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| import pandas as pd | |
| import numpy as np | |
| import gradio as gr | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.metrics import accuracy_score | |
| import matplotlib.pyplot as plt | |
| # Loading the dataset | |
| df = pd.read_csv('assignment-2-k2461469.csv') | |
| # Splitting the data into features and target variable | |
| X = df[["dirty", "wait", "lastyear", "usa"]] | |
| y = df["good"] | |
| # Splitting the dataset into training and test sets | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| # Creating and fitting the logistic regression model | |
| model = LogisticRegression() | |
| model.fit(X_train, y_train) | |
| # Function to make predictions and display them on a graph | |
| def predict_and_plot(dirty, wait, lastyear, usa): | |
| # Making prediction for a single input | |
| input_data = np.array([[dirty, wait, lastyear, usa]]) | |
| predicted_value = model.predict(input_data)[0] | |
| # Predicting on test set for comparison | |
| y_pred = model.predict(X_test) | |
| # Creating subplots for each variable and showing predicted value | |
| fig, axs = plt.subplots(2, 2, figsize=(12, 10)) | |
| # Plot dirty variable distribution with predicted value | |
| axs[0, 0].hist(X_test[:, 0], bins=30, color='gray', alpha=0.5, label='Dirty Distribution') | |
| axs[0, 0].axvline(dirty, color='orange', linestyle='--', label='Input Value (Dirty)') | |
| axs[0, 0].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value') | |
| axs[0, 0].set_title('Distribution of Dirty') | |
| axs[0, 0].set_xlabel('Dirty') | |
| axs[0, 0].set_ylabel('Frequency') | |
| axs[0, 0].legend() | |
| # Plot wait variable distribution with predicted value | |
| axs[0, 1].hist(X_test[:, 1], bins=30, color='gray', alpha=0.5, label='Wait Distribution') | |
| axs[0, 1].axvline(wait, color='orange', linestyle='--', label='Input Value (Wait)') | |
| axs[0, 1].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value') | |
| axs[0, 1].set_title('Distribution of Wait') | |
| axs[0, 1].set_xlabel('Wait') | |
| axs[0, 1].set_ylabel('Frequency') | |
| axs[0, 1].legend() | |
| # Plot lastyear variable distribution with predicted value | |
| axs[1, 0].hist(X_test[:, 2], bins=30, color='gray', alpha=0.5, label='Lastyear Distribution') | |
| axs[1, 0].axvline(lastyear, color='orange', linestyle='--', label='Input Value (Lastyear)') | |
| axs[1, 0].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value') | |
| axs[1, 0].set_title('Distribution of Lastyear') | |
| axs[1, 0].set_xlabel('Lastyear') | |
| axs[1, 0].set_ylabel('Frequency') | |
| axs[1, 0].legend() | |
| # Plot usa variable distribution with predicted value | |
| axs[1, 1].hist(X_test[:, 3], bins=30, color='gray', alpha=0.5, label='USA Distribution') | |
| axs[1, 1].axvline(usa, color='orange', linestyle='--', label='Input Value (USA)') | |
| axs[1, 1].axvline(predicted_value, color='red', linestyle='-', label='Predicted Value') | |
| axs[1, 1].set_title('Distribution of USA') | |
| axs[1, 1].set_xlabel('USA') | |
| axs[1, 1].set_ylabel('Frequency') | |
| axs[1, 1].legend() | |
| # Adjust layout and save the plot | |
| plt.tight_layout() | |
| plt.savefig('output_plot.png') | |
| plt.close() | |
| return predicted_value, 'output_plot.png' | |
| # Creating Gradio UI | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Logistic Regression Prediction") | |
| with gr.Row(): | |
| dirty_slider = gr.Slider(minimum=0, maximum=6, step=0.01, label="Dirty") | |
| wait_slider = gr.Slider(minimum=0, maximum=5.3, step=0.01, label="Wait") | |
| lastyear_slider = gr.Slider(minimum=0, maximum=70, step=0.01, label="Last Year") | |
| usa_slider = gr.Slider(minimum=0, maximum=1, step=0.01, label="USA") | |
| predict_button = gr.Button("Predict") | |
| predicted_value_output = gr.Textbox(label="Predicted Value") | |
| plot_output = gr.Image(label="Actual vs Predicted Graph") | |
| predict_button.click( | |
| fn=predict_and_plot, | |
| inputs=[dirty_slider, wait_slider, lastyear_slider, usa_slider], | |
| outputs=[predicted_value_output, plot_output] | |
| ) | |
| demo.launch() | |