As a data analytics intern at Tranity.in, I had the opportunity to work on various projects that involved collecting, cleaning, analyzing, and visualizing data from different domains and sources. Some of the projects that I worked on are:
- Data Analytics Process: This project aimed to provide a comprehensive overview of the data analytics process, from defining the problem and objectives to acquiring and preparing the data, exploring and modeling the data, to communicating and presenting the results. I used tools such as **Matplotlib**, **Seaborn**, **Numpy**, and **Pandas** to perform data manipulation and visualization in Python.
- Instagram User Analytics: This project focused on analyzing the behavior and preferences of Instagram users based on their posts, likes, comments, and followers. I used tools such as **Pyplot** and **Geopandas** to create interactive maps and charts that showed the geographic distribution and trends of Instagram users.
- Operation & Metric Analytics: This project aimed to measure and improve the performance and efficiency of tranity.in’s operations and processes. I used tools such as **MySQL** and **Advanced Excel** to query, filter, aggregate, and pivot data from various databases and spreadsheets. I also created dashboards and reports that displayed key metrics and indicators for different aspects of the business.
- Hiring Process Analytics: This project involved analyzing the hiring process of tranity. in and identifying the factors that influenced the success and satisfaction of candidates and employees. I used tools such as **Pandas** and **Seaborn** to perform statistical analysis and hypothesis testing on data from surveys, interviews, and assessments. I also created visualizations that showed the correlations and distributions of various variables related to the hiring process.
- IMDB Movie Analysis: This project aimed to explore the characteristics and ratings of movies on IMDB based on their genres, directors, actors, budgets, revenues, etc. I used tools such as **Pandas**, **Seaborn**, and **Pyplot** to scrape, clean, merge, and analyze data from IMDB’s website and API. I also created plots and charts that showed the relationships and patterns among different features of movies.
- Bank Loan Case Study: This project focused on predicting the likelihood of default for bank loan applicants based on their demographic and financial information. I used tools such as **Pandas**, **Numpy**, and **Seaborn** to preprocess, transform, and scale the data. I also used tools such as **scikit-learn** and **TensorFlow** to build, train, evaluate, and compare different machine learning models such as logistic regression, decision tree, random forest, support vector machine, neural network, etc.
- Impact of Car Features: This project aimed to analyze the impact of various car features such as engine size, horsepower, fuel type, transmission type, etc. on the price and mileage of cars. I used tools such as **Pandas**, **Numpy**, **Seaborn**, and **Pyplot** to perform exploratory data analysis and feature engineering on data from car websites. I also used tools such as **scikit-learn** and **statsmodels** to perform linear regression and ANOVA on the data.
- ABC Call Volume Trend: This project involved forecasting the call volume trend for ABC company based on historical data from their call center. I used tools such as **Pandas**, **Numpy**, **Seaborn**, **Pyplot**, and **statsmodels** to perform time series analysis and decomposition on the data. I also used tools such as **Prophet** and **SARIMA** to build, fit, test, and validate different forecasting models for the data.
Working as a data analytics intern at tranity.in was a rewarding experience that helped me develop my skills in data science and gain insights into various domains and industries. I learned how to use different tools and techniques to solve real-world problems with data.
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