Project

Through this data analysis, I aimed to showcase the history of terrorism both globally and at the state level for each nation. The visualizations, including stacked bar charts and bubble graphs, offer insights into which countries and states are more prone to attacks or have a significant history of terrorism. Additionally, the analysis highlights the most commonly used weapons in these attacks, providing a clear understanding of global terrorism patterns. 

 Language and Tools:  Python, Pandas, Numpy, Streamlit

Visualization: Plotly

 Data Source: Kaggle

This project focuses on analyzing startup funding trends and investor activity from 2015 to 2020 in India. It uncovers key trends through MoM charts, sector-specific insights, and city-wise funding patterns. Additionally, it offers a comprehensive view of individual startups, highlighting their funding journeys, the investors, and similar companies. The Dataset was taken from Kaggle.

 Language and Tools:  Python, Pandas, flask, Streamlit

 Data Source: Kaggle

In this case study, I analyzed the IMDb dataset, which contains 50,000 movie reviews, to provide insights into how sentiment classification can be performed using natural language processing (NLP) and text analytics techniques. The objective was to classify the reviews as either positive or negative. Through this study, I aim to demonstrate how textual data can be leveraged to uncover valuable sentiment patterns and improve prediction accuracy.

 Language and Tools:  Python, Pandas

 Data Source: Kaggle

In this customer personality analysis, I explored a dataset designed to help businesses understand their ideal customers and tailor their products accordingly. The dataset includes customer demographics, such as age, income, and household composition, along with detailed purchasing behavior across product categories like wine, meat, and gold. It also tracks customer responses to promotional campaigns and their preferred shopping channels—whether through the web, catalog, or in-store. By analyzing these factors, businesses can identify key customer segments, refine their marketing strategies, and focus on the customers most likely to purchase specific products, ultimately enhancing customer satisfaction and profitability.

Language and Tools:  Python Libraries, Tableau

 Data Source: Kaggle

In this sales analysis, I explored an e-commerce dataset from India with three datasets: List of Orders, Order Details, and Sales Targets. The List of Orders provides customer purchase history, while the Order Details reveal price, quantity, profit, and product categories. The Sales Target dataset helps compare actual sales against targets for each category. This analysis highlights trends in customer behavior, product profitability, and how well sales goals are being met across different product categories.  

Language and Tools:  Python Libraries, Tableau

 Data Source: Kaggle

In this Tableau project, I explored the Olympics dataset spanning from 1896 to 2016, focusing on hosting cities and countries. I created interactive visualizations to provide insights into country-wise medal counts, participation trends, gender representation, and age distribution of athletes over the years. Additionally, I highlighted top-performing athletes and sports to give a comprehensive view of the Olympic history. This analysis aims to help readers understand the evolution of the Olympics in terms of athlete performance, global participation, and demographic shifts across different eras. 

Language and Tools:  Python Libraries, Tableau

 Data Source: Kaggle

This project showcases an NLP app developed in Python, equipped with five powerful features: sentiment analysis, language detection, emotion analysis, text summarization, and named entity recognition (NER). By integrating APIs from various free sources on Google and utilizing JSON for data storage, the app provides a versatile toolset for analyzing and processing textual data. 

 Language:  Python

 Data Source: JSON file(Self created)