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| import streamlit as st | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| # Load data | |
| data = pd.read_csv("P1G5_Set_1_Titan_Russo.csv") | |
| def eda(): | |
| st.title("Eksploratory Data Analysis") | |
| st.write('Analyze the DataFrame for Better Understanding') | |
| st.markdown("<h2><b>Limit Balance vs. Bill Amount by Default Payment Next Month</b></h2>", | |
| unsafe_allow_html=True) | |
| palette = ["#FF0000", "#4129E1"] # custom colors | |
| for i in range(1, 7): | |
| plt.figure() | |
| sns.scatterplot( | |
| x="limit_balance", y=f"bill_amt_{i}", hue="default_payment_next_month", data=data, palette=palette) | |
| plt.title( | |
| f"Limit Balance vs. Pay Amount {i} by Default Payment Next Month") | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| st.pyplot() | |
| st.write("**Explanation**:") | |
| markdown_text = """ | |
| These plots indicate that a higher `limit_balance` means a higher likelihood of non defaulting on payments. | |
| """ | |
| st.markdown(markdown_text) | |
| st.markdown("<h2><b>Heatmap of Correlation Matrix</b></h2>", | |
| unsafe_allow_html=True) | |
| # Heatmap | |
| corr_matrix = data.corr() | |
| plt.figure(figsize=(15, 10)) | |
| sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=".2f") | |
| plt.title('Heatmap of Correlation Matrix') | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| st.pyplot() | |
| st.write("") # Add a blank line | |
| st.write("**Explanation**:") | |
| markdown_text = """ | |
| From the heatmap we can see the correlation between each columns. We can see the `pay_0`, `pay_2`, `pay_3`, `pay_4`, `pay_5`, `pay_6` have correlation each others from categorical columns meanwhile we can see `bill_amt_1`, `bill_amt_2`, `bill_amt_3`, `bill_amt_4`, `bill_amt_5`, `bill_amt_6` have correlation each others from numerical columns | |
| """ | |
| st.markdown(markdown_text) | |
| st.markdown("<h2><b>Checking Distribution Data</b></h2>", | |
| unsafe_allow_html=True) | |
| # Columns to plot | |
| cols_num = ['limit_balance', 'age', 'bill_amt_1', | |
| 'bill_amt_2', 'bill_amt_3', 'bill_amt_4', 'bill_amt_5', 'bill_amt_6', | |
| 'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5', | |
| 'pay_amt_6'] | |
| # creating subplots for histogram | |
| fig, axes = plt.subplots(5, 4, figsize=(18, 15)) | |
| # Flatten axes array | |
| axes = axes.flatten() | |
| # p;ots for each column | |
| for i, col in enumerate(cols_num): | |
| # membuat histogram dengan kernel density estimate | |
| sns.histplot(data[col], ax=axes[i], kde=True) | |
| axes[i].set_title(f'Distribution {col}') | |
| axes[i].set_xlabel(col) | |
| axes[i].set_ylabel('Frequency') | |
| # hapus figure | |
| for j in range(len(cols_num), len(axes)): | |
| axes[j].remove() | |
| # display | |
| plt.tight_layout() | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| st.pyplot() | |
| st.write("") # Add a blank line | |
| st.write("**Explanation**:") | |
| st.write('Checking the distribution data from non categorical columns, we can say the data is positive skewed') | |
| st.write() | |