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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression, Ridge, Lasso
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import make_pipeline
+from pandas.api.types import is_string_dtype
+
+# Set the page configuration
+st.set_page_config(page_title="Data Visualizer & Model Trainer", layout="wide", page_icon="📊", initial_sidebar_state='expanded')
+
+# Title and Introduction
+st.title("📊 Data Visualizer & Model Trainer - Web App")
+st.markdown("""
+This app allows you to upload your data, visualize it through various plots, analyze descriptive statistics, and run multiple regression models to find the best one based on MSE.
+""")
+
+# Sidebar for uploading data
+with st.sidebar:
+    st.header("Upload and Select Data")
+    uploaded_file = st.file_uploader("Choose a CSV file", type=['csv'])
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+        st.success("File successfully uploaded!")
+
+if uploaded_file is not None:
+    # Data Preview Section
+    st.subheader("Data Preview")
+    preview_rows = st.slider("How many rows to display?", 5, 100, 20)
+    st.dataframe(df.head(preview_rows))
+
+    # Preprocess the dataset: Convert dates to numerical features and encode categorical variables
+    for col in df.columns:
+        if is_string_dtype(df[col]):
+            try:
+                df[col] = pd.to_datetime(df[col])
+                df[f"{col}_year"] = df[col].dt.year
+                df[f"{col}_month"] = df[col].dt.month
+                df[f"{col}_day"] = df[col].dt.day
+                df.drop(columns=[col], inplace=True)
+            except Exception:
+                df = pd.get_dummies(df, columns=[col], drop_first=True)
+
+    # Data Analysis Section
+    st.subheader("Data Analysis Tasks")
+    analysis_options = ["Descriptive Statistics", "Missing Values Analysis", "Correlation Heatmap"]
+    selected_analysis = st.multiselect("Select analysis tasks you want to perform:", analysis_options)
+
+    if "Descriptive Statistics" in selected_analysis:
+        st.write("### Descriptive Statistics")
+        st.write(df.describe())
+
+    if "Missing Values Analysis" in selected_analysis:
+        st.write("### Missing Values Analysis")
+        missing_values = df.isnull().sum()
+        missing_values = missing_values[missing_values > 0]
+        st.write(missing_values)
+
+    if "Correlation Heatmap" in selected_analysis:
+        st.write("### Correlation Heatmap")
+        numeric_df = df.select_dtypes(include=[np.number])
+        plt.figure(figsize=(10, 7))
+        sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm')
+        st.pyplot(plt)
+
+    # Data Visualization Section
+    st.subheader("Data Visualization")
+    plot_types = ["Line Plot", "Bar Plot", "Scatter Plot", "Histogram", "Interactive Plot", "Box Plot", "Pair Plot"]
+    selected_plots = st.multiselect("Choose plot types:", plot_types)
+
+    if selected_plots:
+        columns = df.columns.tolist()
+        x_axis = st.selectbox("Select the X-axis", options=columns, index=0)
+        y_axis_options = ['None'] + columns
+        y_axis = st.selectbox("Select the Y-axis", options=y_axis_options, index=0)
+
+    for plot_type in selected_plots:
+        st.write(f"### {plot_type}")
+        if plot_type == "Interactive Plot":
+            fig = px.scatter(df, x=x_axis, y=y_axis if y_axis != 'None' else None, title=f"{y_axis} vs {x_axis}")
+            st.plotly_chart(fig, use_container_width=True)
+        elif plot_type == "Pair Plot":
+            sns.pairplot(df)
+            st.pyplot(plt)
+        else:
+            fig, ax = plt.subplots()
+            if plot_type == "Line Plot" and y_axis != 'None':
+                sns.lineplot(x=x_axis, y=y_axis, data=df, ax=ax)
+            elif plot_type == "Bar Plot" and y_axis != 'None':
+                sns.barplot(x=x_axis, y=y_axis, data=df, ax=ax)
+            elif plot_type == "Scatter Plot" and y_axis != 'None':
+                sns.scatterplot(x=x_axis, y=y_axis, data=df, ax=ax)
+            elif plot_type == "Histogram":
+                sns.histplot(data=df, x=x_axis, kde=True, ax=ax)
+            elif plot_type == "Box Plot" and y_axis != 'None':
+                sns.boxplot(x=x_axis, y=y_axis, data=df, ax=ax)
+            st.pyplot(fig)
+
+    # Model Training and Selection
+    st.subheader("Model Training & Selection")
+    target_column = st.selectbox("Select Target Column", options=df.columns)
+    
+    if st.button("Train Models and Select Best"):
+        feature_columns = [col for col in df.columns if col != target_column]
+        
+        X = df[feature_columns]
+        y = df[target_column]
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        models = {
+            'Linear Regression': make_pipeline(SimpleImputer(strategy='mean'), LinearRegression()),
+            'Ridge Regression': make_pipeline(SimpleImputer(strategy='mean'), Ridge()),
+            'Lasso Regression': make_pipeline(SimpleImputer(strategy='mean'), Lasso()),
+            'Random Forest': make_pipeline(SimpleImputer(strategy='mean'), RandomForestRegressor(random_state=42))
+        }
+
+        mse_scores = {}
+        for name, model in models.items():
+            model.fit(X_train, y_train)
+            predictions = model.predict(X_test)
+            mse_scores[name] = mean_squared_error(y_test, predictions)
+
+        best_model = min(mse_scores, key=mse_scores.get)
+        st.write(f"Best Model: {best_model} with MSE: {mse_scores[best_model]}")
+
+        # Display MSE scores for all models
+        for model_name, mse in mse_scores.items():
+            st.write(f"{model_name}: MSE = {mse}")
+
+# Footer
+st.markdown("---")
+st.markdown("Developed by Isuru Lakshan ekanayaka - A versatile data visualization and model training app built with Streamlit.")

requirements.txt

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+streamlit
+numpy
+pandas
+matplotlib
+seaborn
+plotly
+scikit-learn