📈 Stack Overflow Python Trends Forecasting

📊 This project analyzes and forecasts trends in Python-related questions on Stack Overflow from 2008 to 2024. It applies time series forecasting methods like Holt-Winters and ARIMA to uncover usage patterns and predict future trends.

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🧠 Objective

📌 Forecasting Question
How will the number of Python-related questions on Stack Overflow trend in the coming years?

🎯 Why it matters
Understanding these trends helps tech educators, curriculum designers, and businesses adapt to shifting developer interest and platform demand.

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📦 Dataset Overview

• Source: Stack Overflow dataset (monthly question counts from 2008 to 2024)
• Granularity: Monthly format (YYYY-MM)
• Focus: Python-related tags only
• Preprocessing:
  • Converted to datetime format
  • Filtered and resampled into univariate time series

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🔎 Exploratory Data Analysis (EDA)

📊 Key Patterns Identified

• 🚀 Rapid growth in Python questions from 2008 to 2020
• 📉 Slight decline or flattening observed post-2021
• 📈 Weekly & seasonal spikes around global events (e.g., exams, releases)

📈 Visualizations

• Time series line plots
• Moving averages and rolling statistics
• Seasonal decomposition

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🔮 Forecasting Models Applied

🧠 Models Used

• Naive Forecast
• ETS (Exponential Smoothing)
• ARIMA
• Holt-Winters (Triple Exponential Smoothing)

📏 Accuracy Metrics

• RMSE (Root Mean Square Error)
• MSE / MAE / MAPE
• Residual diagnostics: independence, randomness, and autocorrelation

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🧪 Results & Insights

• Forecast Output:
  Python-related questions are expected to stabilize or slightly decline post-2024

• Best Performing Model:
  Holt-Winters – due to lowest error metrics and clean residuals

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💼 Implications

Recommendation
Tech stakeholders should be aware of Python’s saturation point and consider diversifying content or offerings.

Next Steps
Use job market, GitHub activity, and global events to build multivariate forecasting models in future.

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⚙️ Tools & Libraries

• pandas, numpy
• matplotlib, seaborn
• statsmodels
• prophet, pmdarima
• scikit-learn

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🛠️ How to Run

# Clone this repository
git clone https://github.com/kumarritik24/Forecasting-Python-Question-Trends-on-StackOverflow.git
cd Forecasting-Python-Question-Trends-on-StackOverflow

# Open the notebook
jupyter notebook stackoverflow_python_forecast.ipynb