Harika Kellampalli

• Date of Birth: 01/04/02
• Gender: Female
• Status: Active
• Visa Status: F1, I will be on OPT within 10 days

Served as an anchor at department level events.

Participated in many quiz competitions.

• Listening to music
• Playing video games
• Playing badminton
• Solving Puzzles

1. Investigating Netflix Movies, The problem is to determine whether the average duration of movies on Netflix has been declining over time. To tackle this, you conducted exploratory data analysis (EDA) on the provided dataset, netflix_data.csv, which contains information about various shows available on Netflix. Overall, your project demonstrated your ability to perform data analysis and draw meaningful insights from real-world datasets, showcasing your skills in Python programming and exploratory data analysis techniques., Python, Pandas, Numpy, Matplotlib, CSV file handling, Jupyter Notebook, Trend Analysis: By analyzing the dataset, it was possible to identify trends in the duration of movies available on Netflix over time. This included calculating the average duration of movies for each year and plotting these averages to visualize trends. Data Cleaning and Preparation: The dataset was cleaned and prepared for analysis, including handling missing values, correcting data types, and filtering relevant data. Visual Insights: The use of data visualization techniques helped in understanding the distribution of movie durations and identifying any noticeable patterns or trends over the years. Statistical Analysis: Conducted statistical tests to determine if there is a significant decline in the average duration of movies over time. Conclusion: Based on the analysis, a conclusion was drawn regarding whether the average duration of movies on Netflix has been declining over time. The findings were backed by visual and statistical evidence.

2. Visualizing the history of Nobel prize winners, This project involves analyzing a dataset of Nobel Prize winners from 1901 to 2023. The dataset, provided by the Nobel Foundation and accessible via the Nobel Prize API, contains comprehensive information about the laureates, including their names, award categories, award years, and more. The goal is to explore this data to answer specific questions and uncover interesting insights about the Nobel Prize winners over time. Additionally, there is an opportunity to delve into personal inquiries and further explore the dataset for more detailed analyses., Python, Pandas, Numpy, Matplotlib, CSV file handling, Jupyter Notebook, By analyzing the data, you can identify trends over time, such as the number of prizes awarded per year, the distribution of prizes among different categories, and the demographic changes among laureates. Gain insights into the demographic backgrounds of the laureates, such as their countries of origin, ages at the time of receiving the prize, and gender distribution. Delve into specific categories like Physics, Chemistry, Medicine, Literature, Peace, and Economic Sciences to understand patterns within each field, such as the most awarded subfields or common research topics. Examine how the Nobel Prizes have evolved over time, including changes in the frequency of awards, notable periods of increased or decreased activity, and significant historical events impacting the prizes. Focus on specific laureates to learn about their achievements, the reasons they were awarded the Nobel Prize, and any notable patterns or commonalities among multiple laureates. Encourage exploration of additional questions based on personal interest, such as the impact of Nobel Prize winners on their fields, collaborations between laureates, and the influence of the prizes on scientific and literary advancements.

3. Hypothesis Testing with Men's and Women's Soccer Matches, The objective of this project is to investigate whether more goals are scored in women's international soccer matches compared to men's. This investigation is motivated by an intuition that women's matches tend to have higher goal counts. To ensure the analysis is robust and relevant to contemporary soccer, the data is limited to official FIFA World Cup matches (excluding qualifiers) from January 1, 2002, onwards. Two datasets have been compiled containing results of every official man’s and woman’s international football match since the 19th century. The analysis will involve performing a valid statistical hypothesis test with a 10% significance level to determine if there is a significant difference in the mean number of goals scored between men's and women's matches., Python, Pandas, Numpy, Matplotlib, Scipy, CSV file handling, Jupyter Notebook, Data Cleaning and Preparation: Both datasets (women_results.csv and men_results.csv) were cleaned to ensure they only contained relevant matches (official FIFA World Cup matches from 2002 onwards). This included filtering the data by date and match type. Exploratory Data Analysis (EDA): Initial analysis was conducted to understand the distribution of goals in both men's and women's matches. Descriptive statistics and visualizations (histograms, box plots) were created to compare goal distributions. Hypothesis Testing: A one-tailed t-test was performed to test the null hypothesis (H0) that the mean number of goals scored in women's matches is the same as men's against the alternative hypothesis (HA) that the mean number of goals in women's matches is greater. Statistical Results: The p-value from the t-test was compared to the significance level (0.10) to determine whether to reject the null hypothesis. If the p-value was less than 0.10, the null hypothesis would be rejected, supporting the claim that more goals are scored in women's matches. Conclusion: Based on the statistical analysis, a conclusion was drawn regarding whether there is significant evidence to support the intuition that women's international soccer matches have more goals scored than men's. The results were interpreted and discussed in the context of soccer analysis, providing valuable insights for the investigative article

I hereby declare that all the above information is true to the best of my knowledge.