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bank-marketing-effectiveness-prediction-ml-classification

This project focuses on utilising machine learning techniques to predict the effectiveness of bank marketing campaign. Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Machine, XGBoost, K Nearest Neighbor, Naive Bayes, Support Vector Machine, and Artificial Neaural Networks algorithms are used to build a model for prediction.

bike-sharing-demand-prediction-ml-regression

This project aims to build a predictive model that could predict the number of rental bikes required for each hour using the Seoul Bike Sharing dataset. Linear regression, Lasso (L1), Ridge (L2), ElasticNet, Decision Tree, Random Forest, and XGBoost algorithms are used to build a model to predict the number of rental bikes required for each hour.

cat-vs-dog-popularity-dashboard-using-power-bi

This repository contains a dashboard created in Power BI to visualize the popularity of cats and dogs in the United States. The dashboard provides insights and analysis based on the available data.

data-wrangling-using-pyspark-for-video-games-datasets

The objective was to complete data engineering task for IndiGG interview using pyspark. Includes dataset download, processing, analysis using Python, Spark, AWS Glue, Lambda, Step Functions, and SQL.

eda-hotel-booking-analysis

Conducted exploratory data analysis on the provided dataset and derived valuable conclusions about broad hotel booking trends and how various factors interact to affect hotel bookings. Created dashboard using Tableau.

google-merchandise-store-website-analysis-and-dashboards

kevin_cookies-analytics-report-dashboard

This repository houses a Power BI dashboard that provides comprehensive insights into the performance and key metrics of the Kevin Cookies Company. Analyze sales, inventory, customer engagement, and profitability data through interactive visualizations. Gain valuable business insights and make data-driven decisions.

netflix-movies-and-tv-shows-clustering-ml-unsupervised

The Netflix Movies and TV Shows Clustering Project aims to cluster similar movies and TV shows available on Netflix into different clusters based on their content. The project uses Natural Language Processing (NLP) and unsupervised machine learning techniques to analyze the dataset, including K-Means, Hierarchical clustering, and DBSCAN algorithms.

python-advanced-data-wrangling-practice

Python Advanced Data Wrangling Practice repository offers comprehensive resources and examples for mastering advanced data manipulation techniques using Python.

python-notes

This repository contains a comprehensive set of notes and examples for Python programming language. The notes cover various topics ranging from basic syntax and data structures to advanced concepts such as object-oriented programming, and data science. This repository is a valuable resource for learning and mastering Python.

python-practice

This repository serves as a practice ground for Python programming in the context of data science. It encompasses a collection of code snippets and exercises aimed at enhancing Python skills specifically tailored for data analysis, machine learning, and data visualization.

spam-ham-detection-bert-tensorflow

Email spam/ham detection using BERT & TensorFlow. Implementing ML model to classify emails based on content. Includes BERT fine-tuning, training scripts, evaluation metrics, and dataset preprocessing.

sql-notes

This repository include notes on SQL syntax, examples of SQL queries, best practices for database design, and other useful information for SQL developers and database administrators. In addition to providing a valuable resource for SQL learners and practitioners, a GitHub repository for SQL notes can also foster a community of contributors.

sql-practice

This repository contains a collection of SQL practice exercises to enhance your SQL skills and knowledge. It covers a wide range of SQL topics, including querying, filtering, aggregating, and joining data.