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MLalgoapp.py

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+import streamlit as st
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, RandomForestClassifier, GradientBoostingClassifier
+from sklearn.linear_model import LinearRegression, Ridge, Lasso, LogisticRegression
+from sklearn.svm import SVR, SVC
+from sklearn.metrics import mean_squared_error, r2_score, accuracy_score, classification_report
+from sklearn.impute import SimpleImputer
+from sklearn.datasets import load_iris, fetch_california_housing
+import base64
+
+# Set page config
+st.set_page_config(
+    page_title="Machine Learning App",
+    page_icon="https://i.imgur.com/C6lAamP.png",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# Sidebar logo
+st.sidebar.image("https://i.imgur.com/kpkwAUT.png", use_column_width=True)
+
+#function to load a bg image
+def set_bg_hack_url():
+    '''
+    A function to unpack an image from url and set as bg.
+    Returns
+    -------
+    The background.
+    '''
+        
+    st.markdown(
+         f"""
+         <style>
+         .stApp {{
+             background: url("https://c.wallhere.com/photos/91/50/mountain_top_black_dark_nature_monochrome_landscape_mountains-1296041.jpg!d");
+             background-size: cover
+         }}
+         </style>
+         """,
+         unsafe_allow_html=True
+     )
+
+# Call the function to set background image
+set_bg_hack_url()
+
+# Custom CSS styles with base64-encoded background image
+st.markdown(
+    f"""
+    <style>
+    .sidebar .sidebar-content {{
+        background-image: linear-gradient(#D5DBDB, #F2F3F4);
+    }}
+    .st-cc {{
+        color: #566573;
+    }}
+    .st-cq {{
+        color: #566573;
+    }}
+    .st-c8 {{
+        color: #FFCDD2;
+    }}
+    .st-top-right {{
+    position: fixed;
+    top: 60px;
+    right: 10px;
+    font-size: 16px;
+    color: #FF0000; /* Red text color */
+    background-color: rgba(255, 255, 255, 0.8); /* White background color with some transparency */
+    padding: 5px 10px; /* Add padding to the text */
+    border-radius: 5px; /* Add border radius for rounded corners */
+    font-family: Arial, sans-serif;
+    }}
+    </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+
+# Function to build regression model
+def build_regression_model(df, algorithm, hyperparameters):
+    X = df.iloc[:, :-1]  # Features
+    y = df.iloc[:, -1]   # Target
+
+    # Data splitting
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    st.markdown('**1.2. Data splits**')
+    st.write('Training set')
+    st.info(X_train.shape)
+    st.write('Test set')
+    st.info(X_test.shape)
+
+    st.markdown('**1.3. Variable details**:')
+    st.write('X variable')
+    st.info(list(X.columns))
+    st.write('Y variable')
+    st.info(y.name)
+
+    model = algorithm(**hyperparameters)
+    model.fit(X_train, y_train)
+
+    # Model performance
+    st.subheader('2. Model Performance')
+
+    st.markdown('**2.1. Training set**')
+    y_pred_train = model.predict(X_train)
+    st.write('Coefficient of determination ($R^2$):')
+    st.info(r2_score(y_train, y_pred_train))
+
+    st.write('Error (MSE or MAE):')
+    st.info(mean_squared_error(y_train, y_pred_train))
+
+    st.markdown('**2.2. Test set**')
+    y_pred_test = model.predict(X_test)
+    st.write('Coefficient of determination ($R^2$):')
+    st.info(r2_score(y_test, y_pred_test))
+
+    st.write('Error (MSE or MAE):')
+    st.info(mean_squared_error(y_test, y_pred_test))
+
+    # Model parameters
+    st.subheader('3. Model Parameters')
+    st.write(model.get_params())
+
+    return model
+
+
+# Function to build classification model
+def build_classification_model(df, algorithm, hyperparameters):
+    X = df.iloc[:, :-1]  # Features
+    y = df.iloc[:, -1]   # Target
+
+    # Data splitting
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    st.markdown('**1.2. Data splits**')
+    st.write('Training set')
+    st.info(X_train.shape)
+    st.write('Test set')
+    st.info(X_test.shape)
+
+    st.markdown('**1.3. Variable details**:')
+    st.write('X variable')
+    st.info(list(X.columns))
+    st.write('Y variable')
+    st.info(y.name)
+
+    model = algorithm(**hyperparameters)
+    model.fit(X_train, y_train)
+
+    # Model performance
+    st.subheader('2. Model Performance')
+
+    st.markdown('**2.1. Training set**')
+    y_pred_train = model.predict(X_train)
+    st.write('Accuracy Score:')
+    st.info(accuracy_score(y_train, y_pred_train))
+
+    st.markdown('**2.2. Test set**')
+    y_pred_test = model.predict(X_test)
+    st.write('Accuracy Score:')
+    st.info(accuracy_score(y_test, y_pred_test))
+
+    # Model parameters
+    st.subheader('3. Model Parameters')
+    st.write(model.get_params())
+
+    return model
+
+
+# Disclaimer
+st.markdown(
+    '<p style="color: green; font-family: monospace;">Disclaimer: This app works best when viewed in dark mode.<br> Click three dots in the top right corner -> Settings -> Choose app theme dark.</p>',
+    unsafe_allow_html=True
+)
+
+
+# The Machine Learning App
+st.write(
+    """
+    # Machine Learning App
+    Select the problem type, dataset, model, and hyperparameters.
+    """
+)
+
+# Sidebar - Select problem type
+problem_type = st.sidebar.selectbox("Select Problem Type", ["Regression", "Classification"])
+
+# Sidebar - Select dataset or upload CSV file
+if problem_type == "Classification":
+    st.sidebar.write("### Select Dataset or Upload CSV File for Classification")
+    dataset_option = st.sidebar.selectbox("Select Dataset", ["Iris", "Upload CSV File"])
+elif problem_type == "Regression":
+    st.sidebar.write("### Select Dataset or Upload CSV File for Regression")
+    dataset_option = st.sidebar.selectbox("Select Dataset", ["California Housing", "Upload CSV File"])
+
+# Load dataset
+df = None
+if dataset_option == "Iris":
+    iris = load_iris()
+    df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
+    df["target"] = iris.target
+elif dataset_option == "California Housing":
+    housing = fetch_california_housing()
+    df = pd.DataFrame(data=housing.data, columns=housing.feature_names)
+    df["target"] = housing.target
+elif dataset_option == "Upload CSV File":
+    uploaded_file = st.sidebar.file_uploader("Upload your CSV file", type=["csv"])
+    if uploaded_file is not None:
+        df = pd.read_csv(uploaded_file)
+
+# Sidebar - Select model and hyperparameters
+st.sidebar.write("### Select Model and Hyperparameters")
+
+if problem_type == "Regression":
+    regression_algorithm = st.sidebar.selectbox(
+        "Select Regression Algorithm",
+        ["Linear Regression", "Ridge Regression", "Lasso Regression", "Random Forest Regression", "Gradient Boosting Regression"]
+    )
+    if regression_algorithm == "Linear Regression":
+        hyperparameters = {}
+        algorithm = LinearRegression
+    elif regression_algorithm == "Ridge Regression":
+        alpha = st.sidebar.slider("Alpha", 0.0, 1.0, 0.5, 0.01)
+        hyperparameters = {"alpha": alpha}
+        algorithm = Ridge
+    elif regression_algorithm == "Lasso Regression":
+        alpha = st.sidebar.slider("Alpha", 0.0, 1.0, 0.5, 0.01)
+        hyperparameters = {"alpha": alpha}
+        algorithm = Lasso
+    elif regression_algorithm == "Random Forest Regression":
+        n_estimators = st.sidebar.slider("Number of Estimators", 1, 1000, 100)
+        max_features = st.sidebar.selectbox("Max Features", ["sqrt", "log2"])
+        hyperparameters = {"n_estimators": n_estimators, "max_features": max_features}
+        algorithm = RandomForestRegressor
+    elif regression_algorithm == "Gradient Boosting Regression":
+        n_estimators = st.sidebar.slider("Number of Estimators", 1, 1000, 100)
+        learning_rate = st.sidebar.slider("Learning Rate", 0.01, 1.0, 0.1, 0.01)
+        hyperparameters = {"n_estimators": n_estimators, "learning_rate": learning_rate}
+        algorithm = GradientBoostingRegressor
+
+elif problem_type == "Classification":
+    classification_algorithm = st.sidebar.selectbox(
+        "Select Classification Algorithm",
+        ["Logistic Regression", "Support Vector Classifier", "Random Forest Classifier", "Gradient Boosting Classifier"]
+    )
+    if classification_algorithm == "Logistic Regression":
+        hyperparameters = {"max_iter": 1000}
+        algorithm = LogisticRegression
+    elif classification_algorithm == "Support Vector Classifier":
+        kernel = st.sidebar.selectbox("Kernel", ["linear", "poly", "rbf", "sigmoid"])
+        hyperparameters = {"kernel": kernel}
+        algorithm = SVC
+    elif classification_algorithm == "Random Forest Classifier":
+        n_estimators = st.sidebar.slider("Number of Estimators", 1, 1000, 100)
+        max_features = st.sidebar.selectbox("Max Features", ["sqrt", "log2"])
+        hyperparameters = {"n_estimators": n_estimators, "max_features": max_features}
+        algorithm = RandomForestClassifier
+    elif classification_algorithm == "Gradient Boosting Classifier":
+        n_estimators = st.sidebar.slider("Number of Estimators", 1, 1000, 100)
+        learning_rate = st.sidebar.slider("Learning Rate", 0.01, 1.0, 0.1, 0.01)
+        hyperparameters = {"n_estimators": n_estimators, "learning_rate": learning_rate}
+        algorithm = GradientBoostingClassifier
+
+# Main panel
+st.write("## Results")
+
+if df is not None:
+    st.write("### Data Preview")
+    st.write("First 20 rows of the dataset:")
+    st.write(df.head(20))  # Displaying the first 20 rows of the dataset
+
+    if problem_type == "Regression":
+        model = build_regression_model(df, algorithm, hyperparameters)
+        st.write("### Regression Model")
+        st.write("Model:", algorithm.__name__)
+    elif problem_type == "Classification":
+        model = build_classification_model(df, algorithm, hyperparameters)
+        st.write("### Classification Model")
+        st.write("Model:", algorithm.__name__)
+else:
+    st.info("Please upload a CSV file or select an example dataset.")
+
+# Text in the top-right corner
+st.markdown('<p class="st-top-right">Created by - RAJDEEP CHAKRAVORTY</p>', unsafe_allow_html=True)

requirements.txt

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+# Automatically generated by https://github.com/damnever/pigar.
+
+pandas==2.1.4
+scikit-learn==1.3.2
+streamlit==1.33.0
+scipy