Stock Market Analyzer

Welcome to the Stock Market Analyzer project! This tool is designed to utilize Apache Spark and MongoDB to efficiently retrieve, process, and analyze stock market data. It incorporates machine learning techniques for predictive analysis and clustering. Whether you are a data enthusiast, a financial analyst, or someone interested in machine learning, this project is crafted to cater to your needs.

Project Overview

The Stock Market Analyzer is a robust platform for handling large datasets of stock market data. It enables comprehensive data processing, visualization, and analysis. Additionally, it supports machine learning for predictive modeling and clustering, providing deeper insights into market trends and patterns.

Features

• Data Retrieval and Transformation: Efficiently retrieve and process stock market data from MongoDB using Apache Spark.
• Data Analysis and Visualization: Perform exploratory data analysis (EDA) with a variety of plots and charts.
• Outlier Detection and Removal: Use Z-score based techniques to filter out data outliers.
• Machine Learning: Implement linear regression and K-means clustering for predictive and clustering analysis.

Technologies Used

• Apache Spark: For fast and general-purpose cluster computing.
• MongoDB: A NoSQL database for storing and retrieving stock data.
• Python: The main programming language used for orchestration and analysis.
• Matplotlib and Seaborn: Libraries for data visualization.
• Pandas: A data manipulation library for handling and analyzing data structures.

Project Objectives

• Efficient Data Handling: Utilize Apache Spark and MongoDB for managing and processing large stock market datasets.
• Comprehensive Data Analysis: Provide tools for in-depth data exploration and visualization to uncover market insights.
• Advanced Machine Learning: Implement algorithms like linear regression and K-means clustering for forecasting and segmentation.
• User-Friendly Interface: Ensure an intuitive interface for interacting with and analyzing stock data.

Setup and Configuration

1. Spark and MongoDB Configuration

First, ensure that you have Apache Spark and MongoDB installed and configured. Set up your Spark session to interact with MongoDB:

from pyspark.sql import SparkSession

#  Configure Spark session
spark = SparkSession.builder \
    .appName("Stock Market Analyzer") \
    .config("spark.mongodb.input.uri", "mongodb://127.0.0.1/stockDB.stockData") \
    .config("spark.mongodb.output.uri", "mongodb://127.0.0.1/stockDB.stockData") \
    .getOrCreate()