app.py

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor
from sklearn.svm import SVR
from sklearn.metrics import r2_score

# Uyarıları gizle
import warnings
warnings.filterwarnings("ignore")

# Veri Yükleme ve Ön İşleme
@st.cache_data
def load_data():
    df = pd.read_csv('Housing.csv')

    # Gereksiz sütunu sil (eğer varsa)
    if 'date' in df.columns:
        df = df.drop('date', axis=1)

    # Encoding
    encoding_col = ['furnishingstatus', 'prefarea', 'airconditioning', 'hotwaterheating', 'basement', 'guestroom', 'mainroad']
    encoder = LabelEncoder()
    for col in encoding_col:
        df[col] = encoder.fit_transform(df[col])

    return df

df = load_data()

# Model Eğitimi Fonksiyonu
def train_and_evaluate_model(model, X_train, X_test, y_train, y_test):
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    r2 = r2_score(y_test, y_pred)
    return r2

# Streamlit Arayüzü
st.title("Ev Fiyat Tahmini Uygulaması")

# Kenar Çubuğu - Model Seçimi
st.sidebar.header("Model Seçimi")
selected_model = st.sidebar.selectbox("Model Seçin", ["Linear Regression", "Decision Tree", "Random Forest", "SVR", "Gradient Boosting", "AdaBoost"])

# Kenar Çubuğu - Veri Seti İstatistikleri
st.sidebar.header("Veri Seti İstatistikleri")
if st.sidebar.checkbox("İstatistikleri Göster"):
    st.subheader("Veri Seti İstatistikleri")
    st.write(df.describe())

# Kenar Çubuğu - Grafikler
st.sidebar.header("Grafikler")
if st.sidebar.checkbox("Grafikleri Göster"):
    st.subheader("Grafikler")

    # Count of Bedrooms
    st.subheader("Oda Sayısı Dağılımı")
    bedrooms_count = df['bedrooms'].value_counts()
    fig, ax = plt.subplots(figsize=(8, 3))
    sns.barplot(x=bedrooms_count.index, y=bedrooms_count.values, palette="rocket_r", ax=ax)
    for p in ax.patches:
        ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                     ha='center', va='center', fontsize=8, color='black', xytext=(0, 5),
                     textcoords='offset points')
    st.pyplot(fig)
    
    # Count of Bathrooms
    st.subheader("Banyo Sayısı Dağılımı")
    bathrooms_count = df['bathrooms'].value_counts()
    fig, ax = plt.subplots()
    sns.barplot(x=bathrooms_count.index, y=bathrooms_count.values, palette="mako", ax=ax)
    for p in ax.patches:
        ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                    ha='center', va='center', fontsize=8, color='black', xytext=(0, 5),
                    textcoords='offset points')
    st.pyplot(fig)

    # Count of Stories
    st.subheader("Kat Sayısı Dağılımı")
    stories_count = df['stories'].value_counts()
    fig, ax = plt.subplots()
    sns.barplot(x=stories_count.index, y=stories_count.values, palette="magma", ax=ax)
    for p in ax.patches:
        ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                    ha='center', va='center', fontsize=8, color='black', xytext=(0, 5),
                    textcoords='offset points')
    st.pyplot(fig)

    # Count of Mainroad
    st.subheader("Ana Yola Bağlantı Dağılımı")
    mainroad_count = df['mainroad'].value_counts()
    fig, ax = plt.subplots()
    explode = [0, 0.09]
    colors = sns.color_palette("crest")
    patches, texts, autotexts = ax.pie(mainroad_count.values, labels=mainroad_count.index, autopct='%.0f%%', explode=explode, colors=colors)
    for autotext in autotexts:
        autotext.set_color('black')
    plt.title("Ana Yola Bağlantı")
    plt.legend(loc="best")
    st.pyplot(fig)

    # Count of Guestroom
    st.subheader("Misafir Odası Dağılımı")
    guestroom_count = df['guestroom'].value_counts()
    fig, ax = plt.subplots()
    explode = [0, 0.09]
    colors = sns.color_palette("crest")
    patches, texts, autotexts = ax.pie(guestroom_count.values, labels=guestroom_count.index, autopct='%.0f%%', explode=explode, colors=colors)
    for autotext in autotexts:
        autotext.set_color('black')
    plt.title("Misafir Odası")
    plt.legend(loc="best")
    st.pyplot(fig)
    
    # Count of Furnishing Status
    st.subheader("Eşya Durumu Dağılımı")
    furnishingstatus_count = df['furnishingstatus'].value_counts()
    fig, ax = plt.subplots()
    sns.barplot(x=furnishingstatus_count.index, y=furnishingstatus_count.values, palette="magma", ax=ax)
    for p in ax.patches:
        ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                    ha='center', va='center', fontsize=8, color='black', xytext=(0, 5),
                    textcoords='offset points')
    st.pyplot(fig)

    # Count of Prefarea
    st.subheader("Tercih Edilen Bölge Dağılımı")
    prefarea_count = df['prefarea'].value_counts()
    fig, ax = plt.subplots()
    explode = [0, 0.09]
    colors = sns.color_palette("magma")
    patches, texts, autotexts = ax.pie(prefarea_count.values, labels=prefarea_count.index, autopct='%.0f%%', explode=explode, colors=colors)
    for autotext in autotexts:
        autotext.set_color('black')
    plt.title("Tercih Edilen Bölge")
    plt.legend(loc="best")
    st.pyplot(fig)
    
    # Correlation Heatmap
    st.subheader("Korelasyon Matrisi")
    fig, ax = plt.subplots(figsize=(10, 10))
    sns.heatmap(df.corr(), annot=True, fmt=".2f", linewidths=0.5, cbar=True, ax=ax)
    st.pyplot(fig)

# Ana Bölüm - Model Sonuçları ve Tahmin
st.header("Model Sonuçları")

# Veri Bölme ve Ölçeklendirme
X = df.drop(columns=['price'], axis=1)
y = df['price']
scaler = MinMaxScaler()
X = scaler.fit_transform(X)
y = scaler.fit_transform(y.values.reshape(-1, 1))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=50)

# Model Seçimine Göre Sonuçları Gösterme
if selected_model == "Linear Regression":
    model = LinearRegression()
elif selected_model == "Decision Tree":
    model = DecisionTreeRegressor()
elif selected_model == "Random Forest":
    model = RandomForestRegressor(n_estimators=100)
elif selected_model == "SVR":
    model = SVR(kernel='linear')
elif selected_model == "Gradient Boosting":
    model = GradientBoostingRegressor()
elif selected_model == "AdaBoost":
    model = AdaBoostRegressor()

r2 = train_and_evaluate_model(model, X_train, X_test, y_train, y_test)

st.write(f"{selected_model} R-kare Değeri: {r2:.3f}")