Create u_can_use_any_dataset.py

u_can_use_any_dataset.py

@@ -0,0 +1,97 @@
+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()