This repository contains sample Python projects and code examples to showcase my skills and abilities.
[GOOGLE TRENDS DATA ANALYSIS.py] [GOOGLE TRENDS DATA ANALYSIS.ipynb]
This project involved creating a Python-based dashboard to visualize and analyze Google Trends data. The key goals were to:
- Track search volume trends over time for keywords and categories
- Compare trends across different categories like movies, brands etc.
- Identify seasonal patterns and outlier events driving search spikes
- Forecast future search volume using time series models
- Analyze the top search queries within categories
- Visualize search popularity for keywords as heatmaps
- The dashboard imports Google Trends CSV data into Pandas DataFrames for analysis. It utilizes Matplotlib, Seaborn, Statsmodels, and other libraries for visualization and modeling.
Some key analysis includes:
- Yearly and category-wise search volume trends
- Time series decomposition to identify components
- Exponential Smoothing for forecasting
- Query frequency analysis with wordclouds
- Interactive line charts and heatmaps
[LSOA CRIME RATE ANALYSIS.py] [LSOA CRIME RATE ANALYSIS.ipynb]
This project involved analyzing monthly crime statistics for London neighborhoods (LSOAs) from 2019 to 2021. The goal was to identify insights and trends to support data-driven policing and policy decisions.The analysis utilized Pandas and Matplotlib in Python to process and visualize the complex dataset. Key techniques included:
- Overall crime totals and trends over time
- Breakdown of crimes by major category
- Geographic analysis of crime hotspots
- Correlation analysis between crime types
- Impact of COVID-19 lockdowns
- Seasonal and time series decomposition
Some highlights from the analysis:
- Total crime decreased by 15% from 2019 to 2020, likely due to COVID-19 restrictions
- Violent crimes make up the largest portion of offenses
- Top neighborhoods for total crime were concentrated in certain boroughs
- Property crimes like burglary were strongly correlated with other theft/fraud crimes
- Clear seasonal patterns were identified that repeated yearly
Python is an interpreted, high-level programming language great for data analysis, automation, machine learning, and more. Some key features:
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Great libraries and frameworks for data analysis like Pandas, NumPy, Matplotlib, Seaborn etc.
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Web scraping tools like BeautifulSoup, Scrapy, Selenium.
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Data engineering tools like Luigi, Airflow, Kafka.
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Machine learning libraries like TensorFlow, Keras, scikit-learn, PyTorch etc.
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Ability to containerize and productionize models with Docker.
The projects here demonstrate my hands-on Python programming skills across a variety of domains and use cases.
The source code is located within the subdirectories for each project category. Simply navigate to the project folder and run the Python file to execute the scripts or view the Jupyter notebooks. Make sure to install any required dependencies mentioned at the top of each file.