Added files for the app

.gitignore

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+.venv/

app.py

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+# Pokemon Explorer is an interactive Streamlit app for exploring Pokémon data! 
+# Easily browse Pokémon stats, visualize and predict attributes, and filter by type or name.  
+
+import streamlit as st
+import pandas as pd
+import joblib
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load dataset
+@st.cache_data
+def load_data():
+    return pd.read_csv(r"C:\Users\ACER\Desktop\Feature_Prediction_of_Pokemon\pokemon data\pokemon_cleaned.csv")
+
+df = load_data()
+
+# Load machine learning models (if any)
+model = joblib.load(r"C:\Users\ACER\Desktop\Feature_Prediction_of_Pokemon\pokemon data\random_forest.pkl")  # Uncomment if using a model
+
+# Sidebar Navigation
+st.sidebar.title("🔍 Pokémon Explorer")
+st.sidebar.markdown("Navigate through different sections!")
+page = st.sidebar.radio(" Select a Page", ["Home", "Data Overview", "Visualization and Prediction"])
+
+# Home Page
+if page == "Home":
+    st.title("Pokémon Explorer")
+    st.markdown("Explore Pokémon stats, types, and visualizations interactively! ")
+    st.markdown("### Features:")
+    st.write("✔️ Browse Pokémon data")
+    st.write("✔️ Visualize Pokémon stats")
+    st.write("✔️ Search and filter Pokémon")
+    st.write("✔️ Predict Pokémon attributes")
+
+# Data Overview Page
+elif page == "Data Overview":
+    st.title("Pokémon Data Overview")
+    st.write("Browse and explore the Pokémon dataset.")
+    
+    name_search = st.text_input("🔍 Search Pokémon by Name")
+    type_filter = st.selectbox("🎨 Filter by Type", ["All"] + sorted(df['Type 1'].unique().tolist()))
+    
+    filtered_df = df
+    if name_search:
+        filtered_df = filtered_df[filtered_df['Name'].str.contains(name_search, case=False, na=False)]
+    if type_filter != "All":
+        filtered_df = filtered_df[filtered_df['Type 1'] == type_filter]
+    
+    st.dataframe(filtered_df, use_container_width=True)
+
+# Visualization and Prediction Page
+elif page == "Visualization and Prediction":
+    st.title(" Pokémon Data Visualization and Prediction")
+    st.write("Explore Pokémon stats with interactive graphs!")
+    
+    pokemon_list = df['Name'].unique().tolist()
+    selected_pokemon_name = st.selectbox("🔍 Select a Pokémon", pokemon_list)
+    selected_pokemon = df[df['Name'] == selected_pokemon_name]
+    
+    if not selected_pokemon.empty:
+        st.write(f"### {selected_pokemon.iloc[0]['Name']}")
+        st.write(f"**Type:** {selected_pokemon.iloc[0]['Type 1']} / {selected_pokemon.iloc[0].get('Type 2', 'None')}")
+        st.write(f"**Legendary:** {'Yes' if selected_pokemon.iloc[0]['Legendary'] else 'No'}")
+        st.write(f"**Generation:** {selected_pokemon.iloc[0]['Generation']}")
+        
+        stats = ["HP", "Attack", "Defense", "Speed"]
+        fig, ax = plt.subplots(figsize=(8, 5))
+        sns.barplot(x=stats, y=selected_pokemon.iloc[0][stats].values, ax=ax)
+        ax.set_xlabel("Stats")
+        ax.set_ylabel("Value")
+        ax.set_title(f"Stat Distribution for {selected_pokemon.iloc[0]['Name']}")
+        st.pyplot(fig)
+
+st.sidebar.markdown("---")
+st.sidebar.write("✨ Made by CI-DAVE")

pokemon data/logistic_regression.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e76cc9d145ca63b9089cabf933a4ae720bd75732227fbc51f068c3d5260a699
+size 1416

pokemon data/naive_bayes.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:99c960e2153135fbc2d597321baa8515e47b0b5b926473a83fca48be7dbcd19e
+size 1586

pokemon data/random_forest.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6a98cba74842b09cdb19fee3ec2df2fac9bf1072521445e9fee05c92f327039a
+size 1106515

requirements.txt

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+streamlit
+pandas
+joblib
+matplotlib
+seaborn