Data Analytics Course Delhi | Business Analytics Training Institute

Sales Data Analysis and Forecasting

In this project, students work with historical sales data from an e-commerce or retail company. They clean and preprocess the dataset using Python’s Pandas library and analyze key metrics such as revenue trends, customer purchase behavior, and seasonal fluctuations. Using time-series forecasting techniques, such as ARIMA or Facebook Prophet, they predict future sales trends. The project concludes with interactive dashboards in Tableau or Power BI, allowing stakeholders to visualize trends and make data-driven decisions.

Customer Segmentation Using Clustering

This project focuses on customer behavior analysis using clustering techniques like K-Means and hierarchical clustering. Students analyze data from an online shopping platform, grouping customers based on purchase frequency, spending habits, and demographics. The objective is to help businesses create targeted marketing strategies by identifying high-value customers. Feature engineering and dimensionality reduction techniques, such as PCA, are applied to optimize segmentation. The final insights are presented using data visualization tools like Seaborn and Matplotlib.

Financial Fraud Detection Using ML

In this project, students work with real-world credit card transaction datasets to identify fraudulent activities. They apply data cleaning techniques to handle missing values and outliers. Using classification algorithms such as Logistic Regression, Decision Trees, and Random Forest, they develop a fraud detection model. Precision-recall tradeoffs and confusion matrices are used to evaluate model accuracy. Finally, students build a real-time fraud alert system with Power BI or Streamlit for interactive monitoring.

Social Media Sentiment Analysis

This project involves analyzing public opinions on social media platforms like Twitter. Students use NLP techniques in Python, leveraging libraries like NLTK and TextBlob to preprocess text, remove noise, and classify sentiments as positive, negative, or neutral. The model is trained using supervised learning methods such as Naïve Bayes or LSTM neural networks. The final results are displayed in dynamic dashboards, helping brands track consumer sentiment and adjust marketing strategies accordingly.