Delete pages/5_model training and deployment.py

pages/5_model training and deployment.py

@@ -1,132 +0,0 @@
-import streamlit as st
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.linear_model import LinearRegression, LogisticRegression
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score, classification_report, mean_squared_error
-
-st.set_page_config(page_title="Zero to Hero - ML Model Trainer", layout="wide")
-st.title("📊 Machine Learning: Model Building, Training, and Deployment")
-
-# Sidebar Navigation
-st.sidebar.title("Navigation")
-section = st.sidebar.selectbox("Choose Step", [
-    "Upload Dataset",
-    "Model Selection",
-    "Model Training",
-    "Model Evaluation",
-    "Model Deployment"
-])
-
-# Session state
-if 'df' not in st.session_state:
-    st.session_state.df = None
-if 'model' not in st.session_state:
-    st.session_state.model = None
-
-# Upload dataset
-if section == "Upload Dataset":
-    st.header("📁 Upload Your Dataset")
-    uploaded_file = st.file_uploader("Upload CSV File", type=["csv"])
-    if uploaded_file:
-        df = pd.read_csv(uploaded_file, on_bad_lines='skip')
-        st.session_state.df = df
-        st.dataframe(df.head())
-
-# Model selection
-elif section == "Model Selection":
-    st.header("🧠 Choose Your ML Algorithm")
-    if st.session_state.df is not None:
-        df = st.session_state.df
-        st.write("Select the target column (what you want to predict):")
-        target = st.selectbox("Target", df.columns)
-        features = df.drop(columns=[target])
-        X = features
-        y = df[target]
-
-        task_type = st.radio("What type of problem is it?", ["Regression", "Classification"])
-
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-        scaler = StandardScaler()
-        X_train = scaler.fit_transform(X_train)
-        X_test = scaler.transform(X_test)
-
-        if task_type == "Regression":
-            model_option = st.selectbox("Select Algorithm", ["Linear Regression"])
-            if model_option == "Linear Regression":
-                model = LinearRegression()
-
-        else:
-            model_option = st.selectbox("Select Algorithm", [
-                "Logistic Regression",
-                "K-Nearest Neighbors",
-                "Decision Tree",
-                "Random Forest"
-            ])
-
-            if model_option == "Logistic Regression":
-                model = LogisticRegression()
-            elif model_option == "K-Nearest Neighbors":
-                model = KNeighborsClassifier()
-            elif model_option == "Decision Tree":
-                model = DecisionTreeClassifier()
-            elif model_option == "Random Forest":
-                model = RandomForestClassifier()
-
-        st.session_state.model = model
-        st.session_state.X_train = X_train
-        st.session_state.X_test = X_test
-        st.session_state.y_train = y_train
-        st.session_state.y_test = y_test
-        st.success(f"{model_option} selected and data split for training/testing.")
-
-    else:
-        st.warning("Please upload a dataset first.")
-
-# Model training
-elif section == "Model Training":
-    st.header("🏋️ Model Training")
-    if st.session_state.model is not None:
-        model = st.session_state.model
-        model.fit(st.session_state.X_train, st.session_state.y_train)
-        st.session_state.trained_model = model
-        st.success("Model trained successfully!")
-    else:
-        st.warning("Please select a model first.")
-
-# Evaluation
-elif section == "Model Evaluation":
-    st.header("📈 Model Evaluation")
-    if 'trained_model' in st.session_state:
-        model = st.session_state.trained_model
-        y_pred = model.predict(st.session_state.X_test)
-        if isinstance(model, LinearRegression):
-            mse = mean_squared_error(st.session_state.y_test, y_pred)
-            st.write(f"Mean Squared Error: {mse:.4f}")
-        else:
-            acc = accuracy_score(st.session_state.y_test, y_pred)
-            st.write(f"Accuracy: {acc*100:.2f}%")
-            st.text("Classification Report:")
-            st.text(classification_report(st.session_state.y_test, y_pred))
-    else:
-        st.warning("Train a model first.")
-
-# Deployment
-elif section == "Model Deployment":
-    st.header("🚀 Deployment & Inference")
-    if 'trained_model' in st.session_state:
-        st.write("Upload a new CSV file with same features to test the model on new data:")
-        new_file = st.file_uploader("Upload New Data", type=["csv"], key="new_data")
-        if new_file:
-            new_df = pd.read_csv(new_file)
-            st.write("New Input Data:")
-            st.dataframe(new_df.head())
-            scaled_input = StandardScaler().fit_transform(new_df)
-            predictions = st.session_state.trained_model.predict(scaled_input)
-            st.write("Predictions:")
-            st.write(predictions)
-    else:
-        st.warning("Please train and evaluate a model before deploying.")