Update app.py

app.py

@@ -0,0 +1,90 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+import warnings
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+import time
+
+warnings.filterwarnings('ignore')
+
+st.set_page_config(page_title="Electronics Sales Prediction", layout="wide")
+
+st.title("📊 Consumer Electronics Sales Prediction App")
+
+st.markdown("## 📂 Upload Your Dataset")
+uploaded_file = st.file_uploader("Upload CSV file", type=["csv"])
+
+if uploaded_file is not None:
+    with st.spinner('Loading Data...'):
+        time.sleep(1)
+    
+    data = pd.read_csv(uploaded_file)
+    st.success("Data Uploaded Successfully ✅")
+    
+    st.subheader("👀 Data Preview")
+    st.write(data.head())
+
+    df = data.copy()
+
+    # Rename columns
+    df = df.rename(columns={'Order Date': 'order_date', 'Category': 'category', 'Sub-Category': 'sub_category', 'Sales': 'sales'})
+
+    st.subheader("📊 Data Summary")
+    st.write(df.describe())
+
+    # Data Visualization
+    st.subheader("📈 Sales Distribution")
+    fig, ax = plt.subplots()
+    sns.histplot(df['sales'], kde=True, color='skyblue', ax=ax)
+    st.pyplot(fig)
+
+    st.markdown("### 🔄 Encoding Categorical Variables")
+    le = LabelEncoder()
+    df['category'] = le.fit_transform(df['category'])
+    df['sub_category'] = le.fit_transform(df['sub_category'])
+    st.write("Categorical Encoding Done 🎯")
+
+    # Train-test split
+    X = df[['category', 'sub_category']]
+    y = df['sales']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    st.markdown("### 🚀 Model Training")
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    
+    # Predictions
+    y_pred = model.predict(X_test)
+
+    # Evaluation
+    st.markdown("### 📈 Model Evaluation")
+    accuracy = accuracy_score(y_test, y_pred)
+    st.metric(label="Model Accuracy", value=f"{accuracy:.2%}")
+    st.write("🔍 Classification Report:")
+    st.text(classification_report(y_test, y_pred))
+
+    # Confusion Matrix
+    st.subheader("🎯 Confusion Matrix")
+    fig, ax = plt.subplots()
+    sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d', cmap='Blues', ax=ax)
+    st.pyplot(fig)
+
+    # Additional Feature: Interactive Plot
+    st.subheader("📊 Interactive Sales Analysis")
+    fig = px.scatter(df, x='category', y='sales', color='sub_category', title="Sales by Category and Sub-Category")
+    st.plotly_chart(fig)
+
+    # Sidebar Information
+    st.sidebar.title("📋 App Navigation")
+    st.sidebar.markdown("- Upload Dataset")
+    st.sidebar.markdown("- View Data Summary")
+    st.sidebar.markdown("- Train Model")
+    st.sidebar.markdown("- View Results")
+
+    st.sidebar.info("🔧 **Ensure to preprocess your data properly for accurate results.**")