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app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.ensemble import GradientBoostingRegressor
+import pickle
+
+with open('salPridict.pkl', 'rb') as file:
+  model = pickle.load(file)
+
+# Function to preprocess input data
+def preprocess_input(df):
+    # Preprocess input data here (e.g., encoding categorical variables, transforming date features)
+    # Convert 'DOJ' and 'CURRENT DATE' to datetime
+    df['DOJ'] = pd.to_datetime(df['DOJ'])
+    df['CURRENT DATE'] = pd.to_datetime('2016-07-01')
+
+    # Calculate tenure in days, then convert to years
+    df['TENURE'] = (df['CURRENT DATE'] - df['DOJ']).dt.days // 365.25
+    df['TOTAL_EXP'] = df['PAST_EXP'] + df['TENURE']
+    df = df.drop(columns=['DOJ', 'CURRENT DATE', 'NAME', 'PAST_EXP', 'TENURE', 'AGE'])
+    return df
+# Function to make prediction
+def predict_salary(data):
+    preprocessed_data = preprocess_input(data)
+    salary = model.predict(preprocessed_data)
+    return int(salary)
+
+# Streamlit app
+def main():
+    st.title('Salary Prediction App')
+
+    # Input fields
+    name = st.text_input('Name')
+    age = st.number_input('Age', min_value=0)
+    gender = st.selectbox('Gender', ['M', 'F'])
+    designation = st.selectbox('Designation', ['Analyst', 'Associate', 'Senior Analyst', 'Manager', 'Senior Manager', 'Director'])
+    unit = st.selectbox('Unit', ['Finance', 'IT', 'Marketing' ,'Operations' ,'Web', 'Management'])
+
+
+    past_experience = st.number_input('Past Experience', min_value=0)
+    rating = st.number_input('Rating', min_value=0.0, max_value=5.0, step=0.1)
+    date_of_join = st.date_input('Date of Join')
+
+    # Predict button
+    if st.button('Predict Salary'):
+        input_data = pd.DataFrame({
+            'NAME': [name],
+            'AGE': [age],
+            'SEX': [gender],
+            'DESIGNATION': [designation],
+            'UNIT': [unit],
+            'PAST_EXP': [past_experience],
+            'RATINGS': [rating],
+            'DOJ': [date_of_join]
+        })
+        salary_prediction = predict_salary(input_data)
+        st.success(f'Predicted Salary: {salary_prediction}')
+
+if __name__ == '__main__':
+    main()
+

salPridict.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1fb9f2373d14d3b17e64597cccda52e5ea40e66a41f2bb86e088654fab0354b5
+size 2454062