File size: 2,413 Bytes
c614107 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
import pandas as pd
import streamlit as st
import joblib
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.compose import ColumnTransformer
# Veriyi yükleme ve sütun isimlerini güncelleme
df = pd.read_csv('train.csv')
df.columns = df.columns.str.replace(r'[\s\.]', '_', regex=True)
# Bağımlı ve bağımsız değişkenlerin seçimi
x = df.drop(['id', 'Rings'], axis=1)
y = df[['Rings']]
# Eğitim ve test verilerini ayırma
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)
# Ön işleme (StandardScaler ve OneHotEncoder)
preprocessor = ColumnTransformer(
transformers=[
('num', StandardScaler(), ['Length', 'Diameter', 'Height', 'Whole_weight', 'Whole_weight_1', 'Whole_weight_2', 'Shell_weight']),
('cat', OneHotEncoder(), ['Sex'])
]
)
# Streamlit uygulaması
def rings_pred(Sex, Length, Diameter, Height, Whole_weight, Whole_weight_1, Whole_weight_2, Shell_weight):
input_data = pd.DataFrame({
'Sex': [Sex],
'Length': [Length],
'Diameter': [Diameter],
'Height': [Height],
'Whole_weight': [Whole_weight],
'Whole_weight_1': [Whole_weight_1],
'Whole_weight_2': [Whole_weight_2],
'Shell_weight': [Shell_weight]
})
input_data_transformed = preprocessor.fit_transform(input_data)
model = joblib.load('Abalone.pkl')
prediction = model.predict(input_data_transformed)
return float(prediction[0])
st.title("Abalone Veri seti ile Yaş Tahmini Regresyon Modeli")
st.write("Veri Gir")
Sex = st.selectbox('Sex', df['Sex'].unique())
Length = st.selectbox('Length', df['Length'].unique())
Diameter = st.selectbox('Diameter', df['Diameter'].unique())
Height = st.selectbox('Height', df['Height'].unique())
Whole_weight = st.selectbox('Whole_weight', df['Whole_weight'].unique())
Whole_weight_1 = st.selectbox('Whole_weight_1', df['Whole_weight_1'].unique())
Whole_weight_2 = st.selectbox('Whole_weight_2', df['Whole_weight_2'].unique())
Shell_weight = st.selectbox('Shell_weight', df['Shell_weight'].unique())
if st.button('Predict'):
rings = rings_pred(Sex, Length, Diameter, Height, Whole_weight, Whole_weight_1, Whole_weight_2, Shell_weight)
st.write(f'The predicted rings is: {rings:.2f}') |