can anyone help me?
Hi @mekelgans,
Could you provide some sample code that I can run to reproduce your issue?
Edit: perhaps this stack overflow post is helpful?
yapp, this is my code, and my dataset.
package
import streamlit as st
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use(‘TkAgg’)
import matplotlib.pyplot as plt
from scipy import stats
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
import time
TITLE
st.title(‘Prediction Flood On Jakarta’)
IMPORT THE DATA
df = pd.read_csv(‘data_clean.csv’)
CHECKING THE DATA
st.subheader(‘This is an application to knowing wich area still safe on flood…’)
check_data = st.checkbox(‘show the simple data’)
if check_data:
st.write(df.head())
st.write(“Now let’s find out…”)
MAP
map_data = pd.DataFrame(
np.random.randn(1000, 2) / [80, 10] + [-6.174023, 106.825733],
columns=['lat', 'lon'])
st.map(map_data)
MERUBAH LABEL MENJADI NUMERIC
INPUT TO PREDICT
bulan_kejadian = st.slider(‘what months?’, int(df.bulan_kejadian.min()),int(df.bulan_kejadian.max()), int(df.bulan_kejadian.mean()))
ketinggian_air = st.slider(‘water level?’,int(df.ketinggian_air.min()),int(df.ketinggian_air.max()),int(df.ketinggian_air.mean()))
Unnamed = st.slider(‘which city’,int(df.Unnamed.min()),int(df.Unnamed.max()),int(df.Unnamed.mean()))
SPLITING DATA
X = df.drop(‘bulan_kejadian’, axis = 1)
y = df[‘bulan_kejadian’]
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.2, random_state=45)
MODELING STEP
IMPORT YOUR MODEL
model=LinearRegression()
FITTING AND PREDICT YOUR MODEL
model.fit(X_train, y_train)
model.predict(X_test)
errors = np.sqrt(mean_squared_error(y_test,model.predict(X_test)))
predictions = model.predict([[sqft_liv,bath,bed,floor]])[0]
@mekelgans could you send the code wrapping it as it is being shown here ?