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app.py

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+import streamlit as st
+import pickle
+import numpy as np
+
+# Set page title and icon
+st.set_page_config(page_title="Employee Promotion Prediction", page_icon=":guardsman:", layout="wide")
+
+# Add a big heading
+st.title("Employee Promotion Prediction")
+
+# Change background color to black
+st.markdown(
+    """
+    <style>
+    .reportview-container {
+        background: black
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+model = pickle.load(open('rf.pkl', 'rb'))
+scaling = pickle.load(open('sc.pkl', 'rb'))
+
+# Define the department options
+department_options = ['Analytics', 'Finance', 'HR', 'Legal', 'Operations', 'Procurement', 'R&D', 'Sales & Marketing', 'Technology']
+
+# Define the education options
+education_options = ["Bachelor's", "Master's & above", "Below Secondary"]
+
+# Define the gender options
+gender_options = ['Male', 'Female']
+
+# Define the KPIs met options
+kpis_met_options = ['No', 'Yes']
+
+# Define the awards won options
+awards_won_options = ['No', 'Yes']
+
+# Define the maximum training score
+max_training_score = 100
+
+# Define the maximum number of trainings
+max_trainings = 10
+
+# Define the minimum and maximum age
+min_age, max_age = 18, 60
+
+# Define the minimum and maximum length of service
+min_service_length, max_service_length = 0, 20
+
+# Define the minimum and maximum previous year rating
+min_prev_year_rating, max_prev_year_rating = 1, 5
+
+
+# Define the input components using Streamlit
+
+department = st.selectbox('---> Department', department_options)
+education = st.selectbox('---> Education', education_options)
+gender = st.radio('---> Gender', gender_options)
+no_of_trainings = st.number_input('---> Number of Trainings', min_value=1, max_value=max_trainings, value=1)
+age = st.slider('---> Age', min_value=min_age, max_value=max_age)
+previous_year_rating = st.slider('---> Previous Year Rating', min_value=min_prev_year_rating, max_value=max_prev_year_rating)
+length_of_service = st.number_input('---> Length of Service', min_value=min_service_length, max_value=max_service_length, value=0)
+kpis_met = st.selectbox('---> KPIs Met >80%', kpis_met_options)
+awards_won = st.selectbox('---> Awards Won?', awards_won_options)
+avg_training_score = st.number_input('---> Average Training Score', max_value=max_training_score)
+
+
+department_encoding = {'Analytics':0, 'Finance':1, 'HR':2, 'Legal':3, 'Operations':4, 'Procurement':5, 'R&D':6, 'Sales & Marketing':7, 'Technology':8}
+education_encoding = {"Bachelor's":2, "Master's & above":3, "Below Secondary":1}
+gender_encoding = {'Male':1, 'Female':0}
+kpis_met_encoding = {'No':0, 'Yes':1}
+awards_won_encoding = {'No':0, 'Yes':1}
+
+sum_metric = awards_won_encoding[awards_won] + kpis_met_encoding[kpis_met] + previous_year_rating
+total_score = avg_training_score * no_of_trainings
+
+inputs_to_model = np.array([(department_encoding[department], education_encoding[education],
+                             gender_encoding[gender], no_of_trainings, age, previous_year_rating,
+                             length_of_service, kpis_met_encoding[kpis_met], awards_won_encoding[awards_won], avg_training_score,
+                             sum_metric, total_score)])
+
+final_inputs = scaling.transform(inputs_to_model)
+
+# Print the user inputs
+if st.button('Submit'):
+    # st.write('Department:', department)
+    # st.write('Education:', education)
+    # st.write('Gender:', gender)
+    # st.write('Number of Trainings:', no_of_trainings)
+    # st.write('Age:', age)
+    # st.write('Previous Year Rating:', previous_year_rating)
+    # st.write('Length of Service:', length_of_service)
+    # st.write('KPIs Met >80%:', kpis_met)
+    # st.write('Awards Won?:', awards_won)
+    # st.write('Average Training Score:', avg_training_score)
+    # st.write('sum metric', sum_metric)
+    # st.write('total score', total_score)
+    # st.write('model inputs', inputs_to_model)
+    # st.write('final model inputs', final_inputs)
+
+    prediction = model.predict(final_inputs)
+
+    if prediction[0] == 1:
+        st.header('The Employee should get Promotion')
+        
+    else:
+        st.header('The Employee should not get promotion')
+
+
+
+

rf.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:248eaef4cca11b95ec23dac1239bfd19bf7b1264306a5b4d51b3b14f67c4e215
+size 98524662

sc.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a51acf418ce431df8f9ddcb7153244ab18016d069e90c4fa344b86f5d3e900aa
+size 1003