1st

app.py

@@ -0,0 +1,105 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+import matplotlib.pyplot as plt
+
+# Page Configuration
+st.set_page_config(page_title="Sales Forecasting App", layout="wide")
+
+# App Header
+st.markdown("<h1 style='text-align: center; color: white;'>📈 Sales Forecasting App</h1>", unsafe_allow_html=True)
+
+# Centered Navigation
+st.markdown("<div style='display: flex; justify-content: center;'>", unsafe_allow_html=True)
+tabs = ["📊 Data Visualization", "📈 Model Performance", "🔮 Prediction"]
+selected_tab = st.radio("Navigation", tabs, horizontal=True, key="navigation")
+st.markdown("</div>", unsafe_allow_html=True)
+
+# Example Dataset (Replace with actual data)
+data = {
+    "Time": range(1, 13),  # e.g., months 1 to 12
+    "Sales": [150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700],
+    "OnlineAds": [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120],
+    "SocialMedia": [5, 15, 25, 35, 45, 55, 65, 75, 85, 95, 105, 115]
+}
+df = pd.DataFrame(data)
+
+if selected_tab == "📊 Data Visualization":
+    st.markdown("### Preview of Dataset")
+    st.dataframe(df)
+
+    st.markdown("### Data Visualization")
+    fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+
+    axes[0].scatter(df["Time"], df["Sales"], color='blue', alpha=0.7)
+    axes[0].set_title("Time vs Sales")
+    axes[0].set_xlabel("Time")
+    axes[0].set_ylabel("Sales")
+
+    axes[1].scatter(df["OnlineAds"], df["Sales"], color='green', alpha=0.7)
+    axes[1].set_title("Online Ads vs Sales")
+    axes[1].set_xlabel("Online Ads")
+    axes[1].set_ylabel("Sales")
+
+    axes[2].scatter(df["SocialMedia"], df["Sales"], color='red', alpha=0.7)
+    axes[2].set_title("Social Media vs Sales")
+    axes[2].set_xlabel("Social Media")
+    axes[2].set_ylabel("Sales")
+
+    st.pyplot(fig)
+
+elif selected_tab == "📈 Model Performance":
+    st.markdown("### Train Linear Regression Model")
+
+    # Features and target
+    X = df[["Time", "OnlineAds", "SocialMedia"]]  # Independent variables
+    y = df["Sales"]  # Dependent variable
+
+    # Split data into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Create and train the linear regression model
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+
+    # Predict on test set
+    y_pred = model.predict(X_test)
+
+    # Model evaluation
+    mse = mean_squared_error(y_test, y_pred)
+    r2 = r2_score(y_test, y_pred)
+
+    st.write("Mean Squared Error (MSE):", mse)
+    st.write("R-squared (R2):", r2)
+
+    st.write("### Coefficients and Intercept")
+    st.write("Coefficients:", model.coef_)
+    st.write("Intercept:", model.intercept_)
+
+    st.markdown("### Scatter Plot of Predictions vs Actual Values")
+    fig, ax = plt.subplots(figsize=(8, 6))
+    ax.scatter(y_test, y_pred, alpha=0.7, color='purple')
+    ax.set_title("Predicted vs Actual Sales")
+    ax.set_xlabel("Actual Sales")
+    ax.set_ylabel("Predicted Sales")
+    st.pyplot(fig)
+
+elif selected_tab == "🔮 Prediction":
+    st.markdown("### Predict Future Sales")
+
+    # Input for prediction
+    time_input = st.number_input("Time (e.g., month number):", min_value=1, max_value=100, step=1)
+    online_ads_input = st.number_input("Online Ads Spend:", min_value=0.0, step=10.0)
+    social_media_input = st.number_input("Social Media Spend:", min_value=0.0, step=10.0)
+
+    if st.button("Predict Sales"):
+        future_data = pd.DataFrame({
+            "Time": [time_input],
+            "OnlineAds": [online_ads_input],
+            "SocialMedia": [social_media_input]
+        })
+        future_sales = model.predict(future_data)
+        st.write("Predicted Sales:", future_sales[0])