Open Source Analysis #315

opensource_analysis/README

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+# Stackoverflow Analysis Project
+
+## Setup Instructions
+
+1. **Download and Extract the Project Folder**
+   - Download the project folder and extract it to a desired location on your computer.
+
+2. **Navigate to the Project Directory**
+   ```bash
+   cd /path/to/extracted/project/folder/opensource_analysis
+
+
+## Install the Dependencies
+pip install -r requirements.txt
+
+## Run the Streamlit App 
+streamlit run app.py
+
+## Access the App
+Open the URL http://localhost:8501 in your web browser to access the Streamlit app

opensource_analysis/app.py

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+import os
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, roc_auc_score, confusion_matrix, roc_curve, auc
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Define the path to the data file
+file_path = 'survey_results_sample_2018.csv'
+
+# Check if the file exists
+if not os.path.exists(file_path):
+    st.error(f"File not found: {file_path}. Please ensure the file is in the correct directory.")
+else:
+    # Load the dataset
+    data = pd.read_csv(file_path)
+
+    # Define the necessary columns
+    columns = ['Employment', 'FormalEducation', 'CompanySize', 'DevType', 'Exercise', 'Age', 'OpenSource']
+    data = data[columns].copy()
+
+    # Map age values to numerical values
+    age_mapping = {
+        'Under 18 years old': 0,
+        '18 - 24 years old': 1,
+        '25 - 34 years old': 2,
+        '35 - 44 years old': 3,
+        '45 - 54 years old': 4,
+        '55 - 64 years old': 5,
+        '65 years or older': 6
+    }
+    data['Age'] = data['Age'].map(age_mapping)
+
+    # Define target variable and feature columns
+    target_variable = 'OpenSource'
+    categorical_features = ['Employment', 'FormalEducation', 'CompanySize', 'DevType', 'Exercise', 'Age']
+    numerical_features = []
+
+    # Preprocessing for categorical data
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)
+        ]
+    )
+
+    # Split the data
+    X = data.drop(target_variable, axis=1)
+    y = data[target_variable]
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Create and train the model
+    model = Pipeline(steps=[
+        ('preprocessor', preprocessor),
+        ('classifier', RandomForestClassifier(random_state=42))
+    ])
+    model.fit(X_train, y_train)
+
+    # Evaluate the model
+    y_pred = model.predict(X_test)
+    classification_rep = classification_report(y_test, y_pred)
+    roc_auc = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])
+
+    # Get feature importance
+    importances = model.named_steps['classifier'].feature_importances_
+    feature_names = list(model.named_steps['preprocessor'].transformers_[0][1].get_feature_names_out())
+    feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances}).sort_values(by='Importance', ascending=False)
+
+    # Streamlit App
+    st.title('Machine Learning Model Evaluation')
+
+    # Show classification report
+    st.header('Classification Report')
+    st.text(classification_rep)
+
+    # Show ROC-AUC Score
+    st.header('ROC-AUC Score')
+    st.text(f"ROC-AUC Score: {roc_auc:.2f}")
+
+    # Plot confusion matrix
+    st.header('Confusion Matrix')
+    cm = confusion_matrix(y_test, y_pred)
+    fig, ax = plt.subplots()
+    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['No', 'Yes'], yticklabels=['No', 'Yes'], ax=ax)
+    plt.xlabel('Predicted')
+    plt.ylabel('Actual')
+    st.pyplot(fig)
+
+    # Plot ROC Curve
+    st.header('ROC Curve')
+    y_test_binary = y_test.map({'No': 0, 'Yes': 1})
+    fpr, tpr, _ = roc_curve(y_test_binary, model.predict_proba(X_test)[:, 1])
+    roc_auc = auc(fpr, tpr)
+    fig, ax = plt.subplots()
+    ax.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
+    ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+    ax.set_xlim([0.0, 1.0])
+    ax.set_ylim([0.0, 1.05])
+    ax.set_xlabel('False Positive Rate')
+    ax.set_ylabel('True Positive Rate')
+    ax.set_title('ROC Curve')
+    ax.legend(loc='lower right')
+    st.pyplot(fig)
+
+    # Plot feature importance
+    st.header('Feature Importance')
+    fig, ax = plt.subplots()
+    sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(20), palette='viridis', ax=ax)
+    ax.set_title('Top Feature Importances')
+    ax.set_xlabel('Importance')
+    ax.set_ylabel('Feature')
+    st.pyplot(fig)
+
+    # Section for new data input and prediction
+    st.header('Predict for New Data')
+
+    # Input fields for new data
+    employment = st.selectbox('Employment', data['Employment'].unique())
+    education = st.selectbox('Formal Education', data['FormalEducation'].unique())
+    company_size = st.selectbox('Company Size', data['CompanySize'].unique())
+    dev_type = st.selectbox('Dev Type', data['DevType'].unique())
+    exercise = st.selectbox('Exercise', data['Exercise'].unique())
+    age = st.selectbox('Age', list(age_mapping.keys()))
+
+    # Convert inputs to dataframe
+    new_data = pd.DataFrame({
+        'Employment': [employment],
+        'FormalEducation': [education],
+        'CompanySize': [company_size],
+        'DevType': [dev_type],
+        'Exercise': [exercise],
+        'Age': [age_mapping[age]]
+    })
+
+    # Handle any NaN values
+    new_data = new_data.fillna('')
+
+    # Predict the output for new data
+    if st.button('Predict'):
+        try:
+            prediction = model.predict(new_data)
+            prediction_prob = model.predict_proba(new_data)[:, 1]
+            st.write(f'Prediction: {"Yes" if prediction[0] == "Yes" else "No"}')
+            st.write(f'Prediction Probability: {prediction_prob[0]:.2f}')
+        except Exception as e:
+            st.error(f"An error occurred during prediction: {e}")