Delete machine_learning.py

machine_learning.py

@@ -1,106 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.linear_model import LogisticRegression
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.svm import SVC
-from sklearn.metrics import accuracy_score, classification_report
-from sklearn.cluster import KMeans
-from sklearn.decomposition import PCA
-
-class MachineLearning:
-    def perform_ml_tasks(self, df):
-        task_type = st.selectbox("Select ML task", ["Classification", "Clustering", "Dimensionality Reduction"])
-        
-        if task_type == "Classification":
-            self.perform_classification(df)
-        elif task_type == "Clustering":
-            self.perform_clustering(df)
-        elif task_type == "Dimensionality Reduction":
-            self.perform_dimensionality_reduction(df)
-
-    def perform_classification(self, df):
-        target_column = st.selectbox("Select target column", df.columns)
-        feature_columns = st.multiselect("Select feature columns", df.columns.drop(target_column))
-        
-        if len(feature_columns) > 0:
-            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)
-            
-            scaler = StandardScaler()
-            X_train_scaled = scaler.fit_transform(X_train)
-            X_test_scaled = scaler.transform(X_test)
-            
-            model_type = st.selectbox("Select model type", ["Logistic Regression", "Decision Tree", "Random Forest", "SVM"])
-            
-            if model_type == "Logistic Regression":
-                model = LogisticRegression()
-            elif model_type == "Decision Tree":
-                model = DecisionTreeClassifier()
-            elif model_type == "Random Forest":
-                model = RandomForestClassifier()
-            elif model_type == "SVM":
-                model = SVC()
-            
-            model.fit(X_train_scaled, y_train)
-            y_pred = model.predict(X_test_scaled)
-            
-            st.subheader("Classification Results")
-            st.write(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")
-            st.write("Classification Report:")
-            st.code(classification_report(y_test, y_pred))
-
-    def perform_clustering(self, df):
-        feature_columns = st.multiselect("Select feature columns for clustering", df.columns)
-        
-        if len(feature_columns) > 0:
-            X = df[feature_columns]
-            
-            scaler = StandardScaler()
-            X_scaled = scaler.fit_transform(X)
-            
-            n_clusters = st.slider("Select number of clusters", min_value=2, max_value=10, value=3)
-            
-            kmeans = KMeans(n_clusters=n_clusters, random_state=42)
-            cluster_labels = kmeans.fit_predict(X_scaled)
-            
-            df['Cluster'] = cluster_labels
-            
-            st.subheader("Clustering Results")
-            if len(feature_columns) >= 2:
-                fig = px.scatter(df, x=feature_columns[0], y=feature_columns[1], color='Cluster')
-                st.plotly_chart(fig)
-            
-            st.write("Cluster Centers:")
-            cluster_centers = scaler.inverse_transform(kmeans.cluster_centers_)
-            st.write(pd.DataFrame(cluster_centers, columns=feature_columns))
-
-    def perform_dimensionality_reduction(self, df):
-        feature_columns = st.multiselect("Select feature columns for dimensionality reduction", df.columns)
-        
-        if len(feature_columns) > 0:
-            X = df[feature_columns]
-            
-            scaler = StandardScaler()
-            X_scaled = scaler.fit_transform(X)
-            
-            n_components = st.slider("Select number of components", min_value=2, max_value=min(len(feature_columns), 10), value=2)
-            
-            pca = PCA(n_components=n_components)
-            X_pca = pca.fit_transform(X_scaled)
-            
-            st.subheader("PCA Results")
-            explained_variance_ratio = pca.explained_variance_ratio_
-            st.write(f"Explained Variance Ratio: {explained_variance_ratio}")
-            
-            if n_components >= 2:
-                fig = px.scatter(x=X_pca[:, 0], y=X_pca[:, 1], title="PCA Visualization")
-                st.plotly_chart(fig)
-            
-            st.write("PCA Components:")
-            st.write(pd.DataFrame(pca.components_, columns=feature_columns))