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import streamlit as st |
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import numpy as np |
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import pickle |
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pipe = pickle.load(open('pipe.pkl','rb')) |
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df = pickle.load(open('df.pkl','rb')) |
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st.title("Laptop Price Predictor") |
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company = st.selectbox('Brand',df['Company'].unique()) |
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type = st.selectbox('Type',df['TypeName'].unique()) |
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ram = st.selectbox('RAM(in GB)',[2,4,8,12,16,24,32,64]) |
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weight = st.number_input('Weight of laptop') |
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touchscreen = st.selectbox('Touchscreen',['No','Yes']) |
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ips = st.selectbox('IPS',['No','Yes']) |
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screen_size = st.number_input('Screen Size') |
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resolution = st.selectbox('Screen Resolution',['1920x1080','1600x900','3840x2160','3200x1800','2560x1600','2560x1440','2304x1440','1366x768','2880x1800']) |
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cpu = st.selectbox('CPU',df['Cpu brand'].unique()) |
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hdd = st.selectbox('HDD(in GB)',[0,128,256,512,1024,2048]) |
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ssd = st.selectbox('SSD(in GB)',[0,8,128,256,512,1024,2048]) |
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gpu = st.selectbox('GPU',df['Gpu Brand'].unique()) |
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os = st.selectbox('Os',df['os'].unique()) |
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if st.button('Predict Price'): |
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if touchscreen=='Yes': |
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touchscreen = 1 |
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else: |
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touchscreen = 0 |
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if ips =='Yes': |
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ips = 1 |
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else: |
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ips = 0 |
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X_res = int(resolution.split('x')[0]) |
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Y_res = int(resolution.split('x')[1]) |
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ppi = ((X_res**2) + (Y_res**2))**0.5/screen_size |
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query = np.array([company,type,ram,weight,touchscreen,ips,ppi,cpu,hdd,ssd,gpu,os]) |
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query = query.reshape(1,12) |
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st.title("The Predicted Price: "+str(int(np.exp(pipe.predict(query)[0])))) |