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app.py

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+# Import Library
+import streamlit as st
+
+# Import the created Streamlit Page
+import eda
+import prediction
+
+# Navigation button
+navigasi = st.sidebar.selectbox(label='Select Page:', options=['Home Page', 'Exploratory Data Analysis', 'Prediction'])
+
+# Looping for navigation
+if navigasi == 'Home Page':
+    st.image("https://storage.googleapis.com/danacita-website-v3-prd/website_v3/images/biaya_bootcamp__kursus_hacktiv8_6.original.png")
+    st.header('Milestone 2') 
+    st.write('')
+    st.write('Name      : Salsa Sabitha Hurriyah')
+    st.write('Batch     : HCK - 014')
+    st.write('Classification Model for Predicting Mobile Price Range')
+    st.write('')
+    st.caption('Please select another menu in the Select Box on the left of your screen to start!')
+
+# Displays the EDA page
+elif navigasi == 'Exploratory Data Analysis':
+    eda.run()
+
+# Displays the Predict page
+elif navigasi == 'Prediction':
+    prediction.run()

eda.py

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+# import library
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import plotly.express as px
+import json
+
+# Load feature file
+with open('feature.txt', 'r') as file:
+    feature = json.load(file)
+
+# Set Config Halaman
+st.set_page_config(
+    page_title = "Exploratory Data Analysis (EDA)",
+)
+
+def run():
+
+    # Set Title
+    st.title("Exploratory Data Analysis (EDA)")
+
+    # Load Data
+    df = pd.read_csv("Mobile_Price_Classification.csv")
+
+    # Create dataset header
+    st.subheader("Dataset Mobile Price Classification")
+
+    # Display the dataframe on streamlit
+    st.dataframe(df)
+    st.markdown('---')
+
+    # Display data distribution for each price range
+    st.subheader("Data Distribution for Each Price Range")
+
+    # Check how much data is in each price range
+    price_range_counts = df['price_range'].value_counts()
+
+    # Show the amount of data in each price category
+    st.write("Amount of Data in Each Price Rategory :")
+    st.write(price_range_counts)
+
+    # Show plot of price categories
+    st.write("Plot of the price category :")
+    colors = ['blue', 'green', 'orange', 'red']
+    fig, ax = plt.subplots(figsize=(8, 6))
+    ax.pie(price_range_counts, labels=price_range_counts.index, autopct='%1.1f%%', colors=colors)
+    st.pyplot(fig)
+
+    st.write('The amount of data in each price range is same, as 500 data for each price range.')
+    st.markdown('---')
+
+    # Display data distribution for each feature selected per price range
+    # Looping through each feature
+    for selected_column in feature:
+
+        # View the average in each price_range
+        mean_col = df.groupby('price_range')[selected_column].mean().sort_values()
+
+        # Title for each plot
+        plot_title = 'Average of {} column per Price Range'.format(selected_column)
+        st.subheader(plot_title)
+
+        # Color to use for each bar
+        colors = ['blue', 'green', 'orange', 'red']
+
+        # Visualization of average in each price_range
+        fig, ax = plt.subplots(figsize=(8, 6))
+        mean_col.plot.barh(color=colors, ax=ax)
+
+        # Add labels
+        ax.set_xlabel("Average of '{}' column".format(selected_column))
+        ax.set_ylabel('Price Range')
+
+        # Plot displays
+        st.pyplot(fig)
+        st.markdown('---')
+
+if __name__== '__main__':
+    run()

feature.txt

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+["battery_power", "pc", "px_height", "px_width", "ram", "wifi"]

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8ccb4c16d978c357b3d34bf4a0fbdbfb038b6331bbc7e6f01fc273fed12c452b
+size 31144

prediction.py

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+# import library
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import json
+
+# Load Model
+with open('model.pkl', 'rb') as file:
+    model = pickle.load(file)
+with open('feature.txt', 'r') as file:
+    feature = json.load(file)
+
+# Function to run streamlit model predictor
+def run():
+
+   # Set Title
+    st.title("Predict the Price Range of a Mobile Based on its Features")
+    st.markdown('---')
+
+    # Create a Form for Data Inference
+    st.markdown('## Input Data')
+    with st.form('my_form'):
+        battery_power = st.number_input('Battery Power', min_value=500, max_value=8000)
+        blue = st.selectbox('Has bluetooth or not? 0 = No, Yes = 1', (0,1))
+        clock_speed = st.number_input('Clock Speed (Speed at Wich Microprocessor Execute Instruction)', min_value=0.5, max_value=3.0)
+        dual_sim = st.selectbox('Has dual sim or not? 0 = No, Yes = 1', (0,1))
+        fc = st.number_input('Front Camera mega pixels', min_value=0, max_value=40)
+        four_g = st.selectbox('Has 4G or not? 0 = No, Yes = 1', (0,1))
+        int_memory = st.number_input('Internal Memory in Gigabytes', min_value=2, max_value=256)
+        m_dep = st.number_input('Mobile Depth in cm', min_value=0.1, max_value=1.0)
+        mobile_wt = st.number_input('Weight of Mobilephone', min_value=80, max_value=300)
+        n_cores = st.number_input('Number of Cores of Processor', min_value=1, max_value=10)
+        pc = st.number_input('Primary Camera mega pixels', min_value=0, max_value=20)
+        px_height = st.number_input('Pixel Resolution Height', min_value=0, max_value=2000)
+        px_width = st.number_input('Pixel Resolution Width', min_value=500, max_value=2000)
+        ram = st.number_input('RAM in Megabytes', min_value=256, max_value=4000)
+        sc_h = st.number_input('Screen Height of Mobile in cm', min_value=5, max_value=20)
+        sc_w = st.number_input('Screen Width of Mobile in cm', min_value=1, max_value=15)
+        talk_time = st.number_input('The Longest Time for one battery charge when you use it', min_value=2, max_value=168)
+        three_g = st.selectbox('Has 3G or not? 0 = No, Yes = 1', (0,1))
+        touch_screen = st.selectbox('Has touch_screen or not? 0 = No, Yes = 1', (0,1))
+        wifi = st.selectbox('Has wifi or not? 0 = No, Yes = 1', (0,1))
+
+        # Create a button to make predictions
+        submitted = st.form_submit_button("Predict")
+
+    # Dataframe
+    data = {'battery_power': battery_power,
+            'blue': blue,
+            'clock_speed': clock_speed,
+            'dual_sim': dual_sim,
+            'fc': fc, 
+            'four_g': four_g,
+            'int_memory': int_memory, 
+            'm_dep': m_dep,
+            'mobile_wt': mobile_wt,
+            'n_cores': n_cores,
+            'pc': pc,
+            'px_height': px_height,
+            'px_width': px_width,
+            'ram': ram,
+            'sc_h': sc_h,
+            'sc_w': sc_w,
+            'talk_time': talk_time,
+            'three_g': three_g,
+            'touch_screen': touch_screen,
+            'wifi': wifi}
+
+    df = pd.DataFrame([data])
+    st.dataframe(df)
+
+    if submitted:
+        df_selected = df[feature]
+        y_pred_inf = model.predict(df_selected)
+        if y_pred_inf[0] == 0:
+            st.subheader('~ The Mobile Features you Enter is in "Entry-Level" price ~')
+        elif y_pred_inf[0] == 1:
+            st.write('~ The Mobile Features you Enter is in "Mid-Range" price ~')
+        elif y_pred_inf[0] == 2:
+            st.write('~ The Mobile Features you Enter is in "High-End" price ~')
+        elif y_pred_inf[0] == 3:
+            st.write('~ The Mobile Features you Enter is in "Flagship" price ~')
+
+if __name__== '__main__':
+    run()

requirements.txt

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+seaborn
+pandas
+matplotlib
+pickleshare
+plotly
+scikit-learn==1.4.1.post1