This project utilizes the Prophet library along with Pandas to analyze and forecast search trends data for MercadoLibre. The primary goal is to understand the dynamics of search traffic and its relationship with stock market behavior, particularly focusing on MercadoLibre's stock price. By exploring hourly search trends and correlating them with stock volatility and returns, as well as forecasting future popularity using time series modeling, the project aims to uncover insights into user behavior patterns and potential market influences.
- Prophet: Utilized for time series forecasting, providing a robust framework for modeling and predicting trends in search traffic data.
- Pandas: Employed for data manipulation and analysis, enabling tasks such as reading CSV files, slicing dataframes, and handling missing values efficiently.
- Datetime: Used for datetime operations, facilitating the manipulation and conversion of date and time information within the dataset.
- Numpy: Applied for mathematical operations, enabling tasks such as calculating rolling statistics and percentage changes in stock prices.
- Matplotlib (Inline): Integrated for data visualization, allowing the generation of insightful plots directly within the Jupyter notebook environment.
- Time Series Analysis: The project extensively employs time series analysis techniques to explore hourly, daily, and weekly search traffic trends, providing valuable insights into user behavior patterns over time.
- Data Concatenation: Dataframes are concatenated to merge search trends data with stock price data, enabling comprehensive analysis and correlation between the two datasets.
- Correlation Analysis: Correlation coefficients are computed to investigate relationships between search traffic, stock volatility, and stock returns, offering insights into potential associations between these variables.
- Store Data: Data from external sources is stored in Pandas dataframes, ensuring accessibility and ease of manipulation for further analysis.
- Visualize Trends: Search trends data is visualized through plots, facilitating the interpretation of patterns and trends in search traffic over time.
- Forecast Popularity: Using the Prophet library, future popularity of MercadoLibre is forecasted, providing insights into potential future trends in search traffic.
The analysis of search traffic data reveals several key findings:
- Distinct Time-based Trends: Hourly, daily, and weekly variations in search traffic indicate patterns aligned with human activity, with peak traffic during daytime hours and decreased activity during weekends.
- Relationship with Stock Market: While fluctuations in search trends and stock prices are observed, correlation analysis suggests a weak relationship between lagged search traffic and stock volatility or returns.
- Forecasted Popularity: Near-term forecasts predict a positive trend in MercadoLibre's popularity, with an upward trajectory indicating potential future growth in search traffic.
This project provides valuable insights into the dynamics of search traffic for MercadoLibre and its relationship with stock market behavior. By leveraging time series analysis techniques and Prophet forecasting, the project offers actionable insights for marketers, investors, and decision-makers. For marketers, understanding user behavior patterns can inform content and marketing strategies to optimize engagement. For investors, insights into the relationship between search trends and stock market performance can guide investment decisions. Moreover, the forecasting capabilities of the project enable stakeholders to anticipate future trends in MercadoLibre's popularity, facilitating proactive decision-making. Overall, the methodologies and findings presented in this project can be applied across various industries to analyze user behavior, forecast trends, and inform strategic decision-making in real-world scenarios. For example, e-commerce platforms can utilize similar analyses to understand customer behavior and optimize product offerings, while financial institutions can leverage these insights to inform investment strategies and risk management decisions.