initial commit

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+# Custom Streamlit styling - Dark mode version
+st.markdown(
+    """
+    <style>
+    body {
+        background-color: #1E1E1E;
+        color: #FFFFFF;
+        font-family: 'Arial', sans-serif;
+    }
+    .stButton>button {
+        background-color: #4A90E2;
+        color: #FFFFFF;
+        border-radius: 15px;
+        padding: 12px 24px;
+        font-size: 16px;
+        font-weight: bold;
+    }
+    # .stSlider>div>div>div {
+    #     background-color: #4A90E2;
+    }
+    .title {
+        color: #64FFDA;
+        text-shadow: 1px 1px #FF4C4C;
+    }
+    # .stSlider label, .stSlider>div>div>span {
+    #     color: #FFFFFF !important;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Load the League of Legends dataset
+st.title("🎮 League of Legends Game Win Predictor")
+st.markdown("<h2 class='title'>Predict whether the Blue Team will dominate the game! 💥</h2>", unsafe_allow_html=True)
+
+# Load dataset directly
+file_path = 'high_diamond_ranked_10min.csv'
+df = pd.read_csv(file_path)
+st.write("### 📊 Dataset Preview")
+st.dataframe(df.head())
+
+# Select relevant columns
+df = df[['blueFirstBlood', 'blueKills', 'blueDeaths', 'blueAssists', 'blueTotalGold', 'blueTotalExperience', 'blueDragons', 'blueHeralds', 'blueTowersDestroyed', 'blueWins']]
+df = df.dropna()
+
+# Define features and target
+X = df.drop('blueWins', axis=1)
+y = df['blueWins']
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Scale data
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Train Logistic Regression Model
+model = LogisticRegression(random_state=42)
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+
+# Display model performance
+accuracy = accuracy_score(y_test, y_pred)
+st.write("### 🔥 Model Performance")
+st.write(f"**✅ Model Accuracy:** {accuracy:.2f}")
+
+# Visualizing performance
+st.write("### 📊 Performance Breakdown")
+conf_matrix = confusion_matrix(y_test, y_pred)
+st.write("Confusion Matrix:")
+fig, ax = plt.subplots()
+sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='coolwarm', ax=ax)
+st.pyplot(fig)
+
+# Feature Importance Visualization
+st.write("### 🔍 Feature Importance")
+feature_importance = abs(model.coef_[0])
+features = X.columns
+fig, ax = plt.subplots()
+ax.barh(features, feature_importance, color='#4A90E2')
+ax.set_title("Feature Importance for Blue Team Win Prediction")
+ax.set_xlabel("Importance")
+st.pyplot(fig)
+
+# Prediction section
+st.write("### 🎮 Predict Game Outcome")
+st.markdown("Adjust the stats below to simulate a match scenario!")
+
+first_blood = st.selectbox("Did Blue Team Get First Blood?", [0, 1])
+kills = st.slider("Blue Team Kills", min_value=0, max_value=50, value=5)
+deaths = st.slider("Blue Team Deaths", min_value=0, max_value=50, value=5)
+assists = st.slider("Blue Team Assists", min_value=0, max_value=50, value=10)
+total_gold = st.slider("Blue Team Total Gold", min_value=10000, max_value=100000, value=50000)
+total_exp = st.slider("Blue Team Total Experience", min_value=10000, max_value=100000, value=50000)
+dragons = st.slider("Blue Team Dragons Taken", min_value=0, max_value=5, value=1)
+heralds = st.slider("Blue Team Heralds Taken", min_value=0, max_value=2, value=0)
+towers = st.slider("Blue Team Towers Destroyed", min_value=0, max_value=11, value=2)
+
+if st.button("✨ Predict Win"):
+    input_data = scaler.transform([[first_blood, kills, deaths, assists, total_gold, total_exp, dragons, heralds, towers]])
+    prediction = model.predict(input_data)[0]
+    prediction_proba = model.predict_proba(input_data)[0]
+
+    st.subheader("🔮 Prediction Result")
+    result_text = "🏅 Blue Team is likely to WIN! 🎉" if prediction == 1 else "⚔️ Blue Team is likely to LOSE. 😞"
+    st.success(result_text) if prediction == 1 else st.error(result_text)
+    st.write(f"Confidence: {prediction_proba[prediction]:.2f}")
+
+    # Win/Loss Bar Chart
+    st.write("### 📊 Win Probability Breakdown")
+    fig, ax = plt.subplots()
+    ax.bar(["Win", "Lose"], [prediction_proba[1], prediction_proba[0]], color=["#64FFDA", "#FF4C4C"])
+    ax.set_ylim(0, 1)
+    ax.set_ylabel("Probability")
+    ax.set_title("Blue Team Win/Loss Probability")
+    st.pyplot(fig)

requirements.txt

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+streamlit
+pandas
+numpy
+matplotlib
+seaborn
+scikit-learn