Delete streamlit_app.py

streamlit_app.py

@@ -1,105 +0,0 @@
-import os
-os.system('pip install pdpbox==0.2.1')
-
-from pdpbox.pdp import pdp_isolate, pdp_plot
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_absolute_error
-from sklearn.linear_model import LinearRegression
-from sklearn.pipeline import make_pipeline
-from sklearn.preprocessing import StandardScaler
-from sklearn.feature_selection import SelectKBest
-from sklearn.ensemble import RandomForestRegressor
-import pandas as pd
-from numpy import mean
-import streamlit as st
-
-"""
-# IOT
-"""
-
-
-max_depth_input = st.slider("Max depth", 1, 100, 5)
-colsample_bytree_input = st.slider("Colsample bytree", 0.0, 1.0, 0.5)
-learning_rate_input = st.slider("Learning rate", 0.0, 1.0, 0.2)
-alpha_input = st.slider("Alpha", 1, 100, 10)
-n_estimators_input = st.slider("n estimators", 1, 100, 20)
-city_input = st.selectbox(
-     'Which city do you want to predict rain ?',
-     ("Canberra",
-    "Albury",
-    "Penrith",
-    "Sydney",
-    "MountGinini",
-    "Bendigo",
-    "Brisbane",
-    "Portland"), index=0)
-
-
-df = pd.read_csv("city_temperature.csv")
-
-def mergeStateToCountry():
-    df.loc[df['State'].notna(), 'Country'] = df['State']
-    df = df.loc[:, ~df.columns.str.contains('State')]
-
-i = 0
-
-for region in df["Region"].unique():
-    df["Region"] = df["Region"].replace(region, str(i))
-    i += 1
-    
-i = 0
-
-for country in df["Country"].unique():
-    df["Country"] = df["Country"].replace(country, str(i))
-    i += 1
-    
-i = 0
-
-for state in df["State"].unique():
-    df["State"] = df["State"].replace(state, str(i))
-    i += 1
-    
-i = 0
-
-for city in df["City"].unique():
-    df["City"] = df["City"].replace(city, str(i))
-    i += 1
-
-df = df.astype({"Region": "int"})
-df = df.astype({"Country": "int"})
-df = df.astype({"State": "int"})
-df = df.astype({"City": "int"})
-
-target = 'AvgTemperature'
-# Here Y would be our target
-Y = df[target]
-# Here X would contain the other column
-#X = df.loc[:, df.columns != target]
-X = df[['Month', 'Day', 'Year']]
-
-X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size=0.25, random_state=42)
-
-y_pred = [Y_train.mean()] * len(Y_train)
-
-st.write('Baseline MAE: %f' % (round(mean_absolute_error(Y_train, y_pred), 5)))
-
-lm = make_pipeline(StandardScaler(), LinearRegression(),)
-
-lm.fit(X_train, Y_train)
-
-st.write('Linear Regression Training MAE: %f' % (round(mean_absolute_error(Y_train, lm.predict(X_train)), 5)))
-st.write('Linear Regression Test MAE: %f' % (round(mean_absolute_error(Y_val, lm.predict(X_val)), 5)))
-
-forestModel = make_pipeline(
-    SelectKBest(k="all"), 
-    StandardScaler(), 
-    RandomForestRegressor(
-        n_estimators=100,
-        max_depth=50,
-        random_state=77,
-        n_jobs=-1))
-
-forestModel.fit (X_train, Y_train)
-
-st.write('Random Forest Regressor Model Training MAE: %f' % (mean_absolute_error(Y_train, forestModel.predict(X_train))))
-st.write('Random Forest Regressor Model Test MAE: %f' % (mean_absolute_error(Y_val, forestModel.predict(X_val))))