for a change in reporting structure to prevent stagnation and promote growth.")
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if percent_salary_hike < 5:
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st.write("Consider adjusting salary and benefits packages to remain competitive and reward employee loyalty.")
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if training_times_last_year < 2:
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st.write("Invest in employee development through training programs and continuous learning opportunities.")
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if over_time:
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st.write("Evaluate workload distribution and consider implementing measures to prevent overwork, such as workload balancing and flexible scheduling.")
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if relationship_satisfaction == 1:
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st.write("Foster positive relationships and a supportive work environment through team-building activities and open communication channels.")
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if monthly_income < 5000:
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st.write("Review compensation structures and adjust salaries to align with industry standards and employee expectations.")
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if num_companies_worked > 5:
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st.write("Identify reasons for high turnover and address issues related to job stability, career progression, and organizational culture.")
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if work_life_balance == 1:
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st.write("Promote work-life balance initiatives, such as flexible work arrangements and wellness programs, to support employee well-being.")
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# General recommendation for all negative predictions
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st.write("Conduct exit interviews to gather feedback and identify areas for improvement in retention strategies.")
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if __name__ == "__main__":
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main()
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prediction = model.predict(input_data)
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probability = model.predict_proba(input_data)[:, 1]
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# Convert integer inputs to integers
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age = int(age)
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monthly_income = int(monthly_income)
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num_companies_worked = int(num_companies_worked)
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percent_salary_hike = int(percent_salary_hike)
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training_times_last_year = int(training_times_last_year)
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years_since_last_promotion = int(years_since_last_promotion)
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years_with_curr_manager = int(years_with_curr_manager)
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# Display characteristic-based recommendations
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st.subheader("Suggestions for retaining the employee:")
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if job_satisfaction == 1 or environment_satisfaction == 1:
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st.write("Enhance job and environment satisfaction through initiatives such as recognition programs and improving workplace conditions.")
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if years_since_last_promotion > 5:
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st.write("Implement a transparent promotion policy and provide opportunities for career advancement.")
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if years_with_curr_manager > 5:
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st.write("Offer opportunities for a change in reporting structure to prevent stagnation and promote growth.")
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if percent_salary_hike < 5:
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st.write("Consider adjusting salary and benefits packages to remain competitive and reward employee loyalty.")
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if training_times_last_year < 2:
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st.write("Invest in employee development through training programs and continuous learning opportunities.")
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if over_time:
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st.write("Evaluate workload distribution and consider implementing measures to prevent overwork, such as workload balancing and flexible scheduling.")
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if relationship_satisfaction == 1:
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st.write("Foster positive relationships and a supportive work environment through team-building activities and open communication channels.")
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if monthly_income < 5000:
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st.write("Review compensation structures and adjust salaries to align with industry standards and employee expectations.")
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if num_companies_worked > 5:
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st.write("Identify reasons for high turnover and address issues related to job stability, career progression, and organizational culture.")
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if work_life_balance == 1:
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st.write("Promote work-life balance initiatives, such as flexible work arrangements and wellness programs, to support employee well-being.")
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# General recommendation for all negative predictions
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st.write("Conduct exit interviews to gather feedback and identify areas for improvement in retention strategies.")
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if __name__ == "__main__":
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main()
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