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streamlit.py

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+import pandas as pd
+import streamlit as st
+from sklearn.ensemble import RandomForestClassifier
+import numpy as np
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split, cross_val_score
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import precision_score, f1_score, recall_score
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.svm import SVC
+import joblib 
+
+
+st. write("""
+# Best Model Classifier App
+This App will predict the Best Model for the data 
+### by Saleha Rasid""")
+
+st.sidebar.header("Models List")
+option = st.sidebar.selectbox(
+   '# Select the Best Model',
+   ('Linear Regression', 'Random Forest Classifier', 'KNN Classifer', 'Logistc Regression', ' Decision Tree Classifier', 'SVMs', 'Naive Bayes','Gradient Boosting'))
+st.write('You selected:', option)
+
+st.sidebar.subheader("Data Points")
+def user_input_features():
+
+    people_fully_vaccinated=st.sidebar.slider('People Fully Vaccinated', 4000, 78000000, 4000)
+    New_deaths=st.sidebar.slider('New Deaths', 0, 1400, 1)
+    data= {
+        'People Fully Vaccinated':people_fully_vaccinated,
+          'New Deaths': New_deaths  }
+    feature =pd.DataFrame(data, index=[0])
+    return feature
+df= user_input_features()
+
+st.subheader('COVID-19 Parameters')
+st.write(df)
+
+covid = pd.read_excel('data_covid.xlsx')
+st.subheader('COVID-19 Dataset')
+st.write(covid)
+data_sets = st.container()
+with data_sets:
+
+    st.subheader("New Deaths")
+    st.bar_chart(covid['New_deaths'].value_counts())
+    st.subheader('People Fully Vaccinated')
+    st.bar_chart(covid['people_fully_vaccinated'].value_counts())
+
+import streamlit as st
+import pandas as pd
+
+# Load Data
+
+def load_data():
+    df = pd.read_excel('data_covid.xlsx')
+    return df
+data = load_data()
+# Dropdown Menu for Country
+country = st.sidebar.selectbox(
+    'Choose a Country',
+    data['country'].unique())
+
+# Filter Data
+selected_country = data[data['country'] == country]
+st.header('EDA ANALYSIS')
+# Show New Deaths
+st.subheader('New Deaths')
+st.bar_chart(selected_country['New_deaths'].value_counts())
+st.line_chart(selected_country['New_deaths'].value_counts())
+# Show People Fully Vaccinated
+st.subheader('people_fully_vaccinated')
+st.bar_chart(selected_country['people_fully_vaccinated'].value_counts())
+st.line_chart(selected_country['people_fully_vaccinated'].value_counts())
+
+st.write('## After Normalizing the Data')
+def load_data():
+    new1 = pd.read_csv('covid_preprocessed_data.csv')
+    return new1
+
+# Show New Deaths
+st.subheader('New Deaths')
+st.bar_chart(selected_country['New_deaths'].value_counts())
+st.line_chart(selected_country['New_deaths'].value_counts())
+# Show People Fully Vaccinated
+st.subheader('people_fully_vaccinated')
+st.bar_chart(selected_country['people_fully_vaccinated'].value_counts())
+st.line_chart(selected_country['people_fully_vaccinated'].value_counts())
+
+
+