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import streamlit as st
import pandas as pd
import joblib
# Load your logistic regression model
model = joblib.load('lr1.joblib')
# Streamlit app title
st.title('NBA Rookie Career Longevity Prediction App')
# Create form for user input
with st.form("prediction_form"):
st.write("Enter the NBA Rookie's Season Statistics:")
# Input fields for all relevant features
gp = st.number_input('Games Played', min_value=0)
min = st.number_input('Minutes Played', min_value=0.0, format="%.2f")
pts = st.number_input('Points Per Game', min_value=0.0, format="%.2f")
fgm = st.number_input('Field Goals Made', min_value=0.0, format="%.2f")
fga = st.number_input('Field Goals Attempted', min_value=0.0, format="%.2f")
fg_percent = st.number_input('Field Goal Percentage', min_value=0.0, max_value=100.0, format="%.2f")
threep_made = st.number_input('3-Point Field Goals Made', min_value=0.0, format="%.2f")
threepa = st.number_input('3-Point Field Goals Attempted', min_value=0.0, format="%.2f")
threep_percent = st.number_input('3-Point Field Goal Percentage', min_value=0.0, max_value=100.0, format="%.2f")
ftm = st.number_input('Free Throws Made', min_value=0.0, format="%.2f")
fta = st.number_input('Free Throws Attempted', min_value=0.0, format="%.2f")
ft_percent = st.number_input('Free Throw Percentage', min_value=0.0, max_value=100.0, format="%.2f")
oreb = st.number_input('Offensive Rebounds', min_value=0.0, format="%.2f")
dreb = st.number_input('Defensive Rebounds', min_value=0.0, format="%.2f")
reb = st.number_input('Total Rebounds', min_value=0.0, format="%.2f")
ast = st.number_input('Assists', min_value=0.0, format="%.2f")
stl = st.number_input('Steals', min_value=0.0, format="%.2f")
blk = st.number_input('Blocks', min_value=0.0, format="%.2f")
tov = st.number_input('Turnovers', min_value=0.0, format="%.2f")
# Form submission button
submitted = st.form_submit_button("Predict")
if submitted:
# Create the DataFrame without considering the index as a feature
input_df = pd.DataFrame([[
gp, min, pts, fgm, fga, fg_percent, threep_made, threepa, threep_percent,
ftm, fta, ft_percent, oreb, dreb, reb, ast, stl, blk, tov
]], columns=[
'GP', 'MIN', 'PTS', 'FGM', 'FGA', 'FG%', '3P Made', '3PA', '3P%',
'FTM', 'FTA', 'FT%', 'OREB', 'DREB', 'REB', 'AST', 'STL', 'BLK', 'TOV'
])
# Attempt prediction
try:
prediction = model.predict(input_df)[0]
if prediction == 1:
st.success('The rookie is likely to have a successful career spanning at least 5 years!')
else:
st.error('The rookie might not have a very successful career lasting at least 5 years.')
except ValueError as e:
st.error(f'Error making prediction: {e}') |