Update .gitignore and modify project files

.gitignore

@@ -0,0 +1,19 @@
+# Virtual environment
+.venv/
+# Python cache files
+__pycache__/
+*.py[cod]
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+# Model files
+model/*.pkl
+model/*.h5
+model/*.joblib
+model/*.sav
+model/*.onnx
+# Logs
+logs/
+*.log
+# token files
+*.token
+HUGGINGFACE_TOKEN.txt

README.md

@@ -11,7 +11,7 @@ pinned: false
 short_description: This project about Customer Prediction in Turkiye
 ---
 
-# Welcome to Streamlit!
+# <b>Welcome to Streamlit!</b>
 
 Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
 

requirements.txt

@@ -1,4 +1,15 @@
 altair
 pandas
+scipy
 streamlit
-huggingface_hub
+huggingface_hub
+streamlit_extras
+plotly
+requests
+scikit-learn
+imbalanced-learn
+pickle
+joblib
+onnx
+skl2onnx
+onnxruntime

src/.streamlit/config.toml

@@ -0,0 +1,2 @@
+[deprecation]
+showPyplotGlobalUse = false

src/streamlit_app.py

@@ -1,40 +1,36 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.set_page_config(page_title = 'Customer Prediction', page_icon = '📈', layout = 'wide')
+
+# --- PAGE SETUP -----
+info_page = st.Page(
+    page = 'pages/about.py',
+    title = 'Profile Developer',
+    icon = ':material/person:',
+    default= True
+)
+#---- PAGE PROJECT ------
+dashboard = st.Page(
+    page = 'pages/dashboard.py',
+    title = 'Dashboard Customer Retail',
+    icon = ':material/bar_chart:',
+)
+#---- PAGE PREDICTION ------
+prediction = st.Page(
+    page = 'pages/predict.py',
+    title = 'Customer Category Prediction',
+    icon = ':material/thumb_up:'
+)
+
+page = st.navigation(
+    {
+        "Info": [info_page],
+        "Projects": [dashboard, prediction],
+    }
+)
+
+st.sidebar.info("Source code, find in My Github:")
+st.sidebar.link_button("Github Source", "https://github.com/fendy07/customer-prediction")
+st.sidebar.text(f'Created by Fendy Hendriyanto 👨🏼‍💻')
+
+page.run()

src/train_model.py

@@ -0,0 +1,345 @@
+import onnx
+import pickle
+import joblib
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from sklearn import tree
+import plotly.express as px
+from scipy.stats import zscore
+import matplotlib.pyplot as plt
+from skl2onnx import convert_sklearn
+from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
+from sklearn.feature_selection import RFE
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from skl2onnx.common.data_types import FloatTensorType
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.metrics import accuracy_score, classification_report
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
+
+# Load Dataset
+data_ritel = pd.read_csv('data/customer_shopping_data.csv')
+data_ritel.sample(25)
+
+data_ritel.info()
+
+data_ritel.shape
+
+"""## Exploratory Data Analysis"""
+
+data_ritel.isnull().sum()
+
+# Analisa data pada kolom category,
+category = data_ritel['category'].value_counts()
+print(category)
+
+gender = data_ritel['gender'].value_counts()
+print(gender)
+
+payment_counts = data_ritel['payment_method'].value_counts()
+print(payment_counts)
+
+# Shopping Mall Insights berdasarkan jumlah transaksional
+mall = data_ritel['shopping_mall'].value_counts()
+print(mall)
+
+# Visualize data on payment method and age
+# Set the style for the plot
+sns.set_style("whitegrid")
+# Create a figure and axis object
+fig, ax = plt.subplots(figsize=(10, 6))
+# Plotting the bar chart
+sns.barplot(x=payment_counts.index, y=payment_counts.values, palette="viridis", ax=ax)
+# Set plot title and labels
+ax.set_title("Distribution of Payment Methods", fontsize=16)
+ax.set_xlabel("Payment Method", fontsize=14)
+ax.set_ylabel("Number of Transactions", fontsize=14)
+# Set x-axis tick labels
+ax.set_xticks(payment_counts.index)
+ax.set_xticklabels(payment_counts.index.unique(), fontsize=12)
+# Display the plot
+plt.show()
+
+# Calculate the average prices for each product category
+average_prices = data_ritel.groupby('category')['price'].mean()
+average_prices
+
+# Visualisasi data pada kolom Category dan Harga
+# Create a bar chart for the average prices of each product category
+fig = px.bar(average_prices,
+             x=average_prices.index,
+             y=average_prices.values,
+             labels={'x': 'Kategori Produk', 'y': 'Rata-rata Harga'},
+             title='Rata-rata harga dalam Kategori Produk')
+
+# Show the plot
+fig.show()
+
+# Mengelompokkan data berdasarkan kategori dan menjumlahkan quantity
+category_quantity = data_ritel.groupby('category')['quantity'].sum()
+# Plot pie chart
+plt.figure(figsize=(10, 8))
+plt.pie(category_quantity, labels=category_quantity.index, autopct='%1.1f%%', colors=sns.color_palette("pastel"))
+# Set judul
+plt.title('Distribusi Kategori Produk Berdasarkan Jumlah Quantity', fontsize=16)
+# Tampilkan plot
+plt.show()
+
+# Visualisasi total pendapatan disetiap pusat perbelanjaan
+total_revenue = data_ritel.groupby('shopping_mall')['price'].sum()
+fig = px.bar(total_revenue,
+             x = total_revenue.index,
+             y = total_revenue.values,
+             labels = {'x': 'Shopping Mall', 'y': 'Total Revenue'},
+             title = 'Total Pendapatan Setiap Pusat Perbelanjaan')
+
+# Show the plot
+fig.show()
+
+# Top penjualan pada kategori
+category_quantity = data_ritel.groupby('category')['quantity'].sum().sort_values(ascending=False)
+# Create a bar chart for the top-selling product categories
+fig = px.bar(category_quantity,
+             x = category_quantity.index,
+             y = category_quantity.values,
+             labels = {'x': 'Kategori Produk', 'y': 'Total Kuantitas Terjual'},
+             title = 'Top Penjualan Kuantitas Kategori')
+
+# Show the plot
+fig.show()
+
+# Visualisasi data pada kolom umur
+# Plot bar chart untuk distribusi umur
+plt.figure(figsize=(10, 6))
+sns.histplot(data_ritel['age'], bins=20, kde=False, color='skyblue')
+
+# Set judul dan label
+plt.title('Distribusi Umur Pelanggan', fontsize=16)
+plt.xlabel('Umur', fontsize=14)
+plt.ylabel('Jumlah Pelanggan', fontsize=14)
+# Tampilkan plot
+plt.show()
+# Demografi Pelanggan berdasarkan jenis kelamin dan umur
+demographics_summary = data_ritel[['gender', 'age']].describe(include='all')
+demographics_summary
+# Visualisasi hasil transaksi terbanyak pada pusat perbelanjaan atau mall
+fig = px.bar(data_ritel['shopping_mall'].value_counts(),
+             x = data_ritel['shopping_mall'].value_counts().index,
+             y = data_ritel['shopping_mall'].value_counts().values,
+             labels = {'x': 'Shopping Mall', 'y': 'Nominal Transaksi'},
+             title = 'Jumlah Nominal Transaksi Pada Pusat Perbelanjaan')
+fig.show()
+
+
+### **Data Preprocessing
+# Encoding data kolom dengan feature mapping
+# Encoding pada kolom metode pembayaran
+data_ritel['payment_method'] = data_ritel['payment_method'].map({'Cash': 0, 'Credit Card': 1, 'Debit Card': 2})
+# Encoding pada kolom jenis kelamin
+data_ritel['gender'] = data_ritel['gender'].map({'Female': 0, 'Male': 1})
+# Encoding pada kolom pusat perbelanjaan
+data_ritel['shopping_mall'] = data_ritel['shopping_mall'].map({'Mall of Istanbul': 0,
+                                                               'Kanyon': 1,
+                                                               'Metrocity': 2,
+                                                               'Metropol AVM': 3,
+                                                               'Istinye Park': 4,
+                                                               'Zorlu Center': 5,
+                                                               'Cevahir AVM': 6,
+                                                               'Forum Istanbul': 7,
+                                                               'Viaport Outlet': 8,
+                                                               'Emaar Square Mall': 9})
+
+# Encoding data kolom kategori
+le = LabelEncoder()
+data_ritel['category'] =le.fit_transform(data_ritel['category'])
+data_ritel.sample(10)
+# Analisa statistik deskriptif
+data_ritel.describe().T
+# Hapus kolom data yang tidak diperlukan
+data_ritel = data_ritel.drop(columns = ['invoice_no', 'customer_id', 'invoice_date'])
+data_ritel.sample(10)
+# Korelasi antara kolom data
+plt.figure(figsize = (12, 8))
+sns.heatmap(data_ritel.corr(), annot = True)
+plt.show()
+
+# Pemilihan data fitur dan label
+features = ['age', 'gender', 'price', 'payment_method', 'shopping_mall']
+
+X = data_ritel[features].values
+y = data_ritel['category'].values
+
+"""### **Splitting Data**"""
+
+# Pisahkan data Train dengan test
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 44)
+print('Data training : ', X_train.shape, y_train.shape)
+print('Data Testing : ', X_test.shape, y_test.shape)
+
+data_ritel.category.value_counts()
+
+# Filling Missing Data
+imputer = SimpleImputer(strategy = 'mean')
+X_train_imputed = imputer.fit_transform(X_train)
+X_test_imputed = imputer.transform(X_test)
+
+# Data Scaling
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train_imputed)
+X_test_scaled = scaler.transform(X_test_imputed)
+
+# Outlier detection using Z-Score
+z_scores = np.abs(zscore(X_train_scaled))
+threshold = 5
+outliers = np.where(z_scores > threshold)
+
+X_train_no_outliers = X_train_scaled[(z_scores < threshold).all(axis=1)]
+y_train_no_outliers = y_train[(z_scores < threshold).all(axis=1)]
+
+# Modelling
+# Decision Tree Classifier
+model_dt = DecisionTreeClassifier(random_state = 44)
+rfe = RFE(model_dt, n_features_to_select=5)
+
+X_train_rfe = rfe.fit_transform(X_train_no_outliers, y_train_no_outliers)
+X_test_rfe = rfe.transform(X_test_scaled)
+
+selected_features = np.array(features)[rfe.support_]
+print(selected_features)
+
+"""### **Decision Tree**"""
+
+# Training with Pipeline
+pipeline = Pipeline([
+    ('Classifier', DecisionTreeClassifier(random_state = 44)),
+
+])
+
+param_grid = {
+    'Classifier__max_depth': list(range(2, 10)),
+    'Classifier__max_leaf_nodes': list(range(2, 10))
+}
+
+gridsearch = GridSearchCV(pipeline, param_grid, cv=20)
+gridsearch.fit(X_train_rfe, y_train_no_outliers)
+
+best_model = gridsearch.best_estimator_
+print(gridsearch.best_params_)
+
+# Evaluation Model Decision Tree
+# Predict the model
+y_pred = best_model.predict(X_test_rfe)
+print("Accuracy:", accuracy_score(y_test, y_pred))
+
+scores = cross_val_score(best_model, X_train_no_outliers, y_train_no_outliers, cv=20, scoring='accuracy')
+print("Cross-Validation Accuracy Scores:", scores)
+
+# Classification Report
+target_names = ['Books',
+                'Clothing',
+                'Cosmetics',
+                'Food & Beverage',
+                'Shoes',
+                'Souvenir',
+                'Technology',
+                'Toys']
+
+print('Classification Report in Hyperparameter Tuning Decision Tree:')
+print(classification_report(y_test, y_pred, target_names = le.classes_))
+
+# Plot Decision Tree
+# Assuming 'best_model' is your Pipeline object
+decision_tree = best_model.named_steps['Classifier']  # Replace 'Classifier' with your step name
+plt.figure(figsize = (25, 20))
+tree.plot_tree(decision_tree,
+               feature_names = features,
+               class_names = target_names,
+               filled=True)
+
+plt.show()
+
+# Random Forest
+model_rf = RandomForestClassifier(random_state = 44)
+rfe = RFE(model_rf, n_features_to_select = 5)
+
+X_train_rfe = rfe.fit_transform(X_train_no_outliers, y_train_no_outliers)
+X_test_rfe = rfe.transform(X_test_scaled)
+
+selected_features = np.array(features)[rfe.support_]
+print(selected_features)
+
+# Training with Pipeline
+pipeline = Pipeline([
+    ('Classifier', RandomForestClassifier(random_state = 44)),
+])
+
+param_grid = {
+    'Classifier__n_estimators': [100, 200, 300],
+    'Classifier__max_depth': [None, 5, 10]
+}
+
+grid_search = GridSearchCV(pipeline, param_grid, cv = 5)
+grid_search.fit(X_train_rfe, y_train_no_outliers)
+
+best_model_rf = grid_search.best_estimator_
+print(grid_search.best_params_)
+
+## **Evaluation Model Random Forest
+y_pred_rf = best_model_rf.predict(X_test_rfe)
+print("Accuracy Score :", accuracy_score(y_test, y_pred_rf))
+scores = cross_val_score(best_model_rf, X_train_no_outliers, y_train_no_outliers, cv = 5, scoring = 'accuracy')
+print("Cross-Validation Accuracy Scores: ", scores)
+print('Classification Report in Hyperparameter Tuning Random Forest:')
+print(classification_report(y_test, y_pred_rf, target_names = target_names))
+
+# Assuming 'y_test' and 'y_pred_rf' are your true and predicted labels respectively
+cm = confusion_matrix(y_test, y_pred_rf)
+disp = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = target_names)
+disp.plot(cmap = 'Blues', xticks_rotation = 'vertical')
+plt.title('Confusion Matrix - Random Forest')
+plt.show()
+
+# Assuming 'best_model_rf' is your best performing model (Random Forest in this case)
+# Input features for new data (replace with actual values)
+new_data = np.array([[30, 0, 50, 1, 0]])  # Example: age, gender, price, payment_method, shopping_mall
+# Preprocess the new data (scaling)
+new_data_scaled = scaler.transform(new_data)
+# Feature selection using RFE (if used during training)
+new_data_rfe = rfe.transform(new_data_scaled)
+# Make prediction
+predicted_category = best_model.predict(new_data_rfe)
+# Decode the predicted category (if label encoding was used)
+predicted_category_name = le.inverse_transform(predicted_category)
+print("Predicted Category (Numerical):", predicted_category)
+print("Predicted Category (Name):", predicted_category_name)
+
+# Save model using Pickle
+with open('model/best_model_rf.pkl', 'wb') as file:
+  pickle.dump(best_model_rf, file)
+
+# Save model using Joblib
+joblib.dump(best_model_rf, 'model/best_model_rf.joblib')
+
+# Save model using ONNX
+initial_type = [('float_input', FloatTensorType([None, X_train_rfe.shape[1]]))]
+# Convert the scikit-learn Random Forest model to ONNX format
+onnx_model = convert_sklearn(best_model_rf, initial_types=initial_type)
+# Define the path to save the ONNX model
+onnx_filename = 'model/best_model_rf.onnx'
+# Save the ONNX model to a file
+with open(onnx_filename, "wb") as f:
+    f.write(onnx_model.SerializeToString())
+print(f"Model saved to {onnx_filename} in ONNX format.")
+
+# Optional: Verify the ONNX model
+onnx_model_loaded = onnx.load(onnx_filename)
+onnx.checker.check_model(onnx_model_loaded)
+print("ONNX model check successful!")
+
+# Load Model Pickle
+filename = 'model/best_model_rf.pkl'
+model = pickle.load(open(filename, 'rb'))
+model.score(X_test_rfe, y_test)