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Projects

Welcome to my Projects section, where I showcase my hands-on experience and expertise in implementing innovative solutions across various domains. Explore how I've applied my skills in data analysis, software development, and machine learning to solve real-world problems and drive impactful outcomes.

T20 world cup cricket data analytics

Sport Data Analysis [Python | Web scrapping | Pandas | Power BI]

• Created a Power BI report to identify top 11 players for a T20 cricket team by scraping data from espncricinfo with a Brightdata website tool.

• Cleaned and transformed the data with pandas and evaluating various player performance metrics.

• Used the resulting Power BI dashboard to select players for various categories and ultimately choose the top 11 players to play in the match.

• Selected team using the Power BI dashboard has 90% of chances to win the game.

Analyzing the data

Data Pipeline for Graph Processing

[Python, Kafka, Neo4j, Data Processing]

• Implemented a data pipeline using Minikube as the orchestrator and Kafka as the messaging queue.

• Integerated Neo4j and ensured seamless data flowdata flow from producer to Kafka, Neo4j, and data analytics phase.

• Applied data analytics techniques including PageRank and Breadth-First Search (BFS) to extract valuable insights.

Image by Martin Adams

Face Recognition with AWS Lambda

[Lambda, S3, DynamoDB, Python.]

• Developed a Paas Application using Docker and combining it with AWS Lambda and Elastic Container Registry.

• Lambda Trigger was created from the S3 Buckets and the face recognition logic was implemented in lambda function.

• Employed DynamoDB to retrieve person-specific details through facial recognition and returned as an Output

Illustrated Man

Revenue insights in hospitality domain

Business Analysis [Power BI | Excel]

• Atliq Grands noticed a loss in their market share and revenue over a few months. To understand the cause of this loss, they needed a way to analyze this. I created a dashboard in Power BI using three months of data.

• With the created dashboard, Revenue team of Atliq Grands were able to gain insights about their revenue trend. This could help in regaining their revenue and market share by 20% in the next month.

Image by Luke Chesser

Google Data Analytics Capstone Project

[Data analysis, R, Tableau, Excel]

  • Analyzed a dataset using R for a company, Cyclistic, a bike sharing company in Chicago.

  • Created an interactive dashboard showing how annual members and casual riders use Cyclistic bikes differently.

  • Provided marketing strategies to convert casual riders to annual members.

Image by Carlos Muza

Real Estate Price Prediction

[Python, Pandas, Scikit-learn, Seaborn]

  • Gathered relevant data, Cleaned and prepared the collected data by handling missing values, removing outliers.

  • Created new features and transformed existing ones to capture important information that can influence real estate prices.

  • Explore and visualize the data to gain insights into its distribution, correlations, and patterns.

  • Chose appropriate machine learning algorithm or predictive model for the task.

  • Trained the selected model on the training data, optimizing its parameters to make accurate predictions.

Image by Madison Kaminski

Food Delivery Cost and Profitability Analysis

[Python, Pandas, Matplotlib, Datetime]

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  • Gathered and cleaned data from various sources, ensuring data integrity.

  • Extracted relevant features impacting cost and profitability.

  • Analyzed cost breakdown and revenue generation:

  • Identified fixed and variable costs associated with each order.

  • Calculated revenue from commission fees and order values.

  • Developed strategic recommendations for enhancing profitability.

  • Identified the impact of discounts on profitability and proposed adjustments.

  • Simulated financial impact of proposed changes to discount and commission rates.

  • Implemented a new profitability strategy.

  • Established optimal commission and discount percentages based on profitable order characteristics.

  • Advocated for a higher commission rate and lower discount percentage to improve profitability.

Image by Isaac Smith

Loan Approval Prediction with Machine Learning

[Python, Data Processing, Pandas, Scikit-learn ]

  1. Analyzed and preprocessed dataset:

    • Removed irrelevant columns like 'Loan_ID'.

    • Handled missing values appropriately.

  2. Explored data through EDA:

    • Visualized feature distributions.

    • Investigated relationships with 'Loan_Status'.

  3. Prepared data for modeling:

    • Converted categorical columns using one-hot encoding.

    • Split dataset into training and testing sets.

    • Scaled numerical features using StandardScaler.

  4. Trained and evaluated ML model:

    • Selected Support Vector Machine (SVM) classifier.

    • Trained model on training data.

    • Evaluated performance using accuracy and other metrics.

Graphs
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