!pip install shap==0.39.0 !pip install streamlit==1.13.0 #load in libraries import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt from joblib import dump, load import streamlit as st

import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier import streamlit as st import os from joblib import dump, load # Load your trained model model = load(os.path.join(os.getcwd(), 'DecisionTree_MatchPrediction.joblib')) # Load the dataset try: dataset_path = os.path.join(os.getcwd(), 'speeddating.csv') st.write(f"Loading dataset from: {dataset_path}") df = pd.read_csv(dataset_path) st.write("Dataset loaded successfully") except Exception as e: st.error(f"Failed to load dataset: {e}") df = None # Ensure df is loaded before using it if df is not None: # Define your target value (y) and your features (df_un) y = 'match' df_dt = df.drop(columns=[y]) # Exclude the target value # Optional: Exclude any other columns if necessary # For example, exclude 'age' or any other feature if needed # df_dt = df_dt.loc[:, df_dt.columns != 'age'] # Streamlit interface st.title('Speeddating Match Prediction') st.write('Predict whether there will be a match based on your inputs.') # Collecting user input age = st.sidebar.slider('Your Age', min_value=18, max_value=70, value=25) like = st.sidebar.slider('Like Score', min_value=0, max_value=10, value=5) decision = st.sidebar.selectbox('Your Decision', ['Yes', 'No']) # Map inputs to the corresponding feature values input_data = { 'age': age, 'like': like, 'decision': 1 if decision == 'Yes' else 0 } # Convert the input data to a DataFrame input_df = pd.DataFrame([input_data]) # Handle missing columns that may have been excluded in df_dt input_df = input_df.reindex(columns=df_dt.columns, fill_value=0) # Make prediction if st.sidebar.button('Predict'): prediction = model.predict(input_df)[0] st.write(f"The model predicts a {'match' if prediction == 1 else 'no match'}.") else: st.error("The dataset could not be loaded. Please check the file path and the dataset format.")

!streamlit run speeddating_app.py --server.port 8080