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from sklearn.linear_model import LinearRegression
import streamlit as st
from types import NoneType
def process(data):
if type(data[0]) == NoneType or type(data[1]) == NoneType: # if either training or testing dataset is still missing
st.info('Please Upload Data')
return None
if len(data) == 0:
st.info('Please Upload Data')
return None
x_train = data[0].iloc[:,:-1]
y_train = data[0].iloc[:,-1]
#st.write(x_train.shape)
x_test = data[1].iloc[:,:x_train.shape[1]]
#st.dataframe(data[1])
#st.write(x_test.shape)
if len(x_train.columns) != len(x_test.columns):
st.info('Training and testing datasets have different column number, cannot perform classification.')
return None
if 'object' in list(data[0].dtypes) or 'object' in list(data[1].dtypes):
st.info('Please Upload Numerica Data.')
return None
reg = LinearRegression().fit(x_train, y_train)
cols = x_train.columns
#st.write(list(zip(reg.coef_,cols)))
st.latex(f" {x_train.columns[-1]} = ")
coeffs = ['{:.4f}'.format(float(c)) for c in reg.coef_]
eq = ' + '.join([str(col) +' × '+ (alpha) for col,alpha in zip(coeffs,cols)])
st.markdown(f" $$ {reg.intercept_} {eq} $$")
st.latex(f" R² = {reg.score(x_train, y_train)} ")
pred = reg.predict(x_test)
x_test[data[0].columns[-1]] = pred
return x_test |