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import joblib |
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import pandas as pd |
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import streamlit as st |
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Experience_dict = {'Entry-level':1, |
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'Mid-level':2, |
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'Senior-level':3, |
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'Executive-level':4 |
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} |
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Company_size_dict = {'Small, less than 50 employees':1, |
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'Medium, 50 to 250 employees':2, |
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'Large, more than 250 employees':3 |
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} |
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remote_ratio_dict = {'No remote work':1, |
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'Partially remote':2, |
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'Fully remote':3 |
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} |
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model = joblib.load('model.joblib') |
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unique_values = joblib.load('unique_values.joblib') |
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unique_experience_level = unique_values["experience_level"] |
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unique_employment_type = unique_values["employment_type"] |
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unique_job_title = unique_values["job_title"] |
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unique_remote_ratio = unique_values["remote_ratio"] |
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unique_company_size = unique_values["company_size"] |
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def main(): |
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st.title("Data Science Job Salaries") |
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with st.form("questionaire"): |
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experience_level = st.selectbox("The experience level in the job", options = unique_experience_level) |
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employment_type = st.selectbox("The type of employment for the role", options=unique_employment_type) |
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job_title = st.selectbox("The role worked", options=unique_job_title) |
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remote_ratio = st.selectbox("The overall amount of work done remotely", options=unique_remote_ratio) |
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company_size = st.selectbox("The average number of people that worked for the company", options=unique_company_size) |
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clicked = st.form_submit_button("Predict Data Science Job Salaries") |
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if clicked: |
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result=model.predict(pd.DataFrame({"experience_level": [Experience_dict[experience_level]], |
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"employment_type" : [employment_type], |
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"job_title": [job_title], |
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"remote_ratio": [remote_ratio_dict[remote_ratio]], |
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"company_size": [Company_size_dict[company_size]]})) |
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salary_in_usd = str(round(float(result),2)) |
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st.success("Your predicted Data Science job salaries in USD is "+salary_in_usd) |
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if __name__ == "__main__": |
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main() |
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