Mominali23/relations

1import streamlit as st2import pandas as pd3import numpy as np4import statsmodels.api as sm5from sklearn.linear_model import LinearRegression6 7# App title8st.title("Data Relationship Estimator")9 10# Upload file11uploaded_file = st.file_uploader("Upload your Excel file", type=["xlsx", "xls"])12 13if uploaded_file:14    try:15        # Read the uploaded Excel file16        data = pd.read_excel(uploaded_file)17        st.success("File uploaded successfully!")18        19        # Display data preview20        st.write("### Data Preview")21        st.write(data.head())22        23        # Allow the user to select variables24        st.write("### Select Variables for Analysis")25        numerical_cols = data.select_dtypes(include=["number"]).columns.tolist()26        if len(numerical_cols) < 2:27            st.error("Not enough numerical data for analysis. Please upload valid data.")28        else:29            x_cols = st.multiselect("Select Independent Variables (X)", numerical_cols)30            y_col = st.selectbox("Select Dependent Variable (Y)", numerical_cols)31            32            if x_cols and y_col:33                # Subset data34                X = data[x_cols]35                y = data[y_col]36 37                # Fit a regression model38                st.write("### Regression Model")39                X_with_const = sm.add_constant(X)  # Add intercept term40                model = sm.OLS(y, X_with_const).fit()41                st.write(model.summary())42 43                # Display the equation44                coeffs = model.params45                equation = f"{y_col} = "46                for i, col in enumerate(['Intercept'] + x_cols):47                    term = f"{coeffs[i]:.3f}"48                    if i > 0:49                        term += f" * {col}"50                    equation += term51                    if i < len(coeffs) - 1:52                        equation += " + "53 54                st.write("### Mathematical Relationship")55                st.write(f"**{equation}**")56                57                # Explanation58                st.write("### Explanation")59                st.write("""60                    - **Intercept**: The baseline value of the dependent variable (Y) when all independent variables (X) are zero.61                    - **Coefficients**: These constants quantify the relationship between each independent variable and the dependent variable.62                    - **R-squared**: A measure of how well the model explains the variance in the dependent variable.63                    - **P-values**: Indicate whether the relationships between variables are statistically significant.64                """)65            else:66                st.error("Please select both independent and dependent variables.")67 68    except Exception as e:69        st.error(f"An error occurred: {e}")70else:71    st.info("Please upload an Excel file to begin.")72