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| import pandas as pd | |
| import streamlit as st | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from sklearn.metrics import r2_score | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.model_selection import train_test_split | |
| import seaborn as sns | |
| #loading the data | |
| df = pd.read_csv('train5.csv') | |
| # Renaming columns | |
| df.rename(columns = {'MSSubClass':'MSSubClass','LotArea':'LotArea', 'OverallQual':'OverallQual','OverallCond':'OverallCond', 'YearBuilt':'YearBuilt', | |
| 'BsmtFinSF1':'BsmtFinSF1', 'BsmtFinSF2':'BsmtFinSF2', | |
| 'BsmtUnfSF':'BsmtUnfSF','TotalBsmtSF':'TotalBsmtSF'},inplace = True) | |
| # HEADINGS | |
| st.title('House Price Prediction') | |
| st.sidebar.header('Housing Data') | |
| st.subheader('Training Data Stats') | |
| st.write(df.describe()) | |
| # X AND Y DATA | |
| x = df.drop(['SalePrice'], axis = 1) | |
| y = df.iloc[:, -1] | |
| x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random_state = 0) | |
| # FUNCTION | |
| def user_report(): | |
| MSSubClass = st.sidebar.slider('MSSubClass', 0,60, 200 ) | |
| LotArea = st.sidebar.slider('LotArea', 1300,10000,22000 ) | |
| OverallQual = st.sidebar.slider('OverallQual', 1,5, 10 ) | |
| OverallCond = st.sidebar.slider('OverallCond', 1,5, 9 ) | |
| YearBuilt = st.sidebar.slider('YearBuilt', 1872,1975, 2010 ) | |
| YearRemodAdd = st.sidebar.slider('YearRemodAdd', 1950,1975, 2010 ) | |
| BsmtFinSF1 = st.sidebar.slider('BsmtFinSF1', 0,2500, 5000 ) | |
| BsmtUnfSF = st.sidebar.slider('BsmtUnfSF', 0,2500, 5000 ) | |
| BsmtFinSF2 = st.sidebar.slider('BsmtFinSF2', 0,2500, 5000 ) | |
| TotalBsmtSF = st.sidebar.slider('TotalBsmtSF', 0,2500, 6000 ) | |
| #SalePrice = st.sidebar.slider('SalePrice', 0,300000, 800000 ) | |
| user_report_data = { | |
| 'MSSubClass':MSSubClass, | |
| 'LotArea':LotArea, | |
| 'OverallQual':OverallQual, | |
| 'OverallCond': OverallCond, | |
| 'YearBuilt':YearBuilt, | |
| 'YearRemodAdd': YearRemodAdd, | |
| 'BsmtFinSF1': BsmtFinSF1, | |
| 'BsmtUnfSF': BsmtUnfSF, | |
| 'BsmtFinSF2': BsmtFinSF2, | |
| 'TotalBsmtSF': TotalBsmtSF | |
| #'SalePrice': SalePrice, | |
| } | |
| report_data = pd.DataFrame(user_report_data, index=[0]) | |
| return report_data | |
| # Housing Data | |
| user_data = user_report() | |
| st.subheader('Housing Data') | |
| st.write(user_data) | |
| # MODEL | |
| lr = LinearRegression() | |
| lr.fit(x_train, y_train) | |
| user_result = lr.predict(user_data) | |
| # VISUALISATIONS | |
| st.title('Visualised Housing Data') | |
| # COLOR FUNCTION | |
| if user_result[0]==0: | |
| color = 'blue' | |
| else: | |
| color = 'red' | |
| # OUTPUT | |
| st.subheader('Price of House is : ') | |
| st.write(str(user_result)) | |
| st.title('output') | |
| st.subheader('r2_score: ') | |
| st.write(str(r2_score(y_test, lr.predict(x_test))*100)+'%') |