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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+kiva_loans.csv filter=lfs diff=lfs merge=lfs -text
+loan_theme_ids.csv filter=lfs diff=lfs merge=lfs -text

app - Copy.py

@@ -0,0 +1,188 @@
+# Import necessary libraries
+import streamlit as st
+import pandas as pd
+import altair as alt
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Function to load the dataset
+@st.cache_data  # Cache the function to enhance performance
+def load_data():
+    # Define the file path
+    file_path = 'https://raw.githubusercontent.com/aaubs/ds-master/main/apps/M1-attrition-streamlit/HR-Employee-Attrition-synth.csv'
+    
+    # Load the CSV file into a pandas dataframe
+    df = pd.read_csv(file_path)
+
+    # Create age groups and add as a new column
+    bin_edges = [18, 25, 35, 45, 60]
+    bin_labels = ['18-24', '25-34', '35-44', '45-60']
+    df['AgeGroup'] = pd.cut(df['Age'], bins=bin_edges, labels=bin_labels, right=False)
+
+    return df
+
+# Load the data using the defined function
+df = load_data()
+
+# Set the app title and sidebar header
+st.title("Employee Attrition Dashboard 😊📈")
+st.sidebar.header("Filters 📊")
+
+# Introduction
+
+# HR Attrition Dashboard
+
+st.markdown("""
+            Welcome to the HR Attrition Dashboard. In the backdrop of rising employee turnovers, HR departments are stressing the significance of predicting and understanding employee departures. Through the lens of data analytics, this dashboard unveils the deeper causes of employee churn and proposes strategies to boost employee retention.
+""")
+with st.expander("📊 **Objective**"):
+                 st.markdown("""
+At the heart of this dashboard is the mission to visually decode data, equipping HR experts with insights to tackle these queries:
+- Which company factions face a greater likelihood of employee exits?
+- What might be pushing these individuals to part ways?
+- Observing the discerned trends, what incentives might hold the key to decreasing the attrition rate?
+"""
+)
+                             
+# Tutorial Expander
+with st.expander("How to Use the Dashboard 📚"):
+    st.markdown("""
+    1. **Filter Data** - Use the sidebar filters to narrow down specific data sets.
+    2. **Visualize Data** - From the dropdown, select a visualization type to view patterns.
+    3. **Insights & Recommendations** - Scroll down to see insights derived from the visualizations and actionable recommendations.
+    """)
+
+
+# Sidebar filter: Age Group
+selected_age_group = st.sidebar.multiselect("Select Age Groups 🕰️", df['AgeGroup'].unique().tolist(), default=df['AgeGroup'].unique().tolist())
+if not selected_age_group:
+    st.warning("Please select an age group from the sidebar ⚠️")
+    st.stop()
+filtered_df = df[df['AgeGroup'].isin(selected_age_group)]
+
+# Sidebar filter: Department
+departments = df['Department'].unique().tolist()
+selected_department = st.sidebar.multiselect("Select Departments 🏢", departments, default=departments)
+if not selected_department:
+    st.warning("Please select a department from the sidebar ⚠️")
+    st.stop()
+filtered_df = filtered_df[filtered_df['Department'].isin(selected_department)]
+
+# Sidebar filter: Monthly Income Range
+min_income = int(df['MonthlyIncome'].min())
+max_income = int(df['MonthlyIncome'].max())
+income_range = st.sidebar.slider("Select Monthly Income Range 💰", min_income, max_income, (min_income, max_income))
+filtered_df = filtered_df[(filtered_df['MonthlyIncome'] >= income_range[0]) & (filtered_df['MonthlyIncome'] <= income_range[1])]
+
+# Sidebar filter: Job Satisfaction Level
+satisfaction_levels = sorted(df['JobSatisfaction'].unique().tolist())
+selected_satisfaction = st.sidebar.multiselect("Select Job Satisfaction Levels 😊", satisfaction_levels, default=satisfaction_levels)
+if not selected_satisfaction:
+    st.warning("Please select a job satisfaction level from the sidebar ⚠️")
+    st.stop()
+filtered_df = filtered_df[filtered_df['JobSatisfaction'].isin(selected_satisfaction)]
+
+# Displaying the Attrition Analysis header
+st.header("Attrition Analysis 📊")
+
+# Dropdown to select the type of visualization
+visualization_option = st.selectbox(
+    "Select Visualization 🎨", 
+    ["Attrition by Age Group", 
+     "KDE Plot: Distance from Home by Attrition", 
+     "Attrition by Job Role", 
+     "Attrition Distribution by Gender", 
+     "MonthlyRate and DailyRate by JobLevel"]
+)
+
+# Visualizations based on user selection
+if visualization_option == "Attrition by Age Group":
+    # Bar chart for attrition by age group
+    chart = alt.Chart(filtered_df).mark_bar().encode(
+        x='AgeGroup',
+        y='count()',
+        color='Attrition'
+    ).properties(
+        title='Attrition Rate by Age Group'
+    )
+    st.altair_chart(chart, use_container_width=True)
+
+elif visualization_option == "KDE Plot: Distance from Home by Attrition":
+    # KDE plot for Distance from Home based on Attrition
+    plt.figure(figsize=(10, 6))
+    sns.kdeplot(data=filtered_df, x='DistanceFromHome', hue='Attrition', fill=True, palette='Set2')
+    plt.xlabel('Distance From Home')
+    plt.ylabel('Density')
+    plt.title('KDE Plot of Distance From Home by Attrition')
+    st.pyplot(plt)
+
+elif visualization_option == "Attrition by Job Role":
+    # Bar chart for attrition by job role
+    chart = alt.Chart(filtered_df).mark_bar().encode(
+        y='JobRole',
+        x='count()',
+        color='Attrition'
+    ).properties(
+        title='Attrition by Job Role'
+    )
+    st.altair_chart(chart, use_container_width=True)
+
+elif visualization_option == "Attrition Distribution by Gender":
+    # Pie chart for attrition distribution by gender
+    pie_chart_data = filtered_df[filtered_df['Attrition'] == 'Yes']['Gender'].value_counts().reset_index()
+    pie_chart_data.columns = ['Gender', 'count']
+    
+    chart = alt.Chart(pie_chart_data).mark_arc().encode(
+        theta='count:Q',
+        color='Gender:N',
+        tooltip=['Gender', 'count']
+    ).properties(
+        title='Attrition Distribution by Gender',
+        width=300,
+        height=300
+    ).project('identity')
+    st.altair_chart(chart, use_container_width=True)
+
+elif visualization_option == "MonthlyRate and DailyRate by JobLevel":
+    # Boxplots for MonthlyRate and DailyRate by JobLevel
+    fig, ax = plt.subplots(1, 2, figsize=(15, 7))
+    
+    # MonthlyRate by JobLevel
+    sns.boxplot(x="JobLevel", y="MonthlyRate", data=filtered_df, ax=ax[0], hue="JobLevel", palette='Set2', legend=False)
+    ax[0].set_title('MonthlyRate by JobLevel')
+    ax[0].set_xlabel('Job Level')
+    ax[0].set_ylabel('Monthly Rate')
+
+    # DailyRate by JobLevel
+    sns.boxplot(x="JobLevel", y="DailyRate", data=filtered_df, ax=ax[1], hue="JobLevel", palette='Set2', legend=False)
+    ax[1].set_title('DailyRate by JobLevel')
+    ax[1].set_xlabel('Job Level')
+    ax[1].set_ylabel('Daily Rate')
+    
+    plt.tight_layout()
+    st.pyplot(fig)
+
+# Display dataset overview
+st.header("Dataset Overview")
+st.dataframe(df.describe())
+
+
+# Insights from Visualization Section Expander
+with st.expander("Insights from Visualization 🧠"):
+    st.markdown("""
+    1. **Age Groups & Attrition** - The 'Attrition by Age Group' plot showcases which age brackets face higher attrition.
+    2. **Home Distance's Impact** - The 'KDE Plot: Distance from Home by Attrition' visualizes if being farther away influences leaving tendencies.
+    3. **Roles & Attrition** - 'Attrition by Job Role' reveals which roles might be more attrition-prone.
+    4. **Gender & Attrition** - The pie chart for 'Attrition Distribution by Gender' provides insights into any gender-based patterns.
+    5. **Earnings Patterns** - 'MonthlyRate and DailyRate by JobLevel' boxplots display the compensation distribution across job levels.
+    """)
+
+# Recommendations Expander
+with st.expander("Recommendations for Action 🌟"):
+    st.markdown("""
+    - 🎁 **Incentive Programs:** Introduce incentives tailored for groups showing higher attrition tendencies.
+    - 🏡 **Remote Work Options:** Providing flexibility, especially for those living farther from the workplace, could reduce attrition.
+    - 🚀 **Training & Growth:** Invest in employee development, especially in roles with higher attrition rates.
+    - 👫 **Gender Equality:** Foster an environment that supports equal opportunities regardless of gender.
+    - 💸 **Compensation Review:** Regularly review and adjust compensation structures to stay competitive and retain talent.
+    """)

app.py

@@ -4,185 +4,38 @@ import pandas as pd
 import altair as alt
 import matplotlib.pyplot as plt
 import seaborn as sns
+from scipy.stats import zscore
 
-# Function to load the dataset
-@st.cache_data  # Cache the function to enhance performance
-def load_data():
-    # Define the file path
-    file_path = 'https://raw.githubusercontent.com/aaubs/ds-master/main/apps/M1-attrition-streamlit/HR-Employee-Attrition-synth.csv'
-    
-    # Load the CSV file into a pandas dataframe
-    df = pd.read_csv(file_path)
+st.title('my shitty app ')
 
-    # Create age groups and add as a new column
-    bin_edges = [18, 25, 35, 45, 60]
-    bin_labels = ['18-24', '25-34', '35-44', '45-60']
-    df['AgeGroup'] = pd.cut(df['Age'], bins=bin_edges, labels=bin_labels, right=False)
+file_path= 'kiva_loans.csv'
 
-    return df
+df_kiva_loans = pd.read_csv(file_path)
 
-# Load the data using the defined function
-df = load_data()
+df_kiva_loans = df_kiva_loans.drop(['use', 'disbursed_time','funded_time','posted_time','tags'], axis=1)
 
-# Set the app title and sidebar header
-st.title("Employee Attrition Dashboard 😊📈")
-st.sidebar.header("Filters 📊")
+#drop nas on specific columns not all of them, it doesnt affect the task we actually want to do now, but might need for later use
+df_kiva_loans.dropna(subset=['partner_id','borrower_genders'], inplace=True)
 
-# Introduction
+# Calculate Z-scores
+z_scores = zscore(df_kiva_loans['funded_amount'])
 
-# HR Attrition Dashboard
+# Get boolean array indicating the presence of outliers
+df_kiva_loans['outlier_funded_amount'] = (z_scores > 3) | (z_scores < -3)
+df_kiva_loans_cleaned = df_kiva_loans[~df_kiva_loans['outlier_funded_amount']]
 
-st.markdown("""
-            Welcome to the HR Attrition Dashboard. In the backdrop of rising employee turnovers, HR departments are stressing the significance of predicting and understanding employee departures. Through the lens of data analytics, this dashboard unveils the deeper causes of employee churn and proposes strategies to boost employee retention.
-""")
-with st.expander("📊 **Objective**"):
-                 st.markdown("""
-At the heart of this dashboard is the mission to visually decode data, equipping HR experts with insights to tackle these queries:
-- Which company factions face a greater likelihood of employee exits?
-- What might be pushing these individuals to part ways?
-- Observing the discerned trends, what incentives might hold the key to decreasing the attrition rate?
-"""
-)
-                             
-# Tutorial Expander
-with st.expander("How to Use the Dashboard 📚"):
-    st.markdown("""
-    1. **Filter Data** - Use the sidebar filters to narrow down specific data sets.
-    2. **Visualize Data** - From the dropdown, select a visualization type to view patterns.
-    3. **Insights & Recommendations** - Scroll down to see insights derived from the visualizations and actionable recommendations.
-    """)
+#Grouping by 'repayment interval' and calculating  mean,sum and max for funded amount and loan amount.
 
+selected = st.sidebar.selectbox("Select Variable of Interest", ['country', 'sector', 'repayment_interval'])
+selected2 = st.sidebar.selectbox("Select Variable of Interest", ['funded_amount', 'count'])
 
-# Sidebar filter: Age Group
-selected_age_group = st.sidebar.multiselect("Select Age Groups 🕰️", df['AgeGroup'].unique().tolist(), default=df['AgeGroup'].unique().tolist())
-if not selected_age_group:
-    st.warning("Please select an age group from the sidebar ⚠️")
-    st.stop()
-filtered_df = df[df['AgeGroup'].isin(selected_age_group)]
+if selected2 == 'count':
+    result = df_kiva_loans.groupby(selected).size().reset_index(name='count')
+    result = result.sort_values(by='count', ascending=False)
+else:
+    result = df_kiva_loans.groupby(selected)[selected2].sum().reset_index()
+    result = result.sort_values(by=selected2, ascending=False)
 
-# Sidebar filter: Department
-departments = df['Department'].unique().tolist()
-selected_department = st.sidebar.multiselect("Select Departments 🏢", departments, default=departments)
-if not selected_department:
-    st.warning("Please select a department from the sidebar ⚠️")
-    st.stop()
-filtered_df = filtered_df[filtered_df['Department'].isin(selected_department)]
+st.title("Aggregated Data")
+st.table(result)
 
-# Sidebar filter: Monthly Income Range
-min_income = int(df['MonthlyIncome'].min())
-max_income = int(df['MonthlyIncome'].max())
-income_range = st.sidebar.slider("Select Monthly Income Range 💰", min_income, max_income, (min_income, max_income))
-filtered_df = filtered_df[(filtered_df['MonthlyIncome'] >= income_range[0]) & (filtered_df['MonthlyIncome'] <= income_range[1])]
-
-# Sidebar filter: Job Satisfaction Level
-satisfaction_levels = sorted(df['JobSatisfaction'].unique().tolist())
-selected_satisfaction = st.sidebar.multiselect("Select Job Satisfaction Levels 😊", satisfaction_levels, default=satisfaction_levels)
-if not selected_satisfaction:
-    st.warning("Please select a job satisfaction level from the sidebar ⚠️")
-    st.stop()
-filtered_df = filtered_df[filtered_df['JobSatisfaction'].isin(selected_satisfaction)]
-
-# Displaying the Attrition Analysis header
-st.header("Attrition Analysis 📊")
-
-# Dropdown to select the type of visualization
-visualization_option = st.selectbox(
-    "Select Visualization 🎨", 
-    ["Attrition by Age Group", 
-     "KDE Plot: Distance from Home by Attrition", 
-     "Attrition by Job Role", 
-     "Attrition Distribution by Gender", 
-     "MonthlyRate and DailyRate by JobLevel"]
-)
-
-# Visualizations based on user selection
-if visualization_option == "Attrition by Age Group":
-    # Bar chart for attrition by age group
-    chart = alt.Chart(filtered_df).mark_bar().encode(
-        x='AgeGroup',
-        y='count()',
-        color='Attrition'
-    ).properties(
-        title='Attrition Rate by Age Group'
-    )
-    st.altair_chart(chart, use_container_width=True)
-
-elif visualization_option == "KDE Plot: Distance from Home by Attrition":
-    # KDE plot for Distance from Home based on Attrition
-    plt.figure(figsize=(10, 6))
-    sns.kdeplot(data=filtered_df, x='DistanceFromHome', hue='Attrition', fill=True, palette='Set2')
-    plt.xlabel('Distance From Home')
-    plt.ylabel('Density')
-    plt.title('KDE Plot of Distance From Home by Attrition')
-    st.pyplot(plt)
-
-elif visualization_option == "Attrition by Job Role":
-    # Bar chart for attrition by job role
-    chart = alt.Chart(filtered_df).mark_bar().encode(
-        y='JobRole',
-        x='count()',
-        color='Attrition'
-    ).properties(
-        title='Attrition by Job Role'
-    )
-    st.altair_chart(chart, use_container_width=True)
-
-elif visualization_option == "Attrition Distribution by Gender":
-    # Pie chart for attrition distribution by gender
-    pie_chart_data = filtered_df[filtered_df['Attrition'] == 'Yes']['Gender'].value_counts().reset_index()
-    pie_chart_data.columns = ['Gender', 'count']
-    
-    chart = alt.Chart(pie_chart_data).mark_arc().encode(
-        theta='count:Q',
-        color='Gender:N',
-        tooltip=['Gender', 'count']
-    ).properties(
-        title='Attrition Distribution by Gender',
-        width=300,
-        height=300
-    ).project('identity')
-    st.altair_chart(chart, use_container_width=True)
-
-elif visualization_option == "MonthlyRate and DailyRate by JobLevel":
-    # Boxplots for MonthlyRate and DailyRate by JobLevel
-    fig, ax = plt.subplots(1, 2, figsize=(15, 7))
-    
-    # MonthlyRate by JobLevel
-    sns.boxplot(x="JobLevel", y="MonthlyRate", data=filtered_df, ax=ax[0], hue="JobLevel", palette='Set2', legend=False)
-    ax[0].set_title('MonthlyRate by JobLevel')
-    ax[0].set_xlabel('Job Level')
-    ax[0].set_ylabel('Monthly Rate')
-
-    # DailyRate by JobLevel
-    sns.boxplot(x="JobLevel", y="DailyRate", data=filtered_df, ax=ax[1], hue="JobLevel", palette='Set2', legend=False)
-    ax[1].set_title('DailyRate by JobLevel')
-    ax[1].set_xlabel('Job Level')
-    ax[1].set_ylabel('Daily Rate')
-    
-    plt.tight_layout()
-    st.pyplot(fig)
-
-# Display dataset overview
-st.header("Dataset Overview")
-st.dataframe(df.describe())
-
-
-# Insights from Visualization Section Expander
-with st.expander("Insights from Visualization 🧠"):
-    st.markdown("""
-    1. **Age Groups & Attrition** - The 'Attrition by Age Group' plot showcases which age brackets face higher attrition.
-    2. **Home Distance's Impact** - The 'KDE Plot: Distance from Home by Attrition' visualizes if being farther away influences leaving tendencies.
-    3. **Roles & Attrition** - 'Attrition by Job Role' reveals which roles might be more attrition-prone.
-    4. **Gender & Attrition** - The pie chart for 'Attrition Distribution by Gender' provides insights into any gender-based patterns.
-    5. **Earnings Patterns** - 'MonthlyRate and DailyRate by JobLevel' boxplots display the compensation distribution across job levels.
-    """)
-
-# Recommendations Expander
-with st.expander("Recommendations for Action 🌟"):
-    st.markdown("""
-    - 🎁 **Incentive Programs:** Introduce incentives tailored for groups showing higher attrition tendencies.
-    - 🏡 **Remote Work Options:** Providing flexibility, especially for those living farther from the workplace, could reduce attrition.
-    - 🚀 **Training & Growth:** Invest in employee development, especially in roles with higher attrition rates.
-    - 👫 **Gender Equality:** Foster an environment that supports equal opportunities regardless of gender.
-    - 💸 **Compensation Review:** Regularly review and adjust compensation structures to stay competitive and retain talent.
-    """)

kiva_loans.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b20efc20de600b27608d69fe07e728b00a075c3db29849e146b717098f778d92
+size 195852823

loan_theme_ids.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:48f3d922eef1d329ba913d0ec3c1b88714014f45ec5940a4084c311f4a455baa
+size 31641314