Predictive Modeling for Stock Price Forecasting

In this project, I utilized Python's powerful data analysis libraries—such as Pandas, NumPy, and Matplotlib—to explore, visualize, and derive actionable insights from a dataset containing stock price data for Zomato spanning from July 2021 to February 2024. The project involved a comprehensive approach, combining exploratory data analysis (EDA) with advanced feature engineering to uncover meaningful patterns and trends within the dataset.

I began by conducting a thorough EDA to understand the structure and characteristics of the dataset. This step involved:

Data cleaning to handle missing or inconsistent values.

Analyzing key statistical metrics such as mean, median, standard deviation, and correlation between various stock attributes.

Visualization of stock price trends using line graphs to observe overall market behavior and price fluctuations over time. This also included plotting the distribution of stock prices to examine their variability and volatility.

Next, I employed feature engineering techniques to extract additional insights from the stock price data. This included:

Creating moving averages (e.g., 7-day, 30-day) to smooth out short-term fluctuations and highlight longer-term trends.

Calculating rolling standard deviations to measure stock volatility over different time windows.

Log transformations to stabilize variance and make the data more suitable for modeling, particularly when addressing extreme price movements.

Identifying time-related features such as day of the week, month, and year to observe any cyclical behavior in stock prices.

To prepare the data for any further analysis or modeling, I focused on critical data preprocessing tasks:

Handling missing values through imputation or removal, ensuring the dataset remained clean for accurate analysis.

Normalizing or scaling the data when necessary, especially for features that could be sensitive to outliers or varying scales.

Encoding categorical variables to ensure that time-based features (like weekdays or months) were properly represented for subsequent analysis or machine learning tasks.

I used visualization techniques to enhance understanding and interpretation of the data.

I created candlestick charts to represent daily stock price movements, highlighting opening, closing, high, and low values.

Correlation heatmaps were used to identify relationships between various features, like stock volume and closing price, helping to inform any predictive modeling.

By the end of the analysis, I was able to generate valuable insights regarding the stock price trends of Zomato. This included identifying potential volatility patterns, seasonal effects, and overall market sentiment during specific periods. With feature engineering and data preprocessing, I laid the groundwork for predictive models that could forecast stock price movements or detect anomalies in future trading behavior.

This project demonstrates how Python and its data analysis libraries can be leveraged to perform deep-dive explorations into financial data, unlocking key insights and providing a foundation for more advanced analysis or predictive modeling.