Python Course in Delhi | Python Training Institute

Data Analysis: Sales Data Analysis with Pandas

This project involves analyzing a sales dataset to extract meaningful insights and patterns using the Pandas library in Python. Participants will work with datasets containing information such as sales, customer demographics, and product categories. The project begins with data cleaning, including handling missing values, removing duplicates, and normalizing data. Participants will then use Pandas to manipulate and filter the data, generate summary statistics, and group data by various attributes (e.g., region, product type). Visualization tools like Matplotlib or Seaborn can be integrated to create interactive charts and graphs, highlighting trends such as seasonal sales peaks, top-selling products, and underperforming categories. By the end of the project, participants will create a detailed report with actionable insights, showcasing their ability to handle real-world data analysis tasks.

Web Scraper: Scraping E-commerce Data

In this project, participants will build a web scraper using Python libraries like BeautifulSoup or Scrapy to extract data from an e-commerce website. The project involves fetching product details, such as names, prices, ratings, and availability. Participants will learn how to navigate website structures (HTML, CSS selectors) and handle challenges like pagination, dynamic content, and anti-bot mechanisms. The scraped data will be saved in formats like CSV or a database for analysis. This project demonstrates the ability to automate data collection, a crucial skill in industries like market research, price comparison, and competitive analysis.

Web Application: Building a Blog Using Flask/Django

This project focuses on creating a functional blog application with a backend framework like Flask or Django. Participants will design features such as user authentication (login/signup), post creation, editing, and commenting. The project includes creating a responsive user interface with HTML, CSS, and JavaScript and integrating a database (e.g., SQLite or PostgreSQL) to store posts and user data. Advanced features like search functionality, user roles (admin/editor), and deployment on platforms like Heroku or AWS are optional extensions. This project equips participants with end-to-end web development skills, including backend logic, frontend design, and deployment.

Machine Learning Model: Predicting House Prices

This project involves building a machine learning model to predict house prices based on features like location, size, number of rooms, and amenities. Participants will begin by preprocessing the dataset, handling missing values, encoding categorical variables, and scaling numerical data. They will then split the data into training and testing sets and experiment with algorithms like Linear Regression, Random Forest, or Gradient Boosting. Evaluation metrics such as Mean Squared Error (MSE) and R² will be used to assess model performance. Advanced steps include hyperparameter tuning using GridSearchCV and deploying the model with Flask or Streamlit for real-time predictions. This project provides hands-on experience with the machine learning pipeline, from data preparation to deployment.