Data Analytics Projects using SQL, Python, Power BI, Tableau and more.
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Updated
Jun 3, 2024 - Jupyter Notebook
Data Analytics Projects using SQL, Python, Power BI, Tableau and more.
Pedalling Forward: The Evolution of Dedicated Cycling Infrastructure in Canadian Cities from 2010 to 2022
I used the dataset for Facebook and Microsoft 2014-2018 stock values to analyze the price differences, returns, direction, and the moving averages. After I calculated the moving averages. I graphed the fast signal and the slow signal and visualized the profitability of the trend trading strategy over time.
Pipeline for remotely sensed imagery. The pipeline processes satellite imagery alongside auxiliary data in multiple steps to arrive at a set of trend files related to land-cover changes.
Open source momentum base trend following systematic trading strategies inspired from top trend following traders (Richard Denis, Olivier Seban and Nick Radge) implemented for various trading platforms as TradingView, cTrader, MetaTrader, Multicharts and TradeStation.
Analyzing trends in the AI landscape over time using Google Trends and visualizing the results using Power BI
This repo contains all code that was used for data analysis process for my research paper. my paper was about estimating the rainfall ratio for himalayan catchment using physchometeri method.
The objective of the project was to create innovative and interactive Tableau dashboards that focus sales of countries, year, trade amount and quantity and YOY growth rate,using various charts and features of tableau
Technical Analysis Library using Pandas and Numpy
Classification Report Sentiment Analysis and Trend Analysis
Bayesian Change-Point Detection and Time Series Decomposition
A python package for non parametric Mann Kendall family of trend tests.
📈 A small, fast chart for time series, lines, areas, ohlc & bars
"Conducting a comprehensive evaluation of the North American food and beverage industry, analyzing market trends, emerging flavors, and consumer preferences to provide a holistic understanding of the regional F&B landscape."
Tableau dashboard on natural disasters
Library for automated signal segmentation, trend classification and analysis.
EngageInsight analyzes user interactions in comment data. It provides insights through visualizations created using Python libraries like Pandas and Matplotlib. The project aims to uncover patterns and trends in user engagement. The visualizations provide an overview of comment lengths, the frequency of different types of replies.
Intrinsic Time-Scale Decomposition
A Tableau dashboard project providing comprehensive insights into soft drink sales trends, allowing for detailed analysis and informed decision-making within the beverage industry.
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