Spaces:
Sleeping
Sleeping
File size: 3,572 Bytes
db612bc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
import io
def main():
st.title("Dynamic Regression Model App")
# File uploader for user dataset
uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
if uploaded_file is not None:
df = pd.read_csv(uploaded_file)
st.write("## Data Sample")
st.write(df.head())
st.write("## Data Statistics")
st.write(df.describe())
st.write("## Data Info")
buffer = io.StringIO()
df.info(buf=buffer)
s = buffer.getvalue()
st.text(s)
st.write("## Missing Values")
st.write(df.isnull().sum())
# Drop target variable from the predictors list
columns = df.columns.tolist()
target = st.selectbox('Select the target variable for regression:', options=columns)
predictor_options = [col for col in columns if col != target]
# Add multiselect for user to choose predictor variables
predictors = st.multiselect(
'Select predictor variables for regression:',
options=predictor_options
)
if not predictors or not target:
st.error("Please select at least one predictor and a target variable.")
return
st.write("## Scatter Plot")
if len(predictors) == 1:
fig, ax = plt.subplots()
ax.scatter(df[predictors[0]], df[target])
ax.set_xlabel(predictors[0])
ax.set_ylabel(target)
ax.set_title(f'Relationship between {predictors[0]} and {target}')
st.pyplot(fig)
else:
st.write("Scatter plot is only available for a single predictor.")
# Regression analysis
X = df[predictors]
y = df[target]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
r2 = r2_score(y_test, y_pred)
st.write(f'## Regression Analysis')
st.write(f'RMSE: {rmse}')
st.write(f'R-squared: {r2}')
if len(predictors) == 1:
fig, ax = plt.subplots()
ax.scatter(X_train[predictors[0]], y_train, color='blue', label='Training data')
ax.scatter(X_test[predictors[0]], y_test, color='green', label='Testing data')
ax.plot(X_test[predictors[0]], y_pred, color='red', linewidth=2, label='Regression line')
ax.set_xlabel(predictors[0])
ax.set_ylabel(target)
ax.set_title(f'Linear Regression: {predictors[0]} vs {target}')
ax.legend()
st.pyplot(fig)
else:
fig, ax = plt.subplots(figsize=(10, 6))
ax.scatter(y_test, y_pred, color='blue', label='Predicted vs Actual')
ax.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', linewidth=2, label='Ideal fit')
ax.set_xlabel('Actual ' + target)
ax.set_ylabel('Predicted ' + target)
ax.set_title('Multilinear Regression: Actual vs Predicted')
ax.legend()
st.pyplot(fig)
else:
st.write("Please upload a CSV file to proceed.")
if __name__ == "__main__":
main() |