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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestRegressor
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+from sklearn.model_selection import train_test_split
+
+
+st.write("""
+# Simple House price Prediction App
+
+This app **Predicts the price of House** according to features!
+         
+It is using **Random Forest Regression** algorithm.
+""")
+
+st.sidebar.header('User Input Parameters')
+
+def user_input_features():
+    bedrooms = st.sidebar.slider('Bedrooms',1,15,2,1,key=0)
+
+    bathrooms = st.sidebar.slider('Bathrooms',1,12,2,1,key=1)
+
+    sqft_living = st.sidebar.slider('Area of Living (Sqft)',300,10000,560,50,key=2)
+
+    sqft_land = st.sidebar.slider('Area of Land (Sqft)',400,20000,700,100,key=3)
+
+    floors = st.sidebar.slider('Floors',1,6,1,1,key=4)
+
+    st.sidebar.write("<p style='margin-bottom: 2px;'>Waterfront</p>", unsafe_allow_html=True)
+    waterfront_input = st.sidebar.radio("Select Yes or No", ["Yes", "No"], index=1)
+    waterfront = 1 if waterfront_input == 'Yes' else 0
+
+    st.sidebar.write("<p style='margin-bottom: 2px;'>View</p>", unsafe_allow_html=True)
+    view_input = st.sidebar.radio("Select Yes or No", ["Yes", "No"], index=1, key='Yes')
+    view = 1 if view_input == 'Yes' else 0
+
+    st.sidebar.write("<p style='margin-bottom: 2px;'>Condition</p>", unsafe_allow_html=True)
+    st.sidebar.write("<p style='margin-bottom: 2px;'>1: Poor</p>", unsafe_allow_html=True)
+    st.sidebar.write("<p style='margin-bottom: 2px;'>2: Fair</p>", unsafe_allow_html=True)
+    st.sidebar.write("<p style='margin-bottom: 2px;'>3: Average</p>", unsafe_allow_html=True)
+    st.sidebar.write("<p style='margin-bottom: 2px;'>4: Good</p>", unsafe_allow_html=True)
+    st.sidebar.write("<p style='margin-bottom: 2px;'>5: Excellent</p>", unsafe_allow_html=True)
+    condition = st.sidebar.slider('Select from above',1,5,3,1,key=5)
+
+    st.sidebar.write("<p style='margin-bottom: 2px;'>Grades</p>", unsafe_allow_html=True)
+    st.sidebar.write("<p style='margin-bottom: 2px;'>(1-3): Poor Construction</p>", unsafe_allow_html=True)
+    st.sidebar.write("<p style='margin-bottom: 2px;'>(4-7): Standard Construction</p>", unsafe_allow_html=True)
+    st.sidebar.write("<p style='margin-bottom: 2px;'>(8-9): Good to Very Good Quality</p>", unsafe_allow_html=True)
+    st.sidebar.write("<p style='margin-bottom: 2px;'>(10-12): High Quality with Luxury Features</p>", unsafe_allow_html=True)
+    grade = st.sidebar.slider('Select from above',1,12,6,1,key=6)
+
+    sqft_above = st.sidebar.slider('Sqft Above', 300, 10000, 560, 50,key=7)
+
+    sqft_basement = st.sidebar.slider('Sqft Basement', 0, 5000, 0, 50,key=8)
+
+    yr_built = st.sidebar.slider('Year Built', 1900, 2022, 1970, 1,key=9)
+
+    yr_renovated = st.sidebar.slider('Year Renovated', 1900, 2022, 1970, 1, key=10)
+
+    sqft_living15 = st.sidebar.slider("Average interior square footage of the 15 nearest neighbors' living spaces", 300, 10000, 560, 50, key=11)
+    
+    sqft_lot15 = st.sidebar.slider('The average square footage of the land lots of the 15 nearest neighbors', 400, 20000, 700, 100, key=12)
+
+    zipcode_options = ['98002', '98003', '98004', '98005', '98006', '98007', '98008', '98010', '98011', '98014',
+                       '98019', '98022', '98023', '98024', '98027', '98028', '98029', '98030', '98031', '98032',
+                       '98033', '98034', '98038', '98039', '98040', '98042', '98045', '98052', '98053', '98055',
+                       '98056', '98058', '98059', '98065', '98070', '98072', '98074', '98075', '98077', '98092',
+                       '98102', '98103', '98105', '98106', '98107', '98108', '98109', '98112', '98115', '98116',
+                       '98117', '98118', '98119', '98122', '98125', '98126', '98133', '98136', '98144', '98146',
+                       '98148', '98155', '98166', '98168', '98177', '98178', '98188', '98198', '98199']
+
+    selected_zipcode = st.sidebar.selectbox('Zipcode', zipcode_options)
+
+    data = {
+        'bedrooms': bedrooms,
+        'bathrooms': bathrooms,
+        'sqft_living': sqft_living,
+        'sqft_lot': sqft_land,
+        'floors': floors,
+        'waterfront': waterfront,
+        'view': view,
+        'condition': condition,
+        'grade': grade,
+        'sqft_above': sqft_above,
+        'sqft_basement': sqft_basement,
+        'yr_built': yr_built,
+        'yr_renovated': yr_renovated,
+        'sqft_living15': sqft_living15,
+        'sqft_lot15': sqft_lot15,
+        f'zipcode_{selected_zipcode}': True,  # Set the selected zipcode to True
+    }
+    # Set all zipcodes to False
+    for zipcode_option in zipcode_options:
+        data[f'zipcode_{zipcode_option}'] = False
+
+    # Set the selected zipcode to True
+    data[f'zipcode_{selected_zipcode}'] = True
+    
+    features = pd.DataFrame(data, index=[0])
+    return features
+
+user_features = user_input_features()
+
+st.subheader('User Input Parameters')
+st.write(user_features)
+
+#loading dataset
+
+@st.cache_data
+def load_dataset():
+    return pd.read_csv('house_data.csv')
+
+df = load_dataset()
+y=df['price']
+X=df.drop("price",axis=1)
+
+#splitting dataset
+@st.cache_data
+def split_dataset():
+    return train_test_split(X, y, test_size=0.2, random_state=0)
+
+X_train, X_test, y_train, y_test = split_dataset()
+
+
+#model Training
+@st.cache_data
+def train_model():
+    rfregressor = RandomForestRegressor(n_estimators=500, random_state=0, n_jobs=4,
+                                        max_depth=30, max_features='sqrt', min_samples_leaf=1, min_samples_split=10)
+    rfregressor.fit(X_train, y_train)
+    return rfregressor
+
+model = train_model()
+
+#Prediction
+
+@st.cache_data
+def make_prediction(features):
+    # Load the model inside the function to avoid UnhashableParamError
+    model = train_model()
+
+    # Extract feature names from the training data
+    feature_names = X.columns.tolist()
+
+    # Ensure the input features have the same order as the training data
+    features = features[feature_names]
+
+    return model.predict(features)
+
+y_pred = make_prediction(X_test)
+
+prediction = make_prediction(user_features)
+
+st.write('## Prediction')
+st.write(f'Predicted House Price: **${int(prediction[0]):,}**')
+
+st.write("## Data and Result Analysis")
+st.subheader('Dataframe Sample')
+st.write(df.head(10))
+
+#Model Analysis
+
+def analyze_model(X_test, y_test, model):
+    model_params = (model.get_params(),)
+    y_pred = model.predict(X_test)
+    mse = mean_squared_error(y_test, y_pred)
+    rmse = np.sqrt(mse)
+    mae = mean_absolute_error(y_test, y_pred)
+    r2 = r2_score(y_test, y_pred)
+    return mse, rmse, mae, r2
+
+mse, rmse, mae, r2 = analyze_model(X_test, y_test, model)
+
+# Display the mathematical result analysis
+st.subheader('Mathematical Result Analysis')
+st.write(f'R-squared (R2): **{r2:.2f}**')
+st.write(f'Mean Squared Error (MSE): **{mse:.2f}**')
+st.write(f'Root Mean Squared Error (RMSE): **{rmse:.2f}**')
+st.write(f'Mean Absolute Error (MAE): **{mae:.2f}**')
+
+
+# Add space to push the footer to the bottom
+st.markdown("<br><br>", unsafe_allow_html=True)
+
+st.markdown('<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.2/css/all.min.css">', unsafe_allow_html=True)
+
+footer_html = """
+---
+### Contact Me
+For inquiries or support, please contact me at [[email protected]](mailto:[email protected]).
+
+[<i class="fab fa-github" style="color: white; font-size: 25px;"></i>](https://github.com/vigneshmaradiya)
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
+[<i class="fab fa-linkedin" style="color: white; font-size: 25px;"></i>](https://www.linkedin.com/in/vignesh-maradiya/)
+
+&copy; 2024 Vignesh Maradiya. All rights reserved. Developed by Vignesh Maradiya.
+"""
+
+st.markdown(footer_html, unsafe_allow_html=True)

requirements.txt

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+matplotlib==3.8.2
+numpy==1.26.3
+pandas==2.1.4
+scikit_learn==1.3.2
+seaborn==0.13.1
+streamlit==1.29.0