Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.metrics import mean_squared_error, r2_score | |
| import io | |
| def main(): | |
| st.title("Dynamic Regression Model App") | |
| # File uploader for user dataset | |
| uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"]) | |
| if uploaded_file is not None: | |
| df = pd.read_csv(uploaded_file) | |
| st.write("## Data Sample") | |
| st.write(df.head()) | |
| st.write("## Data Statistics") | |
| st.write(df.describe()) | |
| st.write("## Data Info") | |
| buffer = io.StringIO() | |
| df.info(buf=buffer) | |
| s = buffer.getvalue() | |
| st.text(s) | |
| st.write("## Missing Values") | |
| st.write(df.isnull().sum()) | |
| # Drop target variable from the predictors list | |
| columns = df.columns.tolist() | |
| target = st.selectbox('Select the target variable for regression:', options=columns) | |
| predictor_options = [col for col in columns if col != target] | |
| # Add multiselect for user to choose predictor variables | |
| predictors = st.multiselect( | |
| 'Select predictor variables for regression:', | |
| options=predictor_options | |
| ) | |
| if not predictors or not target: | |
| st.error("Please select at least one predictor and a target variable.") | |
| return | |
| st.write("## Scatter Plot") | |
| if len(predictors) == 1: | |
| fig, ax = plt.subplots() | |
| ax.scatter(df[predictors[0]], df[target]) | |
| ax.set_xlabel(predictors[0]) | |
| ax.set_ylabel(target) | |
| ax.set_title(f'Relationship between {predictors[0]} and {target}') | |
| st.pyplot(fig) | |
| else: | |
| st.write("Scatter plot is only available for a single predictor.") | |
| # Regression analysis | |
| X = df[predictors] | |
| y = df[target] | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| model = LinearRegression() | |
| model.fit(X_train, y_train) | |
| y_pred = model.predict(X_test) | |
| rmse = np.sqrt(mean_squared_error(y_test, y_pred)) | |
| r2 = r2_score(y_test, y_pred) | |
| st.write(f'## Regression Analysis') | |
| st.write(f'RMSE: {rmse}') | |
| st.write(f'R-squared: {r2}') | |
| if len(predictors) == 1: | |
| fig, ax = plt.subplots() | |
| ax.scatter(X_train[predictors[0]], y_train, color='blue', label='Training data') | |
| ax.scatter(X_test[predictors[0]], y_test, color='green', label='Testing data') | |
| ax.plot(X_test[predictors[0]], y_pred, color='red', linewidth=2, label='Regression line') | |
| ax.set_xlabel(predictors[0]) | |
| ax.set_ylabel(target) | |
| ax.set_title(f'Linear Regression: {predictors[0]} vs {target}') | |
| ax.legend() | |
| st.pyplot(fig) | |
| else: | |
| fig, ax = plt.subplots(figsize=(10, 6)) | |
| ax.scatter(y_test, y_pred, color='blue', label='Predicted vs Actual') | |
| ax.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', linewidth=2, label='Ideal fit') | |
| ax.set_xlabel('Actual ' + target) | |
| ax.set_ylabel('Predicted ' + target) | |
| ax.set_title('Multilinear Regression: Actual vs Predicted') | |
| ax.legend() | |
| st.pyplot(fig) | |
| else: | |
| st.write("Please upload a CSV file to proceed.") | |
| if __name__ == "__main__": | |
| main() |