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import streamlit as st
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import seaborn as sns
import re
st.title("Expense Category Prediction")
# Load data from CSV
df = pd.read_csv("financial_data.csv", sep='\s\s+', engine='python')
# Data Preprocessing
def preprocess_data(df):
# Clean the date column
df['Date'] = df['Date'].str.extract(r'(\d{4}-\d{2}-\d{2})')
# Forward fill missing dates
df['Date'] = df['Date'].ffill()
# Remove rows with missing dates
df.dropna(subset=['Date'], inplace=True)
# Convert 'Date' to datetime objects
df['Date'] = pd.to_datetime(df['Date'])
# Fill missing values in 'Expense_Category' and 'Description' with 'Unknown'
df['Expense_Category'] = df['Expense_Category'].fillna('Unknown')
df['Description'] = df['Description'].fillna('Unknown')
# Convert 'Amount' to numeric, fill missing with 0
df['Amount'] = pd.to_numeric(df['Amount'], errors='coerce').fillna(0)
# Date Feature Engineering
df['Month'] = df['Date'].dt.month
df['DayOfWeek'] = df['Date'].dt.dayofweek
# Description Text Processing
def clean_text(text):
text = text.lower()
text = re.sub(r'[^a-zA-Z0-9\s]', '', text)
return text
df['Description_Cleaned'] = df['Description'].apply(clean_text)
# TF-IDF Vectorization
tfidf_vectorizer = TfidfVectorizer(max_features=100) # Limiting features for simplicity
tfidf_features = tfidf_vectorizer.fit_transform(df['Description_Cleaned']).toarray()
tfidf_df = pd.DataFrame(tfidf_features, index=df.index)
# Combine Features
features_df = pd.concat([df[['Amount', 'Month', 'DayOfWeek']], tfidf_df], axis=1)
# Encode the target variable
label_encoder = LabelEncoder()
df['Expense_Category_Encoded'] = label_encoder.fit_transform(df['Expense_Category'])
# Select features and target
X = features_df
y = df['Expense_Category_Encoded']
# Scale the features
scaler = StandardScaler()
X = scaler.fit_transform(X)
return X, y, label_encoder, df # Return the original dataframe
X, y, label_encoder, df = preprocess_data(df.copy())
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42)
# --- Models ---
models = {
"Random Forest": RandomForestClassifier(random_state=42),
"Gradient Boosting": GradientBoostingClassifier(random_state=42)
}
# --- Streamlit Tabs ---
tabs = st.tabs(list(models.keys()))
for tab, model_name in zip(tabs, models.keys()):
with tab:
st.header(model_name)
model = models[model_name]
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
# --- Confusion Matrix ---
st.subheader("Confusion Matrix")
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")
plt.xlabel("Predicted")
plt.ylabel("Actual")
st.pyplot(plt.gcf())
# --- Classification Report ---
st.subheader("Classification Report")
cr = classification_report(y_test, y_pred,
target_names=label_encoder.inverse_transform(
df['Expense_Category_Encoded'].unique()),
zero_division=0) # Get original category names
st.text(cr)
# --- Remarks ---
st.subheader("Remarks")
st.write("Model Performance Analysis:")
st.write(
f"The {model_name} model's performance in predicting Expense Categories is shown above.")
st.write("Key Metrics:")
st.write(
"- The model uses a combination of expense amount, time-based features, and text descriptions to predict the expense category."
)
st.write(
"- The classification report provides insights into the model's precision, recall, and F1-score for each expense category."
) |