farhakousar1601 / globalcoretech-datascience-internship

This repository contains resources, code, and projects related to the Global Core Tech Internship on Data Science with Python. Explore the world of data science with Python, learn NumPy, Pandas, Matplotlib, and EDA, and work on exciting data science projects. Elevate your skills and knowledge in the field of data analysis and visualization.

Hi, I'm Farha Kousar 👋

I'm a student pursuing a B.E. in artificial intelligence and machine learning. I'm passionate about tech and always eager to learn new things. Here's a bit about me:

• 💻 My technical skills include proficiency in programming languages like C, C++, Java, and Python.
• 🌐 I have experience with version control using Git and GitHub.
• 🛠️ I enjoy collaborating as an open-source contributor.
• 📊 I'm currently learning data science with Python.
• 📚 My hobby is learning new things and staying curious.
• 🚀 Ongoing Development: Continuously improving my programming and development skills.

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globalcoretech-datascience-internship's Issues

"Online Shopping Sentiment Analysis Project: Flipkart - Internship Kickoff"

Global Core Tech Internship - Data Science with Python

Welcome to the Global Core Tech Internship on Data Science with Python! This four-week internship program, from October 26th to November 24th, is designed to equip you with the essential skills and knowledge in data science using Python, with a focus on libraries such as NumPy, Pandas, Matplotlib, and exploratory data analysis (EDA). The primary objective is to enable you to execute and work on data science projects in Jupyter Notebook using Python 3.7.0.

Internship Issue

Issue: Data Science Internship - Online Shopping Sentiment Analysis Project: Flipkart

Description: In this internship, your main project is to perform sentiment analysis on online shopping reviews from Flipkart. You will be using Python and various data science libraries to extract insights from the data and gain an understanding of customer sentiment.

Project Tasks

Week 1 (October 26 - November 1)

• Task 1: Set up your development environment. Install Python 3.7.0, Jupyter Notebook, and the necessary libraries (NumPy, Pandas, Matplotlib).

• Task 2: Explore the Flipkart dataset. Download the dataset, load it using Pandas, and understand its structure.

Week 2 (November 2 - November 8)

• Task 3: Data Preprocessing. Clean the data, handle missing values, and perform any necessary data transformations.

• Task 4: Exploratory Data Analysis (EDA). Create visualizations to gain insights into the distribution of reviews and customer sentiment.

Week 3 (November 9 - November 15)

• Task 5: Sentiment Analysis. Implement sentiment analysis using natural language processing techniques. Categorize reviews as positive, negative, or neutral.

• Task 6: Analyze the results of the sentiment analysis. What insights can you gather from the sentiment of the reviews?

Week 4 (November 16 - November 24)

• Task 7: Finalize your project. Prepare a report or presentation summarizing your findings and the insights gained from the sentiment analysis.

• Task 8: Submit your project and findings for evaluation and feedback.

Additional Notes

• Remember to maintain a detailed notebook of your work in Jupyter Notebook, documenting your code, analysis, and findings throughout the project.

• Seek assistance from mentors and fellow interns whenever needed. Collaboration and learning from each other is encouraged.

Good luck with your Data Science Internship project! Feel free to reach out if you have any questions or need further guidance.

Online Shopping Sentiment Analysis Project: Flipkart

Global Core Tech Internship - Data Science with Python

Welcome to the Global Core Tech Internship on Data Science with Python! This four-week internship program, from October 26th to November 24th, is designed to equip you with the essential skills and knowledge in data science using Python, with a focus on libraries such as NumPy, Pandas, Matplotlib, and exploratory data analysis (EDA). The primary objective is to enable you to execute and work on data science projects in Jupyter Notebook using Python 3.7.0.

Internship Issue

Issue: Data Science Internship - Online Shopping Sentiment Analysis Project: Flipkart

Description: In this internship, your main project is to perform sentiment analysis on online shopping reviews from Flipkart. You will be using Python and various data science libraries to extract insights from the data and gain an understanding of customer sentiment.

Project Tasks

Week 1 (October 26 - November 1)

• Task 1: Set up your development environment. Install Python 3.7.0, Jupyter Notebook, and the necessary libraries (NumPy, Pandas, Matplotlib).

• Task 2: Explore the Flipkart dataset. Download the dataset, load it using Pandas, and understand its structure.

Week 2 (November 2 - November 8)

• Task 3: Data Preprocessing. Clean the data, handle missing values, and perform any necessary data transformations.

• Task 4: Exploratory Data Analysis (EDA). Create visualizations to gain insights into the distribution of reviews and customer sentiment.

Week 3 (November 9 - November 15)

• Task 5: Sentiment Analysis. Implement sentiment analysis using natural language processing techniques. Categorize reviews as positive, negative, or neutral.

• Task 6: Analyze the results of the sentiment analysis. What insights can you gather from the sentiment of the reviews?

Week 4 (November 16 - November 24)

• Task 7: Finalize your project. Prepare a report or presentation summarizing your findings and the insights gained from the sentiment analysis.

• Task 8: Submit your project and findings for evaluation and feedback.

Additional Notes

• Remember to maintain a detailed notebook of your work in Jupyter Notebook, documenting your code, analysis, and findings throughout the project.

• Seek assistance from mentors and fellow interns whenever needed. Collaboration and learning from each other is encouraged.

Good luck with your Data Science Internship project! Feel free to reach out if you have any questions or need further guidance.