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housing-price-pred-app.py
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housing-price-pred-app.py
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# General and EDA
import numpy as np
import pandas as pd
# Preprocessing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
# ML Algorithms
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import RandomForestRegressor
# Streamlit
import streamlit as st
st.write("""
# House Price Prediction App
This app predicts the **Dhaka House Price**!
""")
st.write('---')
# Read dataset
df = pd.read_csv('housing_dataset_bd.csv')
# Sidebar
# Header of Specify Input Parameters
st.sidebar.header('Specify Input Parameters')
# Drop null values
df.dropna(axis=0, inplace=True)
# Reset the index after dropping the null values
df.reset_index(drop=True, inplace=True)
st.header('Geographic View of Model Training Data')
st.write("""
This model is trained on House prices in Dhaka
""")
st.map(df)
st.write('---')
# Select features and set target
X = df.drop(['Price', 'Location', 'Type', 'Region', 'Sub_region'], axis=1)
y = df['Price']
# Scale data in a scale of 0-1
scaler = MinMaxScaler()
scaler.fit_transform(X)
# Side bar slider for user input
def user_input_features():
No_Beds = st.sidebar.slider(
'No. Beds', int(X.No_Beds.min()), int(X.No_Beds.max()), int(X.No_Beds.mean()))
No_Baths = st.sidebar.slider(
'No. Baths', int(X.No_Baths.min()), int(X.No_Baths.max()), int(X.No_Baths.mean()))
Area = st.sidebar.slider('Area (Sq.ft.)', float(X.Area.min()), float(
X.Area.max()), float(X.Area.mean()))
latitude = st.sidebar.slider(
'Latitude', float(X.latitude.min()), float(X.latitude.max()), float(X.latitude.mean()))
longitude = st.sidebar.slider(
'Longitude', float(X.longitude.min()), float(X.longitude.max()), float(X.longitude.mean()))
data = {'No. Beds': No_Beds,
'No. Baths': No_Baths,
'Area': Area,
'Latitude': latitude,
'Longitude': longitude}
features = pd.DataFrame(data, index=[0])
return features
df_user_ip = user_input_features()
# Main Panel
# Print specified input parameters
st.header('Specified Input parameters')
st.write(df_user_ip)
st.write('---')
total_pred = 0
list_pred = []
# Model
# KNN Regressor
neigh = KNeighborsRegressor(
n_neighbors=10, weights='uniform', algorithm='auto', p=1)
neigh.fit(X, y)
y_pred_1 = neigh.predict(df_user_ip)
list_pred.append(y_pred_1)
total_pred += y_pred_1
# DecisionTreeRegressor
regr_1 = DecisionTreeRegressor(max_depth=10)
regr_1.fit(X, y)
y_pred_2 = regr_1.predict(df_user_ip)
list_pred.append(y_pred_2)
total_pred += y_pred_2
# AdaBoostRegressor
regr_2 = AdaBoostRegressor(
n_estimators=50, learning_rate=0.3, loss='exponential')
regr_2.fit(X, y)
y_pred_3 = regr_2.predict(df_user_ip)
list_pred.append(y_pred_3)
total_pred += y_pred_3
# RandomForestRegressor
regr_3 = RandomForestRegressor()
regr_3.fit(X, y)
y_pred_4 = regr_3.predict(df_user_ip)
list_pred.append(y_pred_4)
total_pred += y_pred_4
# Mean price prediction
mean_pred = total_pred / 4
st.header('Prediction of House Price (BDT)')
st.write(mean_pred)
st.write('---')
# Model comparison
st.header('Model Prediction Comparison')
st.write('This model uses 4 models- KNeighborsRegressor, DecisionTreeRegressor, AdaBoostRegressor, and RandomForestRegressor')
df_pred = pd.DataFrame(
list_pred, index=['KNN', 'DecisionTree', 'AdaBoost', 'RandomForest'])
st.bar_chart(df_pred)