Upload 9 files

.gitattributes

@@ -33,3 +33,4 @@ 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
+data/Findex_data.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1,157 @@
----
-title: Final Submission BDS App.py
-emoji: 🌖
-colorFrom: gray
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: My Streamlit Project
+emoji: 🔥
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+python_version: '3.10'
+tags:
+- streamlit
+- data-visualization
+app_file: app.py
+sdk_version: 1.38.0
+---
+
+
+# 🌍 **FINDEX Data Analysis & Prediction App** 📊
+
+Welcome to the **FINDEX Data Analysis & Prediction App**! This app provides insights and predictions related to financial inclusion based on the Global Findex 2021 dataset from the World Bank. The app allows users to explore data distributions, statistics, and use machine learning to predict the likelihood of bank account ownership.
+
+## 🚀 **Table of Contents**
+- [Introduction](#introduction)
+- [Features](#features)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Pages Overview](#pages-overview)
+- [Folder Structure](#folder-structure)
+- [Contributing](#contributing)
+- [License](#license)
+- [Contact](#contact)
+
+---
+
+## 🌟 **Introduction**
+
+The **FINDEX Data Analysis & Prediction App** is a multi-page Streamlit application that provides a detailed exploration of financial inclusion data. The app uses machine learning models to predict the likelihood of an individual owning a bank account based on various socioeconomic factors, including income, education, and digital payment usage.
+
+Whether you're looking to explore demographic trends or predict financial behavior, this app delivers intuitive visualizations and powerful AI-driven insights.
+
+---
+
+## 🔥 **Features**
+- **Data Visualization**: Visualize the distribution of key features, including age, income, and account ownership.
+- **Statistics Overview**: View summary statistics on different demographic groups.
+- **Machine Learning Predictions**: Predict the likelihood of bank account ownership using an XGBoost model.
+- **SHAP Explanations**: Understand the key factors behind the predictions with SHAP visualizations.
+- **Filtering Options**: Customize your analysis by filtering data by gender, education, age, and more.
+
+---
+
+## 💻 **Installation**
+
+To run the app locally, follow these steps:
+
+1. **Clone the repository**:
+    ```bash
+    git clone https://github.com/Driisa/Final-submission-BDS-app.py.git
+    ```
+
+2. Navigate to the project directory:
+    ```bash
+    cd FINDEX-App
+    ```
+
+3. Set up a virtual environment (optional but recommended):
+    ```bash
+    python -m venv venv
+    source venv/bin/activate  # On Windows use: venv\Scripts\activate
+    ```
+
+4. Install the required dependencies:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+5. Run the app:
+    ```bash
+    streamlit run app.py
+    ```
+
+6. Open your browser: Visit http://localhost:8501 to start using the app!
+
+---
+
+## 🕹 **Usage**
+
+- **Navigate the App**: Use the sidebar to switch between different pages such as Info, Distribution, Statistics, and Prediction.
+- **Input Your Data**: On the Prediction page, provide the input data for demographic and socioeconomic factors to predict bank account ownership.
+- **Visualize Data**: Use the Distribution and Statistics pages to explore data distributions and key insights from the dataset.
+
+---
+
+## 📚 **Pages Overview**
+
+1️⃣ **Info**  
+Provides an introduction to the Global Findex dataset and the key variables used in the analysis.
+
+2️⃣ **Distribution**  
+Visualize the distribution of various features, including age, mobile ownership, internet access, and account ownership, with bar plots and histograms.
+
+3️⃣ **Statistics**  
+Analyze summary statistics for key demographic groups, including age and education, and view bar plots showing account ownership across income and age groups.
+
+4️⃣ **Prediction**  
+Predict whether an individual has a bank account based on inputted data. The prediction is powered by an XGBoost model, and SHAP visualizations help explain the predictions.
+
+---
+
+## 📁 **Folder Structure**
+
+```bash
+FINDEX-App/
+├── models/
+│   ├── df.joblib
+│   ├── df_sample.joblib
+│   ├── ohe.joblib
+│   ├── scaler.joblib
+│   └── xgb_clf.joblib
+├── app.py
+├── README.md
+├── requirements.txt
+└── .streamlit/
+```
+
+## 💡 **Contributing**
+
+Want to contribute to the project? Here’s how:
+
+1. **Fork the repository**.
+2. **Create a new branch** for your feature or bug fix:
+    ```bash
+    git checkout -b feature-new-feature
+    ```
+3. Make your changes and commit them:
+    ```bash
+    git commit -m "Added a new feature!"
+    ```
+4. Push the changes to your forked repo:
+    ```bash
+    git push origin feature-new-feature
+    ```
+5. Open a Pull Request, and let’s review your changes!
+
+---
+
+## 📜 **License**
+
+This project is licensed under the MIT License. See the LICENSE file for more details.
+
+---
+
+## 📞 **Contact**
+
+If you have any issues or suggestions, feel free to reach out by opening a GitHub issue. We’d love to hear from you and improve the app based on your feedback!
+
+---
+
+🎉 **Enjoy exploring financial inclusion data with the FINDEX Data Analysis & Prediction App!** 🎉

app.py

@@ -0,0 +1,392 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import streamlit as st
+from pathlib import Path
+import os
+import joblib
+import shap
+from streamlit_shap import st_shap
+from streamlit_folium import st_folium  # Import st_folium to embed Folium map in Streamlit
+import folium
+
+
+# Load the model, scaler, one-hot encoder, and pre-processed DataFrame
+@st.cache_resource  # Cache the model objects to avoid reloading on every interaction
+def load_model_objects():
+    models_dir = os.path.join(os.getcwd(), 'models')  # Adjust the 'models' folder if needed
+    xgb_clf = joblib.load(os.path.join(models_dir, 'xgb_clf.joblib'))
+    scaler = joblib.load(os.path.join(models_dir, 'scaler.joblib'))
+    ohe = joblib.load(os.path.join(models_dir, 'ohe.joblib'))
+    df = joblib.load(os.path.join(models_dir, 'df.joblib'))  # Pre-processed DataFrame
+    df_sample = joblib.load(os.path.join(models_dir, 'df_sample.joblib'))  # Sampled DataFrame
+
+    return xgb_clf, scaler, ohe, df, df_sample
+
+# Load the model, scaler, encoder, and pre-processed DataFrame
+xgb_clf, scaler, ohe, df, df_sample = load_model_objects()
+
+
+
+# =============================================================================================================================
+# Sidebar navigation
+# =============================================================================================================================
+
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Go to", ["Info", "Destribution", "Statistics", "Prediction"])
+
+
+
+# =============================================================================================================================
+# Info page
+# =============================================================================================================================
+if page == "Info": # if the page is info then show following
+    st.title("Info")
+    st.write("Welcome to the Streamlit Dashboard of the FINDEX dataset!")
+
+    st.write("This dashboard provides insights from the final submission in Introduction to business data science. The data in this app is from the Global Findex 2021 / World Bank survey.")
+    st.write("The dataset from Findex contains financial inclusion data from 2021. The data covers various demographics, income, and financial behaviors across multiple countries.") 
+
+
+    st.subheader("Understand Business Context - Problem Definition")
+    st.write("Based on the data this app will help to understand the financial inclusion of the respondents. The app wil adress the following questions.")
+    st.write("""
+    - Can we predict whether an individual is likely to own a bank account based on income, education, and other socioeconomic factors?
+    - What factors influence on having and account?
+    - How is the correlation between the diffenrent varibles? 
+    """)
+
+    st.subheader("Key Variable Descriptions (df_sample)")
+    st.write("""
+    - **Account**: Binary variable indicating whether the respondent has a bank account.
+    - **Income**: Income quintile of the respondent.
+    - **Remittances**: Amount of remittances received by the respondent.
+    - **Education Level**: Education level of the respondent.
+    - **Age**: Respondent's age.
+    - **Gender**: Gender of the respondent.
+    - **Mobile Owner**: Binary variable indicating whether the respondent owns a mobile phone.
+    - **Internet Access**: Binary variable indicating whether the respondent has access to the internet.
+    - **Pay Utilities**: Binary variable indicating if the respondent uses digital payment methods for paying utilities.
+    - **Receive Transfers**: Binary variable indicating if the respondent receives money transfers.
+    - **Receive Pension**: Binary variable indicating if the respondent receives a pension.
+    - **Economy**: Country of the respondent.
+    - **Regionwb**: World Bank region of the respondent.
+    - **Digital Payment Usage**: Binary variable indicating if the respondent uses digital payment methods.
+    """)
+    st.write("All these variables are used to predict the account variable in the prediction page.")
+
+
+# =============================================================================================================================
+# Destribution page 
+# =============================================================================================================================
+elif page == "Destribution": # if the page is destribution then show following
+
+    st.title("Visulisation of the data distribution og the data")
+    
+    st.write("Here is a preview of the Age Distribution:")
+    def plot_age_distribution(data):
+        fig, ax = plt.subplots(figsize=(8, 6))
+        sns.histplot(data['age'], kde=True)
+        st.pyplot(fig)
+    plot_age_distribution(df)
+
+
+    st.write("Here is a preview of the percentage of the different features:")
+
+    # Dictionary to map numeric codes to their actual meanings
+    mapping_dict = {
+        'mobile_owner': {1: 'Owns mobile phone', 2: 'Does not own', 3: "Don't know"},
+        'internet_access': {1: 'Has access', 2: 'No access', 3: "Don't know"},
+        'pay_utilities': {1: 'Paid from account', 2: 'Paid in cash', 3: 'Other method', 4: 'Did not pay'},
+        'receive_transfers': {1: 'Received via account', 2: 'Received in cash', 3: 'Other method', 4: 'Did not receive'},
+        'receive_pension': {1: 'Received via account', 2: 'Received in cash', 3: 'Other method', 4: 'Did not receive'},
+        'education_level': {1: 'Primary or less', 2: 'Secondary', 3: 'Tertiary or more'},
+        'gender': {1: 'Female', 2: 'Male'},
+        'account': {1: 'Yes', 0: 'No'},
+        'digital_payment_usage': {1: 'Yes', 0: 'No'}
+    }
+
+    # List of categorical/binary features to plot
+    cat_features = [
+        'account', 'mobile_owner', 'internet_access', 
+        'pay_utilities', 'receive_transfers', 'gender', 
+        'education_level', 'digital_payment_usage'
+    ]
+
+    # Set up the figure for multiple subplots
+    fig, axes = plt.subplots(4, 2, figsize=(10, 20))  # 2 rows, 4 columns abd the firure size
+
+    # Flatten axes to easily iterate over them in a single loop
+    axes = axes.flatten() #
+
+    # Loop through features to create bar plots (instead of doing the same for each plot, we can do it once using loop)
+    for i, col in enumerate(cat_features):
+        # Create a copy of the current column and apply mapping for the plot
+        data_for_plot = df_sample[col].copy().replace(mapping_dict.get(col, {}))  # Use copy() to avoid modifying the original data
+
+        # Calculate percentages for each category
+        percentage_data = data_for_plot.value_counts(normalize=True) * 100
+
+        # Plot the bar plot showing percentage distribution
+        sns.barplot(x=percentage_data.index, y=percentage_data.values, ax=axes[i], palette="Blues_d")
+
+        # Set plot title and labels
+        axes[i].set_title(f'Percentage Distribution of {col}')
+        axes[i].set_ylabel('Percentage (%)')
+        axes[i].set_xlabel(col)
+
+        # Rotate x-axis labels if there are long categories
+        axes[i].set_xticklabels(axes[i].get_xticklabels(), rotation=45, ha='right')
+
+    # Adjust layout for better appearance
+    plt.tight_layout()
+
+    # Display the plot in Streamlit
+    st.pyplot(fig)
+
+
+
+# =============================================================================================================================
+# Statistics page 
+# =============================================================================================================================
+elif page == "Statistics":
+
+    # sidebar filtering settings 
+    # Map gender and education level codes to readable labels for the select boxes
+    gender_mapping = {1: 'Female', 2: 'Male'}
+    education_level_mapping = {1: 'Primary or less', 2: 'Secondary', 3: 'Tertiary or more'}
+
+    # Create new columns for the labels in the df_sample dataframe
+    df_sample['gender_label'] = df_sample['gender'].map(gender_mapping)
+    df_sample['education_level_label'] = df_sample['education_level'].map(education_level_mapping)
+
+    # Sidebar economy dropdown
+    selected_economy = st.sidebar.multiselect('Select Economy', df_sample['economy'].unique(), default=[])
+
+    # Sidebar gender dropdown (using gender_label column)
+    selected_genders = st.sidebar.multiselect('Select Gender', df_sample['gender_label'].unique(), default=[])
+
+    # Sidebar education level dropdown (using education_level_label column)
+    selected_educational_level = st.sidebar.multiselect('Select educational level', df_sample['education_level_label'].unique(), default=[])
+
+    # Sidebar Age Slider
+    st.sidebar.header('Filter by Age')
+    age_range = st.sidebar.slider('Select Age Range', int(df_sample['age'].min()), int(df_sample['age'].max()), (15, 99))
+
+    # Initial filter - apply all conditions cumulatively
+    filtered_data = df_sample[df_sample['age'].between(age_range[0], age_range[1])]
+
+    # Apply economy filter if selections are made
+    if selected_economy:
+        filtered_data = filtered_data[filtered_data['economy'].isin(selected_economy)]
+
+    # Apply gender filter based on the gender_label column
+    if selected_genders:
+        filtered_data = filtered_data[filtered_data['gender_label'].isin(selected_genders)]
+
+    # Apply educational level filter based on the education_level_label column
+    if selected_educational_level:
+        filtered_data = filtered_data[filtered_data['education_level_label'].isin(selected_educational_level)]
+
+
+    st.title("Statistics Page")
+    # Check if filtered data is not empty and calculate statistics, otherwise use "N/A"
+    if not filtered_data.empty:
+        mean_age = f"{filtered_data['age'].mean():.2f}"
+        median_age = f"{filtered_data['age'].median():.2f}"
+        max_age = f"{filtered_data['age'].max():.2f}"
+        min_age = f"{filtered_data['age'].min():.2f}"
+    else:
+        mean_age = median_age = max_age = min_age = "N/A"
+
+    # Display the statistics in columns
+    st.subheader('Age Statistics')
+    col1, col2, col3, col4 = st.columns(4)
+
+    col1.metric('Mean Age', mean_age)
+    col2.metric('Median Age', median_age)
+    col3.metric('Max Age', max_age)
+    col4.metric('Min Age', min_age)
+
+
+
+    # Add your subheader
+    st.subheader("Boxplot of Age")
+
+    # Boxplot before applying the cap and hurdle on age
+    st.write("Boxplot of Age - figure showing the distribution")
+    plt.figure(figsize=(8, 4))  # Define the size of the figure
+    sns.boxplot(x='age', data=filtered_data)  # Create a boxplot based on "age"
+    plt.title("Boxplot of Age")  # Title of the plot
+    st.pyplot(plt)  # Display the plot in Streamlit
+
+
+    # If filtered data is not empty, continue with analysis
+    if not filtered_data.empty:
+        # Barplot: Account Ownership Distribution by Education Level    
+        st.subheader('Account Ownership Distribution by Education Level')
+
+        # Create a crosstab to show the distribution
+        education_account_dist = pd.crosstab(filtered_data['education_level'], filtered_data['account'], normalize='index') * 100
+
+        # Rename columns to be more descriptive
+        education_account_dist.columns = ['No Account (%)', 'Has Account (%)']
+
+        # Bar plot for education level distribution
+        fig, ax = plt.subplots(figsize=(10, 6))
+        education_account_dist.plot(kind='bar', stacked=True, color=['#3498db', '#2ecc71'], ax=ax)
+
+        ax.set_xlabel('Education Level', fontsize=12)
+        ax.set_ylabel('Percentage of Account Ownership (%)', fontsize=12)
+        ax.set_title('Account Ownership by Education Level', fontsize=14)
+        ax.legend(title='Account Ownership', loc='upper right')
+        plt.xticks(rotation=45, ha='right')
+
+        # Display the plot
+        st.pyplot(fig)
+
+
+
+        # Barplot: Income Distribution by Account Ownership
+        st.subheader('Income Distribution by Account Ownership')
+
+        # Create a crosstab to show the distribution
+        income_account_dist = pd.crosstab(filtered_data['income'], filtered_data['account'], normalize='index') * 100
+
+        # Rename columns to be more descriptive
+        income_account_dist.columns = ['No Account (%)', 'Has Account (%)']
+
+        # Bar plot for income quintile distribution
+        fig, ax = plt.subplots(figsize=(10, 6))
+        income_account_dist.plot(kind='bar', stacked=True, color=['#3498db', '#2ecc71'], ax=ax)
+
+        ax.set_xlabel('Income Quintile', fontsize=12)
+        ax.set_ylabel('Percentage of Account Ownership (%)', fontsize=12)
+        ax.set_title('Account Ownership by Income Quintile', fontsize=14)
+        ax.legend(title='Account Ownership', loc='upper right')
+        plt.xticks(rotation=45, ha='right')
+
+        # Display the plot
+        st.pyplot(fig)
+
+
+
+        # Barplot: Percentage of People Having an Account by Age Group
+        st.subheader('Percentage of People Having an Account by Age Group')
+
+        # Calculate the proportion of people having an account in each age group
+        account_by_age = filtered_data.groupby('age_group')['account'].mean().reset_index()
+        account_by_age['account'] = (account_by_age['account'] * 100).round(2)
+
+        # Create the bar plot using Matplotlib and Seaborn
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.barplot(x='age_group', y='account', data=account_by_age, palette="Blues_d", ax=ax)
+        ax.set_xlabel('Age Group', fontsize=12)
+        ax.set_ylabel('Percentage of Account Ownership (%)', fontsize=12)
+        ax.set_title('Percentage of People with an Account by Age Group', fontsize=14)
+
+        # Add values on top of each bar
+        for index, value in enumerate(account_by_age['account']):
+            ax.text(index, value + 1, f'{value}%', ha='center', fontsize=10)
+
+        # Rotate x-axis labels for readability
+        ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
+
+        # Display the plot in Streamlit
+        st.pyplot(fig)
+
+    else:
+        st.write("No data available for the selected filters.")
+
+
+    # Display filtered data
+    st.write("You can download the filtered data here")
+    st.dataframe(filtered_data)
+
+
+
+# Prediction page
+# =============================================================================================================================
+elif page == "Prediction":
+    st.title("Prediction Page")
+
+    # Get valid categories for economy and regionwb from the OneHotEncoder
+    economy_categories = ohe.categories_[0]
+    regionwb_categories = ohe.categories_[1]
+
+    # Create SHAP explainer
+    explainer = shap.TreeExplainer(xgb_clf)
+
+    # App description
+    with st.expander("What's this app?"):
+        st.markdown("""
+        This app predicts whether an individual has a bank account based on their demographic and socioeconomic data.
+        Using advanced AI models trained on relevant data, we provide insights into financial inclusion. 
+        Explore the SHAP explanations to understand the key factors behind the predictions!
+        """)
+
+    st.subheader('Input Your Data')
+
+    # User input section
+    col1, col2 = st.columns(2)
+
+    with col1:
+        inc_q = st.selectbox("Income Quintile", options=[1, 2, 3, 4, 5])
+        remittances = st.selectbox("Receives Remittances", options=[1, 2, 3, 4, 5, 6],
+                                format_func=lambda x: ['Via Account', 'Via MTO', 'Cash Only', 'Other Methods', 'None', 'Don’t Know'][x-1])
+        educ = st.selectbox("Education Level", options=[1, 2, 3], 
+                            format_func=lambda x: ['Primary or Less', 'Secondary', 'Tertiary'][x-1])
+        age = st.slider("Age", 18, 100, 30)
+        female = st.selectbox("Gender", options=[1, 2], format_func=lambda x: 'Female' if x == 1 else 'Male')
+
+    with col2:
+        mobileowner = st.selectbox("Owns Mobile Phone", options=[1, 2, 3, 4],
+                                format_func=lambda x: ['Yes', 'No', 'Don’t Know', 'Refused'][x-1])
+        internetaccess = st.selectbox("Has Internet Access", options=[1, 2, 3, 4],
+                                    format_func=lambda x: ['Yes', 'No', 'Don’t Know', 'Refused'][x-1])
+        pay_utilities = st.selectbox("Utility Payment Method", options=[1, 2, 3, 4, 5],
+                                    format_func=lambda x: ['Account', 'Cash', 'Other', 'None', 'Don’t Know'][x-1])
+        receive_transfers = st.selectbox("Government Transfer Method", options=[1, 2, 3, 4, 5],
+                                        format_func=lambda x: ['Account', 'Cash', 'Other', 'None', 'Don’t Know'][x-1])
+        receive_pension = st.selectbox("Receives Pension", options=[1, 2, 3, 4, 5],
+                                    format_func=lambda x: ['Account', 'Cash', 'Other', 'None', 'Don’t Know'][x-1])
+        economy = st.selectbox("Economy", options=economy_categories)  # Dynamically populated
+        regionwb = st.selectbox("World Bank Region", options=regionwb_categories)  # Dynamically populated
+
+    # Prediction button
+    if st.button('Predict Bank Account Ownership 🚀'):
+        # Prepare categorical and numerical features
+        cat_features = pd.DataFrame({'economy': [economy], 'regionwb': [regionwb]})
+        cat_encoded = pd.DataFrame(ohe.transform(cat_features).todense(), columns=ohe.get_feature_names_out(['economy', 'regionwb']))
+        
+        num_features = pd.DataFrame({
+            'inc_q': [inc_q],
+            'remittances': [remittances],
+            'educ': [educ],
+            'age': [age],
+            'female': [female],
+            'mobileowner': [mobileowner],
+            'internetaccess': [internetaccess],
+            'pay_utilities': [pay_utilities],
+            'receive_transfers': [receive_transfers],
+            'receive_pension': [receive_pension]
+        })
+        
+        # Scale numerical features
+        num_scaled = pd.DataFrame(scaler.transform(num_features), columns=num_features.columns)
+        
+        # Combine categorical and numerical features
+        features = pd.concat([num_scaled, cat_encoded], axis=1)
+        
+        # Make prediction
+        prediction = xgb_clf.predict(features)[0]
+        
+        # Display prediction
+        st.metric(label="Bank Account Prediction", value='Has Account' if prediction == 1 else 'No Account')
+        
+        # SHAP explanation
+        st.subheader('Factors Behind the Prediction 🤖')
+        shap_values = explainer.shap_values(features)
+        st_shap(shap.force_plot(explainer.expected_value, shap_values[0], features), height=400, width=600)
+
+

data/Findex_data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:98ee1367d02f92b04d0933584a4620516b90ed5f9c554f867fa5037f3f721f7a
+size 40174289

models/df.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d4dc76f85d2edff3a7fc698defeecc88ef65b4dc4a6ba5627e577e60bc069c2
+size 15510187

models/df_sample.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df1a6247c842ca48c9dacc1e563bf9202eaaf4f6e1fc33b726c6aeaec2199065
+size 559691

models/ohe.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:293ef71904d41f5f901442ae0f4d96e7496e48a2cf424ce2682601d2cf5bfe41
+size 3789

models/scaler.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d1905891aa05668b71bfe61f2b9b425083b87120bbc0c0802414840b04ffc29
+size 1319

models/xgb_clf.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f5ae910ddf664d98dad33a8d49e1c0d0a958f2d272a9835c01f9104b4cbce62
+size 173738

requirements.txt

@@ -0,0 +1,10 @@
+streamlit==1.38.0
+pandas==1.5.3
+seaborn==0.12.2
+joblib==1.2.0
+shap==0.41.0
+streamlit-shap==0.0.7
+plotly==5.10.0
+folium==0.14.0
+streamlit-folium==0.11.0
+xgboost==1.7.5