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import streamlit as st
import pandas as pd
import numpy as np
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import IsolationForest
from sklearn.decomposition import PCA
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from collections import Counter
import matplotlib.pyplot as plt
# Download necessary NLTK data
nltk.download('vader_lexicon')
nltk.download('punkt')
nltk.download('stopwords')
def main():
st.title("AI in Data Science Demo")
# Sidebar for navigation
page = st.sidebar.selectbox("Choose a demo", ["Feature Engineering", "Anomaly Detection", "NLP Analysis"])
if page == "Feature Engineering":
feature_engineering_demo()
elif page == "Anomaly Detection":
anomaly_detection_demo()
else:
nlp_demo()
def feature_engineering_demo():
st.header("Automated Feature Engineering and Selection")
# Generate sample data
X = np.random.rand(100, 5)
y = np.random.randint(0, 2, 100)
# Feature selection
selector = SelectKBest(f_classif, k=3)
X_new = selector.fit_transform(X, y)
# PCA
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)
# Display results
st.subheader("Original Features")
st.write(pd.DataFrame(X, columns=[f"Feature {i+1}" for i in range(5)]).head())
st.subheader("Selected Top 3 Features")
st.write(pd.DataFrame(X_new, columns=[f"Selected Feature {i+1}" for i in range(3)]).head())
st.subheader("PCA Transformation")
st.write(pd.DataFrame(X_pca, columns=["PC1", "PC2"]).head())
# Visualization
fig, ax = plt.subplots()
ax.scatter(X_pca[:, 0], X_pca[:, 1], c=y)
ax.set_xlabel("First Principal Component")
ax.set_ylabel("Second Principal Component")
ax.set_title("PCA of Dataset")
st.pyplot(fig)
def anomaly_detection_demo():
st.header("Anomaly Detection")
# Generate sample data with anomalies
np.random.seed(42)
X = np.random.randn(100, 2)
X[-5:] = X[-5:] + [4, 4] # Add some anomalies
# Fit Isolation Forest
clf = IsolationForest(contamination=0.1, random_state=42)
y_pred = clf.fit_predict(X)
# Visualization
fig, ax = plt.subplots()
ax.scatter(X[:, 0], X[:, 1], c=y_pred, cmap='viridis')
ax.set_title("Anomaly Detection using Isolation Forest")
ax.set_xlabel("Feature 1")
ax.set_ylabel("Feature 2")
st.pyplot(fig)
st.write("Points in yellow are detected as anomalies.")
def nlp_demo():
st.header("NLP Analysis")
# Sample text input
text = st.text_area("Enter text for analysis", "I love using AI for data analysis. It's exciting and powerful!")
if text:
# Sentiment Analysis
sia = SentimentIntensityAnalyzer()
sentiment = sia.polarity_scores(text)
st.subheader("Sentiment Analysis")
st.write(f"Positive: {sentiment['pos']:.2f}")
st.write(f"Neutral: {sentiment['neu']:.2f}")
st.write(f"Negative: {sentiment['neg']:.2f}")
# Simple keyword extraction
tokens = word_tokenize(text.lower())
stop_words = set(stopwords.words('english'))
keywords = [word for word in tokens if word.isalnum() and word not in stop_words]
keyword_freq = Counter(keywords).most_common(5)
st.subheader("Top Keywords")
st.write(pd.DataFrame(keyword_freq, columns=["Keyword", "Frequency"]))
if __name__ == "__main__":
main() |