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).

Prerequisites
- Python 3.7 Installation
- Setting up the Environment
Getting Started
- Verifying Python Installation
- Installing Libraries
Exploratory Data Analysis (EDA)
Learning Resources
Projects

Prerequisites

Python 3.7 Installation

We'll start by setting up Python 3.7, which is the version we'll be using for this internship.

Download Python 3.7 from python.org.
Run the Windows x86-64 executable installer.
During installation, make sure to:
- Add Python 3.7 to your system PATH by ticking the corresponding option.
- Select "Install launcher for all users" and "Add Python 3.7 to PATH".
Complete the installation process, and ensure that Python 3.7 is installed by opening a command prompt (Win+R, type cmd, press Enter) and running:
```
python --version
```

Setting up the Environment

To ensure the correct environment configuration, follow these steps:

Open a command prompt and execute the following command to set the PATH:
```
set PATH=%PATH%;C:\Program Files\Python3
```
Edit your system variables by adding C:\Program Files\Python3 to the PATH variable.
Verify the Python version again by running:
```
python --version
```

Getting Started

Verifying Python Installation

Make sure Python 3.7 is installed by running the following command in the command prompt:

python --version

Installing Libraries

Install the required Python libraries (NumPy, Pandas, Matplotlib, Seaborn, and Plotly) using pip:

pip install numpy pandas matplotlib seaborn plotly

After installation, close the command prompt.

Verifying Library Installation

To verify that the libraries are installed correctly, open the Python IDLE for Python 3.7.0 and run the following commands:

import pip
import numpy
import pandas
import matplotlib
import seaborn
import plotly

Exploratory Data Analysis (EDA)

Explore and analyze data by using the libraries you've just installed. Perform data analysis, visualization, and gain insights from your datasets.

Learning Resources

As you progress through the internship, you can enhance your knowledge and skills through various online platforms and resources, including:

You can also refer to the book "Tutorial 3.7: Python from Beginner to Advanced" to deepen your Python knowledge.

Projects

During this internship, you will have the opportunity to execute and work on data science projects in Jupyter Notebook using Python 3.7.0. One of the projects you'll be working on is the Online Shopping Sentiment Analysis Project: Flipkart.

Online Shopping Sentiment Analysis Project: Flipkart

Project Description: In this project, you will delve into the world of sentiment analysis in the context of online shopping reviews on Flipkart, a popular e-commerce platform. You will analyze and classify customer reviews into positive, negative, or neutral sentiments, providing valuable insights for both consumers and businesses.
Project Goals:
- Collect and preprocess data from Flipkart reviews.
- Perform text analysis and sentiment classification using Python and data science libraries.
- Create visualizations to present your findings effectively.
- Draw conclusions and make recommendations based on the sentiment analysis results.

This project is an excellent opportunity to apply the knowledge and skills you've gained during the internship to a real-world data science scenario. It will also give you a chance to showcase your abilities as a data scientist.

Feel free to reach out to mentors and fellow interns for guidance and support as you work on the Online Shopping Sentiment Analysis Project: Flipkart during your internship.

FarhaKousar1601/Online_Shopping_Sentiment_Analaysis_Flipkart