Update eda.py

eda.py

@@ -1,142 +1,142 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import streamlit as st
-import os
-from phik import phik_matrix  
-
-# Path to dataset
-data_path = r"C:\Users\handw\Documents\FTDS\p1-ftds036-rmt-m2-ebhon\deployment\adult.csv"
-
-# Load dataset
-@st.cache_data
-def load_data():
-    if not os.path.isfile(data_path):
-        st.error(f"File not found: {data_path}")
-        return None
-    return pd.read_csv(data_path)
-
-def run_eda():
-    # Load data
-    data = load_data()
-
-    # Check if data is loaded successfully
-    if data is not None:
-        # Trim whitespace from column names
-        data.columns = data.columns.str.strip()
-        
-        # Sidebar for chart selection
-        st.sidebar.title("EDA Menu")
-        menu_options = st.sidebar.radio("Select a chart:",
-                                         ("Age Distribution Histogram",
-                                          "Average Age by Income Category",
-                                          "Count by Work Class and Income",
-                                          "Average Capital Gain by Education Level",
-                                          "Total Hours Worked by Income Category",
-                                          "Count by Marital Status and Income",
-                                          "Phik Correlation Matrix"))
-
-        # Histogram of Age distribution
-        if menu_options == "Age Distribution Histogram":
-            st.subheader("Histogram of Age Distribution")
-            if 'age' in data.columns:
-                plt.figure(figsize=(10, 6))
-                sns.histplot(data['age'], bins=30, kde=True)
-                plt.title('Distribusi Usia')
-                plt.xlabel('Usia')
-                plt.ylabel('Frekuensi')
-                st.pyplot(plt)
-                st.write("**Insight:** This histogram shows the age distribution of individuals in the dataset, indicating how age varies among the population.")
-            else:
-                st.error("Column 'age' not found in the dataset.")
-
-        # Average Age by Income Category
-        if menu_options == "Average Age by Income Category":
-            st.subheader("Average Age Based on Income Category")
-            if 'income' in data.columns and 'age' in data.columns:
-                age_income = data.groupby('income')['age'].mean().reset_index()  # Group age by income
-                plt.figure(figsize=(10, 6))
-                sns.barplot(data=age_income, x='income', y='age')
-                plt.title('Rata-rata Usia berdasarkan Kategori Pendapatan')
-                plt.xlabel('Kategori Pendapatan')
-                plt.ylabel('Rata-rata Usia')
-                st.pyplot(plt)
-                st.write("**Insight:** This bar plot displays the average age of individuals based on income categories, showing how age correlates with income.")
-            else:
-                st.error("Required columns not found in the dataset.")
-
-        # Count by Work Class and Income
-        if menu_options == "Count by Work Class and Income":
-            st.subheader("Count by Work Class and Income")
-            if 'workclass' in data.columns and 'income' in data.columns:
-                workclass_income = data.groupby(['workclass', 'income']).size().reset_index(name='count')
-                plt.figure(figsize=(12, 6))
-                sns.barplot(data=workclass_income, x='workclass', y='count', hue='income')
-                plt.title('Jumlah Individu berdasarkan Jenis Pekerjaan dan Pendapatan')
-                plt.xticks(rotation=45)
-                st.pyplot(plt)
-                st.write("**Insight:** This plot illustrates the distribution of individuals by their job types and income levels, highlighting job categories that attract higher income.")
-            else:
-                st.error("Required columns not found in the dataset.")
-
-        # Average Capital Gain by Education Level
-        if menu_options == "Average Capital Gain by Education Level":
-            st.subheader("Average Capital Gain Based on Education Level")
-            if 'education' in data.columns and 'capital-gain' in data.columns:
-                capital_gain_education = data.groupby('education')['capital-gain'].mean().reset_index()
-                plt.figure(figsize=(12, 6))
-                sns.barplot(data=capital_gain_education, x='education', y='capital-gain')
-                plt.title('Rata-rata Keuntungan Modal berdasarkan Tingkat Pendidikan')
-                plt.xticks(rotation=45)
-                st.pyplot(plt)
-                st.write("**Insight:** This bar plot indicates the average capital gain across different education levels, suggesting that higher education is associated with greater financial gains.")
-            else:
-                st.error("Required columns not found in the dataset.")
-
-        # Total Hours Worked by Income Category
-        if menu_options == "Total Hours Worked by Income Category":
-            st.subheader("Total Hours Worked Based on Income Category")
-            if 'income' in data.columns and 'hours-per-week' in data.columns:
-                hours_income = data.groupby('income')['hours-per-week'].sum().reset_index()
-                plt.figure(figsize=(8, 5))
-                sns.barplot(data=hours_income, x='income', y='hours-per-week')
-                plt.title('Total Jam Kerja berdasarkan Kategori Pendapatan')
-                plt.xlabel('Kategori Pendapatan')
-                plt.ylabel('Total Jam Kerja')
-                st.pyplot(plt)
-                st.write("**Insight:** This plot shows the total number of hours worked for each income category, indicating the relationship between working hours and income.")
-            else:
-                st.error("Required columns not found in the dataset.")
-
-        # Count by Marital Status and Income
-        if menu_options == "Count by Marital Status and Income":
-            st.subheader("Count by Marital Status and Income")
-            if 'marital-status' in data.columns and 'income' in data.columns:
-                relationship_income = data.groupby(['marital-status', 'income']).size().reset_index(name='count')
-                plt.figure(figsize=(12, 6))
-                sns.barplot(data=relationship_income, x='marital-status', y='count', hue='income')
-                plt.title('Jumlah Individu berdasarkan Status Perkawinan dan Pendapatan')
-                plt.xticks(rotation=45)
-                st.pyplot(plt)
-                st.write("**Insight:** This plot shows the distribution of individuals by marital status and income category, providing insights into how marital status may affect income.")
-            else:
-                st.error("Required columns not found in the dataset.")
-
-        # Phik Correlation Matrix
-        if menu_options == "Phik Correlation Matrix":
-            st.subheader("Phik Correlation Matrix")
-            # List the required columns
-            required_columns = ['income', 'age', 'capital-gain', 'hours-per-week', 'marital-status', 'education', 'workclass']
-            if all(col in data.columns for col in required_columns):
-                # Calculate the Phik correlation matrix
-                phik_corr = data.phik_matrix()
-                plt.figure(figsize=(12, 8))
-                sns.heatmap(phik_corr, annot=True, fmt=".2f", cmap='coolwarm', square=True)
-                plt.title('Phik Correlation Matrix (Sampled Data)')
-                st.pyplot(plt)
-                st.write("**Insight:** The Phik correlation matrix reveals the strength and direction of relationships between variables, helping identify multicollinearity and associations within the dataset.")
-            else:
-                missing_cols = [col for col in required_columns if col not in data.columns]
-                st.error(f"Required columns not found in the dataset: {', '.join(missing_cols)}")
-    else:
-        st.error("Data not loaded successfully.")
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import streamlit as st
+import os
+from phik import phik_matrix  
+
+# Path to dataset
+data_path = 'adult.csv'
+
+# Load dataset
+@st.cache_data
+def load_data():
+    if not os.path.isfile(data_path):
+        st.error(f"File not found: {data_path}")
+        return None
+    return pd.read_csv(data_path)
+
+def run_eda():
+    # Load data
+    data = load_data()
+
+    # Check if data is loaded successfully
+    if data is not None:
+        # Trim whitespace from column names
+        data.columns = data.columns.str.strip()
+        
+        # Sidebar for chart selection
+        st.sidebar.title("EDA Menu")
+        menu_options = st.sidebar.radio("Select a chart:",
+                                         ("Age Distribution Histogram",
+                                          "Average Age by Income Category",
+                                          "Count by Work Class and Income",
+                                          "Average Capital Gain by Education Level",
+                                          "Total Hours Worked by Income Category",
+                                          "Count by Marital Status and Income",
+                                          "Phik Correlation Matrix"))
+
+        # Histogram of Age distribution
+        if menu_options == "Age Distribution Histogram":
+            st.subheader("Histogram of Age Distribution")
+            if 'age' in data.columns:
+                plt.figure(figsize=(10, 6))
+                sns.histplot(data['age'], bins=30, kde=True)
+                plt.title('Distribusi Usia')
+                plt.xlabel('Usia')
+                plt.ylabel('Frekuensi')
+                st.pyplot(plt)
+                st.write("**Insight:** This histogram shows the age distribution of individuals in the dataset, indicating how age varies among the population.")
+            else:
+                st.error("Column 'age' not found in the dataset.")
+
+        # Average Age by Income Category
+        if menu_options == "Average Age by Income Category":
+            st.subheader("Average Age Based on Income Category")
+            if 'income' in data.columns and 'age' in data.columns:
+                age_income = data.groupby('income')['age'].mean().reset_index()  # Group age by income
+                plt.figure(figsize=(10, 6))
+                sns.barplot(data=age_income, x='income', y='age')
+                plt.title('Rata-rata Usia berdasarkan Kategori Pendapatan')
+                plt.xlabel('Kategori Pendapatan')
+                plt.ylabel('Rata-rata Usia')
+                st.pyplot(plt)
+                st.write("**Insight:** This bar plot displays the average age of individuals based on income categories, showing how age correlates with income.")
+            else:
+                st.error("Required columns not found in the dataset.")
+
+        # Count by Work Class and Income
+        if menu_options == "Count by Work Class and Income":
+            st.subheader("Count by Work Class and Income")
+            if 'workclass' in data.columns and 'income' in data.columns:
+                workclass_income = data.groupby(['workclass', 'income']).size().reset_index(name='count')
+                plt.figure(figsize=(12, 6))
+                sns.barplot(data=workclass_income, x='workclass', y='count', hue='income')
+                plt.title('Jumlah Individu berdasarkan Jenis Pekerjaan dan Pendapatan')
+                plt.xticks(rotation=45)
+                st.pyplot(plt)
+                st.write("**Insight:** This plot illustrates the distribution of individuals by their job types and income levels, highlighting job categories that attract higher income.")
+            else:
+                st.error("Required columns not found in the dataset.")
+
+        # Average Capital Gain by Education Level
+        if menu_options == "Average Capital Gain by Education Level":
+            st.subheader("Average Capital Gain Based on Education Level")
+            if 'education' in data.columns and 'capital-gain' in data.columns:
+                capital_gain_education = data.groupby('education')['capital-gain'].mean().reset_index()
+                plt.figure(figsize=(12, 6))
+                sns.barplot(data=capital_gain_education, x='education', y='capital-gain')
+                plt.title('Rata-rata Keuntungan Modal berdasarkan Tingkat Pendidikan')
+                plt.xticks(rotation=45)
+                st.pyplot(plt)
+                st.write("**Insight:** This bar plot indicates the average capital gain across different education levels, suggesting that higher education is associated with greater financial gains.")
+            else:
+                st.error("Required columns not found in the dataset.")
+
+        # Total Hours Worked by Income Category
+        if menu_options == "Total Hours Worked by Income Category":
+            st.subheader("Total Hours Worked Based on Income Category")
+            if 'income' in data.columns and 'hours-per-week' in data.columns:
+                hours_income = data.groupby('income')['hours-per-week'].sum().reset_index()
+                plt.figure(figsize=(8, 5))
+                sns.barplot(data=hours_income, x='income', y='hours-per-week')
+                plt.title('Total Jam Kerja berdasarkan Kategori Pendapatan')
+                plt.xlabel('Kategori Pendapatan')
+                plt.ylabel('Total Jam Kerja')
+                st.pyplot(plt)
+                st.write("**Insight:** This plot shows the total number of hours worked for each income category, indicating the relationship between working hours and income.")
+            else:
+                st.error("Required columns not found in the dataset.")
+
+        # Count by Marital Status and Income
+        if menu_options == "Count by Marital Status and Income":
+            st.subheader("Count by Marital Status and Income")
+            if 'marital-status' in data.columns and 'income' in data.columns:
+                relationship_income = data.groupby(['marital-status', 'income']).size().reset_index(name='count')
+                plt.figure(figsize=(12, 6))
+                sns.barplot(data=relationship_income, x='marital-status', y='count', hue='income')
+                plt.title('Jumlah Individu berdasarkan Status Perkawinan dan Pendapatan')
+                plt.xticks(rotation=45)
+                st.pyplot(plt)
+                st.write("**Insight:** This plot shows the distribution of individuals by marital status and income category, providing insights into how marital status may affect income.")
+            else:
+                st.error("Required columns not found in the dataset.")
+
+        # Phik Correlation Matrix
+        if menu_options == "Phik Correlation Matrix":
+            st.subheader("Phik Correlation Matrix")
+            # List the required columns
+            required_columns = ['income', 'age', 'capital-gain', 'hours-per-week', 'marital-status', 'education', 'workclass']
+            if all(col in data.columns for col in required_columns):
+                # Calculate the Phik correlation matrix
+                phik_corr = data.phik_matrix()
+                plt.figure(figsize=(12, 8))
+                sns.heatmap(phik_corr, annot=True, fmt=".2f", cmap='coolwarm', square=True)
+                plt.title('Phik Correlation Matrix (Sampled Data)')
+                st.pyplot(plt)
+                st.write("**Insight:** The Phik correlation matrix reveals the strength and direction of relationships between variables, helping identify multicollinearity and associations within the dataset.")
+            else:
+                missing_cols = [col for col in required_columns if col not in data.columns]
+                st.error(f"Required columns not found in the dataset: {', '.join(missing_cols)}")
+    else:
+        st.error("Data not loaded successfully.")